Evaluation Method for Energy Efficiency Grading and Carbon Emission of Communication Base Stations

Abstract

The present invention relates to the technical field of energy management, and discloses an evaluation method for energy efficiency grading and carbon emissions of communication base stations. The method includes: collecting real-time operation data through Internet of Things sensors deployed in communication base stations; generating dynamic weight configuration parameters based on base station types and climate characteristics, and constructing a digital twin model based on the verified dynamic weight configuration parameters and real-time operation data, wherein multi-dimensional energy consumption characteristic curves under different load rates are simulated through the digital twin model; performing spatio-temporal matching on the multi-dimensional energy consumption characteristic curves and regional power grid dynamic carbon emission factors to generate a carbon emission intensity matrix of communication base stations; inputting the multi-dimensional energy consumption characteristic curves and the carbon emission intensity matrix into a pre-trained energy efficiency grading model to output an energy efficiency grading result and a carbon emission evaluation report of the communication base stations. The present invention can improve the accuracy of the evaluation of energy efficiency grading and carbon emissions of communication base stations.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and particularly to an evaluation method for energy efficiency grading and carbon emissions of communication base stations. Background Art

[0002] With the rapid development of 5G networks and the Internet of Things, the number of base stations has increased sharply, and the problems of energy consumption and carbon emissions have become increasingly prominent. In the prior art, the energy efficiency evaluation of base stations is mostly based on a single index (such as the power consumption of a single station), lacking a dynamic comprehensive evaluation of multi-dimensional factors such as load rate, environmental conditions (temperature, humidity), and equipment aging degree, resulting in inaccurate energy efficiency optimization. For example: traditional methods are based on the rated power consumption under fixed working conditions, without considering the impact of load fluctuations (such as day-night traffic differences) on energy efficiency during actual operation; only focusing on the energy consumption of a single station, without incorporating the network efficiency of base stations (such as redundant power consumption in overlapping coverage areas); lacking monitoring of long-term factors such as equipment aging and heat dissipation efficiency, resulting in evaluation results deviating from the real scenario.

[0003] Traditional methods only focus on energy consumption itself, without quantifying carbon emissions (such as the carbon intensity coefficient based on the power source), and cannot directly guide low-carbon transformation; at the same time, the energy efficiency grading standards in the industry are vague, making it difficult for operators to horizontally compare the performance of base stations and unable to effectively formulate energy-saving and carbon-reduction strategies. Summary of the Invention

[0004] The present invention provides an evaluation method for energy efficiency grading and carbon emissions of communication base stations, and its main purpose is to solve the problem of low accuracy in the evaluation of energy efficiency grading and carbon emissions of communication base stations.

[0005] To achieve the above object, an evaluation method for energy efficiency grading and carbon emissions of communication base stations provided by the present invention includes:

[0006] Collecting real-time operation data through Internet of Things sensors deployed in communication base stations;

[0007] Generating dynamic weight configuration parameters based on the base station type of the communication base station and the climate characteristics of the location area, and performing decentralized verification on the dynamic weight configuration parameters through blockchain nodes;

[0008] Constructing a digital twin model of the communication base station based on the verified dynamic weight configuration parameters and the real-time operation data, wherein multi-dimensional energy consumption characteristic curves under different load rates are simulated through the digital twin model;

[0009] Invoking the regional power grid dynamic carbon emission factor database stored in the blockchain distributed ledger, and performing spatio-temporal matching on the multi-dimensional energy consumption characteristic curves and the regional power grid dynamic carbon emission factors in the regional power grid dynamic carbon emission factor database, so as to generate the carbon emission intensity matrix of the communication base station.

[0010] Input the multi-dimensional energy consumption characteristic curve and the carbon emission intensity matrix into a pre-trained energy efficiency grading model to output the energy efficiency grading result and the carbon emission evaluation report of the communication base station.

[0011] Optionally, the real-time operation data includes the real-time load rate, environmental parameters, equipment aging coefficient, and power source type, and the real-time operation data is transmitted to the blockchain node through an encrypted channel, where:

[0012] The environmental parameters are determined based on the dynamic heat dissipation efficiency of the heat dissipation system in the communication base station;

[0013] The equipment aging coefficient is determined based on the ratio of the cumulative operation duration of the base station equipment in the communication base station to the design life;

[0014] The power source type is used to determine the dynamic carbon emission factor of the regional power grid.

[0015] Optionally, the calculation formula of the dynamic heat dissipation efficiency is as follows:

[0016] ;

[0017] Where, is the dynamic heat dissipation efficiency, is the real-time power of the heat dissipation system, is the rated power of the heat dissipation system, is the environmental temperature, is the maximum working temperature threshold allowed for the base station equipment, is the environmental humidity.

[0018] Optionally, generating the dynamic weight configuration parameters based on the base station type of the communication base station and the climate characteristics of the location includes:

[0019] Define the base station type factor of the communication base station according to the energy consumption characteristic differences of macro base stations, micro base stations, and distributed base stations;

[0020] Determine the climate correction factor of the communication base station according to the climate characteristics of the location where the communication base station is located;

[0021] Based on the climate correction factor and the base station type factor, perform weighted fusion on the environmental parameter weight and the equipment aging weight, and perform gain on the real-time load rate weight according to the load dynamic correction factor to obtain the dynamic weight configuration parameters, where the calculation formula of the dynamic weight configuration parameters is as follows:

[0022] ;

[0023] Where, is the dynamic weight configuration parameter, is the base station type factor, is the climate correction factor, is the load dynamic correction factor, is the environmental parameter weight, is the equipment aging weight, is the real-time load rate weight.

[0024] Optionally, the decentralized verification of the dynamic weight configuration parameter by the blockchain node includes:

[0025] Encrypting and broadcasting the dynamic weight configuration parameter to the verification nodes among multiple blockchain nodes;

[0026] Verifying whether the dynamic weight configuration parameter conforms to the dynamic weight mapping rule of the climate characteristics and the base station type through a consensus algorithm, and the dynamic weight mapping rule is stored in the smart contract;

[0027] If the dynamic weight configuration parameter conforms to the dynamic weight mapping rule, trigger the smart contract to write the dynamic weight configuration parameter into the blockchain and synchronously update the input parameters of the digital twin model.

[0028] Optionally, constructing the digital twin model of the communication base station based on the verified dynamic weight configuration parameter and the real-time operation data includes:

[0029] Performing parametric 3D modeling on the main equipment, heat dissipation system and power supply module of the communication base station based on the verified dynamic weight configuration parameter to obtain the 3D model of the communication base station;

[0030] Injecting the equipment aging coefficient into the 3D model to simulate the energy consumption increment caused by equipment performance degradation;

[0031] Determine the wireless signal coverage range of the communication base station under different load rates. If the wireless signal coverage range overlaps with the coverage area of adjacent base stations, add a signal interference redundant power consumption correction term to the multi-dimensional energy consumption characteristic curve.

[0032] Optionally, the multi-dimensional energy consumption characteristic curve includes a main equipment energy consumption curve, a heat dissipation system energy consumption curve and a signal interference redundant power consumption curve, where:

[0033] Calculate the power consumption changes of the baseband unit and the radio frequency unit based on the real-time load rate, thereby generating the main equipment energy consumption curve;

[0034] Generate the heat dissipation system energy consumption curve according to the dynamic heat dissipation efficiency and the real-time temperature difference;

[0035] Identify the wireless signal coverage overlapping area of the communication base station through the base station networking topology structure, so as to generate a signal interference redundant power consumption curve caused by signal interference.

[0036] Optionally, the method for updating the regional power grid dynamic carbon emission factor database includes:

[0037] Based on the blockchain oracle, access the protocol of the real-time data interface of the energy structure of the regional power grid to obtain the real-time energy structure data of the proportion of thermal power generation, the proportion of wind power generation, and the proportion of photovoltaic power generation;

[0038] Input the parameters of the preset carbon emission factor calculation model based on the real-time energy structure data to obtain the regional power grid carbon emission factor at the current moment;

[0039] Bind the spatio-temporal marking of the regional power grid carbon emission factor based on the collection timestamp and the base station geographical coding to obtain the regional power grid dynamic carbon emission factor with spatio-temporal attributes;

[0040] Based on the asymmetric encryption algorithm, perform blockchain node signature processing on the regional power grid dynamic carbon emission factor to obtain a verifiable encrypted carbon emission factor record;

[0041] Based on the blockchain consensus protocol, store the encrypted carbon emission factor record in a distributed ledger to form a regional power grid dynamic carbon emission factor database for the carbon emission intensity matrix to call.

[0042] Optionally, the calculation formula for the carbon emission intensity in the carbon emission intensity matrix is as follows:

[0043] ;

[0044] Wherein, is the carbon emission intensity, is the energy type index, is the total number of energy types, is the consumption of the type of energy, is the regional power grid dynamic carbon emission factor corresponding to the type of energy, is the load sensitivity coefficient, is the real-time load rate,

[0045] Optionally, inputting the multi-dimensional energy consumption characteristic curve and the carbon emission intensity matrix into the pre-trained energy efficiency classification model to output the energy efficiency classification result and carbon emission evaluation report of the communication base station includes:

[0046] Perform energy efficiency grading on the multi-dimensional energy consumption characteristic curve based on a pre-trained energy efficiency grading model to obtain an energy efficiency grading result with initial grading labels;

[0047] Match the low-carbon attribute identification for the energy efficiency grading result based on the carbon emission intensity matrix to obtain a multi-level energy efficiency evaluation system with additional low-carbon labels;

[0048] Perform an energy-saving measure association mapping on the multi-level energy efficiency evaluation system based on an energy-saving strategy knowledge base to obtain a grading response strategy recommendation list including equipment replacement suggestions, load scheduling plans, and heat dissipation optimization;

[0049] Integrate the carbon emission evaluation of the grading response strategy recommendation list based on a structured report template to generate a carbon emission evaluation report including a dynamic optimization path.

[0050] Through dynamic weight configuration parameters and digital twin models, the present invention comprehensively considers multi-dimensional factors such as base station type, climate characteristics, equipment aging, and load fluctuations, realizes multi-factor adaptive weighted evaluation, and significantly improves the accuracy of energy efficiency grading and carbon emission evaluation; at the same time, uses digital twin models to simulate the associated energy consumption of load rate and signal coverage, introduces a signal interference redundancy power consumption correction factor, incorporates the homogeneous networking efficiency into the evaluation system, and simulates complex factors in the real operating environment (such as coverage overlap and equipment performance degradation), making the evaluation results closer to reality; real-time collects data through Internet of Things sensors, updates the carbon emission factors of the regional power grid in real time based on blockchain oracles, and constructs a trusted computing link through asymmetric encryption signatures to ensure the spatio-temporal matching accuracy of the carbon emission intensity matrix and realize the dynamic tracking of base station energy consumption and carbon emissions; in addition, establishes an association mapping between the multi-level energy efficiency evaluation system and low-carbon labels, generates grading response strategies in combination with the energy-saving strategy knowledge base, provides a carbon emission evaluation report with a dynamic optimization path, and thus significantly improves the evaluation accuracy of energy efficiency grading and carbon emissions for communication base stations. Brief Description of the Drawings

[0051] Figure 1 It is a schematic flowchart of a method for evaluating energy efficiency grading and carbon emissions for a communication base station provided by an embodiment of the present invention;

[0052] The realization, functional features, and advantages of the objectives of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0053] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] An embodiment of the present application provides a method for evaluating the energy efficiency grading and carbon emissions of communication base stations. The execution subject of the method for evaluating the energy efficiency grading and carbon emissions of communication base stations includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for evaluating the energy efficiency grading and carbon emissions of communication base stations can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0055] Referring to Figure 1 As shown, it is a schematic flowchart of a method for evaluating the energy efficiency grading and carbon emissions of a communication base station provided by an embodiment of the present invention. In this embodiment, the method for evaluating the energy efficiency grading and carbon emissions of a communication base station includes:

[0056] S1. Collect real-time operation data through Internet of Things sensors deployed in the communication base station.

[0057] In the embodiment of the present invention, the real-time operation data includes real-time load rate, environmental parameters, equipment aging coefficient, and power source type, and the real-time operation data is transmitted to the blockchain node through an encrypted channel, where:

[0058] The environmental parameters are determined based on the dynamic heat dissipation efficiency of the heat dissipation system in the communication base station;

[0059] The equipment aging coefficient is determined based on the ratio of the cumulative operation duration of the base station equipment in the communication base station to the designed life;

[0060] The power source type is used to determine the dynamic carbon emission factor of the regional power grid.

[0061] In the embodiment of the present invention, the calculation formula of the dynamic heat dissipation efficiency is as follows:

[0062] ;

[0063] Wherein, is the dynamic heat dissipation efficiency, is the real-time power of the heat dissipation system, is the rated power of the heat dissipation system, is the environmental temperature, is the maximum operating temperature threshold allowed for the base station equipment, is the ambient humidity.

[0064] Specifically, reflects the proportional relationship between the actual power and the rated power of the cooling system. If is close to , it indicates that the cooling system is operating near full load; if is much smaller than , it means that the cooling system is not fully efficient.

[0065] Specifically, takes into account the influence of ambient temperature on the cooling efficiency. When the ambient temperature is close to the maximum operating temperature threshold allowed for the base station equipment , this value approaches 0, meaning that the cooling efficiency will decrease significantly; conversely, when the ambient temperature is low, this value is close to 1 and the cooling efficiency is relatively high.

[0066] Specifically, takes into account the influence of ambient humidity on the cooling efficiency. The higher the humidity, the larger the denominator and the smaller the value of the whole fraction, that is, the lower the cooling efficiency. This is because high humidity affects the heat dissipation ability of the air.

[0067] Specifically, when evaluating the performance of the cooling system of a communication base station, the real-time power, ambient temperature, and ambient humidity of the cooling system can be obtained through sensors. At the same time, the rated power of the cooling system and the maximum operating temperature threshold allowed for the base station equipment are known. Substituting these values into the formula can calculate the dynamic cooling efficiency. Through the dynamic cooling efficiency, it can be judged whether the cooling system is working properly and the degree of influence of environmental conditions on heat dissipation, providing basic data for subsequent energy efficiency grading and carbon emission evaluation.

[0068] Specifically, an Internet of Things sensor is a device that converts various information in the physical world (such as temperature, humidity, power, etc.) into digital signals and transmits them through a network. In a communication base station, the Internet of Things sensor is responsible for collecting various real-time data during the operation of the base station and is a key component for realizing real-time monitoring.

[0069] Specifically, real-time operation data refers to various status data of a communication base station at the current operating moment, including real-time load rate, environmental parameters, equipment aging coefficient, and power source type. These data reflect the immediate working conditions of the base station and are crucial for evaluating the energy efficiency and carbon emissions of the base station.

[0070] Specifically, the real-time load rate represents the ratio of the current actual service load borne by the base station to the maximum bearing capacity, and is used to measure the busyness of the base station services. Changes in the load rate will directly affect the power consumption of each device in the base station.

[0071] Specifically, the environmental parameters are mainly determined by the dynamic heat dissipation efficiency of the communication base station cooling system, including environmental factors related to heat dissipation, such as temperature, humidity, etc., which will affect the working efficiency of the cooling system and thus affect the overall energy consumption of the base station.

[0072] Specifically, the equipment aging coefficient is calculated by the ratio of the cumulative operating duration of the base station equipment to the designed life, and is used to measure the aging degree of the equipment. Equipment aging will lead to performance degradation and increased energy consumption, which is a factor that cannot be ignored when evaluating the energy consumption of the base station.

[0073] Specifically, the power source type refers to the source of the power used by the base station, such as thermal power generation, wind power generation, photovoltaic power generation, etc. Different power sources have different carbon emission intensities, so it is of great significance for determining the dynamic carbon emission factor of the regional power grid and calculating the carbon emissions of the base station.

[0074] Specifically, an encrypted channel is a communication channel that uses encryption technology to encrypt the transmitted data, which can prevent the data from being stolen or tampered with during the transmission process, and ensure the security and integrity of the data transmission.

[0075] Specifically, a blockchain node is a participating unit in the blockchain network, responsible for verifying, storing, and propagating blockchain data. In the present invention, it is used to receive, verify, and store the real-time operation data collected from the base station.

[0076] Specifically, the dynamic heat dissipation efficiency is an index to measure the actual heat dissipation ability of the cooling system under different environmental conditions, which is calculated by a formula and reflects the relationship between the real-time power, rated power, environmental temperature, the highest working temperature threshold allowed for the base station equipment, and the environmental humidity of the cooling system.

[0077] Furthermore, deploy Internet of Things sensors inside the communication base station. These sensors obtain the various operation data of the base station in real time according to the preset frequency and collection rules, including the real-time load rate, the real-time power of the cooling system, environmental temperature, environmental humidity, etc.

[0078] Furthermore, record the cumulative operating duration of the base station equipment, and combine it with the designed life of the equipment to calculate the equipment aging coefficient.

[0079] Furthermore, identify the power source type of the base station, for example, determine it by querying the power supply contract or monitoring the power supply characteristics, etc., and then determine the corresponding dynamic carbon emission factor of the regional power grid according to this type.

[0080] Furthermore, encrypt the real-time operation data such as the collected real-time load rate, determined environmental parameters, equipment aging coefficient, and power source type through an encryption algorithm, and then use network communication technology to transmit the encrypted data to the blockchain node.

[0081] Specifically, collecting real-time operation data is the basis of the entire evaluation process. The subsequent steps of determining parameters and transmitting data all rely on the collected data. The calculation of dynamic heat dissipation efficiency and the determination of environmental parameters rely on data such as the real-time power of the heat dissipation system, environmental temperature, and environmental humidity collected; the calculation of the equipment aging coefficient relies on the cumulative operation duration data of the base station equipment; the determination of the dynamic carbon emission factor of the regional power grid relies on the power source type data.

[0082] Specifically, the determined parameters constitute an important part of the real-time operation data. After these data are transmitted to the blockchain node through the encrypted channel, they provide data support for subsequent steps such as data verification and digital twin model construction on the blockchain.

[0083] Generally speaking, multi-dimensional data collection breaks through the traditional evaluation method that only focuses on a single indicator (such as the power consumption of a single station), comprehensively collects multi-dimensional data such as real-time load rate, environmental parameters, equipment aging coefficient, and power source type, and provides the possibility for more accurate energy efficiency grading and carbon emission evaluation.

[0084] Generally speaking, introducing scientific parameter determination methods such as using the dynamic heat dissipation efficiency calculation formula to determine environmental parameters and determining the equipment aging coefficient through the ratio of the cumulative operation duration of the equipment to the design life is different from the traditional simple or inaccurate parameter setting methods, and improves the scientificity and accuracy of the evaluation.

[0085] Generally speaking, using the encrypted channel to transmit data and the blockchain node to store data ensures the security and reliability of the data, and solves the problems of easy data tampering and poor security in traditional evaluation methods.

[0086] S2. Generate dynamic weight configuration parameters based on the base station type of the communication base station and the climate characteristics of the region where it is located, and perform decentralized verification of the dynamic weight configuration parameters through the blockchain node.

[0087] In the embodiment of the present invention, the generating dynamic weight configuration parameters based on the base station type of the communication base station and the climate characteristics of the region where it is located includes:

[0088] Define the base station type factor of the communication base station according to the energy consumption characteristic differences of macro base stations, micro base stations, and distributed base stations;

[0089] Determine the climate correction factor of the communication base station according to the climate characteristics of the region where the communication base station is located;

[0090] Weighted fusion of the environmental parameter weight and the equipment aging weight is performed based on the climate correction factor and the base station type factor, and the real-time load rate weight is increased according to the load dynamic correction factor to obtain the dynamic weight configuration parameter. The calculation formula of the dynamic weight configuration parameter is as follows:

[0091] ;

[0092] where, is the dynamic weight configuration parameter, is the base station type factor, is the climate correction factor, is the load dynamic correction factor, is the environmental parameter weight, is the equipment aging weight, is the real-time load rate weight.

[0093] Specifically, Based on the climate correction factor the environmental parameter weight and the equipment aging weight are weighted and fused. reflects the influence degree of climate characteristics on the weight. If is larger, it indicates that the environmental parameter weight is more important; on the contrary, the equipment aging weight is more important.

[0094] Specifically, is the base station type factor, which adjusts the result of the previous weighted fusion, reflecting the influence of the energy consumption characteristics differences of different types of base stations (such as macro base stations, micro base stations, etc.) on the weight configuration.

[0095] Specifically, considers the increase of the real-time load rate weight by the load dynamic correction factor . reflects the influence of load changes on the weight. When the load rate changes, the real-time load rate weight will be adjusted accordingly.

[0096] Specifically, when performing the energy efficiency grading and carbon emission evaluation of communication base stations, it is necessary to first determine the base station type factor, climate correction factor, load dynamic correction factor, environmental parameter weight, equipment aging weight and real-time load rate weight. These parameters can be obtained through historical data, experimental measurements or empirical estimates. Substituting these values into the formula can calculate the dynamic weight configuration parameter. This parameter is used to construct a digital twin model later to more accurately simulate the energy consumption situation of the base station.

[0097] In the embodiment of the present invention, the decentralized verification of the dynamic weight configuration parameter by the blockchain node includes:

[0098] Broadcast the dynamic weight configuration parameters encrypted with the historical verification dataset to the verification nodes among multiple blockchain nodes;

[0099] Verify whether the dynamic weight configuration parameters conform to the dynamic weight mapping rule of the climate characteristics and the base station type through a consensus algorithm, and the dynamic weight mapping rule is stored in the smart contract;

[0100] If the dynamic weight configuration parameters conform to the dynamic weight mapping rule, trigger the smart contract to write the dynamic weight configuration parameters into the blockchain and synchronously update the input parameters of the digital twin model.

[0101] Specifically, the dynamic weight configuration parameters: comprehensively consider factors such as the base station type of the communication base station, the climate characteristics of the location area, and the load change, and allocate weight parameters for different evaluation indicators such as environmental parameters, equipment aging, and real-time load rate, which are used for subsequent evaluation steps such as constructing a digital twin model to make the evaluation more in line with the actual situation.

[0102] Specifically, the base station type factor is a parameter set according to the differences in energy consumption characteristics of macro base stations, micro base stations, and distributed base stations, which is used to reflect the impact of the differences in energy consumption characteristics of different types of base stations on the evaluation weight. For example, macro base stations have high power and wide coverage, which are different from the energy consumption characteristics of micro base stations, and the base station type factor will reflect this difference.

[0103] Specifically, the climate correction factor is a parameter determined according to the climate characteristics of the area where the communication base station is located, which is used to adjust the environmental parameter weight and the equipment aging weight to adapt to the energy consumption characteristics of the base station under different climate conditions. For example, in hot areas, the base station has a large heat dissipation demand, and the climate correction factor will make corresponding adjustments to the relevant weights.

[0104] Specifically, the load dynamic correction factor is a parameter that considers the dynamic change of the load to increase the weight of the real-time load rate, which reflects the impact of load fluctuations on the energy consumption evaluation of the base station. When the load rate changes, this factor adjusts the weight of the real-time load rate to make the evaluation more accurate.

[0105] Specifically, the environmental parameter weight is the weight value of environmental parameters (such as temperature, humidity, etc.) in the entire evaluation system when evaluating the energy efficiency and carbon emissions of the base station, and its size affects the contribution degree of environmental factors to the evaluation result.

[0106] Specifically, the equipment aging weight is the weight of the equipment aging coefficient in the evaluation system, which reflects the importance of equipment aging in the evaluation of the energy consumption and carbon emissions of the base station. Different degrees of equipment aging will affect the evaluation result with this weight.

[0107] Specifically, the real-time load rate weight is the weight of the real-time load rate in the evaluation system, reflecting the impact degree of the load rate on the energy consumption and carbon emissions of the base station. The change of the load rate will affect the evaluation result through this weight.

[0108] Specifically, a blockchain node is a basic unit in a blockchain network, with functions such as storing, verifying, and propagating data. In the present invention, it is used to perform decentralized verification and storage on the dynamic weight configuration parameters.

[0109] Specifically, decentralized verification is a way of data verification that does not rely on a single central institution. Through the joint participation of multiple nodes in the blockchain network for verification, it ensures the authenticity, reliability, and security of the data, and avoids tampering and errors of a single node.

[0110] Specifically, the historical verification data set is a data set containing various types of base station-related data verified in the past and corresponding correct weight configuration parameters and other information, which is used to compare and verify with the currently generated dynamic weight configuration parameters to judge the rationality of the new parameters.

[0111] Specifically, the consensus algorithm is an algorithm for nodes in the blockchain to reach a consensus, enabling each node to reach a consensus on the state of the data and the validity of the transactions. In the present invention, it is used to verify whether the dynamic weight configuration parameters conform to the rules.

[0112] Specifically, the dynamic weight mapping rule is a pre-set corresponding relationship of dynamic weight configuration based on climate characteristics and base station types, which is stored in the smart contract and used as the basis for verifying the dynamic weight configuration parameters.

[0113] Specifically, a smart contract is an automatically executed contract clause, deployed on the blockchain in the form of code, and has the ability of self-verification, execution, and forced performance. In the present invention, it is used to store the dynamic weight mapping rule and perform operations such as writing to the blockchain.

[0114] Furthermore, considering factors such as the base station type and the climate characteristics of the location area, specific calculation methods and rules are used to create dynamic weight configuration parameters; according to the differences in energy consumption characteristics of different types of base stations, the value of the base station type factor is determined to clarify its role and influence in the weight configuration; based on the climate characteristics of the area where the communication base station is located, the corresponding climate correction factor is found to adjust the weight; the climate correction factor and the base station type factor are combined and calculated with the environmental parameter weight and the equipment aging weight according to a specific formula to make the weight distribution more reasonable; the real-time load rate weight is adjusted and increased according to the load dynamic correction factor to highlight the impact of load changes on the evaluation.

[0115] Furthermore, send the encrypted dynamic weight configuration parameters and the historical verification dataset to the verification nodes among multiple blockchain nodes so that they can be widely verified. Through the consensus algorithm, check the correctness and rationality of the dynamic weight configuration parameters according to the dynamic weight mapping rules. When the dynamic weight configuration parameters conform to the rules, start the smart contract to execute corresponding operations, such as writing to the blockchain and updating the input parameters of the digital twin model. Record the verified dynamic weight configuration parameters in the blockchain to ensure the immutability and traceability of the data. Modify the input parameters of the digital twin model so that it can be simulated and evaluated based on the latest weight configuration parameters.

[0116] Specifically, generating the dynamic weight configuration parameters includes the following steps:

[0117] First, analyze the characteristics of macro base stations, micro base stations, and distributed base stations in terms of energy consumption, such as power consumption patterns and equipment configuration differences, and set the base station type factor according to these differences. Then, collect the climate data of the area where the communication base station is located, such as the annual average temperature and humidity change range, and determine the climate correction factor based on these climate characteristics. Then, determine the initial values of the environmental parameter weight, equipment aging weight, and real-time load rate weight. Finally, substitute the above parameters into the calculation formula of the dynamic weight configuration parameters described in the formula to calculate the dynamic weight configuration parameters.

[0118] Specifically, encrypt the generated dynamic weight configuration parameters and the historical verification dataset to prevent the data from being stolen or tampered with during transmission. Through the blockchain network, broadcast the encrypted data to the verification nodes among multiple blockchain nodes. The verification nodes use the consensus algorithm to verify the dynamic weight configuration parameters against the dynamic weight mapping rules stored in the smart contract.

[0119] Furthermore, if the parameters conform to the rules, the smart contract is triggered to write the dynamic weight configuration parameters into the blockchain for storage and synchronously update the input parameters of the digital twin model to ensure that the model uses the latest weight data.

[0120] Specifically, generating the dynamic weight configuration parameters takes into account factors such as base station type and climate characteristics, can more accurately reflect the importance of each evaluation index in different situations, make the energy efficiency grading and carbon emission evaluation more in line with the actual situation, and improve the evaluation accuracy.

[0121] Specifically, through decentralized verification by blockchain nodes, use multiple nodes to jointly verify the data to prevent the data from being maliciously tampered with, ensure the authenticity and reliability of the dynamic weight configuration parameters, and thus enhance the credibility of the entire evaluation process.

[0122] Specifically, after verification, the input parameters of the digital twin model are updated in a timely manner, enabling the model to perform simulation analysis based on the latest weight data, ensuring that the results output by the model can reflect the actual situation of the current base station, and providing more timely and effective support for decision-making.

[0123] Generally speaking, generating dynamic weight configuration parameters is a prerequisite for decentralized verification. Only after determining the weight parameters can verification be carried out. And the verification result determines whether the weight parameters can be used in subsequent steps such as digital twin model construction. If the verification fails, the weight parameters need to be regenerated.

[0124] Generally speaking, the digital twin model depends on the verified dynamic weight configuration parameters as input. Accurate weight parameters can enable the model to more realistically simulate the energy consumption and carbon emission conditions of the base station, providing a reliable basis for subsequent evaluation and decision-making.

[0125] Generally speaking, the weight configuration breaks through the traditional fixed-weight evaluation method, proposes a method for generating dynamic weight configuration parameters based on base station types and climate characteristics, making the evaluation more targeted and flexible; at the same time, applying the decentralized verification technology of blockchain to the verification of weight configuration parameters solves the problems of easy data tampering and lack of credibility in traditional evaluations; through dynamic weight configuration and timely updating of the input parameters of the digital twin model, dynamic real-time evaluation of the energy efficiency and carbon emissions of the base station is achieved, different from traditional static evaluation methods, improving the timeliness and accuracy of the evaluation.

[0126] S3. Construct a digital twin model of the communication base station based on the verified dynamic weight configuration parameters and the real-time operation data, wherein the multi-dimensional energy consumption characteristic curve under different load rates is simulated through the digital twin model.

[0127] In the embodiment of the present invention, the constructing the digital twin model of the communication base station based on the verified dynamic weight configuration parameters and the real-time operation data includes:

[0128] Perform parametric three-dimensional modeling on the main equipment, heat dissipation system, and power supply module of the communication base station based on the verified dynamic weight configuration parameters to obtain a three-dimensional model of the communication base station;

[0129] Inject the equipment aging coefficient into the three-dimensional model to simulate the energy consumption increment caused by equipment performance degradation;

[0130] Determine the wireless signal coverage range of the communication base station under different load rates. If the wireless signal coverage range overlaps with the coverage area of adjacent base stations, add a signal interference redundancy power consumption correction term to the multi-dimensional energy consumption characteristic curve.

[0131] In an embodiment of the present invention, the multi-dimensional energy consumption characteristic curve includes a main device energy consumption curve, a heat dissipation system energy consumption curve, and a signal interference redundant power consumption curve, where:

[0132] Based on the real-time load rate, calculate the power consumption changes of the baseband unit and the radio frequency unit, so as to generate the main device energy consumption curve;

[0133] Generate the heat dissipation system energy consumption curve according to the dynamic heat dissipation efficiency and the real-time temperature difference;

[0134] Identify the wireless signal coverage overlapping area of the communication base station through the base station networking topology structure, so as to generate the signal interference redundant power consumption curve caused by signal interference.

[0135] Specifically, the digital twin model is a digital mapping of the physical entity of the communication base station. It integrates the verified dynamic weight configuration parameters and real-time operation data, and can simulate the operation state of the base station under different working conditions, especially the multi-dimensional energy consumption characteristics under different load rates.

[0136] Specifically, parametric 3D modeling is to accurately set the parameters of the main device, heat dissipation system and power supply module of the communication base station according to the verified dynamic weight configuration parameters, and construct a 3D model reflecting the actual physical structure and electrical characteristics of the base station.

[0137] Specifically, the equipment aging coefficient is an index that measures the degree of performance degradation of the base station equipment due to long-term use. By injecting it into the 3D model, the impact of equipment aging on energy consumption can be simulated.

[0138] Specifically, the wireless signal coverage range is the geographical area that the wireless signal emitted by the communication base station can effectively cover, and its size is related to factors such as the transmission power of the base station and the antenna gain.

[0139] Specifically, the signal interference redundant power consumption correction term is that when the wireless signal coverage range of the communication base station overlaps with that of adjacent base stations, signal interference will occur, resulting in additional power consumption. This correction term is used to reflect this part of the additional power consumption in the multi-dimensional energy consumption characteristic curve.

[0140] Specifically, the multi-dimensional energy consumption characteristic curve includes a main device energy consumption curve, a heat dissipation system energy consumption curve, and a signal interference redundant power consumption curve, which reflects the energy consumption characteristics of the communication base station under different load rates from multiple aspects.

[0141] Specifically, the main device energy consumption curve describes the curve of the power consumption of the main device of the communication base station (such as the baseband unit and the radio frequency unit) changing with the real-time load rate.

[0142] Specifically, the heat dissipation system energy consumption curve reflects the relationship between the energy consumption of the heat dissipation system, the dynamic heat dissipation efficiency and the real-time temperature difference.

[0143] Specifically, the signal interference redundant power consumption curve reflects the curve of the additional power consumption caused by signal interference changing with the wireless signal coverage overlapping area.

[0144] Furthermore, using the verified dynamic weight configuration parameters and real-time operation data, create a digital twin model of the communication base station to accurately simulate the operation state of the base station; add the equipment aging coefficient to the 3D model so that the model can consider the impact of equipment aging on energy consumption; through calculation or measurement, clarify the wireless signal coverage range of the communication base station at different load rates; add a signal interference redundant power consumption correction term to the multi-dimensional energy consumption characteristic curve to more accurately reflect the actual energy consumption of the base station; according to data such as real-time load rate, obtain the power consumption changes of the baseband unit and radio frequency unit for generating the main equipment energy consumption curve; create the main equipment energy consumption curve, heat dissipation system energy consumption curve, and signal interference redundant power consumption curve respectively according to relevant data and algorithms; analyze the base station networking topology structure to find the wireless signal coverage overlapping area of the communication base station.

[0145] Specifically, according to the verified dynamic weight configuration parameters, precisely set the parameters of the main equipment (such as baseband unit, radio frequency unit, etc.), heat dissipation system (such as air conditioner, fan, etc.), and power supply module (such as power supply, battery, etc.) of the communication base station, and use 3D modeling software to construct a 3D model of the communication base station. For example, adjust parameters such as the power of the main equipment and the heat dissipation efficiency of the heat dissipation system according to the dynamic weight configuration parameters.

[0146] Specifically, add the previously calculated equipment aging coefficient to the 3D model. By setting the relationship between equipment performance and the aging coefficient in the model, simulate the energy consumption increment caused by equipment performance degradation. For example, as the equipment aging coefficient increases, the power consumption of the main equipment will increase accordingly.

[0147] Specifically, according to parameters such as the transmission power and antenna gain of the communication base station, calculate the wireless signal coverage range at different load rates. Then, analyze the base station networking topology structure to determine whether the coverage range overlaps with the coverage area of adjacent base stations. If there is an overlap, add a signal interference redundant power consumption correction term to the multi-dimensional energy consumption characteristic curve.

[0148] Specifically, according to the real-time load rate, combined with the power characteristics of the baseband unit and radio frequency unit, calculate the power consumption changes at different load rates. For example, when the load rate increases, the processing tasks of the baseband unit and radio frequency unit increase, and the power consumption will also increase accordingly. Plot these power consumption data into a curve to obtain the main equipment energy consumption curve.

[0149] Specifically, based on the dynamic heat dissipation efficiency and the real-time temperature difference, the energy consumption calculation formula of the heat dissipation system is used to calculate the energy consumption of the heat dissipation system under different conditions. For example, when the real-time temperature difference increases, the heat dissipation system needs to consume more energy to maintain the temperature of the base station. These energy consumption data are plotted into a curve to obtain the heat dissipation system energy consumption curve.

[0150] Specifically, by analyzing the base station networking topology structure, the wireless signal coverage overlapping area of the communication base station is identified. According to the size of the overlapping area and the degree of signal interference, the additional power consumption caused by signal interference is calculated. These additional power consumption data are plotted into a curve to obtain the signal interference redundant power consumption curve.

[0151] Specifically, the digital twin model comprehensively considers factors such as dynamic weight configuration parameters, equipment aging, and signal interference, and can more accurately simulate the actual operating state of the communication base station, thereby improving the accuracy of energy efficiency grading and carbon emission evaluation.

[0152] Specifically, the multi-dimensional energy consumption characteristic curve reflects the energy consumption characteristics of the base station from multiple aspects such as the main equipment, heat dissipation system, and signal interference, providing more comprehensive information for the energy-saving optimization of the base station.

[0153] Specifically, by simulating the energy consumption under different load rates, the digital twin model can help operators predict the energy consumption and carbon emissions of the base station under different working conditions, so as to formulate more reasonable energy-saving strategies and operation plans.

[0154] Specifically, constructing a digital twin model is the basis for generating a multi-dimensional energy consumption characteristic curve. Only by constructing an accurate digital twin model can the energy consumption under different load rates be simulated based on this model, and then a multi-dimensional energy consumption characteristic curve can be generated.

[0155] Specifically, the injection of the equipment aging coefficient and the increase of the signal interference redundant power consumption correction term will affect the shape and value of the multi-dimensional energy consumption characteristic curve. For example, equipment aging will cause the main equipment energy consumption curve to rise, and signal interference will make the signal interference redundant power consumption curve show a non-zero value.

[0156] Specifically, the multi-dimensional energy consumption characteristic curve is one of the output results of the digital twin model. It intuitively shows the energy consumption characteristics of the communication base station under different load rates, providing an important basis for subsequent energy efficiency grading and carbon emission evaluation.

[0157] Generally speaking, the method for constructing a digital twin model based on the verified dynamic weight configuration parameters and real-time operation data proposed by the present invention comprehensively considers the influence of various factors on the base station energy consumption, and is more comprehensive and accurate compared with the traditional single-factor modeling method.

[0158] Generally speaking, the introduction of multi-dimensional energy consumption characteristic curves analyzes the energy consumption of communication base stations from multiple perspectives, providing richer information for the energy efficiency evaluation and energy-saving optimization of base stations, and breaking through the traditional single energy consumption index evaluation method.

[0159] Generally speaking, in the modeling process, actual factors such as equipment aging and signal interference are considered and quantified through corresponding correction terms, making the model closer to the actual situation and improving the reliability and practicality of the evaluation results.

[0160] S4. Call the regional power grid dynamic carbon emission factor database stored in the blockchain distributed ledger, and perform spatio-temporal matching between the multi-dimensional energy consumption characteristic curve and the regional power grid dynamic carbon emission factor in the regional power grid dynamic carbon emission factor database, so as to generate the carbon emission intensity matrix of the communication base station.

[0161] In the embodiment of the present invention, the update method of the regional power grid dynamic carbon emission factor database includes:

[0162] Based on the blockchain oracle, perform protocol access to the real-time data interface of the energy structure of the regional power grid to obtain real-time energy structure data of the proportion of thermal power generation, the proportion of wind power generation, and the proportion of photovoltaic power generation;

[0163] Based on the real-time energy structure data, input parameters into a preset carbon emission factor calculation model to obtain the regional power grid carbon emission factor at the current moment;

[0164] Based on the acquisition timestamp and the base station geographical coding, perform spatio-temporal marking and binding on the regional power grid carbon emission factor to obtain a regional power grid dynamic carbon emission factor with spatio-temporal attributes;

[0165] Based on the asymmetric encryption algorithm, perform blockchain node signature processing on the regional power grid dynamic carbon emission factor to obtain a verifiable encrypted carbon emission factor record;

[0166] Based on the blockchain consensus protocol, store the encrypted carbon emission factor record in the distributed ledger to form a regional power grid dynamic carbon emission factor database for the carbon emission intensity matrix to call.

[0167] In the embodiment of the present invention, the calculation formula of the carbon emission intensity in the carbon emission intensity matrix is as follows:

[0168] ;

[0169] Wherein, is the carbon emission intensity, is the energy type index, is the total number of energy types, is the consumption of the nth type of energy, is the regional grid dynamic carbon emission factor corresponding to the category of energy, is the load sensitivity coefficient, is the real-time load rate, is the base station reference load rate.

[0170] Specifically, the total carbon emissions generated by various types of energy consumed by the communication base station are calculated.

[0171] Specifically, is a Sigmoid function used to consider the impact of the load rate on the carbon emission intensity, where is the load sensitivity coefficient, is the real-time load rate, is the base station reference load rate. When the real-time load rate is close to the base station reference load rate the function value is close to 0.5; when is much greater than the function value approaches 1; when is much less than the function value approaches 0.

[0172] Specifically, when calculating the carbon emission intensity of the communication base station, it is necessary to obtain the consumption of various types of energy, the corresponding regional grid dynamic carbon emission factor, the load sensitivity coefficient, the real-time load rate, and the base station reference load rate. Substituting these values into the formula, the carbon emission intensity can be calculated. Through the carbon emission intensity matrix, the carbon emission situation of the communication base station can be quantitatively evaluated, providing a basis for energy efficiency grading and formulating energy conservation and emission reduction strategies.

[0173] Specifically, the blockchain distributed ledger is a decentralized ledger jointly maintained by many blockchain nodes. The data is stored in a distributed form on each node, with characteristics such as immutability and traceability, and is used to store the regional grid dynamic carbon emission factor database.

[0174] Specifically, the regional grid dynamic carbon emission factor database is a collection of carbon emission factor data for different times and different regional grids. These factors reflect the carbon emissions generated per unit of energy consumed by the regional grid under different energy structures and are the key data source for calculating the carbon emission intensity of communication base stations.

[0175] Specifically, the regional grid dynamic carbon emission factor is closely related to the energy structure of the regional grid (such as the proportion of thermal power, wind power, and photovoltaic power generation), and is a carbon emission factor that dynamically adjusts with time and region, and is used to measure the carbon emission level of different energies in the regional grid.

[0176] Specifically, spatio-temporal matching is to accurately match the energy consumption data in the multi-dimensional energy consumption characteristic curve with the corresponding carbon emission factors in the regional power grid dynamic carbon emission factor database according to the time and space dimensions, ensuring the consistency and accuracy of data when calculating the carbon emission intensity.

[0177] Specifically, the carbon emission intensity matrix is a matrix containing the carbon emission intensity data of communication base stations under different conditions, comprehensively reflecting the relationship between the base station energy consumption and the carbon emission of the regional power grid, and providing a quantitative basis for evaluating the carbon emission of base stations.

[0178] Specifically, the blockchain oracle is a bridge connecting the blockchain with external data (such as real-time data of the regional power grid energy structure). It can securely and reliably obtain off-chain data and introduce it into the blockchain system to ensure the authenticity and timeliness of the data.

[0179] Specifically, the real-time energy structure data interface is an interface through which the regional power grid provides real-time energy structure data (such as the proportion of thermal power, wind power, and photovoltaic power generation). Through this interface, the blockchain oracle can obtain the latest energy structure information.

[0180] Specifically, the preset carbon emission factor calculation model is a mathematical model for calculating carbon emission factors based on the energy structure data of the regional power grid. Based on certain algorithms and assumptions, this model can accurately reflect the carbon emission levels corresponding to different energy structures.

[0181] Specifically, the acquisition timestamp is a mark that records the acquisition time of the regional power grid carbon emission factor data, used to identify the timeliness of the data, and plays a role in identifying the time dimension in spatio-temporal matching.

[0182] Specifically, the base station geocoding is a coding used to uniquely identify the geographical location of a communication base station. In spatio-temporal matching, it is used to determine the area where the base station is located, so as to find the corresponding regional power grid dynamic carbon emission factor.

[0183] Specifically, the asymmetric encryption algorithm is an encryption technology that uses a pair of keys (public key and private key) for encryption and decryption. In this scenario, it is used to sign the regional power grid dynamic carbon emission factors to ensure the security and verifiability of the data.

[0184] Specifically, the blockchain node signature is that the blockchain node uses its own private key to encrypt the regional power grid dynamic carbon emission factors to generate a signature. Other nodes can use the corresponding public key for verification to ensure the authenticity and integrity of the data.

[0185] Specifically, the blockchain consensus protocol is the rules and algorithms for nodes in the blockchain network to reach an agreement, ensuring that each node keeps consistent in storing and updating data. In the present invention, it is used to record and store the encrypted carbon emission factors in the distributed ledger.

[0186] Specifically, the load sensitivity coefficient is a parameter that reflects the sensitivity of carbon emission intensity to changes in the real-time load rate, and is used to adjust the influence degree of the load rate on the result in the carbon emission intensity calculation formula.

[0187] Specifically, the base station reference load rate is the standard value of the base station load rate as a reference, which is used to compare with the real-time load rate and consider the impact of load changes on carbon emissions when calculating the carbon emission intensity.

[0188] Furthermore, obtain the regional power grid dynamic carbon emission factor database from the blockchain distributed ledger for subsequent data matching and calculation; correspond the energy consumption data in the multi-dimensional energy consumption characteristic curve with the regional power grid dynamic carbon emission factors according to the time and space dimensions to find the appropriate carbon emission factors for calculation; through a specific calculation and processing process, create a carbon emission intensity matrix to comprehensively reflect the carbon emission situation of the base station; use the blockchain oracle to connect to the real-time data interface of the energy structure of the regional power grid to obtain the real-time energy structure data; provide the real-time energy structure data to the preset carbon emission factor calculation model as the basis for calculation; associate the collection timestamp and the base station geographical coding with the regional power grid carbon emission factors to endow them with spatio-temporal attributes; use the asymmetric encryption algorithm for the blockchain nodes to sign the regional power grid dynamic carbon emission factors to ensure data security; according to the blockchain consensus protocol, record and save the encrypted carbon emission factors to the distributed ledger to form a database.

[0189] Specifically, the steps of calling the database and performing spatio-temporal matching to generate the carbon emission intensity matrix include: First, call the regional power grid dynamic carbon emission factor database from the blockchain distributed ledger; Then, for the energy consumption data in the multi-dimensional energy consumption characteristic curve, according to its corresponding time and the base station geographical location (determined by the collection timestamp and the base station geographical coding), find the corresponding regional power grid dynamic carbon emission factor in the database. For example, if a communication base station is in a certain area at a certain moment, find the carbon emission factor of that area at that moment. Finally, according to the carbon emission intensity calculation formula, multiply the consumption of various types of energy by the corresponding regional power grid dynamic carbon emission factor and accumulate them, and then calculate in combination with the load sensitivity coefficient, the real-time load rate and the base station reference load rate to obtain the carbon emission intensity, and then generate the carbon emission intensity matrix.

[0190] Specifically, the update of the regional power grid dynamic carbon emission factor database includes the following steps: Using a blockchain oracle to access the real-time data interface of the regional power grid's energy structure according to a specific protocol, and obtaining real-time energy structure data such as the proportion of thermal power generation, wind power generation, and photovoltaic power generation; then, inputting these real-time data into a preset carbon emission factor calculation model, and the model calculates the regional power grid carbon emission factor at the current moment according to the internal algorithm; after that, associate and bind the collection timestamp and the base station geographical coding with the calculated regional power grid carbon emission factor to endow it with spatio-temporal attributes; then, use an asymmetric encryption algorithm, and the blockchain node signs the regional power grid dynamic carbon emission factor to generate a verifiable encrypted carbon emission factor record; finally, according to the blockchain consensus protocol, store these encrypted records in the distributed ledger to complete the update of the database.

[0191] Specifically, by calling the regional power grid dynamic carbon emission factor database and performing spatio-temporal matching, and generating a carbon emission intensity matrix by combining a calculation formula considering factors such as the load rate, it is possible to accurately evaluate the carbon emissions of communication base stations and provide accurate data support for energy conservation and emission reduction.

[0192] Specifically, the update method of the regional power grid dynamic carbon emission factor database uses a blockchain oracle to obtain real-time energy structure data, ensuring the timeliness and accuracy of the carbon emission factors in the database, and making the evaluation results more in line with the actual situation.

[0193] Specifically, an asymmetric encryption algorithm and a blockchain consensus protocol are used to sign and store the data, ensuring the security, verifiability, and immutability of the data during transmission and storage, and improving the credibility of the data.

[0194] Furthermore, the update of the regional power grid dynamic carbon emission factor database is the basis for calling the database to perform spatio-temporal matching to generate a carbon emission intensity matrix; the generation of the carbon emission intensity matrix depends on the spatio-temporal matching of the multi-dimensional energy consumption characteristic curve and the regional power grid dynamic carbon emission factor, and the accuracy of the matching depends on the spatio-temporal marker binding of the data in the database; the database update steps such as the blockchain oracle obtaining data, the preset model calculating factors, encryption signature, and distributed storage cooperate with each other to jointly ensure the quality of the database and provide strong support for the generation of the carbon emission intensity matrix.

[0195] Specifically, the present invention combines the regional power grid dynamic carbon emission factor with the communication base station energy consumption data, and generates a carbon emission intensity matrix through spatio-temporal matching and a calculation formula considering the load factor, which is different from the traditional simple estimation method and can more accurately evaluate the carbon emissions of the base station.

[0196] Generally speaking, the use of blockchain oracles to obtain real-time data, blockchain node signatures, and consensus protocol storage ensures the timeliness, accuracy, and security of the dynamic carbon emission factor data of the regional power grid.

[0197] Generally speaking, considering multiple factors such as energy structure, time, space, and load rate to evaluate the carbon emissions of communication base stations comprehensively and meticulously reflects the actual situation.

[0198] S5. Input the multi-dimensional energy consumption characteristic curve and the carbon emission intensity matrix into a pre-trained energy efficiency grading model to output the energy efficiency grading result and carbon emission evaluation report of the communication base station.

[0199] In the embodiment of the present invention, the inputting the multi-dimensional energy consumption characteristic curve and the carbon emission intensity matrix into a pre-trained energy efficiency grading model to output the energy efficiency grading result and carbon emission evaluation report of the communication base station includes:

[0200] Based on the pre-trained energy efficiency grading model, conduct energy efficiency level division on the multi-dimensional energy consumption characteristic curve to obtain an energy efficiency grading result with initial grading labels;

[0201] Based on the carbon emission intensity matrix, conduct low-carbon attribute label matching on the energy efficiency grading result to obtain a multi-level energy efficiency evaluation system with additional low-carbon labels;

[0202] Based on the energy-saving strategy knowledge base, conduct energy-saving measure association mapping on the multi-level energy efficiency evaluation system to obtain a hierarchical response strategy recommendation list including equipment replacement suggestions, load scheduling plans, and heat dissipation optimization;

[0203] Based on the structured report template, conduct carbon emission evaluation integration on the hierarchical response strategy recommendation list to generate a carbon emission evaluation report including a dynamic optimization path.

[0204] Specifically, the pre-trained energy efficiency grading model is a model pre-trained through a large amount of energy consumption and related data of communication base stations, which can conduct level division on the base station energy efficiency according to the input multi-dimensional energy consumption characteristic curve and is one of the core tools for realizing energy efficiency grading and carbon emission evaluation.

[0205] Specifically, the energy efficiency grading result is obtained by analyzing the multi-dimensional energy consumption characteristic curve according to the pre-trained energy efficiency grading model, which is a grading result representing the high or low energy utilization efficiency of the communication base station and generally can be divided into multiple levels, such as high efficiency, medium efficiency, low efficiency, etc.

[0206] Specifically, the carbon emission evaluation report is a report comprehensively evaluating the carbon emission situation of the communication base station, including the current carbon emission situation of the base station, the impact on the environment, and the optimization strategies proposed for reducing carbon emissions, etc.

[0207] Specifically, the low-carbon attribute label is used to mark the performance of different levels in the energy efficiency grading results in terms of carbon emissions, such as "low-carbon", "medium-carbon", "high-carbon", etc., so as to visually distinguish the carbon emission levels of different energy efficiency levels.

[0208] Specifically, the multi-level energy efficiency evaluation system is a comprehensive evaluation system constructed by combining the energy efficiency grading results and the low-carbon attribute label, which can comprehensively evaluate the energy utilization efficiency and carbon emissions of communication base stations from multiple dimensions, providing richer information for subsequent decision-making.

[0209] Specifically, the energy-saving strategy knowledge base is a database that stores various strategies and methods for energy-saving of communication base stations, including equipment replacement suggestions, load scheduling plans, and heat dissipation optimization measures in different scenarios.

[0210] Specifically, the hierarchical response strategy recommendation list is a list of energy-saving measures suitable for communication base stations with different energy efficiency levels and carbon emission levels, screened from the energy-saving strategy knowledge base according to the multi-level energy efficiency evaluation system, and is targeted and practical.

[0211] Specifically, the structured report template is a pre-designed report format that stipulates the content structure, chapter arrangement, data presentation method, etc. of the carbon emission evaluation report, ensuring the standardization and integrity of the report.

[0212] Specifically, the dynamic optimization path is a series of dynamically adjustable optimization measures and steps formulated according to the current energy efficiency grading and carbon emission situation of communication base stations, aiming to help the base stations achieve the goal of energy-saving and carbon reduction, and can be adjusted according to the actual situation changes.

[0213] Furthermore, the multi-dimensional energy consumption characteristic curve and the carbon emission intensity matrix are provided as data to the pre-trained energy efficiency grading model as the basic data for model analysis and processing; using the pre-trained energy efficiency grading model, according to certain algorithms and rules, analyze the multi-dimensional energy consumption characteristic curve to determine the energy efficiency level of the communication base station and complete the preliminary generation of the energy efficiency grading results; combine the carbon emission data in the carbon emission intensity matrix with the energy efficiency grading results to match corresponding low-carbon attribute labels for different energy efficiency levels to make the energy efficiency evaluation more comprehensive; based on the energy efficiency grading results and low-carbon attribute labels, integrate and form a multi-level energy efficiency evaluation system to evaluate the energy efficiency and carbon emissions of the base station from multiple levels; search in the energy-saving strategy knowledge base for energy-saving measures that match different levels and carbon emission situations in the multi-level energy efficiency evaluation system and establish the corresponding relationship between the two; according to the results of the associated mapping, sort out the hierarchical response strategy recommendation list containing equipment replacement suggestions, load scheduling plans, heat dissipation optimization, etc.; according to the requirements of the structured report template, sort out and summarize the information in the hierarchical response strategy recommendation list and integrate the relevant content of the carbon emission evaluation to form a complete carbon emission evaluation report.

[0214] Specifically, the multi-dimensional energy consumption characteristic curve is input into the pre-trained energy efficiency grading model. According to the algorithms and rules obtained from its internal training, the model analyzes information such as the main equipment energy consumption and the heat dissipation system energy consumption reflected in the curve, classifies the energy utilization efficiency of the communication base station, and outputs the energy efficiency grading result with an initial grading label (such as Grade A, Grade B, Grade C, etc.).

[0215] Specifically, the data in the carbon emission intensity matrix is compared and analyzed with the energy efficiency grading result. For different energy efficiency levels, corresponding low-carbon attribute identifiers are matched according to their carbon emission intensities. For example, a base station with a high energy efficiency level and a low carbon emission intensity is marked as "low-carbon"; a base station with a low energy efficiency level and a high carbon emission intensity is marked as "high-carbon". In this way, a multi-level energy efficiency evaluation system with additional low-carbon identifiers is constructed to comprehensively evaluate the base station from two dimensions: energy utilization efficiency and carbon emission.

[0216] Specifically, according to the constructed multi-level energy efficiency evaluation system, a search is performed in the energy-saving strategy knowledge base. For each combination of energy efficiency level and low-carbon attribute identifier, the corresponding energy-saving measures are found. For example, for a "low efficiency - high carbon" base station, suitable equipment replacement suggestions (such as replacing high-energy-consuming equipment with energy-saving equipment), load scheduling schemes (such as adjusting the business load distribution to reduce the load during peak hours), and heat dissipation optimization measures (such as optimizing the heat dissipation system layout) are found from the knowledge base, and a graded response strategy recommendation list is formed.

[0217] Specifically, according to the structured report template, the content in the graded response strategy recommendation list is sorted out. In the report, first introduce the basic information of the communication base station, the current energy efficiency grading, and the carbon emission situation, then elaborate in detail the energy-saving measures recommended for different problems, as well as the expected effects of these measures on reducing carbon emissions, and finally generate a carbon emission evaluation report containing a dynamic optimization path. For example, the report will clearly point out that in the current situation, first adopt a load scheduling scheme to reduce the load, observe the changes in energy consumption and carbon emissions, and then decide whether to perform subsequent operations such as equipment replacement according to the actual situation.

[0218] Generally speaking, by analyzing the multi-dimensional energy consumption characteristic curve through the pre-trained energy efficiency grading model, the energy efficiency of the communication base station can be accurately graded, enabling the operator to clearly understand the energy utilization efficiency level of the base station.

[0219] Generally speaking, by matching the low-carbon attribute identifiers in combination with the carbon emission intensity matrix, a multi-level energy efficiency evaluation system is constructed to comprehensively evaluate the base station from two key aspects of energy efficiency and carbon emission, providing comprehensive guidance for formulating energy-saving and carbon-reduction strategies.

[0220] Generally speaking, generating a hierarchical response strategy recommendation list based on the energy-saving strategy knowledge base provides targeted and operable energy-saving measures for base stations with different energy efficiency and carbon emission levels, helping operators effectively reduce operating costs and carbon emissions.

[0221] Generally speaking, the carbon emission evaluation report generated using the structured report template has a standardized format and complete content. The dynamic optimization path therein provides a clear direction for the continuous optimization of base stations, contributing to the achievement of long-term energy-saving and carbon reduction goals.

[0222] Furthermore, the energy efficiency level division is the basis for subsequent steps, and its results directly affect the matching of low-carbon attribute identifiers and the construction of a multi-level energy efficiency evaluation system. Different energy efficiency levels correspond to different low-carbon attribute identifiers, which in turn determine the retrieval direction in the energy-saving strategy knowledge base and the recommended energy-saving measures.

[0223] Furthermore, the multi-level energy efficiency evaluation system provides a basis for the associated mapping of energy-saving measures. Only based on the energy efficiency and carbon emission conditions of the base stations determined by this system can appropriate energy-saving measures be found from the energy-saving strategy knowledge base to generate a hierarchical response strategy recommendation list.

[0224] Furthermore, the hierarchical response strategy recommendation list is an important part of generating the carbon emission evaluation report. Integrating it according to the structured report template finally forms a carbon emission evaluation report containing a dynamic optimization path, providing a complete solution for the energy-saving and carbon reduction of base stations.

[0225] Generally speaking, combining multi-dimensional energy consumption characteristic curves and carbon emission intensity matrices to construct a multi-level energy efficiency evaluation system breaks through the traditional single-dimensional evaluation method and more comprehensively and accurately evaluates the energy efficiency and carbon emissions of communication base stations.

[0226] Generally speaking, through the associated mapping between the energy-saving strategy knowledge base and the multi-level energy efficiency evaluation system, a targeted hierarchical response strategy recommendation list is automatically generated, realizing intelligent energy-saving decision support, different from traditional manual experience decision-making, and improving decision-making efficiency and accuracy.

[0227] Generally speaking, using the structured report template to generate a carbon emission evaluation report containing a dynamic optimization path provides a complete solution for the energy-saving and carbon reduction of communication base stations from evaluation to strategy formulation and then to optimization path planning.

[0228] In several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways.

[0229] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0230] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence is the theory, method, and technology that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An evaluation method for energy efficiency grading and carbon emissions of communication base stations, characterized in that, The method includes: Collecting real-time operation data through Internet of Things sensors deployed in communication base stations; Generating dynamic weight configuration parameters based on the base station type of the communication base station and the climate characteristics of the location area, and performing decentralized verification on the dynamic weight configuration parameters through blockchain nodes; Constructing a digital twin model of the communication base station based on the verified dynamic weight configuration parameters and the real-time operation data, wherein multi-dimensional energy consumption characteristic curves under different load rates are simulated through the digital twin model; Invoking the regional power grid dynamic carbon emission factor database stored in the blockchain distributed ledger, and performing spatio-temporal matching between the multi-dimensional energy consumption characteristic curves and the regional power grid dynamic carbon emission factors in the regional power grid dynamic carbon emission factor database, so as to generate the carbon emission intensity matrix of the communication base station; Inputting the multi-dimensional energy consumption characteristic curves and the carbon emission intensity matrix into a pre-trained energy efficiency grading model to output the energy efficiency grading result and carbon emission evaluation report of the communication base station.

2. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 1, characterized in that, The real-time operation data includes real-time load rate, environmental parameters, equipment aging coefficient, and power source type, and the real-time operation data is transmitted to the blockchain node through an encrypted channel, wherein: The environmental parameters are determined based on the dynamic heat dissipation efficiency of the heat dissipation system in the communication base station; The equipment aging coefficient is determined based on the ratio of the cumulative operation duration of the base station equipment in the communication base station to the design life; The power source type is used to determine the regional power grid dynamic carbon emission factor.

3. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 2, characterized in that, The calculation formula of the dynamic heat dissipation efficiency is as follows: ； Among them, is the dynamic heat dissipation efficiency, is the real-time power of the heat dissipation system, is the rated power of the heat dissipation system, is the ambient temperature, is the maximum operating temperature threshold allowed for the base station equipment, is the ambient humidity.

4. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 1, characterized in that, The generating dynamic weight configuration parameters based on the base station type of the communication base station and the climate characteristics of the location area includes: Defining the base station type factor of the communication base station according to the energy consumption characteristic differences of macro base stations, micro base stations, and distributed base stations; Determining the climate correction factor of the communication base station according to the climate characteristics of the location area of the communication base station; Performing weighted fusion on the environmental parameter weight and the equipment aging weight based on the climate correction factor and the base station type factor, and performing gain on the real-time load rate weight according to the load dynamic correction factor to obtain the dynamic weight configuration parameters, wherein the calculation formula of the dynamic weight configuration parameters is as follows: ； Among them, is the dynamic weight configuration parameter, is the base station type factor, is the climate correction factor, is the load dynamic correction factor, is the environmental parameter weight, is the equipment aging weight, is the real-time load rate weight.

5. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 1, characterized in that, The performing decentralized verification on the dynamic weight configuration parameters through blockchain nodes includes: Encrypting and broadcasting the dynamic weight configuration parameters to the verification nodes among multiple blockchain nodes; Verifying whether the dynamic weight configuration parameters conform to the dynamic weight mapping rule of the climate characteristics and the base station type through a consensus algorithm, and the dynamic weight mapping rule is stored in the smart contract; If the dynamic weight configuration parameters conform to the dynamic weight mapping rule, triggering the smart contract to write the dynamic weight configuration parameters into the blockchain and synchronously update the input parameters of the digital twin model.

6. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 2, characterized in that The constructing a digital twin model of the communication base station based on the verified dynamic weight configuration parameters and the real-time operation data includes: Perform parametric 3D modeling on the main equipment, cooling system, and power supply module of the communication base station based on the verified dynamic weight configuration parameters to obtain the 3D model of the communication base station; Inject the equipment aging coefficient into the 3D model to simulate the energy consumption increment caused by the degradation of equipment performance; Determine the wireless signal coverage range of the communication base station under different load rates. If the wireless signal coverage range overlaps with the coverage area of adjacent base stations, add a signal interference redundancy power consumption correction term to the multi-dimensional energy consumption characteristic curve.

7. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 6, characterized in that, The multi-dimensional energy consumption characteristic curve includes a main equipment energy consumption curve, a cooling system energy consumption curve, and a signal interference redundancy power consumption curve, where: Calculate the power consumption changes of the baseband unit and the radio frequency unit based on the real-time load rate, thereby generating the main equipment energy consumption curve; Generate the cooling system energy consumption curve according to the dynamic cooling efficiency and the real-time temperature difference; Identify the wireless signal coverage overlapping area of the communication base station through the base station networking topology structure, thereby generating a signal interference redundancy power consumption curve caused by signal interference.

8. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 1, wherein The update method of the regional power grid dynamic carbon emission factor database includes: Perform protocol access to the real-time data interface of the energy structure of the regional power grid based on the blockchain oracle to obtain the real-time energy structure data of the thermal power generation ratio, wind power generation ratio, and photovoltaic power generation ratio; Input the real-time energy structure data into the preset carbon emission factor calculation model to obtain the regional power grid carbon emission factor at the current moment; Perform spatio-temporal label binding on the regional power grid carbon emission factor based on the collection timestamp and the base station geographical coding to obtain the regional power grid dynamic carbon emission factor with spatio-temporal attributes; Perform blockchain node signature processing on the regional power grid dynamic carbon emission factor based on the asymmetric encryption algorithm to obtain a verifiable encrypted carbon emission factor record; Perform distributed ledger storage on the encrypted carbon emission factor record based on the blockchain consensus protocol to form a regional power grid dynamic carbon emission factor database for the carbon emission intensity matrix to call.

9. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 2, wherein The calculation formula for the carbon emission intensity in the carbon emission intensity matrix is as follows: ； Among them, is the carbon emission intensity, is the energy type index, is the total number of energy types, is the consumption of the th type of energy, is the dynamic carbon emission factor of the regional power grid corresponding to the th type of energy, is the load sensitivity coefficient, is the real-time load rate, is the base station reference load rate.

10. The evaluation method for energy efficiency grading and carbon emissions of a communication base station according to claim 1, characterized in that, Inputting the multi-dimensional energy consumption characteristic curve and the carbon emission intensity matrix into the pre-trained energy efficiency grading model to output the energy efficiency grading result and the carbon emission evaluation report of the communication base station includes: Perform energy efficiency level division on the multi-dimensional energy consumption characteristic curve based on the pre-trained energy efficiency grading model to obtain an energy efficiency grading result with an initial grading label; Perform low-carbon attribute label matching on the energy efficiency grading result based on the carbon emission intensity matrix to obtain a multi-level energy efficiency evaluation system with additional low-carbon labels; Perform energy-saving measure association mapping on the multi-level energy efficiency evaluation system based on the energy-saving strategy knowledge base to obtain a hierarchical response strategy recommendation list including equipment replacement suggestions, load scheduling plans, and cooling optimization; Perform carbon emission evaluation integration on the hierarchical response strategy recommendation list based on the structured report template to generate a carbon emission evaluation report including a dynamic optimization path.

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