Energy-saving and efficiency-improving method and device for 5G base station air conditioner, electronic equipment and storage medium

Abstract

The invention discloses a 5G base station air conditioner energy saving and efficiency improving method and device, electronic equipment and a storage medium. The method comprises the steps that S1, real-time operation data of a 5G base station are collected; s2, on the basis of the real-time operation data, a 5G base station air conditioner dynamic energy-saving control model is constructed; s3, based on the 5G base station air conditioner dynamic energy-saving control model, the total energy efficiency of the air conditioner system is calculated; s4, on the basis of the total energy efficiency of the air conditioning system, an objective function and constraint conditions with the maximum energy-saving rate as an objective are constructed; s5, solving the objective function by adopting a genetic algorithm to obtain a dynamic energy-saving strategy of the air conditioner; and S6, the operation state of the air conditioner is controlled according to the air conditioner dynamic energy-saving strategy, and the 5G base station air conditioner dynamic energy-saving control model is iteratively optimized through the feedback effect. According to the method, the dynamic energy-saving control model is constructed, the real-time base station load and the environment parameters are combined, the genetic algorithm is used for optimizing the set temperature, the wind speed and the start-stop strategy of the air conditioner, and accurate control and energy consumption prediction are achieved.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control technology for 5G base stations, specifically to a method, device, electronic equipment, and storage medium for improving the energy efficiency of 5G base station air conditioning. Background Technology

[0002] As a core component of new infrastructure, 5G base stations possess technological advantages such as high bandwidth, low latency, and massive connectivity. However, they also face the challenge of significantly increased energy consumption. Air conditioning systems account for 30%-50% of the total energy consumption of 5G base stations, making them a key factor affecting overall energy efficiency. Existing energy-saving technologies for 5G base station air conditioning mainly suffer from the following problems:

[0003] 1. Rigid control strategy: Traditional air conditioning energy saving often adopts fixed temperature setting (such as setting 25℃ all year round) or simple time period control (such as running during the day and turning off at night), without taking into account the real-time load of the base station (such as the number of user connections and data transmission volume) and the dynamic changes in ambient temperature and humidity, resulting in the air conditioner running excessively during low load periods and insufficient heat dissipation during high load periods.

[0004] 2. Disconnect between energy consumption and operating conditions: Existing technologies have not established a quantitative correlation model between air conditioning energy consumption and base station load, temperature and humidity, making it impossible to accurately assess the energy-saving potential of air conditioning under different operating conditions. This can easily lead to the problem of "conflict between energy saving and communication quality". For example, excessive shutdown of air conditioning to reduce energy consumption can cause overheating of equipment in the base station, affecting the stability of signal transmission.

[0005] 3. Poor applicability of optimization algorithms: Some energy-saving solutions use simple threshold judgment (such as turning on the air conditioner when the temperature is higher than 28℃), without introducing intelligent optimization algorithms to coordinate the optimization of multiple parameters (temperature, wind speed, start and stop time), making it difficult to achieve the dual goals of "maximizing energy saving rate" and "meeting communication quality standards".

[0006] Therefore, there is an urgent need for a 5G base station air conditioning energy-saving and efficiency-enhancing technology that combines the dynamic characteristics of base station operating conditions, quantifies energy consumption correlation, and has intelligent optimization capabilities to address the shortcomings of existing technologies. Summary of the Invention

[0007] The purpose of this invention is to provide a method, device, electronic device and storage medium for improving the energy efficiency of 5G base station air conditioning. By constructing a dynamic energy-saving control model, the invention achieves coordinated optimization of air conditioning and base station operating conditions, maximizing the energy saving rate of air conditioning while ensuring communication quality.

[0008] To achieve the above objectives, the present invention provides a method for improving the energy efficiency of 5G base station air conditioning, comprising the following steps:

[0009] S1. Collect real-time operational data of 5G base stations;

[0010] S2. Based on the real-time operating data, construct a dynamic energy-saving control model for 5G base station air conditioning;

[0011] S3. Calculate the total energy efficiency of the air conditioning system based on the dynamic energy-saving control model of the 5G base station air conditioner;

[0012] S4. Based on the total energy efficiency of the air conditioning system, construct an objective function and constraints with the goal of maximizing energy saving rate;

[0013] S5. Solve the objective function using a genetic algorithm to obtain the dynamic energy-saving strategy for air conditioning;

[0014] S6. Control the air conditioner's operating status according to the aforementioned dynamic energy-saving strategy, and iteratively optimize the 5G base station air conditioner dynamic energy-saving control model using feedback effects.

[0015] Preferably, the collected real-time operating data includes: base station load data, air conditioning operating parameters, and temperature and humidity data;

[0016] The base station load data includes the number of user connections and the data transmission rate;

[0017] The air conditioner operating parameters include basic power consumption, air conditioner power, set temperature, fan speed level, and running time.

[0018] The temperature and humidity data include ambient temperature, ambient humidity, and real-time temperature inside the base station.

[0019] Preferably, the constructed 5G base station air conditioning dynamic energy-saving control model includes: an air conditioning energy consumption sub-model, a temperature and humidity-load correlation sub-model, and a strategy optimization sub-model.

[0020] The air conditioning energy consumption sub-model calculates air conditioning energy consumption based on the air conditioning basic power consumption, temperature influence coefficient, humidity influence coefficient, and load influence coefficient.

[0021] The temperature-humidity-load correlation sub-model predicts the temperature inside the base station based on the load heat generation coefficient, air conditioning temperature control coefficient, and wind speed influence coefficient.

[0022] The strategy optimization sub-model integrates a genetic algorithm to optimize air conditioning operating parameters.

[0023] Preferably, the method for calculating the total energy efficiency of the air conditioning system includes:

[0024]

[0025] Where, η total λ represents the total energy efficiency of the air conditioning system; λ represents the communication quality coefficient; R represents the communication revenue per unit time of the base station (positively correlated with the number of users connected to the base station and the data transmission rate); t air E represents the operating time of the air conditioner. airThis represents the total energy consumption of the air conditioner.

[0026] Preferably, the constructed objective function and constraints include:

[0027]

[0028] The constraints of the energy-saving and efficiency-improving methods for 5G base station air conditioning are:

[0029]

[0030] in, S represents the air conditioning energy saving rate; S represents the real-time signal strength of the base station. th E is the signal strength threshold. air0 Let D be the air conditioning energy consumption when using a fixed temperature strategy, and D be the base station communication latency. th T is the time delay threshold. in_min T in_max These are the minimum and maximum allowable temperatures inside the base station, respectively, V air For wind speed levels, E air This represents the air conditioning energy consumption when a dynamic strategy is employed.

[0031] Preferably, the method for solving the objective function using a genetic algorithm includes:

[0032] Initialize the genetic algorithm parameters, including population size, number of iterations, crossover probability, and mutation probability;

[0033] Chromosome encoding is performed to encode the air conditioner's set temperature, fan speed level, and start / stop times into chromosomes;

[0034] Perform genetic operations, including selection, crossover, and mutation, to optimize the fitness function;

[0035] When the iteration termination condition is met, the dynamic energy-saving strategy for air conditioning corresponding to the optimal chromosome is output.

[0036] Preferably, S6 includes:

[0037] According to the aforementioned dynamic energy-saving strategy for air conditioning, adjust the air conditioning set temperature, fan speed level, and running time.

[0038] Collect data on air conditioning energy consumption, real-time signal strength of base stations, and base station communication latency;

[0039] Calculate the actual energy saving rate and compare it with the energy saving rate predicted by the model.

[0040] If the error between the actual energy saving rate and the model's predicted energy saving rate exceeds a threshold, the parameters of the 5G base station air conditioning dynamic energy saving control model will be updated.

[0041] The present invention also provides a 5G base station air conditioning energy-saving and efficiency-improving device, the device being used to implement the above method, comprising: a data acquisition module, a model building module, a total energy efficiency calculation module, an objective function construction module, an objective function solving module, and an air conditioning control module;

[0042] The data acquisition module is used to collect real-time operating data of 5G base stations;

[0043] The model building module is used to build a dynamic energy-saving control model for 5G base station air conditioning based on the real-time operating data.

[0044] The total energy efficiency calculation module is used to calculate the total energy efficiency of the air conditioning system based on the 5G base station air conditioning dynamic energy-saving control model;

[0045] The objective function construction module is used to construct an objective function and constraints with the goal of maximizing energy saving rate based on the total energy efficiency of the air conditioning system.

[0046] The objective function solving module is used to solve the objective function using a genetic algorithm to obtain a dynamic energy-saving strategy for air conditioning.

[0047] The air conditioning control module is used to control the air conditioning operation status according to the air conditioning dynamic energy-saving strategy, and to iteratively optimize the 5G base station air conditioning dynamic energy-saving control model by utilizing feedback effects.

[0048] The present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the above-described 5G base station air conditioning energy-saving and efficiency-enhancing method.

[0049] The present invention also provides a storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the above-described 5G base station air conditioning energy-saving and efficiency-enhancing method.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] 1. Strong dynamic adaptability: By constructing a dynamic control model with multiple sub-model coupling, the air conditioning strategy can be matched with the base station load, temperature and humidity in real time, avoiding energy waste or communication quality degradation caused by "one-size-fits-all" control.

[0052] 2. Significant energy-saving effect: By introducing intelligent optimization algorithms for multi-parameter collaborative optimization, the measured energy-saving rate can reach 30%-60%, which is far higher than the 10%-15% of the traditional fixed strategy;

[0053] 3. High practicality: Model parameters can be calibrated using historical data, without relying on complex hardware modifications, and can be directly adapted to existing 5G base station air conditioning systems, resulting in low deployment costs;

[0054] 4. Balancing dual objectives: By strictly ensuring the communication quality of base stations through constraints, a win-win situation of "energy saving and efficiency improvement" and "communication stability" is achieved. Attached Figure Description

[0055] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of the method flow provided in an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of the dynamic energy-saving control model for 5G base station air conditioning according to an embodiment of the present invention;

[0058] Figure 3 This is a flowchart of the genetic algorithm for solving the objective function according to an embodiment of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0061] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0062] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0063] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0064] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0065] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0066] Example 1

[0067] like Figure 1 The diagram shown is a schematic representation of the method flow in this embodiment, and the steps include:

[0068] S1. Collect real-time operational data of 5G base stations.

[0069] By setting a fixed sampling frequency, the following data is collected through a "sensor + terminal module" deployed in 5G base stations:

[0070] 1. Base station load data L: The number of user connections and data transmission rate are obtained through the base station core network interface and converted to GB / h;

[0071] 2. Air Conditioner Operating Parameters: The system communicates with the air conditioner controller via an interface to obtain the air conditioner's basic power consumption P. air0 Air conditioner power P air Air conditioner set temperature T set Wind speed level V air runtime tair ;

[0072] 3. Temperature and humidity data: Outdoor sensors collect ambient temperature T. env Ambient humidity H env Multiple sensors in the computer room collect real-time temperature data (T) inside the base station. in The data is transmitted to the network access layer via the cellular network.

[0073] S2. Based on real-time operating data, construct a dynamic energy-saving control model for 5G base station air conditioning.

[0074] Specifically, such as Figure 2 The diagram shown is a structural schematic of the constructed 5G base station air conditioning dynamic energy-saving control model. Based on the real-time operating data collected by S1, the model parameters are calibrated using the least squares method, specifically including the following steps:

[0075] S201. Fitting parameters of the air conditioning energy consumption sub-model.

[0076] The expression for the air conditioning energy consumption sub-model is:

[0077]

[0078] Among them, E air P represents the total energy consumption of the air conditioner during the time period [t0, t1]. air0 For the basic power consumption of the air conditioner; k T The temperature influence coefficient represents the weight of the difference between the setpoint temperature and the ambient temperature on energy consumption; k H The humidity influence coefficient represents the weight of the impact of environmental humidity on energy consumption; k L is the load impact coefficient, which characterizes the weight of the base station load on the air conditioning heat dissipation demand; t is the time variable.

[0079] The basic power consumption P of the air conditioner is fitted based on the air conditioner energy consumption model. air0 Temperature influence coefficient k T Humidity influence coefficient k H Load influence coefficient k L Ensure that the energy consumption calculation error is ≤10%.

[0080] S202. Verify the parameters of the temperature-humidity-load correlation sub-model.

[0081] The expression for the temperature-humidity-load correlation sub-model is:

[0082]

[0083] Among them, T inV represents the real-time temperature inside the base station; α is the load heat generation coefficient, representing the temperature increment per unit load; β is the air conditioning temperature control coefficient, representing the efficiency of the set temperature in regulating the temperature inside the base station; γ is the wind speed influence coefficient, representing the weight of wind speed level in improving the efficiency of air conditioning temperature control; air This refers to the air conditioner fan speed level (value range 0-5, corresponding to different fan speed settings).

[0084] The load heat production coefficient α, air conditioning temperature control coefficient β, and wind speed influence coefficient γ were verified based on the temperature-humidity-load correlation sub-model to ensure T in The prediction error is within a controllable range.

[0085] S203. Set the initialization parameters of the strategy optimization sub-model.

[0086] The strategy optimization sub-model in this embodiment integrates a genetic algorithm. Initializing the genetic algorithm parameters is fundamental to its startup; proper initialization defines the effective search range, ensures sufficient diversity in the initial population, and prevents early convergence. Simultaneously, it can set suitable initial conditions based on the characteristics of the optimization problem, reducing unnecessary computational waste and providing a high-quality starting point for subsequent "selection, crossover, and mutation" operations. This directly impacts the algorithm's final optimization accuracy and efficiency, and is a necessary guarantee for the stable operation of the genetic algorithm.

[0087] Based on the above issues, the parameters of the preset genetic algorithm are initialized, including population size, number of iterations, crossover probability, and mutation probability.

[0088] S3. Calculate the total energy efficiency of the air conditioning system based on the dynamic energy-saving control model of 5G base station air conditioning.

[0089] Based on the "energy-carbon" coupled evaluation framework, the total energy efficiency of 5G base station air conditioning systems can be used to evaluate the economy and effectiveness of strategies.

[0090] Total Energy Efficiency Ratio (η) of Air Conditioning System total The calculation expression is:

[0091]

[0092] Where λ is the communication quality coefficient (ranging from 0.8 to 1.0, with 1.0 taken when communication quality meets the standard); R is the communication revenue per unit time of the base station (positively correlated with the number of users connected to the base station and the data transmission rate); t air E represents the operating time of the air conditioner. air This represents the total energy consumption of the air conditioner.

[0093] S4. Based on the total energy efficiency of the air conditioning system, construct an objective function and constraints with the goal of maximizing energy saving rate.

[0094] With maximizing air conditioning energy efficiency as the core objective, and combining this with energy efficiency constraints, we construct an objective function for improving the energy efficiency of 5G base station air conditioning, as shown in the following formula:

[0095]

[0096] The constraints of the energy-saving and efficiency-improving methods for 5G base station air conditioning are:

[0097]

[0098] in, S represents the air conditioning energy saving rate; S represents the real-time signal strength of the base station. th E is the signal strength threshold. air0 Let D be the air conditioning energy consumption when using a fixed temperature strategy, and D be the base station communication latency. th T is the time delay threshold. in_min T in_max These are the minimum and maximum allowable temperatures inside the base station, respectively, V air For wind speed levels, E air The energy consumption of air conditioning when using the dynamic strategy of this invention.

[0099] S5. The objective function is solved using a genetic algorithm to obtain the dynamic energy-saving strategy for air conditioning.

[0100] This embodiment selects a genetic algorithm to solve the objective function. Specifically, as shown below... Figure 3 The diagram shown illustrates the flowchart for solving the objective function using a genetic algorithm, including the following specific steps:

[0101] S501. Initialize the strategy optimization sub-model.

[0102] The parameters of the preset genetic algorithm are initialized, including population size, number of iterations, crossover probability, and mutation probability.

[0103] S502. Chromosome encoding.

[0104] Set the air conditioner to temperature T set Wind speed level V air and start / stop time t on / t off As a gene chromosome, it uses a binary-real number hybrid encoding;

[0105] Where T set Encoded with 8 real numbers, V air Encoded with 3 bits, t on / t off The chromosome is encoded using 16 bits of binary code, and its total length is 27 bits.

[0106] S503. Genetic operations.

[0107] Using the objective function as the fitness function, calculate the fitness value for each chromosome;

[0108] Preferably, the selection operation uses the roulette wheel selection method, where chromosomes with higher fitness values ​​have a greater probability of being selected;

[0109] The crossover operation combines arithmetic crossover and single-point crossover, using arithmetic crossover for the real number encoding part and single-point crossover for the binary encoding part.

[0110] The mutation operation combines Gaussian mutation and bit-flip mutation, performing Gaussian mutation on the real number encoding part and bit-flip mutation on the binary encoding part;

[0111] S504. Iteration terminated.

[0112] By generating a new generation of population, the termination condition is determined. When the number of iterations reaches a preset value or the change in the optimal fitness value after 5 consecutive iterations is ≤0.5%, the iteration is terminated, and the dynamic energy-saving strategy for air conditioning corresponding to the optimal chromosome is output.

[0113] S6. Control the air conditioner's operating status according to the air conditioner's dynamic energy-saving strategy, and at the same time use the feedback effect to iteratively optimize the 5G base station air conditioner dynamic energy-saving control model.

[0114] Based on the derived dynamic energy-saving strategy for the air conditioner, the optimal strategy is converted into a control command and sent to the air conditioner controller via the RS485 interface to adjust the air conditioner's operating status. The adjustment strategy is as follows:

[0115] When the base station load L≥ L high (At high load threshold) control the air conditioner set temperature T set Reduced to T set_low Wind speed level V air Upgraded to V high Extend runtime t air .

[0116] When the base station load L≤ L low (Low load threshold) and ambient temperature T env ≤ T env_low When the ambient temperature threshold is low, the air conditioner is controlled to enter intermittent operation mode, and the start-stop cycle t is set. cycle =t on +t off and reduce the wind speed level to V. low .

[0117] When the temperature inside the base station is T in When the temperature exceeds the allowable range, emergency control is triggered, prioritizing adjustments to the air conditioning set temperature and fan speed to ensure T in Return to [T] in_min , Tin_max ] interval.

[0118] One data collection cycle after the strategy is executed, the air conditioning energy consumption E is collected. air The actual energy saving rate ε is calculated based on the base station's real-time signal strength S and communication delay D. actual .

[0119] If the actual energy saving rate ε actual If the error between the energy saving rate ε predicted by the model and the actual energy saving rate ε is greater than 10%, then update the parameter k of the energy consumption sub-model. T k L And retrain the model to ensure long-term optimization results.

[0120] Example 2

[0121] This embodiment also provides a 5G base station air conditioning energy-saving and efficiency-improving device, including: a data acquisition module, a model building module, a total energy efficiency calculation module, an objective function construction module, an objective function solving module, and an air conditioning control module; the data acquisition module is used to collect real-time operating data of the 5G base station; the model building module is used to construct a dynamic energy-saving control model for the 5G base station air conditioning based on the real-time operating data; the total energy efficiency calculation module is used to calculate the total energy efficiency of the air conditioning system based on the dynamic energy-saving control model for the 5G base station air conditioning; the objective function construction module is used to construct an objective function and constraints with the goal of maximizing the energy saving rate based on the total energy efficiency of the air conditioning system; the objective function solving module is used to solve the objective function using a genetic algorithm to obtain the dynamic energy-saving strategy for the air conditioning; the air conditioning control module is used to control the operating state of the air conditioning according to the dynamic energy-saving strategy for the air conditioning and to iteratively optimize the dynamic energy-saving control model for the 5G base station air conditioning using feedback effects.

[0122] Example 3

[0123] Accordingly, embodiments of the present invention provide an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the 5G base station air conditioning energy-saving and efficiency-enhancing method.

[0124] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0125] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0126] Example 4

[0127] Accordingly, this embodiment of the invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the 5G base station air conditioning energy-saving and efficiency-improving method.

[0128] The memory can be used to store the computer program. The processor implements various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart memory card, secure digital card, flash memory card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0129] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0130] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for improving energy efficiency in 5G base station air conditioning, characterized in that, Includes the following steps: S1. Collect real-time operational data of 5G base stations; S2. Based on the real-time operating data, construct a dynamic energy-saving control model for 5G base station air conditioning; S3. Calculate the total energy efficiency of the air conditioning system based on the dynamic energy-saving control model of the 5G base station air conditioner; S4. Based on the total energy efficiency of the air conditioning system, construct an objective function and constraints with the goal of maximizing energy saving rate; S5. Solve the objective function using a genetic algorithm to obtain the dynamic energy-saving strategy for air conditioning; S6. Control the air conditioner's operating status according to the aforementioned dynamic energy-saving strategy, and iteratively optimize the 5G base station air conditioner dynamic energy-saving control model using feedback effects.

2. The method for improving energy efficiency and effectiveness of base station air conditioning according to claim 1, characterized in that, The collected real-time operational data includes: base station load data, air conditioning operating parameters, and temperature and humidity data; The base station load data includes the number of user connections and the data transmission rate; The air conditioner operating parameters include basic power consumption, air conditioner power, set temperature, fan speed level, and running time. The temperature and humidity data include ambient temperature, ambient humidity, and real-time temperature inside the base station.

3. The method for improving energy efficiency and effectiveness of base station air conditioning according to claim 1, characterized in that, The constructed 5G base station air conditioning dynamic energy-saving control model includes: an air conditioning energy consumption sub-model, a temperature and humidity-load correlation sub-model, and a strategy optimization sub-model. The air conditioning energy consumption sub-model calculates air conditioning energy consumption based on the air conditioning basic power consumption, temperature influence coefficient, humidity influence coefficient, and load influence coefficient. The temperature-humidity-load correlation sub-model predicts the temperature inside the base station based on the load heat generation coefficient, air conditioning temperature control coefficient, and wind speed influence coefficient. The strategy optimization sub-model integrates a genetic algorithm to optimize air conditioning operating parameters.

4. The method for improving energy efficiency and effectiveness of base station air conditioning according to claim 1, characterized in that, The method for calculating the total energy efficiency of the air conditioning system includes: Where, η total λ represents the total energy efficiency of the air conditioning system; λ represents the communication quality coefficient; R represents the communication revenue per unit time of the base station (positively correlated with the number of users connected to the base station and the data transmission rate); t air E represents the operating time of the air conditioner. air This represents the total energy consumption of the air conditioner.

5. The method for improving energy efficiency and effectiveness of base station air conditioning according to claim 1, characterized in that, The constructed objective function and constraints include: The constraints of the energy-saving and efficiency-improving methods for 5G base station air conditioning are: in, S represents the air conditioning energy saving rate; S represents the real-time signal strength of the base station. th E is the signal strength threshold. air0 Let D be the air conditioning energy consumption when using a fixed temperature strategy, and D be the base station communication latency. th T is the time delay threshold. in_min T in_max These are the minimum and maximum allowable temperatures inside the base station, respectively, V air For wind speed levels, E air This represents the air conditioning energy consumption when a dynamic strategy is employed.

6. The method for improving energy efficiency and effectiveness of base station air conditioning according to claim 1, characterized in that, The methods for solving the objective function using genetic algorithms include: Initialize the genetic algorithm parameters, including population size, number of iterations, crossover probability, and mutation probability; Chromosome encoding is performed to encode the air conditioner's set temperature, fan speed level, and start / stop times into chromosomes; Perform genetic operations, including selection, crossover, and mutation, to optimize the fitness function; When the iteration termination condition is met, the dynamic energy-saving strategy for air conditioning corresponding to the optimal chromosome is output.

7. The method for improving energy efficiency and effectiveness of base station air conditioning according to claim 1, characterized in that, S6 includes: According to the aforementioned dynamic energy-saving strategy for air conditioning, adjust the air conditioning set temperature, fan speed level, and running time. Collect data on air conditioning energy consumption, real-time signal strength of base stations, and base station communication latency; Calculate the actual energy saving rate and compare it with the energy saving rate predicted by the model. If the error between the actual energy saving rate and the model's predicted energy saving rate exceeds a threshold, the parameters of the 5G base station air conditioning dynamic energy saving control model will be updated.

8. A 5G base station air conditioning energy-saving and efficiency-improving device, the device being used to implement the method described in any one of claims 1-7, characterized in that, include: The system includes a data acquisition module, a model building module, a total energy efficiency calculation module, an objective function construction module, an objective function solving module, and an air conditioning control module. The data acquisition module is used to collect real-time operating data of 5G base stations; The model building module is used to build a dynamic energy-saving control model for 5G base station air conditioning based on the real-time operating data. The total energy efficiency calculation module is used to calculate the total energy efficiency of the air conditioning system based on the 5G base station air conditioning dynamic energy-saving control model; The objective function construction module is used to construct an objective function and constraints with the goal of maximizing energy saving rate based on the total energy efficiency of the air conditioning system. The objective function solving module is used to solve the objective function using a genetic algorithm to obtain a dynamic energy-saving strategy for air conditioning. The air conditioning control module is used to control the air conditioning operation status according to the air conditioning dynamic energy-saving strategy, and to iteratively optimize the 5G base station air conditioning dynamic energy-saving control model by utilizing feedback effects.

9. An electronic device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the 5G base station air conditioning energy-saving and efficiency-enhancing method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the 5G base station air conditioning energy-saving and efficiency-improving method as described in any one of claims 1 to 7.

Images