Energy-saving method and equipment for base station and computer readable storage medium

Abstract

The embodiment of the invention provides an energy-saving method of a base station, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a total power consumption prediction model of the base station, the total power consumption prediction model being determined according to a main equipment power consumption prediction model and a corollary equipment power consumption prediction model of the base station; and within the adjustable range of the adjustable working parameters of the corollary equipment, optimizing the adjustable working parameters of the corollary equipment at the target time so as to enable the estimated total power consumption of the base station at the target time predicted according to the total power consumption prediction model to be the lowest. Taking the adjustable working parameter when the estimated total power consumption is the lowest as a target working value; and adjusting the working value of the adjustable working parameter of the corollary equipment at the target time according to the target working value of the adjustable working parameter. Therefore, the energy consumption of the base station can be effectively reduced.

Description

Technical Field

[0001] This document relates to the field of mobile communication technology, and in particular to a base station energy-saving method, device and computer-readable storage medium. Background Art

[0002] In the operation of mobile communication networks, electricity costs are one of the largest network maintenance costs for operators. For example, with the rapid development of 5G networks, 5G's large bandwidth, ultra-low latency and massive connection characteristics will usher in an era of the Internet of Everything. Although it brings unlimited opportunities, the power consumption of base stations has also increased significantly, which will not only consume a lot of electricity, but also increase the operating costs of operators.

[0003] Therefore, how to effectively reduce the energy consumption of base stations is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of this specification provide a base station energy-saving method, device, and computer-readable storage medium, which are used to solve the problem of how to effectively reduce the energy consumption of the base station.

[0005] To solve the above technical problems, the embodiments of this specification are implemented as follows:

[0006] In a first aspect, a base station energy saving method is provided, including:

[0007] Obtaining a total power consumption prediction model of the base station, wherein the total power consumption prediction model is determined according to a main device power consumption prediction model and an auxiliary device power consumption prediction model of the base station;

[0008] Within the adjustable range of the adjustable working parameters of the supporting equipment, optimizing the adjustable working parameters of the supporting equipment at the target time, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and taking the adjustable working parameters when the estimated total power consumption is the lowest as the target working value;

[0009] According to the target operating value of the adjustable operating parameter, the operating value of the adjustable operating parameter of the supporting equipment at the target time is adjusted.

[0010] In a second aspect, a base station energy saving device is provided, including:

[0011] A model acquisition module, used to acquire a total power consumption prediction model of a base station, wherein the total power consumption prediction model is determined based on a main device power consumption prediction model and a supporting device power consumption prediction model of the base station;

[0012] A parameter value optimization module, used to optimize the adjustable operating parameters of the supporting equipment at the target time within the adjustable range of the adjustable operating parameters of the supporting equipment, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and the adjustable operating parameters when the estimated total power consumption is the lowest are used as target operating values;

[0013] The working value adjustment module is used to adjust the working value of the adjustable working parameter of the supporting equipment at the target time according to the target working value of the adjustable working parameter.

[0014] According to a third aspect, an electronic device is provided, including:

[0015] processor;

[0016] a memory for storing instructions executable by the processor;

[0017] The processor is configured to execute the instructions to implement the method as described in the first aspect.

[0018] According to a fourth aspect, a computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the method described in the first aspect.

[0019] In the embodiments of the present specification, on the one hand, in addition to the power consumption of the main device, the power consumption of the supporting equipment is also included in the scope of consideration for reducing the total power consumption of the base station; on the other hand, after obtaining the total power consumption prediction model of the base station, the adjustable working parameters of the supporting equipment at the target time are optimized within the adjustable range of the adjustable working parameters of the supporting equipment, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and the adjustable working parameters when the estimated total power consumption is the lowest are used as the target working values; and according to the target working values ​​of the adjustable working parameters, the working values ​​of the adjustable working parameters of the supporting equipment at the target time are dynamically adjusted, so that the total power consumption of the base station is minimized, thereby effectively reducing the energy consumption of the base station. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1It is a flowchart of a base station energy saving method provided in an embodiment of this specification.

[0022] Figure 2 It is a flowchart of a base station energy saving method provided by another embodiment of this specification.

[0023] Figure 3 It is a schematic diagram of the implementation structure of a base station energy saving method provided in an embodiment of this specification.

[0024] Figure 4 This is another structural diagram of an implementation of a base station energy saving method provided in an embodiment of this specification.

[0025] Figure 5 is a schematic diagram of the structure of an electronic device according to an embodiment of the present specification;

[0026] Figure 6 It is a structural diagram of an energy-saving device of a base station according to an embodiment of the present specification.

[0027] Figure 7 It is a structural diagram of an energy-saving device of a base station according to another embodiment of the present specification. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. According to the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0029] The terms "first", "second", etc. in this specification and claims are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of this specification can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in this specification and claims means at least one of the connected objects, and the character " / " generally means that the objects associated with each other are in an "or" relationship.

[0030] In order to effectively reduce the energy consumption of the base station, the embodiment of this specification proposes a base station energy saving method, which can be implemented by the base station itself (see Figure 4 ) or by a centralized processing unit independent of the base station (see Figure 3), such as network management, mobile edge computing or multi-access edge computing (MEC). The centralized or distributed processing unit can obtain information such as user load, energy-saving strategy, air conditioning, power supply, etc. in the jurisdiction area to generate a comprehensive energy-saving strategy for the entire site.

[0031] An energy-saving method for a base station provided in an embodiment of this specification aims to coordinately adjust the working parameter values ​​of supporting equipment such as air conditioners according to the load of the main equipment in the base station and the energy-saving strategy (the energy-saving strategy of the base station itself), thereby reducing the total power consumption of the base station as a whole and achieving the goal of optimizing the overall energy consumption of the entire station equipment.

[0032] The following is a detailed description of a base station energy saving method proposed in an embodiment of this specification.

[0033] like Figure 1 As shown, an energy saving method of a base station provided by an embodiment of this specification may include:

[0034] Step 101, obtaining a total power consumption prediction model of a base station, wherein the total power consumption prediction model is determined based on a main equipment power consumption prediction model and an auxiliary equipment power consumption prediction model of the base station.

[0035] The main equipment of the base station includes a cabinet, and the supporting equipment of the base station generally includes a cabinet air conditioner and / or a computer room air conditioner. Of course, the supporting equipment of the base station may also include power supply equipment and the like.

[0036] Among them, the main equipment power consumption prediction model and the supporting equipment power consumption prediction model of the base station are pre-built based on relevant sample data.

[0037] For some base stations, their supporting air conditioning equipment includes computer room air conditioners and cabinet air conditioners; while for other base stations, their supporting air conditioning equipment only includes cabinet air conditioners. For these two situations, the models that need to be pre-built include: base station load prediction model, cabinet power consumption prediction model, computer room air conditioning prediction model and cabinet air conditioning prediction model, among which cabinet power consumption refers to the power consumption of the base station itself or the main equipment of the base station, computer room air conditioner refers to the air conditioner installed in the computer room where the base station is located and located outside the cabinet, and cabinet air conditioner refers to the air conditioner installed in the cabinet of the base station.

[0038] The following is a brief introduction to the construction process of the above model.

[0039] First, collect relevant information for modeling.

[0040] Such information may include but is not limited to at least one of the following:

[0041] 1) Base station information, including but not limited to:

[0042] a. User load information, including but not limited to the number of users, uplink and downlink physical resource blocks (PRBs), PRB utilization, uplink and downlink throughput, uplink and downlink user rates, etc.

[0043] b. Energy consumption information

[0044] c. Cabinet board temperature information

[0045] d. Cabinet fan speed information

[0046] 2) Air conditioning information, including but not limited to: ambient temperature and air conditioning temperature information.

[0047] 3) Public network weather information

[0048] When there is an interface with the external public network, you can crawl the weather and forecast information in the area, including: temperature, weather, humidity, etc.

[0049] 4) Power consumption information, etc.

[0050] Secondly, modeling is performed based on the collected relevant information.

[0051] 1) Modeling of load forecasting model

[0052] Generally speaking, a load forecasting model is constructed based on the historical load time series of base stations.

[0053] Specifically, for the user load information of each cell under the base station, a certain time series modeling method is used to build a load forecasting model so as to predict the user load of the base station at a certain time or period in the future. The load modeling can adopt the prediction modeling methods based on time series such as Long short-term memory (LSTM) and Autoregressive Integrated Moving Average Model (ARIMA).

[0054] 2) Construction of cabinet power consumption prediction model

[0055] The embodiments of this specification have found that the power consumption of the cabinet is related to factors such as the actual load of the base station (number of users, etc.), the energy-saving strategy of the base station, and the cabinet temperature. The higher the actual load of the base station, the more baseband resources the base station uses, the higher the cabinet temperature, and the higher the corresponding cabinet power consumption. At the same time, the power consumption of the cabinet is also affected by the energy-saving strategy of the base station itself. When there are cells in the base station that implement energy-saving strategies such as sleep, the power consumption of the cabinet will be reduced.

[0056] Therefore, as an example, a cabinet power consumption prediction model can be trained based on first sample data, wherein the first sample data may include but is not limited to the historical load of the base station, the historical temperature of the cabinet of the base station, the historical energy-saving strategy of the cabinet, and the historical power consumption of the cabinet.

[0057] Among them, the historical load of the base station is determined based on the user load information collected above; the historical energy-saving strategy of the cabinet refers to the historical energy-saving strategy of the base station itself, specifically refers to the carrier shutdown, deep sleep and other strategies executed by the base station based on the actual load; the historical power consumption of the cabinet refers to the power consumption of the main equipment of the base station, and this data can also be obtained from the relevant information collected above.

[0058] The constructed cabinet power consumption prediction model can be expressed as: nbPower(load, esDuration, temp), where load represents the load of the base station, esDuration represents the energy-saving strategy of the base station, and temp represents the temperature of the cabinet (specifically, it can be the temperature of the cabinet board).

[0059] The basic model for constructing the cabinet power consumption prediction model may be a neural network or a general regression model.

[0060] Optionally, since the cabinet power consumption of base stations of different models may be different, the base station model may be introduced into the first sample data, that is, the base station model may also be taken into consideration for modeling to improve the accuracy of the prediction results given by the constructed model. Of course, different cabinet power consumption prediction models may also be constructed for base stations of different models, which may also improve the accuracy of the prediction results given by the constructed model.

[0061] 3) Construction of cabinet air conditioner power consumption prediction model

[0062] The power consumption of the air conditioner is mainly affected by the indoor and outdoor temperatures and the air conditioner cooling temperature (return air temperature). The higher the indoor temperature and / or the lower the return air temperature setting, the greater the power consumption of the air conditioner. Therefore, the power consumption of the air conditioner can be modeled based on these factors.

[0063] As an example, a cabinet air conditioner power consumption prediction model may be trained based on second sample data, wherein the second sample data includes but is not limited to historical operating values ​​of adjustable operating parameters of the cabinet air conditioner, ambient temperature, and historical power consumption.

[0064] The basic model for constructing the cabinet air conditioner power consumption prediction model may also be a neural network or a general regression model.

[0065] Optionally, since air conditioning cooling conditions are also related to factors such as humidity, cabinet size, and cabinet material, these factors may also be introduced into the above-mentioned second sample data to improve the accuracy of the prediction results given by the constructed cabinet power consumption prediction model.

[0066] 4) Construction of computer room air conditioning power consumption prediction model

[0067] Like cabinet air conditioners, the power consumption of computer room air conditioners is also mainly affected by indoor and outdoor temperatures and the air conditioning cooling temperature (return air temperature). The higher the indoor temperature and / or the lower the return air temperature setting, the greater the power consumption of the air conditioner. Therefore, the power consumption of the air conditioner can be modeled based on these factors.

[0068] As an example, a computer room air conditioner power consumption prediction model may be trained based on third sample data, wherein the third sample data includes historical operating values ​​of adjustable operating parameters of the computer room air conditioner, ambient temperature, and historical power consumption.

[0069] The basic model for constructing the power consumption prediction model of the computer room air conditioner can also be a neural network or a general regression model.

[0070] Optionally, since the air conditioning cooling conditions are also related to factors such as humidity, cabinet size and cabinet material (the material of the computer room's exterior wall affects the heat conduction of the outdoor temperature), these factors can also be introduced into the above-mentioned third sample data to improve the accuracy of the prediction results given by the constructed cabinet power consumption prediction model.

[0071] 5) Construction of cabinet temperature prediction model

[0072] The cabinet temperature refers to the temperature of the cabinet itself, such as the temperature of a cabinet board. The cabinet temperature is related to the temperature of the cabinet air conditioner and / or the room air conditioner. Therefore, as an example, the cabinet temperature prediction model of the base station can be trained based on the fourth sample data, wherein the fourth sample data includes the historical set temperatures of the cabinet air conditioner and / or the room air conditioner of the base station, and the historical temperature of the cabinet, etc.

[0073] After the power consumption prediction model of the main equipment and the power consumption prediction model of the supporting equipment of the base station are constructed, the total power consumption model of the base station can be determined according to the power consumption prediction model of the main equipment and the power consumption prediction model of the supporting equipment of the base station. Specifically, depending on whether the supporting equipment of the base station includes the air conditioner in the computer room or not, it is divided into the following two cases:

[0074] Case 1: The supporting equipment of the base station includes cabinet air conditioners and computer room air conditioners

[0075] In the case where the base station is equipped with a computer room air conditioner and a cabinet air conditioner, the main equipment power consumption prediction model includes a cabinet power consumption prediction model, the supporting equipment power consumption prediction model includes a computer room air conditioner power consumption prediction model and a cabinet air conditioner power consumption prediction model, and the total power consumption prediction model is determined based on the cabinet power consumption prediction model, the computer room air conditioner power consumption prediction model and the cabinet air conditioner power consumption prediction model.

[0076] Specifically, obtain the pre-built cabinet power consumption prediction model, the computer room air conditioner power consumption prediction model and the cabinet air conditioner power consumption prediction model; add the cabinet power consumption prediction model, the computer room air conditioner power consumption prediction model and the cabinet air conditioner power consumption prediction model to obtain the total power consumption prediction model.

[0077] At this time, illustratively, the total power consumption prediction model can be expressed as:

[0078]

[0079] in, Represents the total power consumption, represents the working value (set temperature) of the i-th cabinet air conditioner at time t, and its value ranges from 0 to N, where N is the number of cabinet air conditioners; It represents the working value (set temperature) of the jth computer room air conditioner at time t, and the value of j ranges from 0 to M.

[0080] in, represents the cabinet power consumption prediction model, represents the load of the base station corresponding to the i-th cabinet at time t, represents the energy-saving duration of the base station corresponding to the i-th cabinet at time t, represents the temperature of the i-th cabinet at time t.

[0081] As an example, if t is a time in the future, then: It may be the load of the base station corresponding to the i-th cabinet at time t predicted by the above load prediction model, that is, the energy-saving strategy of the base station at the target time is determined based on the predicted load of the base station at time t, Can be based on predictions And the energy-saving duration corresponding to the i-th cabinet obtained by the energy-saving strategy of the base station itself, It can represent the temperature of the i-th cabinet at time t predicted by the above cabinet temperature prediction model.

[0082] As another example, if t is a time in the future near the current time, then: It can be the load of the i-th cabinet measured in real time at the current moment, It can be the energy-saving duration used by the base station at the current moment measured in real time. The temperature of the i-th cabinet at the current moment can be measured in real time.

[0083] in, represents the cabinet air conditioner power consumption prediction model, Indicates the temperature in the equipment room at time t (indoor temperature), which can be calculated based on the statistics of the most recent measurement results of historical measurements

[0084] in, represents the power consumption prediction model of the computer room air conditioner, Indicates the temperature outside the computer room at time t (outdoor temperature). If there is a web interface, it can be obtained based on the crawled weather forecast information. If there is no web interface, the weather data of the same period in history and the weather data of the recent days can be combined and calculated by weight.

[0085] Case 2: The supporting equipment of the base station includes cabinet air conditioners, but not computer room air conditioners

[0086] In the case that the base station is only equipped with a cabinet air conditioner, the main equipment power consumption prediction model includes a cabinet power consumption prediction model, the supporting equipment power consumption prediction model includes a cabinet air conditioner power consumption prediction model, and the total power consumption prediction model is determined based on the cabinet power consumption prediction model and the cabinet air conditioner power consumption prediction model.

[0087] Specifically, the pre-built cabinet power consumption prediction model and the cabinet air conditioner power consumption prediction model are obtained; the cabinet power consumption prediction model and the cabinet air conditioner power consumption prediction model are added together to obtain the total power consumption prediction model.

[0088] At this time, illustratively, the total power consumption prediction model can be expressed as:

[0089]

[0090] in, Represents the total power consumption, represents the working value (set temperature) of the i-th cabinet air conditioner at time t, and its value ranges from 0 to N, where N is the number of cabinet air conditioners; It represents the working value (set temperature) of the jth computer room air conditioner at time t, and the value of j ranges from 0 to M.

[0091] in, represents the cabinet power consumption prediction model, represents the load of the base station corresponding to the i-th cabinet at time t, represents the energy-saving duration of the base station corresponding to the i-th cabinet at time t, represents the temperature of the i-th cabinet at time t.

[0092] As an example, if t is a time in the future, then: It can be the load of the base station corresponding to the i-th cabinet at time t predicted by the above load prediction model, Can be based on predictions and the energy-saving duration corresponding to the i-th cabinet obtained by the energy-saving strategy of the base station itself, that is, the energy-saving strategy of the base station at the target time is determined based on the predicted load of the base station at time t, It can represent the temperature of the i-th cabinet at time t predicted by the above cabinet temperature prediction model.

[0093] As another example, if t is the current time, then: It can be the load of the i-th cabinet measured in real time at the current moment, It can be the energy-saving duration used by the base station at the current moment measured in real time. The temperature of the i-th cabinet at the current moment can be measured in real time.

[0094] in, represents the cabinet air conditioner power consumption prediction model, Indicates the temperature in the equipment room at time t (indoor temperature), which can be calculated based on the statistics of the most recent measurement results of historical measurements

[0095] Step 102, within the adjustable range of the adjustable working parameters of the supporting equipment, optimize the adjustable working parameters of the supporting equipment at the target time, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and use the adjustable working parameters when the estimated total power consumption is the lowest as the target working value.

[0096] The target time may be one or more moments or a time period. The target time is a time in the future that is far from the current moment, or a time that is close to the current moment. The time granularity and time period of the target time can be determined according to actual application requirements.

[0097] Exemplarily, for the above-described situation 1, predicting the estimated total power consumption of the base station at the target time according to the total power consumption prediction model includes:

[0098] Predicting a first predicted power consumption of the cabinet at the target time according to the load of the base station at the target time, the temperature of the cabinet of the base station at the target time, the energy-saving strategy of the base station at the target time, and the cabinet power consumption prediction model;

[0099] Predicting a second predicted power consumption of the cabinet air conditioner at the target time according to the indoor temperature of the computer room where the base station is located at the target time, the operating temperature of the cabinet air conditioner at the target time, and the cabinet air conditioner power consumption prediction model;

[0100] Predicting a third predicted power consumption of the computer room air conditioner at the target time according to the outdoor temperature of the computer room where the base station is located at the target time, the operating temperature of the computer room air conditioner at the target time, and the power consumption prediction model of the computer room air conditioner;

[0101] The first predicted power consumption, the second predicted power consumption and the third predicted power consumption are added together to obtain the estimated total power consumption of the base station at the target time.

[0102] Exemplarily, for the above-described situation 2, wherein predicting the estimated total power consumption of the base station at the target time according to the total power consumption prediction model includes:

[0103] Predicting a first predicted power consumption of the cabinet at the target time according to the load of the base station at the target time, the temperature of the cabinet of the base station at the target time, the energy-saving strategy of the base station at the target time, and the cabinet power consumption prediction model;

[0104] Predicting a second predicted power consumption of the cabinet air conditioner at the target time according to the indoor temperature of the computer room where the base station is located at the target time, the operating temperature of the cabinet air conditioner at the target time, and the cabinet air conditioner power consumption prediction model;

[0105] The first predicted power consumption and the second predicted power consumption are combined to obtain the estimated total power consumption of the base station at the target time.

[0106] In the above two situations, as an example, if the target time is a future moment far away from the current moment, then: the load of the base station at the target time is predicted by the load prediction model, and the energy-saving strategy of the base station at the target time is determined based on the predicted load of the base station at the target time, and the temperature of the cabinet of the base station at the target time is predicted by the above cabinet temperature prediction model.

[0107] In the above two situations, as another example, if the target time is a future moment close to the current moment, then: the load of the base station at the target time can be the load of the base station at the current moment measured in real time, the energy-saving strategy of the base station at the target time can be the energy-saving duration adopted by the base station at the current moment measured in real time, and the temperature of the cabinet of the base station at the target time can be the temperature of the cabinet at the current moment measured in real time.

[0108] From the above description, it can be found that the estimated total power consumption of the base station is the sum of the cabinet power consumption and the supporting equipment power consumption. It can be understood that for the supporting equipment of the base station - the cabinet air conditioner and / or the computer room air conditioner, when the air conditioner working temperature is set high, the air conditioner power consumption decreases, but the cabinet power consumption increases; conversely, when the air conditioner working temperature is set low, the air conditioner power consumption increases and the cabinet power consumption decreases. Therefore, the working value of the cabinet air conditioner and / or the computer room air conditioner that minimizes the estimated total power consumption at the target time can be found through optimization. That is, the working value of the cabinet air conditioner and / or the computer room air conditioner that minimizes the estimated total power consumption at the target time can be found. and value.

[0109] For supporting equipment such as computer room air conditioners and cabinet air conditioners, since their adjustable working parameters often include multiple items, the adjustable range of each adjustable working parameter includes several optional values. For example, if the cabinet air conditioner is an air-cooled air conditioner, the adjustable working parameters of the cabinet air conditioner include at least one of temperature and wind speed; if the cabinet air conditioner is a liquid-cooled air conditioner, the adjustable working parameters of the cabinet air conditioner include at least one of temperature and liquid flow rate; similarly, if the computer room air conditioner is an air-cooled air conditioner, the adjustable working parameters of the computer room air conditioner include at least one of temperature and wind speed; if the computer room air conditioner is a liquid-cooled air conditioner, the adjustable working parameters of the computer room air conditioner include at least one of temperature and liquid flow rate. Therefore, if the optimal solution is found by enumeration, the amount of calculation is huge and not very realistic. Therefore, the embodiment of this specification proposes to solve the target working value of the adjustable working parameter corresponding to the minimum estimated total power consumption based on a preset optimization algorithm.

[0110] Specifically, step 102 may include: optimizing the adjustable working parameters of the supporting equipment at the target time according to a preset optimization algorithm within the adjustable range of the adjustable working parameters of the supporting equipment. More specifically, within the adjustable range of the adjustable working parameters of the supporting equipment, according to a preset optimization algorithm, the working value of the adjustable working parameter is continuously adjusted and input into the total power consumption prediction model to find the target working value of the adjustable working parameter that minimizes the estimated total power consumption output by the total power consumption prediction model.

[0111] The preset optimization algorithm includes but is not limited to one of a genetic algorithm, an ant colony algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm and a differential evolution algorithm.

[0112] Taking the genetic algorithm as an example, the process of continuously adjusting the working value of the adjustable working parameter within the adjustable range of the supporting equipment and inputting it into the total power consumption prediction model to find the target working value of the adjustable working parameter that makes the estimated total power consumption output by the total power consumption prediction model the lowest is briefly described as follows:

[0113] (1) Initializing A Xs, where X represents a value of an adjustable working parameter of the supporting equipment (cabinet air conditioner and / or cabinet air conditioner and computer room air conditioner), and A is an integer greater than 1;

[0114] (2) A Xs are input into the total power consumption prediction model respectively, and A estimated total power consumptions are outputted. The A Xs are evaluated and ranked according to the A estimated total power consumptions, and the first B better Xs (Xs with lower estimated total power consumptions) are selected as the candidate set;

[0115] (3) within the adjustable range of the adjustable working parameters of the supporting equipment, the genes of X in the candidate set are exchanged to generate some new X;

[0116] (4) within the adjustable range of the adjustable working parameters of the supporting equipment, changing the genes of X in the candidate set to generate some new X;

[0117] (5) Return to (2), taking one round as one generation, and terminating after N generations, and output the best X.

[0118] It can be understood that the best X is the operating value of the cabinet air conditioner and / or computer room air conditioner with the lowest estimated total power consumption.

[0119] Step 103: adjusting the working value of the adjustable working parameter of the supporting equipment at the target time according to the target working value of the adjustable working parameter.

[0120] Wherein, adjusting the working value of the adjustable working parameter of the supporting equipment at the target time according to the target working value of the adjustable working parameter may include:

[0121] Generate an energy-saving strategy for adjusting the working value of the adjustable working parameter of the supporting equipment at the target time according to the target working value of the adjustable working parameter;

[0122] Before the target time, the energy-saving strategy is sent to the air-conditioning adjustment module, so that the air-conditioning adjustment module reduces the actual total power consumption of the base station at the target time by executing the energy-saving strategy.

[0123] The air conditioning adjustment module may be a functional module of the base station, such as Figure 4 As shown, the air conditioning temperature adjustment module 4014 is located in the base station / cabinet 400. The air conditioning adjustment module can also be a functional module independent of the base station. As shown in FIG. 3 , the air conditioning adjustment module can be the air conditioning temperature adjustment module 3032 located in the air conditioning environment unit 303.

[0124] Specifically, the energy-saving strategy is sent to the air conditioning adjustment module before the target time so that the air conditioning adjustment strategy can be adjusted according to the strategy when the target time is reached, rather than obtaining the strategy only when the target time is reached.

[0125] An energy-saving method for a base station provided in an embodiment of the present specification, on the one hand, in addition to the power consumption of the main device, the power consumption of the supporting equipment is also included in the scope of consideration for reducing the total power consumption of the base station; on the other hand, after obtaining the total power consumption prediction model of the base station, the estimated total power consumption of the base station at the target time is predicted according to the total power consumption prediction model within the adjustable range of the adjustable working parameters of the supporting equipment, and optimization is performed to find the target working value of the adjustable working parameter that minimizes the estimated total power consumption; and according to the target working value of the adjustable working parameter, the working value of the adjustable working parameter of the supporting equipment is dynamically adjusted at the target time, so that the total power consumption of the base station is minimized, thereby effectively reducing the energy consumption of the base station.

[0126] Optional, such as Figure 2 As shown, in another embodiment, if the target time includes a future time adjacent to the current time, the energy saving method of a base station provided in this specification may further include, before step 103:

[0127] Step 104, obtaining the actual total power consumption of the base station at the current moment;

[0128] Step 105, determining whether the minimum estimated total power consumption is less than the actual total power consumption, if so, executing step 103, otherwise returning to executing step 102, or ending the process.

[0129] That is, when the minimum estimated total power consumption is less than the actual total power consumption, the working value of the adjustable working parameter of the supporting device at the target time is adjusted according to the target working value of the adjustable working parameter. It can be understood that if the actual total power consumption at the current moment is already lower than the minimum estimated total power consumption, then for the time in the future adjacent to the current moment, it may not be necessary to adjust the working value of the adjustable working parameter of the supporting device; if the actual total power consumption at the current moment is not lower than or much higher than the minimum estimated total power consumption, then for the time in the future adjacent to the current moment, it is necessary to adjust the working value of the adjustable working parameter of the supporting device to reduce the actual total power consumption.

[0130] Optionally, the supporting equipment of the base station further includes a power supply device, and the supporting equipment power consumption prediction model further includes a power supply device power consumption prediction model. Before step 102, a base station energy saving method provided in an embodiment of this specification may further include:

[0131] The power supply device power consumption prediction model is added as a sum item to the total power consumption prediction model.

[0132] Correspondingly, in step 102, the estimated total power consumption of the base station at the target time also includes a fourth predicted power consumption predicted by the power consumption prediction model of the power supply device. And through optimization, a target operating value of an adjustable operating parameter of the power supply device that minimizes the estimated total power consumption of the base station at the target time is also obtained.

[0133] Correspondingly, in step 103, the operating value of the adjustable operating parameter of the power supply device at the target time may also be adjusted according to the target operating value of the adjustable operating parameter of the power supply device.

[0134] The adjustable operating parameters of the power supply device include at least one of the number of power supplies, the voltage of the power supply device and the current of the power supply device.

[0135] It can be understood that in addition to considering the impact of cabinet air conditioners and / or computer room air conditioners on the total power consumption of base stations, incorporating power supply equipment into the scope of base station energy saving considerations can more comprehensively and effectively reduce the total power consumption of base stations and achieve the goal of optimizing the overall energy consumption of the entire station equipment.

[0136] In addition, as mentioned above, a base station energy saving method provided in an embodiment of this specification can be executed by the base station itself, or by a centralized processing unit independent of the base station, such as a network management, mobile edge computing, or multi-access edge computing (Mobile Edge Computing / Muti-access Edge Computing, MEC). Figure 3 and Figure 4 Provide a brief description.

[0137] In one embodiment, several processing units independent of the base station may be used as the execution subject of the energy saving method of the base station provided in the embodiment of this specification. Figure 3 As shown, a centralized processing unit 301 and an air conditioning environment unit 303 may be arranged outside the base station 302, and the base station 302 and the air conditioning environment unit 303 are respectively connected to the centralized processing unit 301 through interfaces.

[0138] The centralized processing unit 301 may include:

[0139] The information collection and modeling module 3011 is used to collect relevant information and model it, for example, to receive information reported by the air conditioning environment unit 303 and the base station 302.

[0140] The energy-saving strategy generation module 3012 is used to generate the energy-saving strategy of the base station itself and send it to the base station 302.

[0141] The air conditioning strategy generation module 3012 is used to generate an air conditioning energy-saving strategy and send it to the air conditioning environment unit 303 .

[0142] The base station 302 may include:

[0143] The base station load monitoring module 3021 is used to detect the load and other information of the base station and report it to the central processing unit 301.

[0144] The energy-saving strategy execution module 3022 is used to execute the energy-saving strategy issued by the central processing unit.

[0145] The air conditioning environment unit 303 may include:

[0146] The ambient temperature monitoring module 3031 is used to monitor ambient temperature information and its own working value and report them to the central processing unit 301 .

[0147] The air conditioner temperature adjustment module 3032 is used to adjust the working value of the air conditioner according to the air conditioner energy saving strategy issued by the central processing unit 301.

[0148] In another embodiment, the base station may be used as an execution subject of a base station energy saving method provided in an embodiment of this specification. Figure 4 As shown, the following functional modules may be provided in the base station 400:

[0149] Information collection and modeling module 4011 is used to collect relevant information and model. For details, please refer to the above Figure 1 Description of step 101 in FIG.

[0150] The energy-saving strategy generating module 4012 is used to generate the energy-saving strategy of the base station itself.

[0151] Air conditioning strategy generation module 4013 is used to generate air conditioning energy saving strategy. Figure 1 The description of step 102 and step 103 in FIG.

[0152] The air conditioning temperature adjustment module 4014 is used to adjust the working value of the air conditioning according to the air conditioning energy saving strategy generated by the air conditioning strategy generation module 4013.

[0153] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0154] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this specification. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0155] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0156] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0157] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an energy-saving device of the base station at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0158] Obtaining a total power consumption prediction model of the base station, wherein the total power consumption prediction model is determined according to a main device power consumption prediction model and an auxiliary device power consumption prediction model of the base station;

[0159] Within the adjustable range of the adjustable working parameters of the supporting equipment, optimizing the adjustable working parameters of the supporting equipment at the target time, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and taking the adjustable working parameters when the estimated total power consumption is the lowest as the target working value;

[0160] According to the target operating value of the adjustable operating parameter, the operating value of the adjustable operating parameter of the supporting equipment at the target time is adjusted.

[0161] The above is as in this manual Figure 5 The method performed by the energy-saving device of the base station disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of this specification can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of this specification can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0162] The electronic device may also perform Figure 1 or Figure 2 The method and the energy saving device of the base station are implemented Figure 1 or Figure 2 The functions in the illustrated embodiments will not be described in detail in this specification.

[0163] Of course, in addition to software implementation, the electronic device of this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0164] The embodiment of the present specification also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figure 1 or Figure 2 The method of the embodiment shown is specifically used to perform the following operations:

[0165] Obtaining a total power consumption prediction model of the base station, wherein the total power consumption prediction model is determined according to a main device power consumption prediction model and an auxiliary device power consumption prediction model of the base station;

[0166] Within the adjustable range of the adjustable working parameters of the supporting equipment, optimizing the adjustable working parameters of the supporting equipment at the target time, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and taking the adjustable working parameters when the estimated total power consumption is the lowest as the target working value;

[0167] According to the target operating value of the adjustable operating parameter, the operating value of the adjustable operating parameter of the supporting equipment at the target time is adjusted.

[0168] Figure 6 This is a schematic diagram of a base station energy saving device 600 provided by an embodiment of this specification. Figure 6 In a software implementation, the energy-saving device 600 of the base station may include: a model acquisition module 601, a parameter value optimization module 602 and a working value adjustment module 603, wherein:

[0169] The model acquisition module 601 is used to acquire the total power consumption prediction model of the base station, wherein the total power consumption prediction model is determined based on the main equipment power consumption prediction model and the supporting equipment power consumption prediction model of the base station.

[0170] The main equipment of the base station includes a cabinet, and the supporting equipment of the base station generally includes a cabinet air conditioner and / or a computer room air conditioner. Of course, the supporting equipment of the base station may also include a power supply device.

[0171] Among them, the main equipment power consumption prediction model and the supporting equipment power consumption prediction model of the base station are pre-built based on relevant sample data.

[0172] For some base stations, their supporting air conditioning equipment includes computer room air conditioners and cabinet air conditioners; while for other base stations, their supporting air conditioning equipment only includes cabinet air conditioners. For these two situations, the models that need to be pre-built include: base station load prediction model, cabinet power consumption prediction model, computer room air conditioning prediction model and cabinet air conditioning prediction model, among which cabinet power consumption refers to the power consumption of the base station itself or the main equipment of the base station, computer room air conditioner refers to the air conditioner installed in the computer room where the base station is located and located outside the cabinet, and cabinet air conditioner refers to the air conditioner installed in the cabinet of the base station.

[0173] Regarding the construction process of the above model, please refer to the above description of the method implementation example, which will not be repeated here.

[0174] The parameter value optimization module 602 is used to optimize the adjustable working parameters of the supporting equipment at the target time within the adjustable range of the adjustable working parameters of the supporting equipment, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and the adjustable working parameters when the estimated total power consumption is the lowest are used as the target working values.

[0175] The target time may be one or more moments or a time period. The target time is a time in the future that is far from the current moment, or a time that is close to the current moment. The time granularity and time period of the target time can be determined according to actual application requirements.

[0176] Exemplarily, for the above-mentioned situation 1, the parameter value optimization module 602 may be used to: predict the first predicted power consumption of the cabinet at the target time according to the load of the base station at the target time, the temperature of the cabinet of the base station at the target time, the energy-saving strategy of the base station at the target time, and the cabinet power consumption prediction model;

[0177] Predicting a second predicted power consumption of the cabinet air conditioner at the target time according to the indoor temperature of the computer room where the base station is located at the target time, the operating temperature of the cabinet air conditioner at the target time, and the cabinet air conditioner power consumption prediction model;

[0178] Predicting a third predicted power consumption of the computer room air conditioner at the target time according to the outdoor temperature of the computer room where the base station is located at the target time, the operating temperature of the computer room air conditioner at the target time, and the power consumption prediction model of the computer room air conditioner;

[0179] The first predicted power consumption, the second predicted power consumption and the third predicted power consumption are added together to obtain the estimated total power consumption of the base station at the target time.

[0180] Exemplarily, for the above-mentioned situation 2, the parameter value optimization module 602 may be used to:

[0181] Predicting a first predicted power consumption of the cabinet at the target time according to the load of the base station at the target time, the temperature of the cabinet of the base station at the target time, the energy-saving strategy of the base station at the target time, and the cabinet power consumption prediction model;

[0182] Predicting a second predicted power consumption of the cabinet air conditioner at the target time according to the indoor temperature of the computer room where the base station is located at the target time, the operating temperature of the cabinet air conditioner at the target time, and the cabinet air conditioner power consumption prediction model;

[0183] The first predicted power consumption and the second predicted power consumption are combined to obtain the estimated total power consumption of the base station at the target time.

[0184] In the above two situations, as an example, if the target time is a future moment far away from the current moment, then: the load of the base station at the target time is predicted by the load prediction model, and the energy-saving strategy of the base station at the target time is determined based on the predicted load of the base station at the target time, and the temperature of the cabinet of the base station at the target time is predicted by the above cabinet temperature prediction model.

[0185] In the above two situations, as another example, if the target time is a future moment close to the current moment, then: the load of the base station at the target time can be the load of the base station at the current moment measured in real time, the energy-saving strategy of the base station at the target time can be the energy-saving duration adopted by the base station at the current moment measured in real time, and the temperature of the cabinet of the base station at the target time can be the temperature of the cabinet at the current moment measured in real time.

[0186] From the above description, it can be found that the estimated total power consumption of the base station is the sum of the cabinet power consumption and the supporting equipment power consumption. It can be understood that for the supporting equipment of the base station - the cabinet air conditioner and / or the computer room air conditioner, when the air conditioner working temperature is set high, the air conditioner power consumption decreases, but the cabinet power consumption increases; conversely, when the air conditioner working temperature is set low, the air conditioner power consumption increases and the cabinet power consumption decreases. Therefore, the working value of the cabinet air conditioner and / or the computer room air conditioner that minimizes the estimated total power consumption at the target time can be found through optimization.

[0187] For supporting equipment such as computer room air conditioners and cabinet air conditioners, since their adjustable working parameters often include multiple items, the adjustable range of each adjustable working parameter includes several optional values. For example, if the cabinet air conditioner is an air-cooled air conditioner, the adjustable working parameters of the cabinet air conditioner include at least one of temperature and wind speed; if the cabinet air conditioner is a liquid-cooled air conditioner, the adjustable working parameters of the cabinet air conditioner include at least one of temperature and liquid flow rate; similarly, if the computer room air conditioner is an air-cooled air conditioner, the adjustable working parameters of the computer room air conditioner include at least one of temperature and wind speed; if the computer room air conditioner is a liquid-cooled air conditioner, the adjustable working parameters of the computer room air conditioner include at least one of temperature and liquid flow rate. Therefore, if the optimal solution is found by enumeration, the amount of calculation is huge and not very realistic. Therefore, the embodiment of this specification proposes to solve the target working value of the adjustable working parameter corresponding to the minimum estimated total power consumption based on a preset optimization algorithm.

[0188] Specifically, the parameter value optimization module 602 can be used to: optimize the adjustable working parameters of the supporting equipment at the target time according to a preset optimization algorithm within the adjustable range of the adjustable working parameters of the supporting equipment. Specifically, according to the preset optimization algorithm, within the adjustable range of the adjustable working parameters of the supporting equipment, the working value of the adjustable working parameter is continuously adjusted and input into the total power consumption prediction model to find the target working value of the adjustable working parameter that minimizes the estimated total power consumption output by the total power consumption prediction model.

[0189] The preset optimization algorithm includes but is not limited to one of a genetic algorithm, an ant colony algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm and a differential evolution algorithm.

[0190] The working value adjustment module 603 is used to adjust the working value of the adjustable working parameter of the supporting equipment at the target time according to the target working value of the adjustable working parameter.

[0191] Specifically, the working value adjustment module 603 may be used to:

[0192] Generate an energy-saving strategy for adjusting the working value of the adjustable working parameter of the supporting equipment at the target time according to the target working value of the adjustable working parameter;

[0193] Before the target time, the energy-saving strategy is sent to the air-conditioning adjustment module, so that the air-conditioning adjustment module reduces the actual total power consumption of the base station at the target time by executing the energy-saving strategy.

[0194] The energy-saving device 600 of a base station provided in the embodiment of the present specification, on the one hand, in addition to the power consumption of the main device, the power consumption of the supporting device is also included in the consideration range of reducing the total power consumption of the base station; on the other hand, after obtaining the total power consumption prediction model of the base station, the estimated total power consumption of the base station at the target time is predicted according to the total power consumption prediction model within the adjustable range of the adjustable working parameters of the supporting device and optimized to find the target working value of the adjustable working parameter that makes the estimated total power consumption the lowest; and according to the target working value of the adjustable working parameter, the working value of the adjustable working parameter of the supporting device at the target time is dynamically adjusted, so that the total power consumption of the base station is minimized, thereby effectively reducing the energy consumption of the base station.

[0195] Optional, such as Figure 7 As shown, in another embodiment, a base station energy saving device 600 provided in this specification may further include: a power consumption acquisition module 604 and a judgment module 605.

[0196] The power consumption acquisition module 604 is used to acquire the actual total power consumption of the base station at the current moment before triggering the working value adjustment module 603 if the target moment includes a future time close to the current moment.

[0197] The judgment module 605 is used to judge whether the minimum estimated total power consumption is less than the actual total power consumption. If so, the working value adjustment module 603 is triggered, otherwise, the triggering parameter value optimization module 602 is returned.

[0198] That is, when the minimum estimated total power consumption is less than the actual total power consumption, the working value of the adjustable working parameter of the supporting device at the target time is adjusted according to the target working value of the adjustable working parameter. It can be understood that if the actual total power consumption at the current moment is already lower than the minimum estimated total power consumption, then for the time in the future adjacent to the current moment, it may not be necessary to adjust the working value of the adjustable working parameter of the supporting device; if the actual total power consumption at the current moment is not lower than or much higher than the minimum estimated total power consumption, then for the time in the future adjacent to the current moment, it is necessary to adjust the working value of the adjustable working parameter of the supporting device to reduce the actual total power consumption.

[0199] Optionally, the supporting equipment of the base station also includes power supply equipment, and the supporting equipment power consumption prediction model also includes a power supply equipment power consumption prediction model. In a base station energy-saving device 600 provided in an embodiment of the present specification, the model acquisition module 601 can also be used to: add the power supply equipment power consumption prediction model as an addition item to the total power consumption prediction model.

[0200] Correspondingly, in the parameter value optimization module 602, the estimated total power consumption of the base station at the target time also includes the fourth predicted power consumption predicted by the power consumption prediction model of the power supply device. And through optimization, the target operating value of the adjustable operating parameter of the power supply device that minimizes the estimated total power consumption of the base station at the target time is also obtained.

[0201] Correspondingly, in the working value adjustment module 603, the working value of the adjustable working parameter of the power supply device at the target time can also be adjusted according to the target working value of the adjustable working parameter of the power supply device.

[0202] The adjustable operating parameters of the power supply device include at least one of the number of power supplies, the voltage of the power supply device and the current of the power supply device.

[0203] It can be understood that in addition to considering the impact of cabinet air conditioners and / or computer room air conditioners on the total power consumption of base stations, incorporating power supply equipment into the scope of base station energy saving considerations can more comprehensively and effectively reduce the total power consumption of base stations and achieve the goal of optimizing the overall energy consumption of the entire station equipment.

[0204] The energy-saving device 600 of a base station provided in the embodiment of this specification can also execute Figure 1 or Figure 2 A method is provided, and a base station energy-saving device 600 is implemented in Figure 1 or Figure 2 The functions of the illustrated embodiments will not be described in detail in the embodiments of this specification.

[0205] In short, the above description is only a preferred embodiment of this specification and is not intended to limit the protection scope of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included in the protection scope of this specification.

[0206] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0207] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0208] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0209] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A method for energy saving of a base station, comprising: Obtaining a total power consumption prediction model of the base station, wherein the total power consumption prediction model is determined according to a main device power consumption prediction model and an auxiliary device power consumption prediction model of the base station; Within the adjustable range of the adjustable working parameters of the supporting equipment, optimizing the adjustable working parameters of the supporting equipment at the target time, so that the estimated total power consumption of the base station at the target time predicted by the total power consumption prediction model is the lowest, and taking the adjustable working parameters when the estimated total power consumption is the lowest as the target working value; According to the target operating value of the adjustable operating parameter, the operating value of the adjustable operating parameter of the supporting equipment at the target time is adjusted.

2. The method according to claim 1, wherein: In the case where the base station is equipped with a computer room air conditioner and a cabinet air conditioner, the main equipment power consumption prediction model includes a cabinet power consumption prediction model, the supporting equipment power consumption prediction model includes a computer room air conditioner power consumption prediction model and a cabinet air conditioner power consumption prediction model, and the total power consumption prediction model is determined based on the cabinet power consumption prediction model, the computer room air conditioner power consumption prediction model and the cabinet air conditioner power consumption prediction model.

3. The method according to claim 2, wherein obtaining the total power consumption prediction model of the base station comprises: Obtaining the cabinet power consumption prediction model, the computer room air conditioner power consumption prediction model and the cabinet air conditioner power consumption prediction model; The cabinet power consumption prediction model, the computer room air conditioner power consumption prediction model and the cabinet air conditioner power consumption prediction model are added together to obtain the total power consumption prediction model.

4. The method according to claim 3, wherein: Predicting the estimated total power consumption of the base station at the target time according to the total power consumption prediction model includes: Predicting a first predicted power consumption of the cabinet at the target time according to the load of the base station at the target time, the temperature of the cabinet of the base station at the target time, the energy-saving strategy of the base station at the target time, and the cabinet power consumption prediction model; Predicting a second predicted power consumption of the cabinet air conditioner at the target time according to the indoor temperature of the computer room where the base station is located at the target time, the operating temperature of the cabinet air conditioner at the target time, and the cabinet air conditioner power consumption prediction model; Predicting a third predicted power consumption of the computer room air conditioner at the target time according to the outdoor temperature of the computer room where the base station is located at the target time, the operating temperature of the computer room air conditioner at the target time, and the power consumption prediction model of the computer room air conditioner; The first predicted power consumption, the second predicted power consumption and the third predicted power consumption are added together to obtain the estimated total power consumption of the base station at the target time.

5. The method according to claim 1, wherein: In the case where the base station is equipped with a cabinet air conditioner, the main equipment power consumption prediction model includes a cabinet power consumption prediction model, the supporting equipment power consumption prediction model includes a cabinet air conditioner power consumption prediction model, and the total power consumption prediction model is determined based on the cabinet power consumption prediction model and the cabinet air conditioner power consumption prediction model.

6. The method according to claim 5, wherein obtaining the total power consumption prediction model of the base station comprises: Obtaining the cabinet power consumption prediction model and the cabinet air conditioner power consumption prediction model; The cabinet power consumption prediction model and the cabinet air conditioner power consumption prediction model are added together to obtain the total power consumption prediction model.

7. The method according to claim 6, wherein: Predicting the estimated total power consumption of the base station at the target time according to the total power consumption prediction model includes: Predicting a first predicted power consumption of the cabinet at the target time according to the load of the base station at the target time, the temperature of the cabinet of the base station at the target time, the energy-saving strategy of the base station at the target time, and the cabinet power consumption prediction model; Predicting a second predicted power consumption of the cabinet air conditioner at the target time according to the indoor temperature of the computer room where the base station is located at the target time, the operating temperature of the cabinet air conditioner at the target time, and the cabinet air conditioner power consumption prediction model; The first predicted power consumption and the second predicted power consumption are combined to obtain the estimated total power consumption of the base station at the target time.

8. The method according to claim 4 or 7, wherein the target time comprises a specified time in the future, and the load of the base station at the target time is predicted by a load prediction model, and the method further comprises: The load prediction model is constructed according to the historical load time series of the base station.

9. The method according to claim 8, The energy saving strategy of the base station at the target time is determined based on the predicted load of the base station at the target time.

10. The method according to any one of claims 2 to 7, before acquiring the total power consumption prediction model of the base station, further comprising: A cabinet power consumption prediction model is trained based on first sample data, wherein the first sample data includes a historical load of the base station, a historical temperature of a cabinet of the base station, a historical energy-saving strategy of the cabinet, and a historical power consumption of the cabinet.

11. The method according to any one of claims 2 to 7, before acquiring the total power consumption prediction model of the base station, further comprising: The cabinet air conditioner power consumption prediction model is trained according to the second sample data, wherein the second sample data includes historical operating values ​​of adjustable operating parameters of the cabinet air conditioner, ambient temperature, and historical power consumption.

12. The method according to any one of claims 2 to 4, before acquiring the total power consumption prediction model of the base station, further comprising: The computer room air conditioner power consumption prediction model is trained according to the third sample data, wherein the third sample data includes historical operating values ​​of adjustable operating parameters of the computer room air conditioner, ambient temperature, and historical power consumption.

13. The method according to any one of claims 1 to 7, wherein the target time comprises a future time adjacent to the current time, and before adjusting the adjustable operating parameter of the supporting equipment to the operating value of the target time according to the target operating value of the adjustable operating parameter, the method further comprises: Obtaining the actual total power consumption of the base station at the current moment; In a case where the lowest estimated total power consumption is less than the actual total power consumption, the operating value of the adjustable operating parameter of the supporting equipment at the target time is adjusted according to the target operating value of the adjustable operating parameter.

14. The method according to claim 1, wherein optimizing the adjustable working parameters of the supporting equipment at the target time within the adjustable range of the adjustable working parameters of the supporting equipment comprises: Within the adjustable range of the adjustable working parameters of the supporting equipment, the adjustable working parameters of the supporting equipment at the target time are optimized according to a preset optimization algorithm, wherein the preset optimization algorithm includes one of a genetic algorithm, an ant colony algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm and a differential evolution algorithm.

15. The method according to any one of claims 2 to 7, wherein the supporting equipment power consumption prediction model further comprises a power supply equipment power consumption prediction model, and the method further comprises: The power supply device power consumption prediction model is added as a sum item to the total power consumption prediction model.

16. The method according to claim 15, The estimated total power consumption of the base station at the target time also includes a fourth predicted power consumption predicted according to the power consumption prediction model of the power supply device.

17. An electronic device comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 16.

18. A computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 16.

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