Energy saving strategy determination method, apparatus, device, medium and program product

Abstract

The application provides an energy-saving strategy determination method, device, equipment, medium and program product, relates to the technical field of artificial intelligence, and is used for providing a reliable scheme for energy saving and emission reduction of inventory machine rooms. The specific technical scheme is as follows: a first evaluation parameter set corresponding to a first machine room is acquired; the first evaluation parameter set is input into a machine room energy efficiency evaluation model corresponding to the first machine room, energy efficiency scores of the first evaluation parameter set are calculated, a first energy efficiency score corresponding to the first machine room is output, the machine room energy efficiency evaluation model is constructed based on machine room information corresponding to M dimensions of the first machine room, and M is a positive integer; based on the first energy efficiency score, an energy-saving reconstruction strategy corresponding to the first machine room is determined; wherein the energy-saving reconstruction strategy comprises a machine room environment adjustment strategy, a machine room layout adjustment strategy, a machine room energy consumption adjustment strategy and a device performance adjustment strategy. The application is applied to the scene of energy-saving scheme strategies.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, equipment, medium, and program product for determining energy-saving strategies. Background Technology

[0002] Currently, with the deepening of global efforts to address climate change and the continuous advancement of the "dual carbon" strategic goals, the green transformation of the information and communication industry, as the core infrastructure of the digital economy, has become an inevitable trend. Existing data centers and communication equipment rooms, as major energy consumers in the industry, generally suffer from high Power Usage Effectiveness (PUE) and an unreasonable energy structure due to aging equipment, outdated technical architecture, and low system efficiency.

[0003] In existing technologies, for energy-saving renovations of the aforementioned computer rooms, the computer room parameters can be collected before the energy-saving renovation to establish a computational fluid dynamics (CFD) model and calculate the power efficiency (PUE). Then, a preset engineering renovation plan (such as closing cold / hot aisles, sealing cabinet gaps, etc.) can be used to carry out energy-saving renovations of the computer room. After the renovation, the PUE can be calculated again using the CFD model. By comparing the PUE values ​​before and after the renovation, a decision can be made on whether to use the engineering renovation plan.

[0004] However, since the above solutions only compare PUE and lack a multi-dimensional and systematic quantitative assessment system for the current status of the data center, the transformation effect cannot meet expectations due to the single decision-making basis and poor adaptability of the solutions. Thus, the accuracy of data center energy-saving transformation is poor. Summary of the Invention

[0005] This application provides a method for determining energy-saving strategies to improve the accuracy of energy-saving retrofits for computer rooms.

[0006] In a first aspect, embodiments of this application provide a method for determining an energy-saving strategy. The method includes: obtaining a first set of evaluation parameters corresponding to a first data center, the first set of evaluation parameters including: data center energy consumption parameters, equipment performance parameters, data center layout parameters, and data center environmental parameters; inputting the first set of evaluation parameters into a data center energy efficiency evaluation model corresponding to the first data center, performing an energy efficiency score on the first set of evaluation parameters, and outputting a first energy efficiency score corresponding to the first data center. The data center energy efficiency evaluation model is constructed based on data center information corresponding to M dimensions of the first data center, the M dimensions including at least: data center equipment dimension, energy dimension, energy-saving dimension, and energy consumption control dimension, where M is a positive integer; and determining an energy-saving renovation strategy corresponding to the first data center based on the first energy efficiency score; wherein the energy-saving renovation strategy includes: a data center environment adjustment strategy, a data center layout adjustment strategy, a data center energy consumption adjustment strategy, and an equipment performance adjustment strategy.

[0007] The technical solution provided in this application brings at least the following beneficial effects: by constructing a data center energy efficiency assessment model in multiple dimensions, such as data center equipment dimension, energy dimension, energy saving dimension and energy consumption control dimension, the data center can output accurate energy-saving transformation strategies for the first data center by combining multi-dimensional decision information and data center energy efficiency assessment model, thereby achieving precise transformation of one policy for one data center, thus improving the accuracy of data center energy-saving transformation.

[0008] One possible implementation is that the above-mentioned data center equipment dimension information includes: basic equipment indicators and a first indicator, the first indicator including at least one of the following: energy consumption data indicators and equipment status assessment indicators; the energy dimension includes: energy efficiency indicators and a second indicator, the second indicator including at least one of the following: power utilization efficiency (PUE) indicator, water use efficiency (WUE) control indicator, and green electricity consumption indicator; the energy saving dimension includes: energy saving indicators and a third indicator, the third indicator including at least one of the following: cooling system configuration indicators, power supply and distribution system configuration indicators, and information technology (IT) equipment configuration indicators; the energy consumption control dimension includes: green energy consumption management indicators and a fourth indicator, the fourth indicator including at least one of the following: energy consumption monitoring indicators, management system certification indicators, and energy saving indicators.

[0009] Another possible implementation involves inputting the first set of evaluation parameters into the energy efficiency evaluation model corresponding to the first data center, scoring the first set of evaluation parameters for energy efficiency, and outputting the first energy efficiency score corresponding to the first data center. This includes: inputting the first set of evaluation parameters into the data center energy efficiency evaluation model, calculating the energy efficiency score based on the weights corresponding to the equipment dimension information, the energy dimension, the energy saving dimension, the energy consumption control dimension, the evaluation score corresponding to the first indicator, the evaluation score corresponding to the second indicator, the evaluation score corresponding to the third indicator, and the evaluation score corresponding to the fourth indicator, and outputting the first energy efficiency score.

[0010] Another possible implementation method, the above-mentioned determination of the energy-saving renovation strategy corresponding to the first computer room based on the first energy efficiency score, includes: determining the set of energy-saving renovation strategies corresponding to the first energy efficiency score based on the first energy efficiency score; scoring each energy-saving renovation strategy in the set of energy-saving renovation strategies to obtain a set of energy-saving scores, where one energy-saving score in the set of energy-saving scores corresponds to one energy-saving renovation strategy; and determining the energy-saving renovation strategy corresponding to the largest energy-saving score in the set of energy-saving scores as the energy-saving renovation strategy corresponding to the first computer room.

[0011] Another possible implementation involves scoring each energy-saving renovation strategy in the set of energy-saving renovation strategies to obtain a set of energy-saving scores. This includes: obtaining the equipment performance parameters, data center layout parameters, and data center environmental parameters corresponding to each energy-saving renovation strategy; predicting the energy consumption of each energy-saving renovation scheme in the set of energy-saving renovation strategies to obtain the energy consumption value corresponding to each energy-saving renovation strategy; and inputting the energy consumption value, equipment performance parameters, data center layout parameters, and data center environmental parameters corresponding to each energy-saving renovation strategy into the data center energy efficiency assessment model to perform energy efficiency assessment and output a set of energy-saving scores.

[0012] Secondly, embodiments of this application provide an energy-saving strategy determination device, comprising: an acquisition module, a processing module, and a determination module. The acquisition module acquires a first set of evaluation parameters corresponding to a first computer room, the first set of evaluation parameters including: computer room energy consumption parameters, equipment performance parameters, computer room layout parameters, and computer room environmental parameters. The processing module inputs the first set of evaluation parameters into a computer room energy efficiency evaluation model corresponding to the first computer room, performs energy efficiency scoring on the first set of evaluation parameters, and outputs a first energy efficiency score for the first computer room. The computer room energy efficiency evaluation model is constructed based on computer room information corresponding to M dimensions of the first computer room, the M dimensions including at least: computer room equipment dimension, energy dimension, energy-saving dimension, and energy consumption control dimension, where M is a positive integer. The determination module determines an energy-saving renovation strategy corresponding to the first computer room based on the first energy efficiency score; wherein the energy-saving renovation strategy includes: computer room environment adjustment strategy, computer room layout adjustment strategy, computer room energy consumption adjustment strategy, and equipment performance adjustment strategy.

[0013] One possible implementation is that the aforementioned data center equipment dimension information includes: basic equipment indicators and a first indicator, the first indicator including at least one of the following: energy consumption data indicators and equipment status assessment indicators; the energy dimension includes: energy efficiency indicators and a second indicator, the second indicator including at least one of the following: power utilization efficiency (PUE) indicator, water utilization efficiency (WUE) control indicator, and green electricity consumption indicator; the energy saving dimension includes: energy saving indicators and a third indicator, the third indicator including at least one of the following: cooling system configuration indicators, power supply and distribution system configuration indicators, and information technology (IT) equipment configuration indicators; the energy consumption control dimension includes: green energy consumption management indicators and a fourth indicator, the fourth indicator including at least one of the following: energy consumption monitoring indicators, management system certification indicators, and energy saving indicators.

[0014] In one possible implementation, the processing module is specifically used to calculate the energy efficiency score of the first set of evaluation parameters after the input module inputs the first set of evaluation parameters into the data center energy efficiency evaluation model, based on the weights corresponding to the equipment dimension information, the weights corresponding to the energy dimension, the weights corresponding to the energy saving dimension, the weights corresponding to the energy consumption control dimension, the evaluation scores corresponding to the first indicator, the evaluation scores corresponding to the second indicator, the evaluation scores corresponding to the third indicator, and the evaluation scores corresponding to the fourth indicator, and output the first energy efficiency score.

[0015] Another possible implementation involves the determining module specifically used to determine a set of energy-saving retrofit strategies corresponding to the first energy efficiency score. The processing module is further used to score each energy-saving retrofit strategy in the set, obtaining a set of energy-saving scores, where each energy-saving score in the set corresponds to one energy-saving retrofit strategy. The determining module is also used to determine the energy-saving retrofit strategy corresponding to the largest energy-saving score in the set as the energy-saving retrofit strategy for the first computer room.

[0016] In another possible implementation, the energy-saving strategy determining device described above further includes an acquisition module that acquires the equipment performance parameters, data center layout parameters, and data center environmental parameters corresponding to each energy-saving renovation strategy. The processing module is further configured to predict the energy consumption of each energy-saving renovation scheme in the set of energy-saving renovation strategies, obtaining the energy consumption value corresponding to each strategy; and input the energy consumption value, equipment performance parameters, data center layout parameters, and data center environmental parameters corresponding to each strategy into the data center energy efficiency assessment model for energy efficiency assessment, outputting a set of energy-saving scores.

[0017] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory stores a program or instructions executable on the processor, wherein the program or instructions, when executed by the processor, implement the method of the first aspect described above.

[0018] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a computer, implement the method of the first aspect described above.

[0019] Fifthly, this application provides a computer program product stored in a storage medium, which, when executed by a computer, implements the method described in the first aspect.

[0020] In a sixth aspect, embodiments of this application provide a chip including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method described in the first aspect.

[0021] The beneficial effects of the second to sixth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0022] Figure 1 A schematic diagram of the network architecture for an energy-saving strategy determination method provided in this application embodiment;

[0023] Figure 2 A flowchart illustrating a method for determining an energy-saving strategy provided in an embodiment of this application;

[0024] Figure 3 A flowchart illustrating a method for determining an energy-saving strategy provided in an embodiment of this application;

[0025] Figure 4 A flowchart illustrating a method for determining an energy-saving strategy provided in an embodiment of this application;

[0026] Figure 5 A flowchart illustrating a method for determining an energy-saving strategy provided in an embodiment of this application;

[0027] Figure 6 A flowchart illustrating another energy-saving strategy determination method provided in an embodiment of this application;

[0028] Figure 7 A schematic diagram of another energy-saving strategy determination system provided in this application embodiment;

[0029] Figure 8 A schematic diagram of another energy-saving strategy determination device provided in the embodiments of this application;

[0030] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] The following is a detailed description of the energy-saving strategy determination method, apparatus, equipment, medium, and procedure products provided in this application, with reference to the accompanying drawings.

[0032] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0033] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0034] The terms "at least one," "at least one of," etc., used in the specification and claims of this application refer to any one, any two, or a combination of two or more of the included items. For example, at least one of a, b, and c can mean: "a," "b," "c," "a and b," "a and c," "b and c," and "a, b, and c," where a, b, and c can be single or multiple. Similarly, "at least two" refers to two or more items, and its meaning is similar to that of "at least one."

[0035] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0036] The technical terms used in the embodiments of this application will be explained in detail below.

[0037] Existing data centers: Existing data centers refer to data centers that have been built and put into use, typically meaning data centers that exist at the current point in time and have not been demolished or abandoned. These data centers include older data centers that require energy-saving renovations.

[0038] Model: A model is a simulation or abstraction of certain characteristics and inherent relationships of objective reality. A model is a role. The concept of a model can be defined as follows: a thing is called a "model" because of its role or purpose in a specific situation—in that situation, it directly or indirectly carries certain attributes of another thing, and based on these attributes, it acts as a substitute or representation of that thing; thus, by using the attributes obtained from the model, the correlation between operations and the corresponding attributes of that thing can be achieved.

[0039] Energy-saving renovation: Energy-saving renovation of existing data centers refers to the process of systematically optimizing the energy efficiency of old data centers that have been in operation for a long time and whose PUE does not meet the standards, in order to meet the increasingly stringent energy efficiency standards and green operation requirements.

[0040] The present application provides an energy-saving strategy determination method, apparatus, equipment, medium, and program product that can be applied to energy-saving renovation of existing computer rooms.

[0041] Driven by global climate change and my country's "dual-carbon" strategy, the information and communication industry, as the cornerstone of the digital economy, is facing increasingly prominent issues regarding energy consumption and carbon emissions. Existing data centers and communication equipment rooms, as major energy consumers in the industry, generally suffer from high Power Usage Effectiveness (PUE) and unreasonable energy structures due to aging equipment, outdated technical architecture, and low system efficiency. Industry surveys and actual measurement data show that the PUE values ​​of many existing data centers in my country are still in the relatively high range of 1.5 to 2.5, far below the advanced level of below 1.3 required for the development of new data centers. The ineffective losses of their air conditioning and power supply systems often exceed 55%, indicating huge potential for renovation. Energy-saving renovation of existing data centers has become a rigid requirement for achieving the industry's green transformation.

[0042] In one existing technology, the computer room parameters can be collected before the energy-saving renovation of the computer room to establish a computational fluid dynamics (CFD) model and calculate the power consumption effect (PUE). Then, a preset engineering renovation plan (such as closing the cold / hot aisle, sealing the cabinet gaps, etc.) is used to carry out the energy-saving renovation of the computer room. The PUE is calculated again by the CFD model. The decision on whether to use the engineering renovation plan is made by comparing the PUE values ​​before and after the renovation.

[0043] However, the core decision-making basis of the above-mentioned solutions is singular (i.e., only comparing PUE), lacking a multi-dimensional and systematic quantitative assessment system for the current state of the data center. Furthermore, its "engineering transformation model" is pre-set, and the solution generation lacks an intelligent matching and grading mechanism with the specific current status scores of the data center (such as equipment health and management maturity). The adaptability and economic optimization of the transformation solution rely on pre-set human experience rather than automatic derivation based on quantitative scores. In addition, this method focuses on the simulation verification of a single transformation, failing to form a complete closed-loop optimization mechanism that includes effect verification and continuous calibration of model parameters, making it difficult to accumulate experience and achieve self-evolution of the predictive model.

[0044] Another approach involves sealing the sides, top, and ends of the cold aisle using a detachable structure and sealing strips. This isolates cold air within the enclosed space, preventing it from reaching the servers and improving heat dissipation, reducing air conditioning energy consumption, and stabilizing temperature and humidity. However, this approach is a specific "point-based" technical modification (i.e., sealing the cold aisle), rather than a systemic modification decision-making method. It focuses entirely on the engineering implementation of a single technical measure, without addressing the quantitative assessment of the overall energy efficiency of the data center, the comparison and selection of multiple energy-saving technologies, or the quantitative verification and optimization loop of the post-modification effects. Its application presupposes that the "sealed cold aisle" measure has already been decided upon, failing to answer higher-level systemic decision-making questions such as "Should this data center be modified?", "Why choose this option among many modification plans?", and "How much will the overall energy efficiency be improved after the modification?". It lacks a holistic perspective and quantitative decision support.

[0045] In summary, most existing energy-saving retrofit methods and technical solutions for existing data centers focus on using CFD simulations to verify the energy-saving effect of a single technical solution (such as a closed cold aisle) or to provide specific engineering retrofit structural solutions. They fail to consider that in the systematic retrofit of existing data centers, due to the complexity of the current state of the data center, the diversity of retrofit objectives (energy efficiency, economy, management), and the variety of technical solutions, the retrofit results are prone to falling short of expectations, resulting in low return on investment and an inability to continuously optimize due to the lack of quantitative evaluation standards, poor solution adaptability, and single decision-making basis.

[0046] To address the aforementioned technical issues, embodiments of this application provide a method, apparatus, equipment, medium, and program product for determining energy-saving strategies. By constructing a data center energy efficiency assessment model across multiple dimensions, including data center equipment dimensions, energy dimensions, energy-saving dimensions, and energy consumption control dimensions, for the first data center, the system can combine multi-dimensional decision information with the data center energy efficiency assessment model to output accurate energy-saving renovation strategies for the first data center. This enables precise renovation tailored to each data center, thereby improving the accuracy of data center energy-saving renovations.

[0047] The following description, in conjunction with the accompanying drawings, details the energy-saving strategy determination method, apparatus, equipment, medium, and program products provided in the embodiments of this application.

[0048] Figure 1 The diagram illustrates a network architecture for an energy-saving strategy determination method provided in an embodiment of this application. For example... Figure 1 As shown, the network architecture includes an energy-saving strategy determination device 101 and a terminal device 102. The energy-saving strategy determination device 101 and the terminal device 102 are interconnected.

[0049] In some embodiments, the energy-saving strategy determination device 101 may be a server, a computer, or a processor or processing unit within a server or computer. The server may be a single server or a server cluster consisting of multiple servers. It should be noted that the embodiments of this application do not limit the specific device form of the energy-saving strategy determination device 101. Figure 1 The energy-saving strategy determination device 101 is illustrated using a single server as an example.

[0050] In some embodiments, the terminal device may be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, personal computer (PC), ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., and the embodiments of this application do not specifically limit it. Figure 1 The example shown is a mobile phone, with terminal device 102 as an example.

[0051] In some embodiments, the terminal device 102 sends a first set of evaluation parameters to the energy-saving strategy determination device 101. The energy-saving strategy determination device 101 receives the first set of evaluation parameters sent by the terminal device 102, inputs the first set of evaluation parameters into the energy efficiency evaluation model of the first data center, performs an energy efficiency score on the first set of evaluation parameters, and outputs a first energy efficiency score for the first data center. Then, based on the first energy efficiency score, it determines an energy-saving renovation strategy for the first data center. Finally, the energy-saving strategy determination device 101 sends the energy-saving renovation strategy for the first data center to the terminal device 102.

[0052] It should be noted that the network architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As network architectures evolve, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0053] See Figure 2 This is a flowchart illustrating a method for determining an energy-saving strategy provided in an embodiment of this application. Figure 2 As shown, the energy-saving strategy determination method provided in this application embodiment can be implemented by the above-mentioned energy-saving strategy determination device, specifically including the following steps 201 to 203.

[0054] Step 201: The energy-saving strategy determination device obtains the first set of evaluation parameters corresponding to the first computer room.

[0055] In some embodiments, the first set of evaluation parameters includes: data center energy consumption parameters, equipment performance parameters, data center layout parameters, and data center environment parameters.

[0056] For example, the first data center mentioned above can be an existing data center.

[0057] For example, the aforementioned data center energy consumption parameters include at least one of the following: the total annual input power of the existing data center, and the energy consumption within the existing data center. The annual total power consumption of the equipment, the annual total power consumption of the air conditioning and refrigeration system of the existing computer room, the annual power loss of the power supply and distribution system of the existing computer room, and the annual power consumption of lighting equipment and other auxiliary equipment of the existing computer room.

[0058] For example, the total annual power consumption of the existing computer room's air conditioning and refrigeration system may include: the total annual power consumption of the chiller unit, the total annual power consumption of the water pump unit, the total annual power consumption of the cooling tower unit, and the total annual power consumption of the terminal air conditioning unit.

[0059] It should be noted that the data units for the above-mentioned data room energy consumption parameters can be...

[0060] For example, the above-mentioned equipment performance parameters include at least one of the following: cooling capacity of the existing computer room's cooling system, overall energy efficiency ratio of the cooling system, operating load rate of the cooling system, rated capacity of the uninterruptible power supply (UPS) in the power supply and distribution system of the existing computer room, operating efficiency of the UPS, transformer load rate, and total power of the IT system in the existing computer room. Average power density of servers in IT systems.

[0061] For example, the above-mentioned data center layout parameters include at least one of the following: data center area and number of server racks.

[0062] For example, the above-mentioned data center environmental parameters include at least one of the following: indoor design temperature, indoor actual operating temperature, and typical meteorological data of the data center location in a given year.

[0063] In some embodiments, the energy-saving strategy determination device can store data center energy consumption parameters, equipment performance parameters, data center layout parameters, and data center environmental parameters in an array or set to obtain the aforementioned first evaluation parameter set.

[0064] In some embodiments, the energy-saving strategy determination device can acquire the aforementioned data room energy consumption parameters, equipment performance parameters, data room layout parameters, and data room environmental parameters through data sensors in the first data room.

[0065] Step 202: The energy-saving strategy determination device inputs the first set of evaluation parameters into the energy efficiency evaluation model of the first computer room, performs energy efficiency scoring on the first set of evaluation parameters, and outputs the first energy efficiency score corresponding to the first computer room.

[0066] In some embodiments, the above-mentioned data center energy efficiency assessment model is constructed based on data center information corresponding to M dimensions of the first data center. The M dimensions include at least: data center equipment dimension, energy dimension, energy saving dimension and energy consumption control dimension, where M is a positive integer.

[0067] In some embodiments, the above-mentioned data center equipment dimension information includes: basic equipment indicators and a first indicator, wherein the first indicator includes at least one of the following: energy consumption data indicators and equipment status assessment indicators.

[0068] For example, the aforementioned basic equipment indicators are used to evaluate the completeness and data quality of the basic work before the renovation.

[0069] For example, the above energy consumption data indicators are used to assess the completeness of sub-item energy consumption data for at least 12 consecutive months.

[0070] For example, the above-mentioned equipment status assessment indicators are used to evaluate the completeness of performance test reports and the accuracy of key parameters of major energy-consuming equipment (such as refrigeration, power supply and distribution, and IT).

[0071] The aforementioned energy dimensions include: energy efficiency indicators and a second indicator, which includes at least one of the following: PUE indicator, WUE control indicator, and green electricity consumption indicator.

[0072] For example, the above-mentioned energy efficiency indicators are used to evaluate the direct efficiency of energy and resource utilization and are core outcome indicators.

[0073] For example, the above PUE index is used to evaluate the average annual electricity usage efficiency.

[0074] For example, the above-mentioned WUE control indicators are used to evaluate the average annual water resource use efficiency.

[0075] For example, the above-mentioned green electricity consumption index is used to evaluate the proportion of renewable energy generation in total energy consumption.

[0076] The aforementioned energy-saving dimensions include: energy-saving indicators and a third indicator, which includes at least one of the following: refrigeration system configuration indicators, power supply and distribution system configuration indicators, and IT equipment configuration indicators.

[0077] For example, the above energy-saving indicators are used to evaluate the advancement, applicability, and systematic nature of the energy-saving technologies adopted.

[0078] For example, the above-mentioned refrigeration system configuration indicators are used to evaluate the energy efficiency level of refrigeration technology paths.

[0079] For example, the above power supply and distribution system configuration indicators are used to evaluate the efficiency level of power supply and distribution equipment and systems.

[0080] For example, the above IT equipment configuration indicators are used to evaluate the energy efficiency and virtualization level of IT equipment.

[0081] The aforementioned energy consumption control dimensions include: green energy consumption management indicators and a fourth indicator, which includes at least one of the following: energy consumption monitoring indicators, management system certification indicators, and energy-saving indicators.

[0082] For example, the aforementioned green energy management indicators are used to evaluate long-term operation and maintenance management capabilities and the application of technological innovation.

[0083] For example, the above energy consumption monitoring indicators are used to evaluate the coverage and functionality of the energy consumption monitoring system (EMS).

[0084] For example, the above management system certification indicators are used to evaluate whether an energy or environmental management system certification has been passed.

[0085] For example, the above energy-saving indicators are used to evaluate the application of innovative technologies such as waste heat recovery, artificial intelligence (AI) energy saving, and energy storage.

[0086] In this way, the energy-saving strategy determination device constructs a multi-dimensional data center energy efficiency assessment model that includes "data center equipment dimension, energy dimension, energy saving dimension and energy consumption control dimension," collects and calculates assessments of existing data centers, and calculates the energy efficiency health score of existing data centers based on the data center energy efficiency assessment model. This effectively improves the success rate and economy of the transformation, and provides a reliable solution for the energy conservation, emission reduction and green low-carbon transformation of existing data centers.

[0087] In some embodiments, combined with Figure 2 ,like Figure 3 As shown, step 202 above can be implemented through step 202a as follows.

[0088] Step 202a: The energy-saving strategy determination device inputs the first set of evaluation parameters into the data center energy efficiency evaluation model. Based on the weights corresponding to the equipment dimension information, the weights corresponding to the energy dimension, the weights corresponding to the energy-saving dimension, the weights corresponding to the energy consumption control dimension, the evaluation scores corresponding to the first indicator, the evaluation scores corresponding to the second indicator, the evaluation scores corresponding to the third indicator, and the evaluation scores corresponding to the fourth indicator, the device calculates the energy efficiency score of the first set of evaluation parameters and outputs the first energy efficiency score.

[0089] It should be noted that the first, second, third, and fourth indicators mentioned above are referred to as secondary indicators below.

[0090] In some embodiments, the energy-saving strategy determination device can calculate the weight of the secondary indicator based on the indicator type corresponding to the secondary indicator.

[0091] In some embodiments, the above indicator types may include condition-compliant indicator types and continuous numerical indicator types.

[0092] In some embodiments, the index type corresponding to the above-mentioned secondary index can be preset by the energy-saving strategy determination device; or it can be determined by the energy-saving strategy determination device based on the semantic information of the data corresponding to the secondary index. The specific type can be determined according to actual usage requirements, and this application embodiment does not impose any limitations.

[0093] For example, the indicator types corresponding to the above secondary indicators can be shown in Table 1 below.

[0094] Table 1

[0095]

[0096] In some embodiments, for the above-mentioned condition-compliant index types, the energy-saving strategy determination device can calculate the weights corresponding to the compliant index types using a step-by-step weighting method; for the above-mentioned continuous numerical index types, the weights corresponding to the continuous numerical index types can be calculated using a linear interpolation weighting method.

[0097] For example, a linear interpolation weighting method is used for continuous numerical indicators in the energy dimension mentioned above: a full score threshold is set. and zero threshold Let the measured data be The score obtained is ,in Representing dimension, ; This indicates the corresponding label for the secondary indicator. A represents the data center equipment dimension, B represents the energy dimension, C represents the energy saving dimension, and D represents the energy consumption management dimension.

[0098] like Superior Then the score is... .

[0099] like Between and Between, the score is .

[0100] like Superior Then the score is... .

[0101] For the condition-compliant indicators in A, C, and D, a tiered weighting method is used. Scores can be directly obtained by referring to a pre-set scoring table based on the actual computer room conditions and technology application. The specific scoring criteria need to be formulated based on the location, policies, and national standards of the data center.

[0102] It should be noted that the above-mentioned basic equipment indicators, energy efficiency indicators, energy saving indicators and green energy consumption management indicators are referred to as primary indicators below.

[0103] In some embodiments, the weights corresponding to the aforementioned primary indicators can be preset by the energy-saving strategy determination device; or, user-defined. The specific weights can be determined based on actual usage requirements.

[0104] In some embodiments, after the energy-saving strategy determination device obtains the scores of various secondary indicators of the existing computer room according to the above-mentioned linear interpolation weighting method and step weighting method, it can use a two-level weighted aggregation function to calculate the first energy efficiency score of the existing computer room, which can be achieved by the following formula (1).

[0105] (1)

[0106] in, This is the first set of evaluation parameters. For dimension The weight, For dimension Secondary indicators The weight, These are the scores for each secondary indicator.

[0107] In some embodiments, the above-described data center energy efficiency assessment model can be constructed based on four dimensions and eleven secondary indicators. Alternative solutions may employ different dimensional divisions (e.g., merging the "energy consumption management dimension" into other dimensions, or adding a "security and reliability" dimension) and indicator numbers (e.g., increasing or decreasing secondary indicators). For example, the indicators in the "energy consumption management dimension" can be incorporated into the "energy saving dimension," forming three core dimensions for evaluation.

[0108] In some embodiments, this application employs a linear interpolation weighting method for continuous indicators and a stepped weighting method for condition-compliant indicators. Alternative solutions may employ different mathematical methods to achieve similar quantitative objectives, such as using nonlinear mappings like exponential or logarithmic functions to score continuous indicators; and using fuzzy comprehensive evaluation or the Analytic Hierarchy Process (AHP) based on expert scoring to determine scores for condition-compliant indicators.

[0109] In this embodiment, after assigning weights to four dimensions and eleven secondary indicators, a two-level weighted aggregation function is used to calculate the first energy efficiency score of the existing data center, thereby achieving accurate "diagnosis" of the existing data center.

[0110] Step 203: The energy-saving strategy determination device determines the energy-saving renovation strategy corresponding to the first computer room based on the first energy efficiency score.

[0111] In some embodiments, the above-mentioned energy-saving renovation strategies include: data center environment adjustment strategies, data center layout adjustment strategies, data center energy consumption adjustment strategies, and equipment performance adjustment strategies.

[0112] In some embodiments, the energy-saving strategy determination device can determine the energy-saving renovation strategy corresponding to the first computer room based on user needs and the first energy efficiency score.

[0113] For example, the aforementioned user requirements may include a budget for data center renovation.

[0114] In some embodiments, the energy-saving strategy determining device can determine an energy-saving renovation strategy corresponding to the first energy efficiency score based on the first energy efficiency score.

[0115] For example, the above levels can be deep green, efficient and economical, or simple and optimized.

[0116] It should be noted that the specific implementation of step 203 above can be found in the following embodiments, and will not be repeated here to avoid repetition.

[0117] In some embodiments, combined with Figure 2 ,like Figure 4 As shown, step 203 above can be implemented through steps 203a to 203c.

[0118] Step 203a: The energy-saving strategy determination device determines the set of energy-saving retrofit strategies corresponding to the first energy efficiency score based on the first energy efficiency score.

[0119] For example, regarding the above-mentioned deep green solution: when the first energy efficiency score is close to the effect score of the data center renovation (90 points), the energy-saving strategy determination device can determine the energy-saving renovation strategy set corresponding to the first energy efficiency score as {indirect evaporative cooling or liquid cooling, high-efficiency amorphous alloy transformer, modular UPS, photovoltaic or energy storage system, AI energy efficiency optimization platform}.

[0120] Regarding the above-mentioned efficient and economical solutions: when the first energy efficiency score is close to the score of 80-90 after the data center renovation, the energy-saving strategy determination device can determine the set of energy-saving renovation strategies corresponding to the first energy efficiency score as {fluorine pump air conditioning or fresh air system, closed hot and cold aisle, replacement of modular uninterruptible power supply (UPS), decommissioning of old IT systems, and basic energy management system (EMS)}.

[0121] Regarding the above simplified optimization scheme: when the first energy efficiency score is close to the score of 60-80 after the data center renovation, the energy-saving strategy determination device can determine the set of energy-saving renovation strategies corresponding to the first energy efficiency score as {Coefficient of Performance (COP) air conditioning replacement, simplified airflow organization optimization, and light-emitting diode (LED) lighting replacement}.

[0122] In some embodiments, the energy-saving strategy determination device can cluster historical successful cases based on a clustering algorithm (e.g., K-means), compare the first energy efficiency score with each cluster center, match the most similar case cluster, and obtain a set of energy-saving renovation strategies from the solutions of that cluster.

[0123] Step 203b: The energy-saving strategy determination device scores each energy-saving renovation strategy in the energy-saving renovation strategy set to obtain an energy-saving score set.

[0124] In some embodiments, one energy-saving score in the above set of energy-saving scores corresponds to one energy-saving renovation strategy.

[0125] In some embodiments, the energy-saving strategy determination device can calculate the score corresponding to each energy-saving renovation strategy in the set of energy-saving renovation strategies through the above-mentioned data center energy efficiency assessment model, so as to obtain a set of energy-saving scores.

[0126] It should be noted that the specific implementation of step 203b above can be found in the following embodiments, and will not be repeated here to avoid repetition.

[0127] In some embodiments, combined with Figure 4 ,like Figure 5As shown, step 203b above can be implemented through steps 203b1 to 203b3.

[0128] Step 203b1: The energy-saving strategy determination device acquires the equipment performance parameters, computer room layout parameters, and computer room environmental parameters corresponding to each energy-saving renovation strategy.

[0129] In some embodiments, the energy-saving strategy determination device can obtain the equipment performance parameters, computer room layout parameters, and computer room environment parameters corresponding to each energy-saving renovation strategy through the renovation text in each energy-saving renovation strategy.

[0130] Step 203b2: The energy-saving strategy determination device performs energy consumption prediction for each energy-saving renovation scheme in the energy-saving renovation strategy set to obtain the energy consumption value corresponding to each energy-saving renovation strategy.

[0131] In some embodiments, the energy consumption forecast corresponding to each of the above energy-saving renovation schemes may include: energy consumption forecast for refrigeration system renovation, energy consumption forecast for power supply and distribution system renovation, energy consumption forecast for IT equipment system renovation, energy consumption forecast for lighting system renovation, forecast for new green energy (e.g., photovoltaic) power generation, total energy consumption forecast, and PUE calculation.

[0132] For example, the energy consumption prediction of the above-mentioned refrigeration system renovation can be achieved by the following formula (2).

[0133] (2)

[0134] in, To predict the annual energy consumption of the air conditioner after the renovation; This represents the current annual energy consumption of the air conditioner. This is the reduction factor for cooling demand. ; This represents the percentage of natural cooling time. This refers to the power consumption ratio in natural cooling mode.

[0135] The energy consumption prediction for the above-mentioned power supply and distribution system renovation can be achieved through the following formula (3).

[0136] (3)

[0137] in, To predict the annual power supply and distribution losses after the renovation; Average power of IT; To improve the power supply efficiency of the new UPS; Hour; For other forms of predicting annual power supply and distribution losses (kWh), such as transformer losses, distribution line losses, and switching power supply losses.

[0138] The energy consumption prediction for the above-mentioned IT equipment system upgrade can be achieved through the following formula (4).

[0139] (4)

[0140] in, This indicates the predicted annual energy consumption of IT after the upgrade (kWh); This indicates the current annual energy consumption of IT (kWh); This indicates the percentage of outdated equipment that has been decommissioned from the network.

[0141] The energy consumption prediction for the above-mentioned lighting system renovation can be achieved through the following formula (5).

[0142] (5)

[0143] in, This indicates the predicted annual energy consumption of the upgraded lighting system. This indicates the current annual energy consumption of the lighting system; This indicates that the energy saving rate of LED replacement can typically reach 60%-70%; This indicates the additional energy saving factor brought about by intelligent control (such as infrared sensing), which can typically reach 30%-50%.

[0144] The above-mentioned forecast of new green energy (photovoltaic) power generation can be achieved through the following formula (6).

[0145] (6)

[0146] in, This indicates the projected annual power generation (kWh) of the newly added photovoltaic system; Indicates the installed area of ​​photovoltaic panels; Indicates the conversion efficiency of photovoltaic modules; This indicates the local annual peak sunshine hours (h); This represents the system performance ratio, typically between 0.75 and 0.85.

[0147] The above total energy consumption prediction and PUE calculation can be achieved through the following formulas (7) and (8).

[0148] (7)

[0149] (8)

[0150] in, This represents the projected total annual power consumption of the computer room after the implementation of the upgrade plan. This indicates the predicted annual average PUE index of the computer room after the implementation and renovation of the plan.

[0151] Step 203b3: The energy-saving strategy determination device inputs the energy consumption value, equipment performance parameters, computer room layout parameters, and computer room environmental parameters corresponding to each energy-saving renovation strategy into the computer room energy efficiency assessment model, performs energy efficiency assessment, and outputs a set of energy-saving scores.

[0152] In some embodiments, the energy-saving strategy determination device can concatenate the energy consumption value, equipment performance parameters, computer room layout parameters, and computer room environmental parameters corresponding to each energy-saving renovation strategy to obtain a predicted data set corresponding to each energy-saving renovation strategy. Then, the predicted data set corresponding to each energy-saving renovation strategy is input into the computer room energy efficiency assessment model to obtain an energy-saving score set, which is: ;

[0153] .

[0154] In this embodiment of the application, the energy consumption of the transformation effect of the cooling system, power supply and distribution system and IT equipment system of each pre-selected scheme is predicted, and the energy efficiency improvement and economic benefits are comprehensively evaluated so as to select the optimal transformation scheme from all pre-selected transformation schemes, so that the transformation of the computer room is more in line with the current situation and actual conditions of the computer room.

[0155] Step 203c: The energy-saving strategy determination device determines the energy-saving renovation strategy corresponding to the largest energy-saving score in the energy-saving score set as the energy-saving renovation strategy corresponding to the first computer room.

[0156] For example, steps 203b and 203c can be implemented by the following formula (9).

[0157] (9)

[0158] in, This indicates the final selected renovation plan; This represents the modification scheme that maximizes the function value. ; and Represents the weighting coefficients, satisfying These represent preferences for "energy efficiency improvement" and "return on investment," respectively, and can be set by the user. Representation scheme Annual electricity savings; Representation scheme Total investment amount.

[0159] In some embodiments, the energy-saving strategy determination device can carry out construction and renovation of the existing computer room according to the above-mentioned energy-saving renovation strategy. After the computer room is running stably, it can collect the actual operating data of the computer room and calculate the actual energy efficiency health score, and conduct verification analysis of the renovation effect and parameter adjustment.

[0160] In some embodiments, the energy-saving strategy determination device can perform verification analysis based on the actual retrofit results, comparing the predicted total energy efficiency score of the optimal retrofit scheme. Total energy efficiency score in actual operation The relationship with the set allowable deviation threshold is analyzed to determine the implementation effect of the scheme. The verification analysis of the modification effect can be achieved by the following formula (10) 7.

[0161] (10)

[0162] in, For example, the set allowable deviation threshold wait.

[0163] It should be noted that if the deviation obtained from the effect verification is large, the key parameters in the transformation plan need to be analyzed, the cause of the deviation needs to be analyzed, and an analysis report needs to be generated for the data center operation and maintenance personnel to check and maintain, until the deviation reaches within the allowable deviation threshold, and the data center transformation process is considered to be completed.

[0164] In some embodiments, after the energy-saving strategy determination device completes the renovation of the existing computer room, it can record the case completely in the case experience database. When renovating other existing computer rooms, it can retrieve and call similar cases in the case experience database, and use their "actual value / predicted value" ratio to calibrate the current prediction parameters, so as to continuously optimize the above-mentioned computer room energy efficiency assessment model.

[0165] In this embodiment, actual operational data collected again after the data center is running stably is input into the model for effect verification. The deviation between actual and predicted data is stored in a case experience database for continuous calibration of the model's prediction parameters. This forms a complete closed loop of "data collection → current status scoring → solution prediction (re-scoring) → decision-making → implementation → verification (re-scoring) → feedback optimization." This effectively improves the success rate and cost-effectiveness of the transformation, providing a reliable solution for energy conservation, emission reduction, and green low-carbon transformation of existing data centers.

[0166] The energy-saving strategy determination method of this application will be described below through specific embodiments.

[0167] like Figure 6 As shown, the implementation process of the energy-saving strategy determination method provided in this application embodiment includes the following S1 to S11.

[0168] S1. The energy-saving strategy determination device constructs a quantitative scoring function model for the energy efficiency and health of existing computer rooms based on four dimensions of existing computer room renovation and eleven related secondary scoring indicators.

[0169] S2. The energy-saving strategy determination device collects data and scores the current status of the existing computer room through the power equipment acquisition terminal and environmental status acquisition terminal in the existing computer room.

[0170] S3. The energy-saving strategy determination device proposes a three-tiered pre-selected renovation plan based on the calculated total score of the energy efficiency and health of the existing computer room, which combines multiple energy-saving technologies: deep green, high efficiency and economy, and simple optimization.

[0171] S4. The energy-saving strategy determination device performs simulation prediction and multi-objective decision-making for each pre-selected scheme, comprehensively evaluates the energy efficiency improvement and economic benefits, and selects the optimal renovation scheme from all pre-selected renovation schemes.

[0172] S5. The energy-saving strategy determination device will carry out construction and modification according to the selected optimal modification scheme, and verify the modification effect after the computer room is completed and running stably.

[0173] S6. The energy-saving strategy determination device verifies whether there is a deviation between the actual operating effect and the predicted effect of the computer room.

[0174] For example, if there is a discrepancy between the running result and the predicted result, S7 is executed; if there is no discrepancy between the running result and the predicted result, S10 is executed.

[0175] S7. The energy-saving strategy determines whether the device verification deviation is within the set threshold range.

[0176] For example, if the verification deviation is within the set threshold range, step S8 is executed; if the verification deviation is not within the set threshold range, step S9 is executed.

[0177] S8. The energy-saving strategy determination device archives case studies and optimizes model parameters.

[0178] S9. The energy-saving strategy determination device performs error analysis and coordinates operation and maintenance rectification.

[0179] S10. The energy-saving strategy determination device completes the renovation of the existing computer room, and this case is fully recorded and stored in the case experience database.

[0180] S11, the energy-saving strategy determination device provides reference data for new projects.

[0181] In this way, by constructing a quantitative evaluation model that includes multiple dimensions such as data center equipment, energy, energy saving, and energy consumption management, and with corresponding scoring rules, data is collected and calculated for existing data centers. Based on the energy efficiency health score of existing data centers calculated by the model, corresponding energy-saving renovation strategies are determined, which effectively improves the accuracy of data center energy-saving renovation.

[0182] It should be noted that the descriptions of each step S1 to S11 in this embodiment can be found in the descriptions in the above embodiments, and will not be repeated here.

[0183] It should be noted that the above-described method embodiments, or the various possible implementations of the method embodiments, can be executed individually, or, provided there is no conflict, they can be combined with each other. The specific implementation can be determined according to actual usage requirements, and this application embodiment does not impose any restrictions on this.

[0184] Figure 7 This is a schematic diagram of an energy-saving strategy determination system provided in an embodiment of this application. Figure 7 As shown, the energy-saving strategy determination system 800 may include: a physical sensing interface 801, a data acquisition and transmission interface 802, a data storage interface 803, a model simulation calculation and multi-objective decision-making interface 804, and a decision report and parameter configuration interface 805. The physical sensing interface 801 is connected to the data acquisition and transmission interface 802; the data acquisition and transmission interface 802 is connected to the data storage interface 803; the data storage interface 803 is connected to the model simulation calculation and multi-objective decision-making interface 804; and the model simulation calculation and multi-objective decision-making interface 804 is connected to the decision report and parameter configuration interface 805. The physical sensing interface 801, data acquisition and transmission interface 802, and data storage interface 803 are connected as a whole to the model simulation calculation and multi-objective decision-making interface 804.

[0185] The aforementioned physical sensing interface 801 includes: an existing computer room equipment status acquisition terminal 8011; an existing computer room environment status acquisition terminal 8012; an equipment acquisition terminal for new equipment added during construction and renovation 8013; and an environment acquisition terminal for new equipment added during construction and renovation 8014.

[0186] The aforementioned data acquisition and transmission interface 802 includes: data center energy consumption data acquisition 8021; data center equipment status information acquisition 8022; data center environmental parameter data acquisition 8023; and manual data entry and data verification 8024.

[0187] The aforementioned data storage interface 803 includes: a case experience database 8031, which stores historical case data for model parameter calibration; a computer room status database 8032, which stores collected raw data, scoring results, analysis reports, etc.; and a renovation scheme knowledge base 8033, which stores various renovation scheme technical measures, technical parameters, cost data, etc.

[0188] The above-mentioned model simulation calculation and multi-objective decision interface 804 includes: calculation of data center energy efficiency and health score 8041; prediction of energy consumption of the transformation effect of the pre-selected transformation scheme 8042; multi-objective decision optimization analysis 8043; and verification of transformation effect and deviation analysis 8044.

[0189] The aforementioned decision report and parameter configuration interface 805 includes: analysis and decision report generation 8051; visualization of transformation plans 8052; and display of existing data center parameter configurations 8053.

[0190] The existing data center equipment status acquisition terminal 8011 and the newly added equipment acquisition terminal 8013 are used to sense equipment data such as cooling capacity, comprehensive energy efficiency ratio, and air conditioning load rate of refrigeration equipment. The existing data center environmental status acquisition terminal 8012 and the newly added environmental acquisition terminal 8014 are used to sense environmental data such as data center area, number of server racks, indoor design temperature, and actual indoor operating temperature. The data center energy consumption data acquisition terminal 8021 is used to collect data such as total annual power input and annual IT equipment power consumption. The data center equipment status information acquisition terminal 8022 and the data center environmental parameter data acquisition terminal 8023 are used to transmit the sensed equipment data and data center environmental data. The manual data entry and verification terminal 8024 is used for manual data entry and verification under specific conditions. These modules are applied to step 201 and related solutions.

[0191] The following modules are used for data center energy efficiency and health rating calculation (8041): Energy consumption prediction for pre-selected renovation schemes (8042): Predicting and scoring the renovation effects of pre-selected schemes. Multi-objective decision optimization analysis (8043): Selecting the optimal renovation scheme from the pre-selected schemes. Renovation effect verification and deviation analysis (8044): Verifying and analyzing the actual operation of the data center after renovation. Analysis and decision report generation (8051): Generating an analysis report after analyzing the actual operation of the data center. Renovation scheme visualization (8052): Presenting the renovation effects of the schemes in a user-friendly interface for understanding and comparison. Existing data center parameter configuration display (8053): Displaying various renovation parameters of the existing data center. These modules are applied to steps 202 and 203, and related schemes.

[0192] The case study database 803 stores historical case data for model parameter calibration. The computer room status database 8032 stores collected raw data, scoring results, analysis reports, etc. The renovation plan knowledge base 8033 stores various renovation plan technical measures, technical parameters, cost data, etc. These modules are applied to step 203 and related plans.

[0193] It should be noted that for a detailed explanation of the steps performed by each module and their beneficial effects, please refer to the description in the above embodiments, which will not be repeated here.

[0194] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0195] This application embodiment can divide the energy-saving strategy determination device into functional modules according to the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0196] In some embodiments, this application also provides an energy-saving strategy determination apparatus. This energy-saving strategy determination apparatus may include one or more functional modules for implementing the energy-saving strategy determination method of the above method embodiments.

[0197] For example, Figure 8 This is a schematic diagram of an energy-saving strategy determination device provided in an embodiment of this application. Figure 8 As shown, the energy-saving strategy determination device 900 includes: an acquisition module 901, a processing module 902, and a determination module 903.

[0198] The acquisition module is used to acquire the first set of evaluation parameters corresponding to the first data center. This first set of evaluation parameters includes: data center energy consumption parameters, equipment performance parameters, data center layout parameters, and data center environmental parameters. The processing module is used to input the first set of evaluation parameters into the data center energy efficiency evaluation model corresponding to the first data center, perform energy efficiency scoring on the first set of evaluation parameters, and output the first energy efficiency score for the first data center. The data center energy efficiency evaluation model is constructed based on data center information corresponding to M dimensions of the first data center. These M dimensions include at least: data center equipment dimension, energy dimension, energy saving dimension, and energy consumption control dimension, where M is a positive integer. The determination module is used to determine the energy-saving renovation strategy corresponding to the first data center based on the first energy efficiency score. This energy-saving renovation strategy includes: data center environment adjustment strategy, data center layout adjustment strategy, data center energy consumption adjustment strategy, and equipment performance adjustment strategy.

[0199] The energy-saving strategy determination device provided in this application constructs a data center energy efficiency assessment model by building multiple dimensions, including data center equipment dimensions, energy dimensions, energy-saving dimensions, and energy consumption control dimensions, corresponding to the first data center. By combining multi-dimensional decision information and the data center energy efficiency assessment model, accurate energy-saving renovation strategies can be output for the first data center, thereby achieving precise renovation with a one-system-one-policy approach. This improves the accuracy of data center energy-saving renovation.

[0200] In some embodiments, the above-mentioned data center equipment dimension information includes: basic equipment indicators and a first indicator, the first indicator including at least one of the following: energy consumption data indicators and equipment status assessment indicators; the energy dimension includes: energy efficiency indicators and a second indicator, the second indicator including at least one of the following: power utilization efficiency (PUE) indicator, water utilization efficiency (WUE) control indicator, and green electricity consumption indicator; the energy saving dimension includes: energy saving indicators and a third indicator, the third indicator including at least one of the following: cooling system configuration indicators, power supply and distribution system configuration indicators, and information technology (IT) equipment configuration indicators; the energy consumption control dimension includes: green energy consumption management indicators and a fourth indicator, the fourth indicator including at least one of the following: energy consumption monitoring indicators, management system certification indicators, and energy saving indicators.

[0201] In other embodiments, the above-mentioned processing module is specifically used to calculate the energy efficiency score of the first evaluation parameter set after the input module inputs the first evaluation parameter set into the data center energy efficiency evaluation model, based on the weights corresponding to the equipment dimension information, the weights corresponding to the energy dimension, the weights corresponding to the energy saving dimension, the weights corresponding to the energy consumption control dimension, the evaluation scores corresponding to the first indicator, the evaluation scores corresponding to the second indicator, the evaluation scores corresponding to the third indicator, and the evaluation scores corresponding to the fourth indicator, and output the first energy efficiency score.

[0202] In some other embodiments, the determining module is specifically used to determine a set of energy-saving retrofit strategies corresponding to the first energy efficiency score. The processing module is further used to score each energy-saving retrofit strategy in the set of energy-saving retrofit strategies to obtain a set of energy-saving scores, where one energy-saving score in the set corresponds to one energy-saving retrofit strategy. The determining module is also used to determine the energy-saving retrofit strategy corresponding to the largest energy-saving score in the set of energy-saving scores as the energy-saving retrofit strategy corresponding to the first computer room.

[0203] In some other embodiments, the energy-saving strategy determining device further includes an acquisition module for acquiring equipment performance parameters, data center layout parameters, and data center environmental parameters corresponding to each energy-saving renovation strategy. The processing module is further configured to predict the energy consumption of each energy-saving renovation scheme in the set of energy-saving renovation strategies, obtaining the energy consumption value corresponding to each energy-saving renovation strategy; inputting the energy consumption value, equipment performance parameters, data center layout parameters, and data center environmental parameters corresponding to each energy-saving renovation strategy into the data center energy efficiency assessment model for energy efficiency assessment, and outputting a set of energy-saving scores.

[0204] It should be noted that by constructing a quantitative evaluation model encompassing multiple dimensions, including data center equipment, energy, energy conservation, and energy consumption management, and with corresponding scoring rules, data is collected and evaluated from existing data centers. Based on the energy efficiency and health score of the existing data centers calculated by the model, corresponding energy-saving renovation strategies are determined, effectively improving the accuracy of data center energy-saving renovations. The device can implement all the processes described in the above method embodiments and achieve the same beneficial effects; therefore, to avoid repetition, it will not be elaborated further here.

[0205] In the case where the functions of the integrated modules described above are implemented in hardware, this application provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 9 As shown, the electronic device 90 includes: a processor 92, a communication interface 93, and a bus 94. Optionally, the electronic device 90 may also include a memory 91.

[0206] Processor 92 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0207] Communication interface 93 is used to connect with other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0208] The memory 91 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0209] As one possible implementation, the memory 91 can exist independently of the processor 92. The memory 91 can be connected to the processor 92 via a bus 94 and is used to store instructions or program code. When the processor 92 calls and executes the instructions or program code stored in the memory 91, it can implement the energy-saving strategy determination method provided in the embodiments of this application.

[0210] In another possible implementation, memory 91 can also be integrated with processor 92.

[0211] Bus 94 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 94 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0212] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0213] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described energy-saving strategy determination method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0214] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0215] This application also provides a readable storage medium storing a program or instructions. When executed by a computer, the program or instructions implement the energy-saving strategy determination method provided in the above embodiments. It is understood that all or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The readable storage medium can be any of the foregoing embodiments or memory. The readable storage medium can also be an external storage device of the service invocation device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, flash card, etc., equipped on the service invocation device. Further, the readable storage medium can include both internal storage units of the service invocation device and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the service invocation device. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0216] This application also provides a computer program product, which is stored in a storage medium and, when executed by a computer, implements the energy-saving strategy determination method provided in the above embodiments.

[0217] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0218] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0219] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for determining an energy-saving strategy, characterized in that, include: Obtain the first set of evaluation parameters corresponding to the first computer room. The first set of evaluation parameters includes: computer room energy consumption parameters, equipment performance parameters, computer room layout parameters, and computer room environment parameters. The first set of evaluation parameters is input into the energy efficiency evaluation model of the first data center, the first set of evaluation parameters is scored for energy efficiency, and the first energy efficiency score of the first data center is output. The energy efficiency evaluation model of the data center is constructed based on the data center information corresponding to M dimensions of the first data center. The M dimensions include at least: data center equipment dimension, energy dimension, energy saving dimension and energy consumption control dimension, where M is a positive integer. Based on the first energy efficiency score, an energy-saving renovation strategy corresponding to the first computer room is determined; wherein, the energy-saving renovation strategy includes: computer room environment adjustment strategy, computer room layout adjustment strategy, computer room energy consumption adjustment strategy, and equipment performance adjustment strategy.

2. The method for determining energy-saving strategies according to claim 1, characterized in that, The data center equipment dimension information includes: basic equipment indicators and a first indicator, wherein the first indicator includes at least one of the following: energy consumption data indicators and equipment status assessment indicators; The energy dimension includes: energy efficiency indicators and a second indicator, wherein the second indicator includes at least one of the following: power utilization efficiency (PUE) indicator, water utilization efficiency (WUE) control indicator, and green electricity consumption indicator. The energy-saving dimension includes: energy-saving indicators and a third indicator, wherein the third indicator includes at least one of the following: refrigeration system configuration indicators, power supply and distribution system configuration indicators, and information technology (IT) equipment configuration indicators; The energy consumption control dimensions include: green energy consumption management indicators and a fourth indicator, wherein the fourth indicator includes at least one of the following: energy consumption monitoring indicators, management system certification indicators, and energy-saving indicators.

3. The method for determining energy-saving strategies according to claim 2, characterized in that, The step of inputting the first set of evaluation parameters into the energy efficiency evaluation model corresponding to the first data center, performing an energy efficiency score on the first set of evaluation parameters, and outputting the first energy efficiency score corresponding to the first data center includes: The first set of evaluation parameters is input into the data center energy efficiency evaluation model. Based on the weights corresponding to the equipment dimension information, the energy dimension, the energy saving dimension, the energy consumption control dimension, the evaluation score corresponding to the first indicator, the evaluation score corresponding to the second indicator, the evaluation score corresponding to the third indicator, and the evaluation score corresponding to the fourth indicator, the energy efficiency score is calculated and the first energy efficiency score is output.

4. The method for determining energy-saving strategies according to claim 1, characterized in that, The step of determining the energy-saving renovation strategy corresponding to the first computer room based on the first energy efficiency score includes: Based on the first energy efficiency score, determine the set of energy-saving renovation strategies corresponding to the first energy efficiency score; Each energy-saving renovation strategy in the set of energy-saving renovation strategies is scored to obtain a set of energy-saving scores, and one energy-saving score in the set of energy-saving scores corresponds to one energy-saving renovation strategy; The energy-saving renovation strategy corresponding to the largest energy-saving score in the set of energy-saving scores is determined as the energy-saving renovation strategy corresponding to the first computer room.

5. The method for determining energy-saving strategies according to claim 4, characterized in that, The step of scoring each energy-saving renovation strategy in the set of energy-saving renovation strategies to obtain a set of energy-saving scores includes: Obtain the equipment performance parameters, data center layout parameters, and data center environment parameters corresponding to each energy-saving renovation strategy; Energy consumption prediction is performed on each energy-saving renovation scheme in the set of energy-saving renovation strategies to obtain the energy consumption value corresponding to each energy-saving renovation strategy. The energy consumption value, equipment performance parameters, computer room layout parameters, and computer room environment parameters corresponding to each energy-saving renovation strategy are input into the computer room energy efficiency assessment model to conduct energy efficiency assessment and output the energy-saving score set.

6. An energy-saving strategy determination device, characterized in that, include: The module consists of an acquisition module, a processing module, and a determination module. The acquisition module is used to acquire a first set of evaluation parameters corresponding to the first computer room. The first set of evaluation parameters includes: computer room energy consumption parameters, equipment performance parameters, computer room layout parameters, and computer room environment parameters. The processing module inputs the first set of evaluation parameters into the energy efficiency evaluation model of the first data center, performs energy efficiency scoring on the first set of evaluation parameters, and outputs the first energy efficiency score of the first data center. The energy efficiency evaluation model of the data center is constructed based on the data center information corresponding to M dimensions of the first data center. The M dimensions include at least: data center equipment dimension, energy dimension, energy saving dimension and energy consumption control dimension, where M is a positive integer. The determining module is used to determine the energy-saving renovation strategy corresponding to the first data center based on the first energy efficiency score; wherein, the energy-saving renovation strategy includes: data center environment adjustment strategy, data center layout adjustment strategy, data center energy consumption adjustment strategy, and equipment performance adjustment strategy.

7. The energy-saving strategy determination device according to claim 6, characterized in that, The data center equipment dimension information includes: basic equipment indicators and a first indicator, wherein the first indicator includes at least one of the following: energy consumption data indicators and equipment status assessment indicators; The energy dimension includes: energy efficiency indicators and a second indicator, wherein the second indicator includes at least one of the following: power utilization efficiency (PUE) indicator, water utilization efficiency (WUE) control indicator, and green electricity consumption indicator. The energy-saving dimension includes: energy-saving indicators and a third indicator, wherein the third indicator includes at least one of the following: refrigeration system configuration indicators, power supply and distribution system configuration indicators, and information technology (IT) equipment configuration indicators; The energy consumption control dimensions include: green energy consumption management indicators and a fourth indicator, wherein the fourth indicator includes at least one of the following: energy consumption monitoring indicators, management system certification indicators, and energy-saving indicators.

8. The energy-saving strategy determination device according to claim 7, characterized in that, The step of inputting the first set of evaluation parameters into the energy efficiency evaluation model corresponding to the first data center, performing an energy efficiency score on the first set of evaluation parameters, and outputting the first energy efficiency score corresponding to the first data center includes: The first set of evaluation parameters is input into the data center energy efficiency evaluation model. Based on the weights corresponding to the equipment dimension information, the energy dimension, the energy saving dimension, the energy consumption control dimension, the evaluation score corresponding to the first indicator, the evaluation score corresponding to the second indicator, the evaluation score corresponding to the third indicator, and the evaluation score corresponding to the fourth indicator, the energy efficiency score is calculated and the first energy efficiency score is output.

9. The energy-saving strategy determination device according to claim 6, characterized in that, The step of determining the energy-saving renovation strategy corresponding to the first computer room based on the first energy efficiency score includes: Based on the first energy efficiency score, determine the set of energy-saving renovation strategies corresponding to the first energy efficiency score; Each energy-saving renovation strategy in the set of energy-saving renovation strategies is scored to obtain a set of energy-saving scores, and one energy-saving score in the set of energy-saving scores corresponds to one energy-saving renovation strategy; The energy-saving renovation strategy corresponding to the largest energy-saving score in the set of energy-saving scores is determined as the energy-saving renovation strategy corresponding to the first computer room.

10. The energy-saving strategy determination device according to claim 9, characterized in that, The step of scoring each energy-saving renovation strategy in the set of energy-saving renovation strategies to obtain a set of energy-saving scores includes: Obtain the equipment performance parameters, data center layout parameters, and data center environment parameters corresponding to each energy-saving renovation strategy; Energy consumption prediction is performed on each energy-saving renovation scheme in the set of energy-saving renovation strategies to obtain the energy consumption value corresponding to each energy-saving renovation strategy. The energy consumption value, equipment performance parameters, computer room layout parameters, and computer room environment parameters corresponding to each energy-saving renovation strategy are input into the computer room energy efficiency assessment model to conduct energy efficiency assessment and output the energy-saving score set.

11. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the energy-saving strategy determination method as described in any one of claims 1-5.

12. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a computer, implement the energy-saving strategy determination method as described in any one of claims 1-5.

13. A computer program product, characterized in that, The computer program product is stored in a storage medium, and when executed by a computer, the computer program product implements the energy-saving strategy determination method as described in any one of claims 1-5.

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