Energy efficiency evaluation method, device and equipment of data center, medium and product

Abstract

The invention discloses an energy efficiency evaluation method and device for a data center, equipment, a medium and a product, and the method comprises the steps: obtaining a multi-dimensional energy efficiency evaluation index of the data center, wherein the dimensions of the multi-dimensional energy efficiency evaluation index comprise a social dimension, an ecological dimension and a technical dimension; hierarchically dividing the multi-dimensional energy efficiency evaluation indexes, and establishing an evaluation index matrix between any two indexes under each hierarchy; determining the comprehensive weight of the data center according to each evaluation index matrix; establishing a membership matrix of the data center according to the multi-dimensional energy efficiency evaluation indexes; and determining an energy efficiency level score of the data center according to a preset evaluation set, the comprehensive weight and the membership matrix. Multi-dimensional energy efficiency evaluation indexes under multiple dimensions of a data center are obtained and subjected to hierarchical division, and an evaluation index matrix and a final energy efficiency level score are determined according to hierarchies. And the hierarchical structure of the energy efficiency level of the data center is clearly reflected, so that the energy efficiency level of the data center is clearly and accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of data centers, and in particular, to an energy efficiency evaluation method, device, equipment, medium and product for a data center. Background Art

[0002] Currently, new generation information technologies such as 5G, cloud computing, and artificial intelligence are developing rapidly. The information technology is accelerating the integration with traditional industries, and the digital economy is booming. As the physical carrier for the operation of information systems in various industries, the data center has become an essential key infrastructure for the operation of the economic society and plays a crucial role in the development of the digital economy.

[0003] In the related technologies, the energy efficiency evaluation of the data center is all carried out based on software and hardware equipment such as IT and communication, power supply system, refrigeration system, and security system, and there is no more comprehensive method for evaluating the energy efficiency level of the data center. Summary of the Invention

[0004] The present invention provides an energy efficiency evaluation method, device, equipment, medium and product for a data center to realize the determination of the energy efficiency level of the data center from multiple dimensions.

[0005] According to the first aspect of the present invention, an energy efficiency evaluation method for a data center is provided, including: Obtaining multi-dimensional energy efficiency evaluation indicators of the data center, where the dimensions of the multi-dimensional energy efficiency evaluation indicators include social dimension, ecological dimension, and technical dimension; Performing hierarchical division on the multi-dimensional energy efficiency evaluation indicators, and establishing an evaluation index matrix between any two indicators under each level; Determining the comprehensive weight of the data center according to each of the evaluation index matrices; Establishing a membership degree matrix of the data center according to the multi-dimensional energy efficiency evaluation indicators; Determining the energy efficiency level score of the data center according to a preset evaluation set, the comprehensive weight, and the membership degree matrix.

[0006] According to the second aspect of the present invention, an energy efficiency evaluation device for a data center is provided, including: An index acquisition module, configured to obtain multi-dimensional energy efficiency evaluation indicators of the data center, where the dimensions of the multi-dimensional energy efficiency evaluation indicators include social dimension, ecological dimension, and technical dimension; A first establishment module, configured to perform hierarchical division on the multi-dimensional energy efficiency evaluation indicators, and establish an evaluation index matrix between any two indicators under each level; A weight determination module, configured to determine the comprehensive weight of the data center according to each of the evaluation index matrices; A second establishment module, configured to establish a membership matrix of the data center according to the multi-dimensional energy efficiency evaluation index; A level determination module, configured to determine an energy efficiency level score of the data center according to a preset evaluation set, the comprehensive weight, and the membership matrix.

[0007] According to a third aspect of the present invention, there is provided an electronic device, where the electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy efficiency evaluation method of the data center according to any embodiment of the present invention.

[0008] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the energy efficiency evaluation method of the data center according to any embodiment of the present invention when executed by a processor.

[0009] According to a fifth aspect of the present invention, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and the computer program implements the energy efficiency evaluation method of the data center according to any embodiment of the present invention when executed by a processor.

[0010] The technical solution of the embodiment of the present invention obtains a multi-dimensional energy efficiency evaluation index of a data center, where the dimensions of the multi-dimensional energy efficiency evaluation index include a social dimension, an ecological dimension, and a technical dimension; hierarchically divides the multi-dimensional energy efficiency evaluation index, and establishes an evaluation index matrix between any two indexes at each level; determines the comprehensive weight of the data center according to each evaluation index matrix; establishes a membership matrix of the data center according to the multi-dimensional energy efficiency evaluation index; determines the energy efficiency level score of the data center according to a preset evaluation set, the comprehensive weight, and the membership matrix. By obtaining the multi-dimensional energy efficiency evaluation index of the data center in multiple dimensions and hierarchically dividing it, the evaluation index matrix and the final energy efficiency level score are determined according to the levels. Clearly reflecting the hierarchical structure of the energy efficiency level of the data center, thereby clearly and accurately evaluating the energy efficiency level of the data center.

[0011] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0013] Figure 1 is a flowchart of an energy efficiency evaluation method for a data center provided in Embodiment 1 of the present invention; Figure 2 is a flowchart of an energy efficiency evaluation method for a data center provided in Embodiment 2 of the present invention; Figure 3 is an example diagram of comprehensive weights in an energy efficiency evaluation method for a data center provided in Embodiment 2 of the present invention; Figure 4 is a schematic structural diagram of an energy efficiency evaluation device for a data center provided in Embodiment 3 of the present invention; Figure 5 is a schematic structural diagram of an electronic device for implementing the embodiments of the present invention. Detailed Embodiments

[0014] In order to enable those skilled in the art to better understand the solutions of the present invention, the following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0015] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0016] Embodiment 1 Figure 1The following is a flowchart of an energy efficiency evaluation method for a data center provided in Embodiment 1 of the present invention. This embodiment is applicable to the energy efficiency evaluation of a data center based on the Social-Ecological-Technical System (SETS). This method can be executed by an energy efficiency evaluation device of the data center. The energy efficiency evaluation device of the data center can be implemented in the form of hardware and / or software, and the energy efficiency evaluation device of the data center can be configured in an electronic device. As Figure 1 shown, the method includes: S110. Obtain multi-dimensional energy efficiency evaluation indicators of the data center. The dimensions of the multi-dimensional energy efficiency evaluation indicators include the social dimension, the ecological dimension, and the technical dimension.

[0017] In this embodiment, the data center can be understood as a physical carrier containing the operation of information systems in various industries, and its data includes multiple parts. The multi-dimensional energy efficiency evaluation indicators can be understood as indicators related to evaluating energy efficiency in the data center, which can include the social dimension, the ecological dimension, and the technical dimension.

[0018] Specifically, the processor can obtain the multi-dimensional energy efficiency evaluation indicators of the data center. The setting of the multi-dimensional energy efficiency evaluation indicators can be determined by referring to relevant data center literature and other methods. In the present invention, the dimensions of the multi-dimensional energy efficiency evaluation indicators include the social dimension, the ecological dimension, and the technical dimension. The processor can obtain the multi-dimensional energy efficiency evaluation indicators related to the data center through these dimensions. For example, the indicators of the social dimension are composed of two secondary indicators, namely energy-saving measures and the human environment; the indicators of the ecological dimension are composed of one secondary indicator, namely the ecological environment; the indicators of the technical dimension are composed of two secondary indicators, namely energy-saving measures and energy consumption.

[0019] S120. Perform hierarchical division on the multi-dimensional energy efficiency evaluation indicators, and establish an evaluation index matrix between any two indicators at each level.

[0020] In this embodiment, the evaluation index matrix can be understood as a matrix used to represent the relationship between any two indicators at a level.

[0021] Specifically, the processor can perform hierarchical division on the multi-dimensional energy efficiency evaluation indicators in a way such as by category, and then divide different multi-dimensional energy efficiency evaluation indicators into different levels. The processor can determine the importance between any two indicators at the same level, and establish an evaluation index matrix based on the obtained importance to obtain the evaluation index matrix at different levels.

[0022] S130. Determine the comprehensive weight of the data center according to each evaluation index matrix.

[0023] In this embodiment, the comprehensive weight can be understood as the weight used to represent the influence of each multi-dimensional energy efficiency evaluation index on the energy efficiency evaluation of the data center.

[0024] Specifically, the processor can verify each evaluation index matrix through a consistency check. If the check is successful, the weight of each evaluation index matrix is obtained, and the comprehensive weight of the data center is determined based on the weight of each evaluation index matrix. If the check fails, the evaluation index matrix is optimized until the check is successful.

[0025] S140. Establish a membership degree matrix of the data center according to the multi-dimensional energy efficiency evaluation index.

[0026] In this embodiment, the membership degree matrix can be understood as a matrix used to represent the evaluation situation of each multi-dimensional energy efficiency index.

[0027] Specifically, the processor can determine the evaluation result of each multi-dimensional energy efficiency evaluation index and establish a membership degree matrix of the data center based on the evaluation result.

[0028] S150. Determine the energy efficiency level score of the data center according to the preset evaluation set, the comprehensive weight, and the membership degree matrix.

[0029] In this embodiment, the preset evaluation set can be understood as a set of scoring criteria for different pre-established evaluations. For example, excellent is 80 points, good is 60 points, medium is 40 points, poor is 20 points, etc. The energy efficiency level can be understood as reflecting the energy efficiency situation of the data center through a numerical value.

[0030] Specifically, the processor can multiply the transpose of the membership degree matrix, the comprehensive weight, and the preset evaluation set to determine the energy efficiency level score of the data center.

[0031] The technical solution of the embodiment of the present invention obtains the multi-dimensional energy efficiency evaluation index of the data center. The dimensions of the multi-dimensional energy efficiency evaluation index include the social dimension, the ecological dimension, and the technical dimension; hierarchically divide the multi-dimensional energy efficiency evaluation index and establish an evaluation index matrix between any two indexes at each level; determine the comprehensive weight of the data center according to each evaluation index matrix; establish a membership degree matrix of the data center according to the multi-dimensional energy efficiency evaluation index; determine the energy efficiency level score of the data center according to the preset evaluation set, the comprehensive weight, and the membership degree matrix. By obtaining the multi-dimensional energy efficiency evaluation index of the data center in multiple dimensions and hierarchically dividing it, the evaluation index matrix and the final energy efficiency level score are determined according to the level. Clearly reflect the hierarchical structure of the energy efficiency level of the data center, so as to conduct a clear and accurate energy efficiency level assessment of the data center.

[0032] Embodiment 2 Figure 2The flowchart of an energy efficiency evaluation method for a data center provided in the second embodiment of the present invention. This embodiment further refines the above embodiments. As Figure 2 shown, the method includes: S201. Obtain the multi-dimensional energy efficiency evaluation indicators of the data center. The dimensions of the multi-dimensional energy efficiency evaluation indicators include the social dimension, the ecological dimension, and the technical dimension.

[0033] S202. Divide the multi-dimensional energy efficiency evaluation indicators into criterion layer indicators and sub-criterion layer indicators belonging to the criterion layer indicators.

[0034] In this embodiment, the criterion layer indicators can be understood as the criteria and standards for solving problems established to achieve the overall goal. The sub-criterion layer indicators can be understood as all the indicators included under the criterion layer indicators.

[0035] Specifically, the processor can divide the multi-dimensional energy efficiency evaluation indicators into criterion layer indicators and sub-criterion layer indicators belonging to the criterion layer indicators.

[0036] Exemplarily, the processor can be divided into three layers, namely the target layer, the criterion layer, and the sub-criterion layer. Target layer T: Evaluation of the energy efficiency level of the data center; Criterion layer C: {C1, C2... CN}, where Ci represents the i-th indicator in the criterion layer indicators (i = 1, 2, 3... N); Sub-criterion layer P: {P1-1, P1-2... Pi-j}, where Pi-j represents that each criterion layer indicator Ci has j sub-criterion layer evaluation indicators (j = 1, 2, 3...); The criterion layer (C) can include: C1: Energy consumption; C2: Energy-saving measures; C3: Human environment; C4: Ecological environment; The sub-criterion layer (P) indicators corresponding to each criterion layer include: Under C1 energy consumption, the sub-criterion layer indicators include: P1-1: Power consumption (photovoltaic and other clean energy power generation); P1-2: Power consumption (thermal power and other non-clean energy power generation); P1-3: Water resource consumption; P1-4: Natural gas consumption; P1-5: Oil consumption; P1-6: Purchased heat consumption. Under C2 energy-saving measures, the sub-criterion layer indicators include: P2-1: Resource conservation policy; P2-2: Installation rate of energy-saving equipment; P2-3: Resource energy-saving effect; P2-4: Resource recycling measures. Under C3 human environment, the sub-criterion layer indicators: P3-1: Strategic goal of the data center; P3-2: Supplier equipment situation; P3-3: Low-carbon publicity activities and implementation; P3-4: Carbon emission management policy; Under C4 ecological environment, the sub-criterion layer indicators: P4-1: Proportion of green buildings; P4-2: Gas waste treatment technology; P4-3: Proportion of ecological greening; P4-4: Impact on the ecological environment; P4-5: Protection of the local ecological environment. The meanings of each indicator are shown in Table 1 below: Table 1 Indicator Meanings

[0037] S203. Pairwise evaluate the criterion layer indicators included under the criterion layer through the consistent matrix method to determine the criterion layer evaluation index matrix of the criterion layer.

[0038] In this embodiment, the consistent matrix method can be understood as an algorithm for determining the weights between factors at each level. The criterion layer evaluation index matrix can be understood as an evaluation index matrix constructed based on the criterion layer.

[0039] Specifically, the processor can pairwise evaluate the criterion layer indicators included under the criterion layer through the consistent matrix method to determine the criterion layer evaluation index matrix of the criterion layer.

[0040] Further, based on the above embodiment, the step of pairwise evaluating the criterion layer indicators included under the criterion layer through the consistent matrix method to determine the criterion layer evaluation index matrix of the criterion layer can be refined as follows: Determine the relative importance between any two criterion layer indicators included under the criterion layer based on a preset scale assignment table; form a criterion layer evaluation index matrix based on each relative importance.

[0041] In this embodiment, the preset scale assignment table can be understood as a table preset to represent the corresponding relationship between different importance levels and scales. The criterion layer can be understood as including all criterion layer indicators. The relative importance can be understood as using a numerical value to represent the importance of one indicator relative to another for the energy efficiency evaluation of the data center. The criterion layer evaluation index matrix can be understood as a tool for determining the importance of each criterion layer relative to the target layer.

[0042] Specifically, the processor can determine the relative importance between any two criterion layer indicators included under the criterion layer based on the preset scale assignment table. For example, experts can compare any two indicators within the criterion layer, assign scale values according to the relative importance between the indicators, and form a criterion layer evaluation index matrix through the relative importance determined pairwise for all criterion layer indicators.

[0043] Exemplarily, a specific example can be used to show the preset scale assignment table: Table 2 Preset Scale Assignment Table

[0044] Exemplarily, following the criterion layer indicators C1 - C4 in the above example, the determined criterion layer evaluation index matrix can be represented by the following formula:

[0045] Where C 11Represents the importance between the index C1 of the comparison criterion layer and the index C1 of the criterion layer; C N1 and C 1N Are reciprocal relations with each other.

[0046] This example lists the judgment matrix of the criterion layer as shown in Table 3 below: Table 3. Judgment Matrix of the Criterion Layer (A)

[0047] S204. Pairwise evaluate the sub-criterion layer indicators included under the sub-criterion layer through the consistent matrix method to determine the sub-criterion layer evaluation index matrix of the sub-criterion layer.

[0048] In this embodiment, the sub-criterion layer can be understood as a level including all sub-criterion layer indicators. The sub-criterion layer evaluation index matrix can be understood as a tool for determining the importance of each sub-criterion layer relative to the target layer.

[0049] Specifically, the processor can determine the relative importance between any two sub-criterion layer indicators included under the sub-criterion layer based on a preset scale assignment table. For example, experts can compare any two indicators within the sub-criterion layer, assign scale values according to the relative importance between the indicators, and form the sub-criterion layer evaluation index matrix through the relative importance determined pairwise for all sub-criterion layer indicators.

[0050] Exemplarily, the sub-criterion layer evaluation index matrix can be represented by the following formula:

[0051] where P 11 represents the importance between the sub-criterion layer indicator P1 and the sub-criterion layer indicator P1; P N1 and P 1N Are reciprocal relations with each other.

[0052] S205. Determine the eigenvalues corresponding to each evaluation index matrix.

[0053] In this embodiment, the eigenvalue can be understood as the largest eigenvalue in the judgment matrix.

[0054] Specifically, the processor can perform normalization processing on the evaluation index matrix, use the arithmetic mean method to obtain the basic weight matrix of each index, and then combine the evaluation index matrices at different levels to determine the eigenvalues of each evaluation index matrix.

[0055] Exemplarily, through the normalization processing of the data and using the arithmetic mean method, the basic weight matrix of each index is obtained , and the evaluation index matrix A is multiplied by , to obtain A 。And calculate the eigenvalues :

[0056] Wherein, A is the evaluation index matrix (including the evaluation index matrix of the criterion layer and the evaluation index matrix of the sub-criterion layer), is the weight matrix, and n is the matrix order.

[0057] S206. Determine the index weights that meet the verification conditions at each level according to each eigenvalue and the preset optimization algorithm.

[0058] In this embodiment, the verification condition can be understood as the set consistency index value. The preset optimization algorithm can be understood as an algorithm for automatic optimization, such as including a Stochastic Polyak Step-Size Optimizer (SPS), etc. The index weight can be understood as the determined weight of each index.

[0059] Specifically, the processor can first determine the consistency index according to each eigenvalue, determine the consistency ratio through the consistency index, compare the consistency ratio with the value in the verification condition. If it is less than, the verification is passed, and the above determined basic weight is the index weight. Otherwise, the processor can optimize the evaluation index matrix that does not meet the verification condition through the preset optimization algorithm until the basic weight that meets the verification condition is obtained.

[0060] Exemplarily, the consistency ratio can be first determined through the following formula:

[0061] Wherein, CI is the consistency index;

[0062] Wherein, CR is the consistency ratio, RI is the average random consistency index.

[0063] For example, the value of the verification condition is 0.1. When CR is less than 0.1, the verification is passed, and the basic weight of each above index is the index weight. Otherwise, the judgment matrix needs to be optimized to make it reasonable; Further explanation, according to the above Table 3 verification, CR = 0.081 < 0.1, the verification is qualified, and the index weights of the criterion layer indexes and the index weights of the sub-criterion layer indexes are obtained.

[0064] S207. Determine the comprehensive weight of the data center according to each index weight.

[0065] Specifically, the processor can multiply the index weights of the criterion layer by the corresponding index weights of the sub-criterion layer to obtain the comprehensive weights.

[0066] Exemplarily, following the sub-criterion layer indexes in the above example, a specific example is used for demonstration. Figure 3 This is an example diagram of the comprehensive weights in an energy efficiency evaluation method for a data center provided in the second embodiment of the present invention. As Figure 3 shown, the comprehensive weights of each sub-criterion layer index are shown, and the sum of all comprehensive weights is 1.

[0067] S208. Construct an energy efficiency level evaluation set and assign scores to each evaluation item included in the energy efficiency level evaluation set to obtain a target evaluation set.

[0068] In this embodiment, the energy efficiency level evaluation set can be understood as a set used for evaluation. For example, the evaluation criteria can be divided into excellent, good, medium, and poor. The target evaluation set can be understood as the evaluation set after score assignment.

[0069] Specifically, the processor can construct an energy efficiency level evaluation set according to user settings and assign scores to each evaluation item included in the energy efficiency level evaluation set to obtain a target evaluation set.

[0070] Exemplarily, an energy efficiency level evaluation set V = (excellent, good, medium, poor) is established and scores are assigned to it, that is, V 分 = (80, 60, 40, 20).

[0071] S209. Evaluate the multi-dimensional energy efficiency evaluation indexes based on the target evaluation set to determine the evaluation results.

[0072] In this embodiment, the evaluation results can be understood as the results after scoring each multi-dimensional energy efficiency evaluation index.

[0073] Specifically, the processor can evaluate the multi-dimensional energy efficiency evaluation indexes based on the target evaluation set to determine the evaluation results. For example, it can be done by relevant personnel in the data center filling out an investigation report formed through the target evaluation set (for example, four options are provided through the target evaluation set, and relevant personnel choose one of the four), and the processor traverses the number of times of evaluation of good or bad for each evaluation index.

[0074] S210. According to each evaluation result and the preset evaluation set, establish a membership matrix of the data center.

[0075] Specifically, the processor can determine the evaluation weights of each sub-criterion layer index under different evaluation items according to each evaluation result and the number of evaluation times, in accordance with the evaluation indexes of the preset evaluation set, to obtain the evaluation set weights, and then summarize the evaluation set weights of all sub-criterion layer indexes to establish a membership matrix of the data center.

[0076] Exemplarily, the evaluation set weights of each sub-criterion layer index can be determined by the following formula W V :

[0077] wherein, W V is the evaluation set weight, and P is the evaluation index, that is, each sub-criterion layer index.

[0078] Exemplarily, the evaluation items included in the evaluation set weights of each P can be summarized to obtain the membership matrix shown in Table 4: Table 4 Membership Matrix

[0079] wherein, one row represents the evaluation set weight of a sub-criterion layer, and the evaluation set weight includes the evaluation weights of four evaluation items: excellent, good, medium, and poor.

[0080] S211. Determine the comprehensive membership matrix according to the comprehensive weight and the membership matrix.

[0081] In this embodiment, the comprehensive membership matrix can be understood as the membership matrix balanced by the comprehensive weight.

[0082] Specifically, the processor can multiply the transpose of the membership matrix by the comprehensive weight matrix to obtain the comprehensive membership matrix.

[0083] Exemplarily, the following formula can be used for calculation:

[0084] wherein, S is the comprehensive membership matrix, S` is the membership matrix, W is the comprehensive weight matrix.

[0085] S212. Determine the energy efficiency level score of the data center according to the comprehensive membership matrix and the preset evaluation set.

[0086] Specifically, the processor can multiply the transpose of the comprehensive membership matrix by the preset evaluation set, and multiply by the set scores in the preset evaluation set. The obtained value is the energy efficiency level score of the data center.

[0087] Exemplarily, the following formula can be used for calculation:

[0088] wherein, is the energy efficiency level score of the data center.

[0089] Using the above example, the comprehensive membership degree matrix can be calculated as follows:

[0090] Furthermore, the energy efficiency level score is determined as:

[0091] The above 62.4 is between 60 and 80, so the evaluation of this data center is good.

[0092] The technical solution of the embodiment of the present invention obtains multi-dimensional energy efficiency evaluation indicators of the data center including social, ecological and technical dimensions, hierarchically divides the multi-dimensional energy efficiency evaluation indicators, determines the evaluation indicators of the criterion layer and the sub-criterion layer, clearly reflects the hierarchical structure of the energy efficiency level of the data center, determines the weights of the criterion layer indicators and the sub-criterion layer indicators respectively, and determines the comprehensive weight, realizing evaluation from multiple aspects, reducing subjective influence, and improving the objectivity of evaluation. By establishing a membership degree matrix through the fuzzy comprehensive evaluation method, the data center management personnel can effectively solve many fuzzy problems that occur in the evaluation process, quantitatively process these fuzzy problems, and organically combine qualitative evaluation and quantitative calculation, greatly improving the evaluation accuracy. Through the membership degree matrix and the comprehensive weight, the energy efficiency level evaluation result of the data center is determined, improving the accuracy of the evaluation.

[0093] Embodiment 3 Figure 4 The following is a schematic structural diagram of an energy efficiency evaluation device for a data center provided by Embodiment 3 of the present invention. As Figure 4 shown, the device includes: an index acquisition module 41, a first establishment module 42, a weight determination module 43, a second establishment module 44, and a level determination module 45.

[0094] The index acquisition module 41 is used to acquire multi-dimensional energy efficiency evaluation indicators of the data center, and the dimensions of the multi-dimensional energy efficiency evaluation indicators include social dimension, ecological dimension and technical dimension; The first establishment module 42 is used to hierarchically divide the multi-dimensional energy efficiency evaluation indicators and establish an evaluation index matrix between any two indicators at each level; The weight determination module 43 is used to determine the comprehensive weight of the data center according to each of the evaluation index matrices; The second establishment module 44 is used to establish the membership degree matrix of the data center according to the multi-dimensional energy efficiency evaluation indicators; The level determination module 45 is used to determine the energy efficiency level score of the data center according to a preset evaluation set, the comprehensive weight and the membership degree matrix.

[0095] The technical solution of the embodiment of the present invention is as follows: by obtaining the multi-dimensional energy efficiency evaluation indexes of the data center, the dimensions of the multi-dimensional energy efficiency evaluation indexes include the social dimension, the ecological dimension and the technical dimension; hierarchically dividing the multi-dimensional energy efficiency evaluation indexes, and establishing an evaluation index matrix between any two indexes under each level; determining the comprehensive weight of the data center according to each evaluation index matrix; establishing a membership degree matrix of the data center according to the multi-dimensional energy efficiency evaluation indexes; and determining the energy efficiency level score of the data center according to the preset evaluation set, the comprehensive weight and the membership degree matrix. By obtaining the multi-dimensional energy efficiency evaluation indexes of the data center in multiple dimensions and hierarchically dividing them, the evaluation index matrix and the final energy efficiency level score are determined according to the levels. The hierarchical structure of the energy efficiency level of the data center is clearly reflected, so as to conduct a clear and accurate energy efficiency level assessment of the data center.

[0096] Further, the first establishment module 42 includes: A first determination unit, configured to divide the multi-dimensional energy efficiency evaluation indexes into criterion layer indexes and sub-criterion layer indexes belonging to the criterion layer indexes; A second determination unit, configured to perform pairwise evaluation on the criterion layer indexes included under the criterion layer by using the consistent matrix method, and determine the criterion layer evaluation index matrix of the criterion layer; A third determination unit, configured to perform pairwise evaluation on the sub-criterion layer indexes included under the sub-criterion layer by using the consistent matrix method, and determine the sub-criterion layer evaluation index matrix of the sub-criterion layer.

[0097] Wherein, the second determination unit is specifically configured to: Determine the relative importance between any two criterion layer indexes included under the criterion layer based on a preset scale assignment table; Form a criterion layer evaluation index matrix based on each of the relative importance.

[0098] Further, the weight determination module 43 is specifically configured to: Determine the eigenvalues corresponding to each of the evaluation index matrices; Determine the index weights satisfying the verification condition under each level according to each of the eigenvalues and a preset optimization algorithm; Determine the comprehensive weight of the data center according to each of the index weights.

[0099] Further, the second establishment module 44 is specifically configured to: Construct an energy efficiency level evaluation set and assign scores to each evaluation item included in the energy efficiency level evaluation set to obtain a target evaluation set; Evaluate the multi-dimensional energy efficiency evaluation indexes based on the target evaluation set, and determine the evaluation result; Establish a membership degree matrix of the data center according to each of the evaluation results and the preset evaluation set.

[0100] Further, the level determination module 45 is specifically configured to: Determine a comprehensive membership degree matrix according to the comprehensive weight and the membership degree matrix; Determine the energy efficiency level score of the data center according to the comprehensive membership degree matrix and the preset evaluation set.

[0101] The energy efficiency evaluation device of the data center provided by the embodiments of the present invention can execute the energy efficiency evaluation method of the data center provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0102] Embodiment Four Figure 5 FIG. shows a schematic structural diagram of an electronic device 50 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0103] As Figure 5 shown, the electronic device 50 includes at least one processor 51, and a memory communicatively connected to at least one processor 51, such as a read only memory (ROM) 52, a random access memory (RAM) 53, etc. The memory stores a computer program executable by at least one processor. The processor 51 can perform various appropriate actions and processes according to the computer program stored in the read only memory (ROM) 52 or the computer program loaded from the storage unit 58 into the random access memory (RAM) 53. Various programs and data required for the operation of the electronic device 50 can also be stored in the RAM 53. The processor 51, the ROM 52, and the RAM 53 are connected to each other through a bus 54. The input / output (I / O) interface 55 is also connected to the bus 54.

[0104] Multiple components in the electronic device 50 are connected to the I / O interface 55, including: an input unit 56, such as a keyboard, a mouse, etc.; an output unit 57, such as various types of displays, speakers, etc.; a storage unit 58, such as a magnetic disk, an optical disc, etc.; and a communication unit 59, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0105] The processor 51 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 51 executes the various methods and processes described above, such as the energy efficiency evaluation method of the data center.

[0106] In some embodiments, the energy efficiency evaluation method of the data center can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 58. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 50 via the ROM 52 and / or the communication unit 59. When the computer program is loaded into the RAM 53 and executed by the processor 51, one or more steps of the energy efficiency evaluation method of the data center described above can be executed. Alternatively, in other embodiments, the processor 51 can be configured to execute the energy efficiency evaluation method of the data center in any other suitable manner (e.g., by means of firmware).

[0107] Various embodiments of the systems and technologies described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0108] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0109] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input received from the user can be in any form (including acoustic input, speech input, or tactile input).

[0111] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0112] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0113] In one embodiment, the embodiment of the present invention further includes a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the energy efficiency evaluation method of the data center in any embodiment of the present invention.

[0114] In the process of implementation, the computer program product can write computer program code for performing the operations of the present invention in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (e.g., by connecting through an Internet service provider via the Internet).

[0115] It should be understood that the various forms of processes shown above can be used, with steps reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0116] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data center energy efficiency evaluation method, characterized in that: include: Obtaining a multidimensional energy efficiency evaluation index of a data center, wherein the dimensions of the multidimensional energy efficiency evaluation index include a social dimension, an ecological dimension, and a technical dimension; Divide the multidimensional energy efficiency evaluation indicators into levels, and establish an evaluation indicator matrix between any two indicators at each level; Determining the comprehensive weight of the data center according to each of the evaluation indicator matrices; According to the multi-dimensional energy efficiency evaluation index, establishing a membership matrix of the data center; The energy efficiency level score of the data center is determined according to the preset evaluation set, the comprehensive weight and the membership matrix.

2. The method according to claim 1, characterized in that: The multi-dimensional energy efficiency evaluation index is divided into levels, and an evaluation index matrix between any two indicators at each level is established, including: Dividing the multidimensional energy efficiency evaluation index into a criterion layer index and a sub-criterion layer index belonging to the criterion layer index; By using a consistent matrix method, the criterion layer indicators contained in the criterion layer are evaluated in pairs to determine the criterion layer evaluation indicator matrix of the criterion layer; The sub-criteria layer indicators contained in the sub-criteria layer are evaluated in pairs by the consistent matrix method to determine the sub-criteria layer evaluation indicator matrix of the sub-criteria layer.

3. The method according to claim 2, characterized in that The step of evaluating the criterion layer indicators contained in the criterion layer in pairs by using the consistent matrix method to determine the criterion layer evaluation indicator matrix of the criterion layer includes: Determining the relative importance between any two criteria layer indicators contained in the criteria layer based on a preset scale assignment table; A criterion layer evaluation index matrix is ​​formed based on the relative importance of each.

4. The method according to claim 1, characterized in that: Determining the comprehensive weight of the data center according to each of the evaluation index matrices includes: Determine the eigenvalues ​​corresponding to each of the evaluation index matrices; Determine the weight of indicators that meet the verification conditions at each level according to the characteristic values ​​and the preset optimization algorithm; According to the weights of the indicators, the comprehensive weight of the data center is determined.

5. The method according to claim 1, characterized in that The step of establishing a membership matrix of the data center according to the multi-dimensional energy efficiency evaluation index comprises: Constructing an energy efficiency level evaluation set and assigning points to each evaluation item included in the energy efficiency level evaluation set to obtain a target evaluation set; Evaluate the multidimensional energy efficiency evaluation index based on the target evaluation set and determine the evaluation result; A membership matrix of the data center is established according to each of the evaluation results and the preset evaluation set.

6. The method according to claim 1, characterized in that Determining the energy efficiency level score of the data center according to the preset evaluation set, the comprehensive weight and the membership matrix includes: Determining a comprehensive membership matrix according to the comprehensive weight and the membership matrix; The energy efficiency level score of the data center is determined based on the comprehensive membership matrix and the preset evaluation set.

7. An energy efficiency evaluation device for a data center, characterized in that: include: An indicator acquisition module is used to obtain a multi-dimensional energy efficiency evaluation indicator of a data center, wherein the dimensions of the multi-dimensional energy efficiency evaluation indicator include a social dimension, an ecological dimension, and a technical dimension; The first establishment module is used to divide the multi-dimensional energy efficiency evaluation indicators into levels and establish an evaluation indicator matrix between any two indicators at each level; A weight determination module, used to determine the comprehensive weight of the data center according to each evaluation index matrix; A second establishing module is used to establish a membership matrix of the data center according to the multi-dimensional energy efficiency evaluation index; The level determination module is used to determine the energy efficiency level score of the data center based on a preset evaluation set, the comprehensive weight and the membership matrix.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy efficiency evaluation method for a data center according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy efficiency evaluation method for a data center according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the energy efficiency evaluation method for a data center according to any one of claims 1 to 6.

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