Multi-stage quantitative measurement evaluation method and device for energy consumption of digital intelligent base

Abstract

The invention relates to a multi-level quantitative measurement evaluation method and device for energy consumption of a digital intelligent base, and the method comprises the steps: determining a digital intelligent base typical task corresponding to each digital intelligent base; acquiring a test data set corresponding to the typical task of the digital intelligent base, and executing the typical task of the digital intelligent base to obtain corresponding operation time, total energy consumption, real-time power and time-phased energy consumption; determining an energy consumption evaluation strategy corresponding to the typical task of the digital intelligent base, calculating an average power corresponding to the typical task of the digital intelligent base based on the energy consumption evaluation strategy, the operation time, the real-time power, the divided-period energy consumption and the total energy consumption, and constructing an energy consumption measurement index value group according to the average power, the operation time and the corresponding energy consumption, and performing multi-stage quantitative measurement evaluation on the operation energy consumption of each digital intelligent base by using the average power and the energy consumption measurement index value group. Therefore, the problems that at present, a digital intelligent base energy consumption measuring method is lacked, and the performance of a large number of digital intelligent applications in the aspects of green, environmental protection and energy conservation cannot be evaluated are solved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent devices and energy consumption management, and in particular to a multi-level quantitative measurement evaluation method and device for the energy consumption of a digital intelligence base. Background Art

[0002] With the advent of the intelligent era, digital intelligence base applications represented by large AI models and high-performance database systems have developed rapidly, gradually penetrating into all walks of life, and becoming an important driving force for digital transformation.

[0003] However, these digital bases, especially the application of large-scale model technology, require a large amount of electricity to maintain operation, which not only increases the pressure on energy supply, but also leads to increased carbon emissions, posing challenges to environmental protection and sustainable development, which need to be urgently addressed. Summary of the invention

[0004] The present application provides a multi-level quantitative measurement and evaluation method and device for the energy consumption of a digital intelligence base, so as to solve the current problems of the lack of a digital intelligence base energy consumption measurement method and the inability to evaluate the performance of a large number of digital intelligence applications in terms of greenness, environmental protection, and energy saving.

[0005] The first aspect of the present application provides a multi-level quantitative measurement and evaluation method for the energy consumption of a digital intelligence base, comprising the following steps: determining a target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base; obtaining a test data set corresponding to the target digital intelligence base typical task, and executing the target digital intelligence base typical task based on the test data set to obtain the running time, total energy consumption, real-time power and time period energy consumption corresponding to the target digital intelligence base typical task; determining a target energy consumption evaluation strategy corresponding to the target digital intelligence base typical task, and calculating the average power corresponding to the target digital intelligence base typical task based on the target energy consumption evaluation strategy, the running time, the real-time power, the time period energy consumption and the total energy consumption, and constructing an energy consumption measurement indicator value group according to the average power, the running time and the total energy consumption, and using the average power and the energy consumption measurement indicator value group to obtain the multi-level quantitative measurement evaluation result of the running energy consumption of each preset digital intelligence base.

[0006] Optionally, in one embodiment of the present application, determining the target digital intelligence base typical tasks corresponding to each preset digital intelligence base in at least one preset digital intelligence base includes: identifying the digital intelligence base type corresponding to each preset digital intelligence base; selecting the target digital intelligence base typical tasks corresponding to each preset digital intelligence base according to the digital intelligence base type, wherein the at least one preset digital intelligence base includes at least one of a target AI big model, a target database, a target big data management software and a computing platform IT infrastructure.

[0007] Optionally, in one embodiment of the present application, the test data set corresponding to the typical task of the target digital intelligence base is obtained, and based on the test data set, the typical task of the target digital intelligence base is executed to obtain the running time, total energy consumption, real-time power and time period energy consumption of the preset digital intelligence base corresponding to the typical task of the target digital intelligence base, including: based on the typical task of the target digital intelligence base, obtaining a test data set of the preset digital intelligence base that executes the typical task of the target digital intelligence base, wherein the typical task of the target digital intelligence base includes any one of a preset large model typical task, a preset big data processing typical task and a preset large model-big data hybrid task; executing the typical task of the target digital intelligence base according to the test data set to generate the running time, total energy consumption, real-time power and time period energy consumption of the preset digital intelligence base that executes the typical task of the target digital intelligence base.

[0008] Optionally, in one embodiment of the present application, the target energy consumption evaluation strategy corresponding to the typical task of the target digital and intelligent base is determined to calculate the average power corresponding to the typical task of the target digital and intelligent base based on the target energy consumption evaluation strategy, the running time, the real-time power, the time-segment energy consumption and the total energy consumption, and an energy consumption measurement indicator value group is constructed according to the average power, the running time and the total energy consumption, and the average power and the energy consumption measurement indicator value group are used to obtain a multi-level quantitative measurement evaluation result of the running energy consumption of each preset digital and intelligent base, including: when the target energy consumption evaluation strategy is an evaluation strategy based on total energy consumption, the first actual running time corresponding to the evaluation strategy based on total energy consumption is determined according to the running time, and the first actual total energy consumption of the preset digital and intelligent base is determined according to the total energy consumption, and the ratio of the first actual total energy consumption to the first actual running time is calculated, To obtain the average power of the preset digital and intelligent base that executes the typical tasks of the target digital and intelligent base; when the target energy consumption assessment strategy is an idle consumption exclusion assessment strategy, determine the start execution time of the target digital and intelligent base typical tasks corresponding to the idle consumption exclusion assessment strategy and the regression idle time of the preset digital and intelligent base according to the running time, and calculate the second actual running time corresponding to the target digital and intelligent base typical tasks according to the start execution time and the regression idle time, and remove the idle energy consumption in the total energy consumption to obtain the second actual total energy consumption of the preset digital and intelligent base, and calculate the ratio of the second actual total energy consumption to the second actual running time to obtain the average power of the preset digital and intelligent base that executes the typical tasks of the target digital and intelligent base; perform a first-level quantitative measurement evaluation on the operating energy consumption of each preset digital and intelligent base through the average power corresponding to the idle consumption exclusion assessment strategy or the assessment strategy based on all energy consumption.

[0009] Optionally, in one embodiment of the present application, the energy consumption measurement indicator value group is constructed according to the average power, the running time and the total energy consumption, and the multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital and intelligent base is obtained by using the average power and the energy consumption measurement indicator value group, which also includes: based on the average power, the running time and the total energy consumption, constructing an energy consumption measurement indicator value group corresponding to each typical task of the target digital and intelligent base, and constructing an indicator vector space according to the energy consumption measurement indicator value groups of the typical tasks of all target digital and intelligent bases; and performing a secondary quantitative measurement evaluation on the operating energy consumption of each preset digital and intelligent base through the indicator vector space.

[0010] According to a second aspect of the present application, an embodiment provides a multi-level quantitative measurement and evaluation device for the energy consumption of a digital intelligence base, including: a determination module, used to determine a target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base; an execution module, used to obtain a test data set corresponding to the target digital intelligence base typical task, and execute the target digital intelligence base typical task based on the test data set to obtain the running time, total energy consumption, real-time power and time period energy consumption of the preset digital intelligence base corresponding to the target digital intelligence base typical task; an evaluation module, used to determine a target energy consumption evaluation strategy corresponding to the target digital intelligence base typical task, and calculate the average power corresponding to the target digital intelligence base typical task based on the target energy consumption evaluation strategy, the running time, the real-time power, the time period energy consumption and the total energy consumption, and construct an energy consumption measurement indicator value group according to the average power, the running time and the total energy consumption, and use the average power and the energy consumption measurement indicator value group to obtain the multi-level quantitative measurement evaluation result of the running energy consumption of each preset digital intelligence base.

[0011] Optionally, in one embodiment of the present application, the determination module includes: a type analysis unit, used to identify the type of digital intelligence base corresponding to each preset digital intelligence base; a selection unit, used to select the target digital intelligence base typical task corresponding to each preset digital intelligence base according to the digital intelligence base type, wherein at least one preset digital intelligence base includes at least one of a target AI big model, a target database, a target big data management software and a computing platform IT infrastructure.

[0012] Optionally, in one embodiment of the present application, the execution module includes: an acquisition unit, used to acquire a test data set of a preset digital intelligence base that executes the target digital intelligence base typical task based on the target digital intelligence base typical task, wherein the target digital intelligence base typical task includes any one of a preset large model typical task, a preset big data processing typical task, and a preset large model-big data hybrid task; a generation unit, used to execute the target digital intelligence base typical task according to the test data set to generate the running time, total energy consumption, real-time power and time period energy consumption of the preset digital intelligence base that executes the target digital intelligence base typical task.

[0013] Optionally, in one embodiment of the present application, the evaluation module includes: a total energy consumption evaluation unit, which is used to determine, when the target energy consumption evaluation strategy is an evaluation strategy based on total energy consumption, a first actual operating time corresponding to the evaluation strategy based on total energy consumption according to the operating time, and determine the first actual total energy consumption of the preset digital and intelligent base according to the total energy consumption, and calculate the ratio of the first actual total energy consumption to the first actual operating time to obtain the average power of the preset digital and intelligent base that executes typical tasks of the target digital and intelligent base; an idle consumption exclusion evaluation unit, which is used to determine, when the target energy consumption evaluation strategy is an idle consumption exclusion evaluation strategy, the target energy consumption corresponding to the idle consumption exclusion evaluation strategy according to the operating time. The start execution time of the typical task of the digital and intelligent base and the regression idle time of the preset digital and intelligent base, and calculate the second actual running time corresponding to the typical task of the target digital and intelligent base based on the start execution time and the regression idle time, and remove the idling energy consumption in the total energy consumption to obtain the second actual total energy consumption of the preset digital and intelligent base, and calculate the ratio of the second actual total energy consumption to the second actual running time to obtain the average power of the preset digital and intelligent base that executes the typical task of the target digital and intelligent base; a first-level evaluation unit, used to perform a first-level quantitative measurement evaluation on the operating energy consumption of each preset digital and intelligent base through the average power corresponding to the idle consumption exclusion evaluation strategy or the evaluation strategy based on all energy consumption.

[0014] Optionally, in one embodiment of the present application, the evaluation module also includes: a vector space construction unit, used to construct an energy consumption measurement index value group corresponding to each typical task of each target digital and intelligent base based on the average power, the operating time and the total energy consumption, and to construct an indicator vector space according to the energy consumption measurement index value group of the typical tasks of all target digital and intelligent bases; a secondary evaluation unit, used to perform a secondary quantitative measurement evaluation on the operating energy consumption of each preset digital and intelligent base through the indicator vector space.

[0015] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-level quantitative measurement evaluation method for the energy consumption of a digital intelligence base as described in the above embodiment.

[0016] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base.

[0017] The fifth aspect embodiment of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base.

[0018] Therefore, the embodiments of the present application have the following beneficial effects:

[0019] The embodiments of the present application can be performed by determining the typical tasks of the target digital intelligence base corresponding to each preset digital intelligence base in at least one preset digital intelligence base; obtaining the test data set corresponding to the typical tasks of the target digital intelligence base, and executing the typical tasks of the target digital intelligence base based on the test data set to obtain the running time, total energy consumption, real-time power and time-divided energy consumption corresponding to the typical tasks of the target digital intelligence base; determining the target energy consumption evaluation strategy corresponding to the typical tasks of the target digital intelligence base, and calculating the average power corresponding to the typical tasks of the target digital intelligence base based on the target energy consumption evaluation strategy, running time, real-time power, time-divided energy consumption and total energy consumption, and constructing an energy consumption measurement index value group according to the average power, running time and total energy consumption, and using the average power and energy consumption measurement index value group to obtain the multi-level quantitative measurement evaluation results of the operating energy consumption of each preset digital intelligence base. The present application selects appropriate typical tasks for testing according to different types of digital intelligence bases, quantifies and analyzes the energy consumption data of the digital intelligence base in the running state, and selects a suitable evaluation method for evaluation, which provides a feasible way for the energy consumption evaluation of the digital intelligence base, and thus provides the possibility of reducing the resource energy consumption of the digital intelligence base and improving the operating efficiency. This solves the current problems of lack of energy consumption measurement methods for digital intelligence bases and inability to evaluate the performance of a large number of digital intelligence applications in terms of greenness, environmental protection, and energy conservation.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1A flowchart of a multi-level quantitative measurement evaluation method for energy consumption of a digital intelligence base provided according to an embodiment of the present application;

[0023] Figure 2 A schematic diagram of the logical architecture of a multi-level quantitative measurement evaluation method for energy consumption of a digital intelligence base provided in accordance with an embodiment of the present application;

[0024] Figure 3 A schematic diagram of a power variation of a digital intelligence base device over time provided for one embodiment of the present application;

[0025] Figure 4 A schematic diagram of an average power calculation strategy for a large model energy consumption evaluation solution provided for an embodiment of the present application;

[0026] Figure 5 This is an example diagram of a multi-level quantitative measurement and evaluation device for energy consumption of a digital intelligence base according to an embodiment of the present application;

[0027] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] Among them, 10-a multi-level quantitative measurement and evaluation device for the energy consumption of a digital intelligence base; 100-a determination module, 200-an execution module, 300-an evaluation module; 601-a memory, 602-a processor, 603-a communication interface. DETAILED DESCRIPTION

[0029] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] The following describes the multi-level quantitative measurement evaluation method and device for the energy consumption of the digital intelligence base of the embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base, in which the typical task of the target digital intelligence base corresponding to each preset digital intelligence base in at least one preset digital intelligence base is determined; a test data set corresponding to the typical task of the target digital intelligence base is obtained, and based on the test data set, the typical task of the target digital intelligence base is executed to obtain the running time, total energy consumption, real-time power and time-divided energy consumption corresponding to the typical task of the target digital intelligence base; the target energy consumption evaluation strategy corresponding to the typical task of the target digital intelligence base is determined, and the average power corresponding to the typical task of the target digital intelligence base is calculated based on the target energy consumption evaluation strategy, running time, real-time power, time-divided energy consumption and total energy consumption, and an energy consumption measurement index value group is constructed according to the average power, running time and total energy consumption, and the multi-level quantitative measurement evaluation result of the running energy consumption of each preset digital intelligence base is obtained by using the average power and the energy consumption measurement index value group. This application selects appropriate typical tasks for testing according to different types of digital intelligence bases, quantifies and analyzes the energy consumption data of the digital intelligence base in operation, and selects an appropriate evaluation method for evaluation, which provides a feasible way for the energy consumption evaluation of the digital intelligence base, thereby providing the possibility of reducing the resource energy consumption of the digital intelligence base and improving the operating efficiency. This solves the current problem of the lack of energy consumption measurement methods for digital intelligence bases and the inability to evaluate the performance of a large number of digital intelligence applications in terms of green, environmental protection, and energy saving.

[0031] Specifically, Figure 1 A flowchart of a multi-level quantitative measurement and evaluation method for energy consumption of a digital intelligence base provided in an embodiment of the present application.

[0032] like Figure 1 As shown, the multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base includes the following steps:

[0033] In step S101, a typical task of a target digital intelligence base corresponding to each preset digital intelligence base in at least one preset digital intelligence base is determined.

[0034] The embodiments of the present application can first select a suitable typical digital intelligence base task for each preset digital intelligence base, thereby providing technical and data support for the implementation of multi-level quantitative measurement evaluation of the energy consumption of the digital intelligence base.

[0035] Optionally, in one embodiment of the present application, determining a target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base includes: identifying the digital intelligence base type corresponding to each preset digital intelligence base; selecting the target digital intelligence base typical task corresponding to each preset digital intelligence base according to the digital intelligence base type, wherein at least one preset digital intelligence base includes at least one of a target AI big model, a target database, a target big data management software and a computing platform IT infrastructure.

[0036] In the actual implementation process, Figure 2 As shown, the digital intelligence base facilities in the embodiments of the present application mainly include IT infrastructure such as AI big models, databases, big data management related software and computing platforms.

[0037] It should be noted that the above-mentioned AI large model refers to a deep learning model with ultra-large-scale parameters and computing power, which can be widely used in fields such as natural language processing, can handle complex tasks and has the ability to understand and generate data.

[0038] The embodiments of the present application can determine the digital intelligence base type corresponding to each digital intelligence base, so as to select and execute appropriate typical tasks according to different digital intelligence base types, and provide corresponding parameter data for subsequent energy consumption evaluation and analysis.

[0039] In step S102, a test data set corresponding to the typical tasks of the target digital and intelligent base is obtained, and based on the test data set, the typical tasks of the target digital and intelligent base are executed to obtain the operating time, total energy consumption, real-time power and time-segment energy consumption of the preset digital and intelligent base corresponding to the typical tasks of the target digital and intelligent base.

[0040] Furthermore, the embodiments of the present application also provide the data set required for testing for the corresponding digital intelligence base facility through the target digital intelligence base typical tasks, and use the data set required for the test as input to enable the digital intelligence base facility to generate parameter data that can perform energy consumption analysis.

[0041] Optionally, in one embodiment of the present application, a test data set corresponding to a typical task of the target digital intelligence base is obtained, and based on the test data set, the typical task of the target digital intelligence base is executed to obtain energy consumption data of a preset digital intelligence base corresponding to the typical task of the target digital intelligence base, including: based on the typical task of the target digital intelligence base, obtaining a test data set of a preset digital intelligence base that executes the typical task of the target digital intelligence base, wherein the typical task of the target digital intelligence base includes any one of a preset large model typical task, a preset big data processing typical task, and a preset large model-big data hybrid task; executing the typical task of the target digital intelligence base according to the test data set to generate the running time, total energy consumption, real-time power and time period energy consumption of the preset digital intelligence base that executes the typical task of the target digital intelligence base.

[0042] It should be noted that the typical tasks of the digital intelligence base in the embodiments of the present application include typical tasks of large models, typical tasks of big data processing, and tasks that are a mixture of the two.

[0043] Typical tasks for large models include data sets related to the functions of large models, such as the Alpaca data set and the GSM8K data set. The Alpaca data set contains text data in multiple languages, which can be used for a series of natural language processing tasks, such as sentiment analysis and text classification. The GSM8K data set contains thousands of math problems to test the model's logical reasoning and problem-solving capabilities. The comprehensive use of the above data sets can evaluate the performance of the model in various tasks and evaluate how different types of data affect energy consumption and reasoning performance.

[0044] Typical big data processing tasks include those that can simulate complex business-related query operations in databases and other big data-related software to test the performance of related databases and big data-related software, such as TPC-H, TPC-IoT, TPC-AI and other benchmark tasks; TPC-H simulates complex query and large-scale data processing scenarios, consisting of a set of business-oriented ad hoc queries and concurrent data modifications. It predefines multiple complex SQL operations and uses indicators such as QphH@Size (number of queries per hour), $ / kQphH@Size (cost per thousand queries) and system availability to test database performance. TPC-IoT is a benchmark designed specifically for measuring the performance of IoT gateway systems, mainly using indicators such as IoTps (number of IoT transactions processed per second), $ / kIoTps (cost per thousand IoT transactions) and system availability; TPCx-AI is a benchmark for evaluating the performance of artificial intelligence and machine learning applications, mainly using indicators such as AIUCpm (number of AI user queries processed per minute), $ / AIUCpm (cost per AI user query processed) and system availability for evaluation.

[0045] Afterwards, the embodiments of the present application can obtain a test data set of a digital intelligence base that performs typical tasks of the digital intelligence base, and perform typical tasks of the target digital intelligence base based on the test data set, thereby generating multiple energy consumption data such as the operating time, total energy consumption, real-time power, and segmented energy consumption in different time periods of the corresponding preset digital intelligence base.

[0046] In step S103, the target energy consumption assessment strategy corresponding to the typical tasks of the target digital and intelligent base is determined, and the average power corresponding to the typical tasks of the target digital and intelligent base is calculated based on the target energy consumption assessment strategy, running time, real-time power, time-segment energy consumption and total energy consumption. An energy consumption measurement indicator value group is constructed according to the average power, running time and total energy consumption, and the average power and energy consumption measurement indicator value group are used to obtain a multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital and intelligent base.

[0047] Furthermore, as an achievable method, the embodiment of the present application targets an energy consumption assessment strategy for big data and databases, and includes multiple indicators that can evaluate the energy consumption of big data and database-related software. It can use a series of indicators to evaluate the energy consumption of big data and database-related software. This series of indicators includes TPC-Energy evaluation indicators, such as power consumption per unit performance throughput, idle power, etc., and uses it as the average power corresponding to each digital intelligence base.

[0048] Those skilled in the art should understand that due to the different optimization strategies of different digital intelligence platforms, there may be a situation where the average power is similar, but the completion speed is different, resulting in differences in total power consumption. In order to more completely and comprehensively describe and measure the energy consumption of the digital intelligence base, the embodiments of the present application also need to introduce a multi-level quantitative measurement evaluation strategy (such as a two-level quantitative measurement evaluation) to evaluate the operating energy consumption of each digital intelligence base.

[0049] Optionally, in one embodiment of the present application, a target energy consumption evaluation strategy corresponding to a typical task of a target digital and intelligent base is determined, and the average power corresponding to a typical task of the target digital and intelligent base is calculated based on the target energy consumption evaluation strategy, operating time, real-time power, time-segmented energy consumption and total energy consumption, and an energy consumption measurement indicator value group is constructed according to the average power, operating time and total energy consumption, so as to obtain a multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital and intelligent base by using the average power and the energy consumption measurement indicator value group, including: when the target energy consumption evaluation strategy is an evaluation strategy based on total energy consumption, determining a first actual operating time corresponding to the evaluation strategy based on total energy consumption according to the operating time, and determining a first actual total energy consumption of the preset digital and intelligent base according to the total energy consumption, and calculating the ratio of the first actual total energy consumption to the first actual operating time to obtain the execution The average power of the preset digital and intelligent base for the typical tasks of the target digital and intelligent base; when the target energy consumption assessment strategy is the idle consumption exclusion assessment strategy, the start execution time of the typical tasks of the target digital and intelligent base corresponding to the idle consumption exclusion assessment strategy and the regression idle time of the preset digital and intelligent base are determined according to the running time, and the second actual running time corresponding to the typical tasks of the target digital and intelligent base is calculated according to the start execution time and the regression idle time, and the idling energy consumption in the total energy consumption is removed to obtain the second actual total energy consumption of the preset digital and intelligent base, and the ratio of the second actual total energy consumption to the second actual running time is calculated to obtain the average power of the preset digital and intelligent base for executing the typical tasks of the target digital and intelligent base; the operating energy consumption of each preset digital and intelligent base is evaluated at a first-level quantitative measurement level through the average power corresponding to the idle consumption exclusion assessment strategy or the assessment strategy based on all energy consumption.

[0050] It should be noted that the embodiments of the present application can divide the energy consumption assessment strategy into two parts according to different tasks, namely, the energy consumption assessment strategy for AI large models and the energy consumption assessment strategy for big data and databases; among them, the large model energy consumption assessment scheme for AI large models (i.e., the energy consumption assessment strategy) mainly includes an assessment method that considers all energy consumption (i.e., an assessment strategy based on all energy consumption) and an assessment method that excludes idle consumption when there is no task (i.e., an assessment strategy that excludes idle consumption), so as to accurately evaluate the energy consumption of the large model.

[0051] Figure 3 This is a schematic diagram of the power of the digital intelligence base equipment changing over time. Figure 3 As shown, the orange curve represents the function curve P(t) of power changing with time, and the green dotted line shows the idling power P0 when there is no task. Figure 3 Two tasks are inputted. Task 1 starts at time t1, the power increases, and reaches a stable power P1 at time t2. After maintaining for a period of time, the task ends, and the power returns to P0 at time t3. Task 2 starts at time t4, the power reaches a stable power P2 at time t5, the task is completed, and the power returns to the idling power P0 at time t6.

[0052] In the specific implementation process, when the target energy consumption evaluation strategy is an evaluation strategy based on total energy consumption, when the embodiment of the present application performs an energy consumption test on a specific digital intelligence base, its average power is calculated as the ratio of the total energy consumption of executing the task (i.e., the first actual total energy consumption) and the total running time (i.e., the first actual running time). The total running time is the time from the start of the task to the end of the task, that is, Figure 3 In the time period from t1 to t6 (i.e. the first actual running time), the total energy consumption is Figure 3 The integral of the real-time power P(t) in the time period from t1 to t6 (i.e., the first actual total energy consumption); Figure 4 As shown, the average power calculation expression corresponding to the evaluation strategy based on total energy consumption is as follows:

[0053]

[0054] When the target energy consumption assessment strategy is to exclude idle consumption assessment strategy, the embodiment of the present application may deduct the idle energy consumption from the energy consumption of executing the task as the total energy consumption (i.e., the second actual total energy consumption), and calculate the ratio of the total energy consumption (i.e., the second actual total energy consumption) to the running time (i.e., the second actual running time) to obtain the corresponding average power; wherein, the running time (i.e., the second actual running time) may be the running time of a single task from the beginning to the end (i.e., returning to idle) or the running time of a batch of tasks from the beginning to the end of all tasks (i.e., returning to idle). If only a single task is calculated, the ratio of the total energy consumption (i.e., the second actual total energy consumption) to the running time (i.e., the second actual running time) may be the running time of a single task from the beginning to the end (i.e., returning to idle). Figure 3 For example, task 1 in the example has a running time from t1 to t3 (i.e., the second actual running time). The total energy consumption is the integral of P(t) from t1 to t3 (i.e., the second actual total energy consumption). The idling power is constant, and the idling energy consumption during this period is P0*(t3-t1) (i.e., the second actual total energy consumption). If a batch of tasks is calculated, Figure 3 Take all the tasks in as an example, the running time (i.e. the second actual running time) is from t1 to t6, the total energy consumption is the integral of P(t) from t1 to t6 (i.e. the second actual total energy consumption), and the idling energy consumption is P0*(t6-t1), which is then divided by the running time (i.e. the second actual running time) to obtain the corresponding average power; Figure 4 As shown, the average power calculation expression corresponding to the idle consumption exclusion evaluation strategy is as follows:

[0055] or

[0056] Therefore, the embodiments of the present application can obtain the corresponding average power through the above-mentioned different energy consumption evaluation strategies, thereby performing a first-level quantitative evaluation of the operating energy consumption of each preset digital intelligence base.

[0057] Optionally, in one embodiment of the present application, a target energy consumption assessment strategy corresponding to a typical task of a target digital and intelligent base is determined, and an energy consumption assessment operation is performed using energy consumption data through the target energy consumption assessment strategy to obtain the average power corresponding to each preset digital and intelligent base. It also includes: based on the average power, operating time and total energy consumption, constructing an energy consumption measurement index value group corresponding to each target digital and intelligent base typical task, and constructing an indicator vector space based on the energy consumption measurement index value groups of all target digital and intelligent base typical tasks; performing a secondary quantitative measurement evaluation on the operating energy consumption of each preset digital and intelligent base through the indicator vector space.

[0058] As an achievable method, the embodiment of the present application can also combine the average power P, completion time t (i.e., running time), and total energy consumption W of each target digital intelligence base typical task to construct an energy consumption measurement index value group (P, t, W): m ; When there are n typical tasks of the target digital intelligence base, the corresponding indicator vector space (P, t, W) can be constructed through n sets of energy consumption measurement indicator values. m , where m ranges from 1 to n, so as to use the indicator vector space and energy consumption measurement indicator value group to perform a secondary quantitative evaluation of the operating energy consumption of each digital intelligence base.

[0059] Therefore, the embodiments of the present application introduce a secondary quantitative measurement and evaluation operation to more comprehensively and completely describe and measure the energy consumption of the digital intelligence base, thereby greatly improving the reliability of energy consumption evaluation.

[0060] To sum up, the embodiments of the present application select suitable typical tasks for testing according to different types of digital and intelligent bases, quantify the energy consumption data of the digital and intelligent bases in operation, and select a suitable evaluation method for evaluation, which provides a feasible way for energy consumption evaluation of the digital and intelligent bases, and further provides the possibility to reduce the energy consumption of digital and intelligent base resources and improve operating efficiency.

[0061] According to the multi-level quantitative measurement and evaluation method of the energy consumption of the digital intelligence base proposed in the embodiment of the present application, a target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base is determined; a test data set corresponding to the target digital intelligence base typical task is obtained, and based on the test data set, the target digital intelligence base typical task is executed to obtain the running time, total energy consumption, real-time power and time period energy consumption corresponding to the target digital intelligence base typical task; the target energy consumption evaluation strategy corresponding to the target digital intelligence base typical task is determined, and the average power corresponding to the target digital intelligence base typical task is calculated based on the target energy consumption evaluation strategy, running time, real-time power, time period energy consumption and total energy consumption, and an energy consumption measurement indicator value group is constructed according to the average power, running time and total energy consumption, and the multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital intelligence base is obtained by using the average power and the energy consumption measurement indicator value group. This application selects appropriate typical tasks for testing according to different types of digital and intelligent bases, quantifies the energy consumption data of the digital and intelligent bases in operation, and selects a suitable evaluation method for evaluation, thereby providing a feasible way for energy consumption evaluation of the digital and intelligent bases, and thus providing the possibility to reduce the resource energy consumption of the digital and intelligent bases and improve operating efficiency.

[0062] Secondly, the multi-level quantitative measurement and evaluation device for the energy consumption of the digital intelligence base proposed in accordance with the embodiment of the present application is described with reference to the accompanying drawings.

[0063] Figure 5 It is a block diagram of a multi-level quantitative measurement and evaluation device for the energy consumption of a digital intelligence base in an embodiment of the present application.

[0064] like Figure 5 As shown, the multi-level quantitative measurement and evaluation device 10 for the energy consumption of the digital intelligence base includes: a determination module 100, an execution module 200 and an evaluation module 300.

[0065] Among them, the determination module 100 is used to determine the typical tasks of the target digital intelligence base corresponding to each preset digital intelligence base in at least one preset digital intelligence base.

[0066] The execution module 200 is used to obtain a test data set corresponding to the typical tasks of the target digital and intelligent base, and execute the typical tasks of the target digital and intelligent base based on the test data set to obtain the operating time, total energy consumption, real-time power and time-phased energy consumption of the preset digital and intelligent base corresponding to the typical tasks of the target digital and intelligent base.

[0067] The evaluation module 300 is used to determine the target energy consumption evaluation strategy corresponding to the typical tasks of the target digital and intelligent base, and calculate the average power corresponding to the typical tasks of the target digital and intelligent base based on the target energy consumption evaluation strategy, running time, real-time power, time period energy consumption and total energy consumption, and construct an energy consumption measurement indicator value group according to the average power, running time and total energy consumption, and use the average power and energy consumption measurement indicator value group to obtain a multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital and intelligent base.

[0068] Optionally, in one embodiment of the present application, the determination module 100 includes: a type analysis unit and a selection unit.

[0069] Among them, the type analysis unit is used to identify the digital intelligence base type corresponding to each preset digital intelligence base.

[0070] A selection unit is used to select a target digital intelligence base typical task corresponding to each preset digital intelligence base according to the type of the digital intelligence base, wherein at least one preset digital intelligence base includes at least one of a target AI big model, a target database, a target big data management software and a computing platform IT infrastructure.

[0071] Optionally, in one embodiment of the present application, the execution module 200 includes: an acquisition unit and a generation unit.

[0072] Among them, the acquisition unit is used to obtain a test data set of a preset digital intelligence base that executes typical tasks of the target digital intelligence base based on the typical tasks of the target digital intelligence base, wherein the typical tasks of the target digital intelligence base include any one of preset large model typical tasks, preset big data processing typical tasks and preset large model-big data hybrid tasks.

[0073] A generation unit is used to execute typical tasks of the target digital intelligence base according to a test data set to generate the running time, total energy consumption, real-time power and time-segment energy consumption of a preset digital intelligence base that executes the typical tasks of the target digital intelligence base.

[0074] Optionally, in one embodiment of the present application, the evaluation module 300 includes: a total energy consumption evaluation unit, an idle consumption exclusion evaluation unit and a primary evaluation unit.

[0075] Among them, the total energy consumption evaluation unit is used to determine the first actual running time corresponding to the evaluation strategy based on the total energy consumption according to the running time when the target energy consumption evaluation strategy is an evaluation strategy based on the total energy consumption, and determine the first actual total energy consumption of the preset digital and intelligent base according to the total energy consumption, and calculate the ratio of the first actual total energy consumption to the first actual running time to obtain the average power of the preset digital and intelligent base that executes typical tasks of the target digital and intelligent base.

[0076] An idle consumption exclusion assessment unit is used to determine the start execution time of a typical task of a target digital intelligent base corresponding to the idle consumption exclusion assessment strategy and the regression idle time of a preset digital intelligent base according to the running time when the target energy consumption assessment strategy is the idle consumption exclusion assessment strategy, and calculate the second actual running time corresponding to the typical task of the target digital intelligent base according to the start execution time and the regression idle time, and remove the idle energy consumption in the total energy consumption to obtain the second actual total energy consumption of the preset digital intelligent base, and calculate the ratio of the second actual total energy consumption to the second actual running time to obtain the average power of the preset digital intelligent base that executes the typical task of the target digital intelligent base.

[0077] The first-level evaluation unit is used to perform a first-level quantitative measurement evaluation on the operating energy consumption of each preset digital intelligence base by excluding the idle consumption evaluation strategy or the average power corresponding to the evaluation strategy based on the total energy consumption.

[0078] Optionally, in one embodiment of the present application, the evaluation module 300 further includes: a vector space construction unit and a secondary evaluation unit.

[0079] Among them, the vector space construction unit is used to construct an energy consumption measurement index value group corresponding to each target digital intelligence base typical task based on average power, running time and total energy consumption, and to construct an index vector space according to the energy consumption measurement index value group of all target digital intelligence base typical tasks.

[0080] The secondary evaluation unit is used to perform secondary quantitative measurement evaluation on the operating energy consumption of each preset digital intelligence base through the indicator vector space.

[0081] It should be noted that the above explanation of the embodiment of the multi-level quantitative measurement and evaluation method for the energy consumption of the digital intelligence base is also applicable to the multi-level quantitative measurement and evaluation device for the energy consumption of the digital intelligence base of this embodiment, and will not be repeated here.

[0082] According to the multi-level quantitative measurement and evaluation device of the energy consumption of the digital intelligence base proposed in the embodiment of the present application, it includes a determination module, which is used to determine the typical task of the target digital intelligence base corresponding to each preset digital intelligence base in at least one preset digital intelligence base; an execution module, which is used to obtain a test data set corresponding to the typical task of the target digital intelligence base, and execute the typical task of the target digital intelligence base based on the test data set to obtain the running time, total energy consumption, real-time power and time period energy consumption of the preset digital intelligence base corresponding to the typical task of the target digital intelligence base; an evaluation module, which is used to determine the target energy consumption evaluation strategy corresponding to the typical task of the target digital intelligence base, and calculate the average power corresponding to the typical task of the target digital intelligence base based on the target energy consumption evaluation strategy, running time, real-time power, time period energy consumption and total energy consumption, and construct an energy consumption measurement indicator value group according to the average power, running time and total energy consumption, and use the average power and the energy consumption measurement indicator value group to obtain the multi-level quantitative measurement evaluation result of the running energy consumption of each preset digital intelligence base. This application selects appropriate typical tasks for testing according to different types of digital and intelligent bases, quantifies the energy consumption data of the digital and intelligent bases in operation, and selects a suitable evaluation method for evaluation, thereby providing a feasible way for energy consumption evaluation of the digital and intelligent bases, and thus providing the possibility to reduce the resource energy consumption of the digital and intelligent bases and improve operating efficiency.

[0083] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0084] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0085] When the processor 602 executes the program, the multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base provided in the above embodiment is implemented.

[0086] Furthermore, the electronic device further comprises:

[0087] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0088] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0089] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0090] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0091] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0092] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0093] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base.

[0094] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base.

[0095] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0096] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0097] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0099] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0100] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0101] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0102] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A multi-level quantitative measurement evaluation method for energy consumption of a digital intelligence base, characterized in that: The following steps are involved: Determine a target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base; Obtain a test data set corresponding to the typical task of the target digital intelligence base, and execute the typical task of the target digital intelligence base based on the test data set to obtain the running time, total energy consumption, real-time power and time-divided energy consumption corresponding to the typical task of the target digital intelligence base; Determine the target energy consumption assessment strategy corresponding to the typical task of the target digital and intelligent base, and calculate the average power corresponding to the typical task of the target digital and intelligent base based on the target energy consumption assessment strategy, the running time, the real-time power, the time period energy consumption and the total energy consumption, and construct an energy consumption measurement indicator value group according to the average power, the running time and the total energy consumption, and use the average power and the energy consumption measurement indicator value group to obtain a multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital and intelligent base.

2. The method according to claim 1, characterized in that The determining of a typical task of a target digital intelligence base corresponding to each preset digital intelligence base in at least one preset digital intelligence base includes: Identify the type of digital intelligence base corresponding to each preset digital intelligence base; According to the type of the digital intelligence base, a target digital intelligence base typical task corresponding to each preset digital intelligence base is selected, wherein at least one of the preset digital intelligence base includes at least one of a target AI big model, a target database, a target big data management software and a computing platform IT infrastructure.

3. The method according to claim 1, characterized in that The step of obtaining a test data set corresponding to the typical task of the target digital intelligence base, and executing the typical task of the target digital intelligence base based on the test data set to obtain the running time, total energy consumption, real-time power, and time-divided energy consumption of the preset digital intelligence base corresponding to the typical task of the target digital intelligence base includes: Based on the target digital intelligence base typical task, obtain a test data set of a preset digital intelligence base that executes the target digital intelligence base typical task, wherein the target digital intelligence base typical task includes any one of a preset large model typical task, a preset big data processing typical task, and a preset large model-big data hybrid task; The typical tasks of the target digital intelligence base are executed according to the test data set to generate the running time, total energy consumption, real-time power and time-segment energy consumption of the preset digital intelligence base that executes the typical tasks of the target digital intelligence base.

4. The method according to claim 3, characterized in that The target energy consumption evaluation strategy corresponding to the typical task of the target digital intelligent base is determined, and the average power corresponding to the typical task of the target digital intelligent base is calculated based on the target energy consumption evaluation strategy, the running time, the real-time power, the time-segment energy consumption and the total energy consumption, and an energy consumption measurement index value group is constructed according to the average power, the running time and the total energy consumption, and the multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital intelligent base is obtained by using the average power and the energy consumption measurement index value group, including: When the target energy consumption evaluation strategy is an evaluation strategy based on total energy consumption, a first actual running time corresponding to the evaluation strategy based on total energy consumption is determined according to the running time, and a first actual total energy consumption of the preset digital intelligence base is determined according to the total energy consumption, and a ratio of the first actual total energy consumption to the first actual running time is calculated to obtain an average power of the preset digital intelligence base that executes a typical task of the target digital intelligence base; When the target energy consumption assessment strategy is an idle consumption exclusion assessment strategy, the start execution time of the target digital intelligence base typical task corresponding to the idle consumption exclusion assessment strategy and the regression idle time of the preset digital intelligence base are determined according to the running time, and the second actual running time corresponding to the target digital intelligence base typical task is calculated according to the start execution time and the regression idle time, and the idle energy consumption in the total energy consumption is removed to obtain the second actual total energy consumption of the preset digital intelligence base, and the ratio of the second actual total energy consumption to the second actual running time is calculated to obtain the average power of the preset digital intelligence base that executes the target digital intelligence base typical task; A first-level quantitative measurement evaluation is performed on the operating energy consumption of each preset digital intelligence base through the average power corresponding to the idle consumption exclusion evaluation strategy or the evaluation strategy based on total energy consumption.

5. The method according to claim 4, characterized in that The step of constructing an energy consumption metric index value group according to the average power, the operating time and the total energy consumption, and obtaining a multi-level quantitative metric evaluation result of the operating energy consumption of each preset digital intelligence base by using the average power and the energy consumption metric index value group, further includes: Based on the average power, the running time and the total energy consumption, construct an energy consumption measurement index value group corresponding to each target digital intelligence base typical task, and construct an index vector space according to the energy consumption measurement index value groups of all target digital intelligence base typical tasks; A secondary quantitative measurement evaluation is performed on the operating energy consumption of each preset digital intelligence base through the indicator vector space.

6. A multi-level quantitative measurement and evaluation device for energy consumption of a digital intelligence base, characterized in that: include: A determination module, used to determine a target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base; An execution module is used to obtain a test data set corresponding to the typical task of the target digital intelligence base, and execute the typical task of the target digital intelligence base based on the test data set to obtain the running time, total energy consumption, real-time power and time-divided energy consumption of the preset digital intelligence base corresponding to the typical task of the target digital intelligence base; An evaluation module is used to determine the target energy consumption evaluation strategy corresponding to the typical task of the target digital and intelligent base, and calculate the average power corresponding to the typical task of the target digital and intelligent base based on the target energy consumption evaluation strategy, the running time, the real-time power, the time period energy consumption and the total energy consumption, and construct an energy consumption measurement indicator value group according to the average power, the running time and the total energy consumption, and use the average power and the energy consumption measurement indicator value group to obtain a multi-level quantitative measurement evaluation result of the operating energy consumption of each preset digital and intelligent base.

7. The device according to claim 6, characterized in that The determination module comprises: A type analysis unit, used to identify the type of digital intelligence base corresponding to each preset digital intelligence base; A selection unit is used to select a target digital intelligence base typical task corresponding to each preset digital intelligence base according to the type of the digital intelligence base, wherein at least one of the preset digital intelligence base includes a target AI big model, a target database, a target big data management software and a computing platform IT infrastructure.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-level quantitative measurement evaluation method for energy consumption of a digital intelligence base as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the multi-level quantitative measurement evaluation method for the energy consumption of a digital intelligence base as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the multi-level quantitative measurement evaluation method for the energy consumption of the digital intelligence base as described in any one of claims 1-5.

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