Method and device for measuring the correlation between temperature sensitivity and energy consumption of digital intelligent base stations

Abstract

This application relates to a method and apparatus for measuring the correlation between temperature sensitivity and energy consumption of a smart data infrastructure. The method includes: determining a typical task for each of at least one preset smart data infrastructure; executing the typical task to obtain temperature and energy consumption parameters, and acquiring workload parameters; and performing a multivariate linear fitting operation on the temperature and energy consumption parameters based on the workload parameters to obtain a correlation measurement between the temperature sensitivity and energy consumption parameters, thereby providing a more comprehensive and balanced quantification of the green, energy-saving, and environmentally friendly capabilities of smart data infrastructures, computing centers, and other environments. This solves the problem that existing technologies and measurement methods lack the ability to simultaneously assess the energy consumption of computing power in smart data infrastructure applications and address the secondary energy consumption caused by cooling requirements due to temperature sensitivity of the operating environment.

Description

Technical Field

[0001] This application relates to the fields of intelligent devices and energy management technology, and in particular to a method and device for measuring the correlation between temperature sensitivity and energy consumption of a digital intelligent base. Background Technology

[0002] The energy demand of data centers continues to grow. Rapid innovation in technologies such as artificial intelligence has driven the construction of large-scale computing infrastructure. The power required by these infrastructures far exceeds that required by traditional data centers. However, existing technologies and measurement methods lack the ability to measure and evaluate the energy consumption of computing power in applications such as data intelligence bases while also taking into account the secondary energy consumption caused by the cooling requirements due to the temperature sensitivity of the operating environment. This issue urgently needs to be addressed. Summary of the Invention

[0003] This application provides a method and apparatus for measuring the correlation between temperature sensitivity and energy consumption of a smart base station, in order to solve the problems of existing technologies and measurement methods that fail to take into account the secondary energy consumption caused by cooling requirements due to the temperature sensitivity of the operating environment when measuring and evaluating computing power energy consumption in smart base station applications.

[0004] The first aspect of this application provides a method for measuring the correlation between temperature sensitivity and energy consumption of a smart base station, comprising the following steps: determining a target smart base station typical task corresponding to each of at least one preset smart base station; executing the target smart base station typical task to obtain temperature parameters and energy consumption parameters corresponding to the target smart base station typical task, and obtaining workload parameters corresponding to the target smart base station typical task; and performing a multivariate linear fitting operation on the temperature parameters and the energy consumption parameters based on the workload parameters to obtain the temperature sensitivity corresponding to the temperature parameters and the energy consumption correlation metric corresponding to the energy consumption parameters.

[0005] Optionally, in one embodiment of this application, determining the 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 the at least one preset digital intelligence base includes at least one of the target AI big data model, target database, target big data management software and computing platform IT infrastructure.

[0006] Optionally, in one embodiment of this application, the step of executing the typical task of the target smart base to obtain the temperature parameters and energy consumption parameters corresponding to the typical task of the target smart base, and obtaining the workload parameters corresponding to the typical task of the target smart base, includes: obtaining a test dataset of a preset smart base corresponding to the typical task of the target smart base, wherein the typical task of the target smart 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 smart base according to the test dataset to obtain the temperature parameters and power information corresponding to the typical task of the target smart base, and calculating the energy consumption parameters corresponding to the typical task of the target smart base using the power information; and obtaining the workload parameters generated during the execution of the typical task of the target smart base corresponding to each preset smart base, wherein the workload parameters include at least one of the total number of tokens, the number of transactions, and the number of task batches.

[0007] Optionally, in one embodiment of this application, after calculating the energy consumption correlation metric corresponding to each preset smart base, the method further includes: establishing a target binary parameter measurement index corresponding to each preset smart base based on the temperature sensitivity and the energy consumption correlation metric; and optimizing the operating performance and operating environment of each preset smart base through the target binary parameter measurement index.

[0008] Optionally, in one embodiment of this application, the multivariate linear fitting calculation expression corresponding to the multivariate linear fitting operation is:

[0009] W = α*T + β*E + γ

[0010] Wherein, W represents the workload parameter; α represents the temperature sensitivity; T represents the temperature parameter; β represents the energy consumption correlation metric; E represents the energy consumption parameter; and γ represents the multivariate linear fitting compensation constant.

[0011] A second aspect of this application provides a device for measuring the correlation between temperature sensitivity and energy consumption of a smart base station, comprising: a determination module for determining a target smart base station typical task corresponding to each of at least one preset smart base station; an execution module for executing the target smart base station typical task to obtain temperature parameters and energy consumption parameters corresponding to the target smart base station typical task, and obtaining workload parameters corresponding to the target smart base station typical task; and a multiple linear fitting module for performing a multiple linear fitting operation on the temperature parameters and the energy consumption parameters based on the workload parameters to obtain the temperature sensitivity corresponding to the temperature parameters and the energy consumption correlation metric corresponding to the energy consumption parameters.

[0012] Optionally, in one embodiment of this application, the determining module includes: a type analysis unit, used to identify the type of digital intelligence base corresponding to each preset digital intelligence base; and a selection unit, used to select a typical task of a target digital intelligence base corresponding to each preset digital intelligence base according to the type of digital intelligence base, 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 computing platform IT infrastructure.

[0013] Optionally, in one embodiment of this application, the execution module includes: a first acquisition unit, configured to acquire a test dataset of a preset smart infrastructure corresponding to the target smart infrastructure typical task, wherein the target smart infrastructure 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 running unit, configured to execute the target smart infrastructure typical task according to the test dataset to obtain the temperature parameter and power information corresponding to the target smart infrastructure typical task, and to calculate the energy consumption parameter corresponding to the target smart infrastructure typical task using the power information; and a second acquisition unit, configured to acquire the workload parameter generated during the execution of the target smart infrastructure typical task corresponding to each preset smart infrastructure, wherein the workload parameter includes at least one of the total number of tokens, the number of transactions, and the number of task batches.

[0014] Optionally, in one embodiment of this application, it further includes: an establishment module, configured to establish a target binary parameter measurement index for each preset smart base based on the temperature sensitivity and the energy consumption correlation metric after calculating the energy consumption correlation metric for each preset smart base; and a measurement module, configured to optimize the operating performance and operating environment of each preset smart base through the target binary parameter measurement index.

[0015] Optionally, in one embodiment of this application, the multivariate linear fitting calculation expression corresponding to the multivariate linear fitting operation is:

[0016] W = α*T + β*E + γ

[0017] Wherein, W represents the workload parameter; α represents the temperature sensitivity; T represents the temperature parameter; β represents the energy consumption correlation metric; E represents the energy consumption parameter; and γ represents the multivariate linear fitting compensation constant.

[0018] A third aspect of this 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 method for measuring the correlation between temperature sensitivity and energy consumption of a digital smart base as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for measuring the correlation between temperature sensitivity and energy consumption of a smart base station.

[0020] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described method for measuring the correlation between temperature sensitivity and energy consumption of a digital smart base station.

[0021] Therefore, the embodiments of this application have the following beneficial effects:

[0022] The embodiments of this application can be implemented by determining a typical task for each of at least one preset intelligent data base; executing the typical task to obtain temperature and energy consumption parameters corresponding to the task, and acquiring workload parameters; and performing a multivariate linear fitting operation on the temperature and energy consumption parameters based on the workload parameters to obtain the temperature sensitivity and energy consumption correlation metrics. This application analyzes the energy consumption levels of different intelligent data bases, designs a standard energy consumption testing method, and proposes a scientific strategy to quantify the correlation between temperature sensitivity and energy consumption. This provides a more comprehensive and balanced quantification of the green, energy-saving, and environmentally friendly capabilities of intelligent data bases, computing centers, and other environments, offering technical support for the design of intelligent infrastructure and improving overall system performance. This solves the problem that existing technologies and measurement methods lack the ability to simultaneously assess the energy consumption of computing power in intelligent data base applications and address the secondary energy consumption caused by cooling requirements due to temperature sensitivity in the operating environment.

[0023] 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 practice of the present application. Attached Figure Description

[0024] 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:

[0025] Figure 1 A flowchart illustrating a method for measuring the correlation between temperature sensitivity and energy consumption of a digital intelligent base station according to an embodiment of this application;

[0026] Figure 2 A schematic diagram of the logical architecture of a method for measuring the correlation between temperature sensitivity and energy consumption of a digital intelligent base station, provided as an embodiment of this application;

[0027] Figure 3 A schematic diagram of the calculation logic for the correlation between temperature sensitivity and energy consumption is provided as an embodiment of this application;

[0028] Figure 4 This is an example diagram of a device for measuring the correlation between temperature sensitivity and energy consumption of a digital smart base according to an embodiment of this application;

[0029] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0030] Among them, 10 is a measurement device for the correlation between temperature sensitivity and energy consumption of the digital intelligent base; 100 is a determination module, 200 is an execution module, and 300 is a multivariate linear fitting module; 501 is a memory, 502 is a processor, and 503 is a communication interface. Detailed Implementation

[0031] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0032] The following describes a method and apparatus for measuring the correlation between temperature sensitivity and energy consumption of a smart data base according to embodiments of this application, with reference to the accompanying drawings. Addressing the problems mentioned in the background art, this application provides a method for measuring the correlation between temperature sensitivity and energy consumption of a smart data base. In this method, a typical task for a target smart data base is determined for each of at least one preset smart data bases; the typical task for the target smart data base is executed to obtain temperature parameters and energy consumption parameters corresponding to the typical task, and workload parameters corresponding to the typical task are obtained; based on the workload parameters, a multivariate linear fitting operation is performed on the temperature parameters and energy consumption parameters to obtain the temperature sensitivity corresponding to the temperature parameters and the energy consumption correlation metric corresponding to the energy consumption parameters. This application analyzes the energy consumption levels of different smart data bases and designs a standard energy consumption testing method to propose a scientific strategy for quantifying the correlation between temperature sensitivity and energy consumption. This provides a more comprehensive and balanced quantification of the green, energy-saving, and environmentally friendly capabilities of smart data bases, computing centers, and other environments, providing technical indicator support for the design of intelligent infrastructure and improving the overall system performance. This solves the problem that existing technologies and measurement methods lack the ability to measure and evaluate computing power energy consumption in applications such as digital intelligence infrastructure, while also taking into account the secondary energy consumption caused by the temperature sensitivity of the operating environment and the resulting cooling requirements.

[0033] Specifically, Figure 1 This is a flowchart illustrating a method for measuring the correlation between temperature sensitivity and energy consumption of a digital smart base, as provided in an embodiment of this application.

[0034] like Figure 1 As shown, the method for measuring the correlation between temperature sensitivity and energy consumption of the intelligent base includes the following steps:

[0035] In step S101, the target digital intelligent base typical task corresponding to each preset digital intelligent base in at least one preset digital intelligent base is determined.

[0036] The embodiments of this application can first select appropriate typical tasks for each preset smart base station, thereby providing technical and data support for the realization of smart base station energy consumption assessment.

[0037] Optionally, in one embodiment of this application, determining the 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 the target AI big model, target database, target big data management software and computing platform IT infrastructure.

[0038] In actual implementation, such as Figure 2 As shown, the digital infrastructure in the embodiments of this application mainly includes IT infrastructure such as AI big data models, databases, big data management related software, and computing platforms.

[0039] It should be noted that the aforementioned large AI model refers to a deep learning model with extremely large parameters and computing power. It can be widely used in fields such as natural language processing, and is capable of handling complex tasks and has the ability to understand and generate data.

[0040] The embodiments of this application can determine the type of digital base station corresponding to each digital base station, so as to select and execute appropriate typical tasks according to different digital base station types, and provide corresponding parameter data for energy consumption analysis.

[0041] In step S102, the typical task of the target smart base is executed to obtain the temperature parameters and energy consumption parameters corresponding to the typical task of the target smart base, and to obtain the workload parameters corresponding to the typical task of the target smart base.

[0042] Furthermore, embodiments of this application can execute typical tasks of the target intelligent digital base and monitor relevant parameters such as temperature and energy consumption generated by the intelligent digital base when executing typical tasks. In addition, embodiments of this application can also obtain workload parameters corresponding to typical tasks of the target intelligent digital base, thereby providing reliable data guidance and basis for subsequent optimization analysis of the intelligent digital base.

[0043] Optionally, in one embodiment of this application, executing a typical task for a target smart base station to obtain temperature parameters and energy consumption parameters corresponding to the typical task, and acquiring workload parameters corresponding to the typical task, includes: acquiring a test dataset of a preset smart base station corresponding to the typical task, wherein the 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; executing the typical task according to the test dataset to obtain temperature parameters and power information corresponding to the typical task, and calculating energy consumption parameters corresponding to the typical task using the power information; and acquiring workload parameters generated during the execution of the typical task for each preset smart base station, wherein the workload parameters include at least one of the total number of tokens, the number of transactions, and the number of task batches.

[0044] It should be noted that the typical tasks of the digital infrastructure in this application embodiment include typical large model tasks, typical big data processing tasks, and mixed tasks of the two. These tasks provide the data set (i.e., test data set) required for testing the digital infrastructure and use it as input to enable the digital infrastructure to generate temperature parameters and power information that can be used for energy consumption analysis, and calculate the corresponding energy consumption parameters through the power information.

[0045] Typical tasks for large models include datasets related to the model's functionality; for example, the Alpaca dataset and the GSM8K dataset. The Alpaca dataset contains text data in multiple languages, which can be used for a range of natural language processing tasks, such as sentiment analysis and text classification; the GSM8K dataset contains thousands of mathematical problems to test the model's logical reasoning and problem-solving abilities. By using these datasets together, we can evaluate the model's performance in various tasks and assess how different types of data affect energy consumption and inference performance.

[0046] Typical big data processing tasks include those that simulate complex business-related query operations in databases and other big data-related software to test the performance of these databases and software, such as benchmark tasks like TPC-H, TPC-IoT, and TPC-AI. TPC-H simulates complex queries and large-scale data processing scenarios, consisting of a set of business-oriented ad-hoc queries and concurrent data modifications. It tests database performance using metrics such as QphH@Size (queries per hour), $ / kQphH@Size (cost per thousand queries), and system availability dates through multiple predefined complex SQL operations. TPC-IoT is a benchmark specifically designed to measure the performance of IoT gateway systems, primarily using metrics such as IoTps (IoT transactions per second), $ / kIoTps (cost per thousand IoT transactions), and system availability dates. TPCx-AI is a benchmark for evaluating the performance of artificial intelligence and machine learning applications, primarily using metrics such as AIUCpm (AI user queries processed per minute), $ / AIUCpm (cost per AI user query), and system availability dates.

[0047] Subsequently, embodiments of this application also need to obtain workload parameters generated during the execution of typical tasks corresponding to each preset digital intelligence base. These workload parameters are indicators measuring the overall workload of a particular digital intelligence base, and can be the number of transactions processed or the amount of computation performed. In actual execution, for large AI models, the workload in this embodiment can be the total number of tokens during the execution of typical tasks corresponding to each preset digital intelligence base; for databases or big data software, the workload can be the number of transactions processed or the number of task batches in a test.

[0048] In step S103, based on the workload parameter, a multiple linear fitting operation is performed on the temperature parameter and the energy consumption parameter to obtain the temperature sensitivity corresponding to the temperature parameter and the energy consumption correlation metric corresponding to the energy consumption parameter.

[0049] Furthermore, embodiments of this application can obtain linear fitting parameters by performing multiple linear regression based on temperature parameters and energy consumption parameters, namely, temperature sensitivity and energy consumption correlation metrics, respectively.

[0050] Optionally, in one embodiment of this application, the multivariate linear fitting calculation expression corresponding to the multivariate linear fitting operation is:

[0051] W = α*T + β*E + γ

[0052] Where W represents the workload parameter; α represents the temperature sensitivity; T represents the temperature parameter; β represents the energy consumption correlation metric; E represents the energy consumption parameter; and γ represents the multivariate linear fitting compensation constant.

[0053] In the specific implementation process, such as Figure 3 As shown, embodiments of this application can obtain workload data W when selecting appropriate typical tasks for testing on different digital base devices, and acquire the aforementioned temperature parameter T and power information P through corresponding sensor devices, and calculate the corresponding energy consumption parameter E through the power information P; secondly, embodiments of this application can perform a multivariate linear fitting operation on the temperature parameter and energy consumption parameter based on the workload parameter, as shown in the following formula:

[0054] W = α*T + β*E + γ

[0055] Where W represents the workload parameter; α represents the temperature sensitivity; T represents the temperature parameter; β represents the energy consumption correlation metric; E represents the energy consumption parameter; and γ represents the multivariate linear fitting compensation constant.

[0056] It is worth noting that in the specific implementation process, for some applications, if it is necessary to focus on one or more specific tasks in the task group, the workload, temperature parameters and power information of one or more specific tasks can be respectively subjected to bilinear fitting to form a binary pair of temperature sensitivity and energy consumption correlation, thereby supporting more granular measurement.

[0057] Therefore, the embodiments of this application calculate the bilinear fitting parameters of workload, temperature, and energy consumption, and use the linear fitting parameter between the workload parameter and the temperature parameter in the bilinear fitting parameters as a temperature sensitivity analysis index, which reflects the linear influence of temperature on workload when energy consumption remains constant; at the same time, the linear fitting parameter between the workload parameter and the energy consumption parameter in the bilinear fitting parameters is used as an energy consumption correlation measurement analysis index, which reflects the linear influence of energy consumption on workload when temperature remains constant.

[0058] Optionally, in one embodiment of this application, after calculating the energy consumption correlation metric corresponding to each preset smart base, the method further includes: establishing a target binary parameter measurement index corresponding to each preset smart base based on temperature sensitivity and energy consumption correlation metric; and optimizing the operating performance and operating environment of each preset smart base through the target binary parameter measurement index.

[0059] As one possible approach, embodiments of this application can also establish a combined index target (α,β) (i.e., a binary parameter measurement index) for each preset smart base station based on temperature sensitivity and energy consumption correlation measurement, so as to measure and optimize the measured operating performance (such as equipment energy consumption performance) and operating environment (such as ambient temperature and cooling factors) of each preset smart base station through the target binary parameter measurement index.

[0060] Therefore, the embodiments of this application analyze the temperature and energy consumption data generated when the digital intelligent base performs typical tasks to obtain the relationship between workload, work efficiency and temperature and energy consumption. For equipment that is greatly affected by temperature, cooling measures can be considered; for equipment that is more related to energy consumption, efficiency can be improved by optimizing energy consumption allocation. Thus, by analyzing the binary index of temperature sensitivity and energy consumption correlation, different equipment can be selected for different tasks, and directional references can be provided for optimizing equipment performance.

[0061] Furthermore, this application can also construct a corresponding measurement system for the correlation between temperature sensitivity and energy consumption of intelligent base stations based on the measurement method for the correlation between temperature sensitivity and energy consumption of intelligent base stations. The measurement system for the correlation between temperature sensitivity and energy consumption of intelligent base stations of this application will be described and introduced below.

[0062] The measurement system for the correlation between temperature sensitivity and energy consumption of the digital intelligent base station in this application mainly consists of two parts: an input module and an analysis module.

[0063] The input module includes the digital base station device and typical tasks of the digital base station. It is responsible for monitoring the temperature, energy consumption and other related parameters generated by the digital base station when performing typical tasks, and transmitting these data to the analysis module for processing and analysis.

[0064] The analysis module performs linear fitting based on temperature and energy consumption for workload, and obtains linear fitting parameters through multiple linear regression, which are temperature sensitivity and energy consumption correlation metrics, to be used as indicators to measure equipment performance and environmental factors.

[0065] According to the measurement method for the correlation between temperature sensitivity and energy consumption of intelligent base stations proposed in this application, the typical task of the target intelligent base station is determined for each of the at least one preset intelligent base station; the typical task of the target intelligent base station is executed to obtain the temperature parameters and energy consumption parameters corresponding to the typical task of the target intelligent base station, and the workload parameters corresponding to the typical task of the target intelligent base station are obtained; based on the workload parameters, a multivariate linear fitting operation is performed on the temperature parameters and energy consumption parameters to obtain the temperature sensitivity corresponding to the temperature parameters and the energy consumption correlation measurement corresponding to the energy consumption parameters. This application analyzes the energy consumption levels of different intelligent base stations, designs a standard energy consumption testing method, and proposes a scientific strategy to quantify the correlation between temperature sensitivity and energy consumption. This provides a more comprehensive and balanced quantification of the green, energy-saving, and environmentally friendly capabilities of intelligent base stations, computing centers, and other environments, providing technical indicator support for the design of intelligent infrastructure and improving the overall system performance.

[0066] Secondly, with reference to the accompanying drawings, a measurement device for the correlation between temperature sensitivity and energy consumption of a digital intelligent base station according to an embodiment of this application is described.

[0067] Figure 4 This is a block diagram of a device for measuring the correlation between temperature sensitivity and energy consumption of a digital smart base according to an embodiment of this application.

[0068] like Figure 4 As shown, the measurement device 10 for the correlation between temperature sensitivity and energy consumption of the smart base includes: a determination module 100, an execution module 200, and a multivariate linear fitting module 300.

[0069] The determining module 100 is used to determine the target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base.

[0070] The execution module 200 is used to execute typical tasks of the target smart base to obtain the temperature parameters and energy consumption parameters corresponding to the typical tasks of the target smart base, and to obtain the workload parameters corresponding to the typical tasks of the target smart base.

[0071] The multiple linear fitting module 300 is used to perform multiple linear fitting operations on temperature parameters and energy consumption parameters based on workload parameters, so as to obtain the temperature sensitivity corresponding to the temperature parameter and the energy consumption correlation metric corresponding to the energy consumption parameter.

[0072] Optionally, in one embodiment of this application, the determining module 100 includes a type analysis unit and a selection unit.

[0073] The type analysis unit is used to identify the type of digital base corresponding to each preset digital base.

[0074] The selection unit is used to select the target digital intelligence base typical task corresponding to each preset digital intelligence base according to the type of digital intelligence base, wherein at least one preset digital intelligence base includes at least one of the target AI big model, target database, target big data management software and computing platform IT infrastructure.

[0075] Optionally, in one embodiment of this application, the execution module 200 includes: a first acquisition unit, a running unit, and a second acquisition unit.

[0076] The first acquisition unit is used to acquire the test dataset of the preset digital intelligence base corresponding to the typical task of the target digital intelligence base. The typical task of the target digital intelligence base includes any one of the preset large model typical task, the preset big data processing typical task, and the preset large model-big data hybrid task.

[0077] The execution unit is used to execute typical tasks of the target number of smart base stations according to the test dataset, so as to obtain the temperature parameters and power information corresponding to the typical tasks of the target number of smart base stations, and to calculate the energy consumption parameters corresponding to the typical tasks of the target number of smart base stations using the power information.

[0078] The second acquisition unit is used to acquire the workload parameters generated during the execution of typical tasks of the target digital infrastructure corresponding to each preset digital infrastructure. The workload parameters include at least one of the total number of tokens, the number of transactions, and the number of task batches.

[0079] Optionally, in one embodiment of this application, the measurement device 10 for measuring the correlation between temperature sensitivity and energy consumption of the digital intelligent base station further includes: an establishment module and a measurement module.

[0080] The module is used to establish a target binary parameter measurement index for each preset smart base station after calculating the energy consumption correlation metric for each preset smart base station based on temperature sensitivity and energy consumption correlation metric.

[0081] The measurement module is used to optimize the operating performance and operating environment of each preset digital intelligent base station by using target binary parameter measurement indicators.

[0082] Optionally, in one embodiment of this application, the multivariate linear fitting calculation expression corresponding to the multivariate linear fitting operation is:

[0083] W = α*T + β*E + γ

[0084] Where W represents the workload parameter; α represents the temperature sensitivity; T represents the temperature parameter; β represents the energy consumption correlation metric; E represents the energy consumption parameter; and γ represents the multivariate linear fitting compensation constant.

[0085] It should be noted that the explanation of the aforementioned embodiment of the method for measuring the correlation between temperature sensitivity and energy consumption of the smart base also applies to the device for measuring the correlation between temperature sensitivity and energy consumption of the smart base in this embodiment, and will not be repeated here.

[0086] The device for measuring the correlation between temperature sensitivity and energy consumption of a smart infrastructure, as proposed in this application, includes a determination module for determining a typical task of a target smart infrastructure corresponding to each of at least one preset smart infrastructure; an execution module for executing the typical task of the target smart infrastructure to obtain temperature parameters and energy consumption parameters corresponding to the typical task of the target smart infrastructure, and to obtain workload parameters corresponding to the typical task of the target smart infrastructure; and a multiple linear fitting module for performing a multiple linear fitting operation on the temperature parameters and energy consumption parameters based on the workload parameters to obtain the temperature sensitivity corresponding to the temperature parameters and the energy consumption correlation measurement corresponding to the energy consumption parameters. This application analyzes the energy consumption levels of different smart infrastructures, designs a standard energy consumption testing method, and proposes a scientific strategy to quantify the correlation between temperature sensitivity and energy consumption. This provides a more comprehensive and balanced quantification of the green, energy-saving, and environmentally friendly capabilities of smart infrastructures, computing centers, and other environments, offering technical indicator support for the design of intelligent infrastructure and improving the overall system performance.

[0087] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0088] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0089] When the processor 502 executes the program, it implements the measurement method for the correlation between temperature sensitivity and energy consumption of the digital intelligent base provided in the above embodiments.

[0090] Furthermore, electronic devices also include:

[0091] Communication interface 503 is used for communication between memory 501 and processor 502.

[0092] The memory 501 is used to store computer programs that can run on the processor 502.

[0093] The memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0094] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0095] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0096] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0097] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-mentioned method for measuring the correlation between temperature sensitivity and energy consumption of a smart base station.

[0098] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for measuring the correlation between temperature sensitivity and energy consumption of a digital smart base.

[0099] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0100] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0101] 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 a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0103] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0104] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0105] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0106] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for measuring the correlation between temperature sensitivity and energy consumption of a digital intelligent base station, characterized in that, Includes the following steps: 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; Execute the typical task of the target smart base to obtain the temperature parameters and energy consumption parameters corresponding to the typical task of the target smart base, and obtain the workload parameters corresponding to the typical task of the target smart base; Based on the workload parameters, a multivariate linear fitting operation is performed on the temperature parameters and the energy consumption parameters to obtain the temperature sensitivity corresponding to the temperature parameters and the energy consumption correlation metric corresponding to the energy consumption parameters. The step of executing the typical task of the target smart base station to obtain the temperature parameters and energy consumption parameters corresponding to the typical task of the target smart base station, and to obtain the workload parameters corresponding to the typical task of the target smart base station, includes: Based on the typical tasks of the target digital intelligence infrastructure, obtain the test dataset of the preset digital intelligence infrastructure corresponding to the typical tasks of the target digital intelligence infrastructure. The typical tasks of the target digital intelligence infrastructure include any one of the preset large model typical tasks, the preset big data processing typical tasks, and the preset large model-big data hybrid tasks. The target number of smart base stations is executed according to the test dataset to obtain the temperature parameters and power information corresponding to the target number of smart base stations, and the energy consumption parameters corresponding to the target number of smart base stations are calculated using the power information. Obtain the workload parameters generated during the execution of typical tasks for each preset digital infrastructure, wherein the workload parameters include at least one of the total number of tokens, the number of transactions, and the number of task batches; The multivariate linear fitting calculation expression corresponding to the multivariate linear fitting operation is: in, This represents the workload parameter; This indicates the temperature sensitivity; This indicates the temperature parameter; This represents the energy consumption correlation metric; This represents the energy consumption parameter; This represents the compensation constant for multivariate linear fitting.

2. The method according to claim 1, characterized in that, The step of determining the typical task of the 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 base station corresponding to each preset digital base station; Select the target digital intelligence base typical task 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 the target AI big model, target database, target big data management software and computing platform IT infrastructure.

3. The method according to claim 1, characterized in that, After calculating the energy consumption correlation metric corresponding to each preset smart base station, the method further includes: Based on the temperature sensitivity and the energy consumption correlation metric, establish a target binary parameter measurement index for each preset digital intelligent base station; The operating performance and operating environment of each preset digital intelligent base station are optimized using the target binary parameter measurement index.

4. A device for measuring the correlation between temperature sensitivity and energy consumption of a digital intelligent base station, characterized in that, include: The determination module is used to determine the target digital intelligence base typical task corresponding to each preset digital intelligence base in at least one preset digital intelligence base; The execution module is used to execute the typical task of the target smart base to obtain the temperature parameters and energy consumption parameters corresponding to the typical task of the target smart base, and to obtain the workload parameters corresponding to the typical task of the target smart base. The multiple linear fitting module is used to perform multiple linear fitting operations on the temperature parameter and the energy consumption parameter based on the workload parameter, so as to obtain the temperature sensitivity corresponding to the temperature parameter and the energy consumption correlation metric corresponding to the energy consumption parameter. The execution module includes: The first acquisition unit is used to acquire a test dataset of a preset digital intelligence base corresponding to the typical task of the target digital intelligence base, wherein the typical task of the target digital intelligence base includes any one of the preset large model typical task, the preset big data processing typical task, and the preset large model-big data hybrid task. The running unit is used to execute the typical task of the target smart base according to the test dataset, so as to obtain the temperature parameters and power information corresponding to the typical task of the target smart base, and use the power information to calculate the energy consumption parameters corresponding to the typical task of the target smart base; The second acquisition unit is used to acquire the workload parameters generated during the execution of the typical task of the target digital infrastructure corresponding to each preset digital infrastructure, wherein the workload parameters include at least one of the total number of tokens, the number of transactions, and the number of task batches; The multivariate linear fitting calculation expression corresponding to the multivariate linear fitting operation is: in, This represents the workload parameter; This indicates the temperature sensitivity; This indicates the temperature parameter; This represents the energy consumption correlation metric; This represents the energy consumption parameter; This represents the compensation constant for multivariate linear fitting.

5. The apparatus according to claim 4, characterized in that, The determining module includes: The type analysis unit is used to identify the type of digital base corresponding to each preset digital base; The selection unit is used to select the target digital intelligence base typical task corresponding to each preset digital intelligence base according to the type of digital intelligence base, wherein the at least one preset digital intelligence base includes at least one of the target AI big model, target database, target big data management software and computing platform IT infrastructure.

6. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for measuring the correlation between temperature sensitivity and energy consumption of the digital smart base as described in any one of claims 1-3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the measurement method for the correlation between temperature sensitivity and energy consumption of the digital smart base as described in any one of claims 1-3.

8. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the method for measuring the correlation between temperature sensitivity and energy consumption of the digital smart base as described in any one of claims 1-3.

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