Data processing method, apparatus and system, and related device

Abstract

A data processing method, comprising: acquiring a running state of a hardware object; and on the basis of the running state of the hardware object, obtaining information of a digital product passport (DPP) of the hardware object, the information of the DPP comprising at least one of the following target information: the energy-saving health of the hardware object, the amount of energy savings of the hardware object, or the energy consumption of the hardware object, wherein the amount of energy savings is used for measuring at least one of the amount of reduced power consumption and the amount of energy savings / reduced carbon emissions of the hardware object in the running state, and the energy consumption is used for measuring at least one of the power consumption level and the amount of carbon emissions of the hardware object in the running state. According to the present application, the energy-saving state of the hardware object is written into the information of the DPP, such that a user (e.g., operations and maintenance personnel) can use the DPP to accurately identify the energy-saving state of the hardware object in real time.

Description

Data processing method, device, system and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on January 19, 2024, with application number 202410084473.8 and application name “Data processing methods, devices, systems and related equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of computer technology, and in particular to a data processing method, apparatus, system and related equipment. Background Art

[0003] The Digital Product Passport (DPP) is used to record product-related information so that it can be electronically registered, processed and shared between supply chain companies, management agencies and consumers.

[0004] With the rapid development of energy-saving and emission-reduction technologies in the IT industry, driven by policies promoting digitalization, intelligence, and green development, users are increasingly focusing on energy conservation, electricity cost reduction, environmental protection, recycling, and carbon reduction assessments for hardware (such as IT equipment). Understanding the precise energy-saving status of hardware allows users to manage and improve equipment, or determine whether hardware needs to be retired or replaced.

[0005] Therefore, there is an urgent need for a method for identifying the energy-saving state of a hardware object.

[0006] Summary of the Invention

[0007] This application provides a data processing method that writes the energy-saving status of a hardware object into the information of a digital product passport. This method can provide energy-saving status monitoring and energy-saving opportunity identification for the hardware object during actual operation, allowing users (such as operation and maintenance personnel) to accurately identify the energy-saving status of the hardware object. In addition, this application also provides a corresponding device, data processing system, computer-readable storage medium, and computer program product.

[0008] In a first aspect, the present application provides a data processing method, specifically including: obtaining the operating status of a hardware object; obtaining information of a digital product passport DPP of the hardware object based on the operating status of the hardware object, the information of the digital product passport DPP including at least one of the following target information: the energy-saving health of the hardware object, the energy-saving health is used to measure the energy efficiency level of the hardware object in the operating state; the energy-saving amount of the hardware object, the energy-saving amount is used to measure at least one of the power consumption savings and energy-saving and carbon reduction amounts of the hardware object in the operating state; or, the energy consumption of the hardware object, the energy consumption is used to measure at least one of the power consumption level and carbon consumption of the hardware object in the operating state.

[0009] In the embodiments of the present application, the energy-saving status of the hardware object is written into the information of the digital product passport. This can provide energy-saving status monitoring and energy-saving and carbon-reduction calculation of the hardware object during actual operation, allowing users (such as operation and maintenance personnel) to accurately identify the energy-saving status of the hardware object, supporting the design and optimization of operation and maintenance energy-saving measures. At the same time, it can provide technical support for the management of the computing power and efficiency of IT equipment in data centers and supercomputing centers. In addition, the energy-saving and carbon-reduction indicators can provide basic data for the carbon emissions and carbon reduction during the operation phase of the user's computing equipment, facilitating the calculation of annual indicators.

[0010] Among them, carbon emissions can refer to the amount of carbon dioxide emissions generated per unit area or per unit energy per unit time.

[0011] In a possible implementation, the information of the digital product passport DPP specifically includes the identifier of the hardware object and the mapping relationship of the target information.

[0012] In a possible implementation, the power consumption saving is specifically the power consumption saving of the hardware object in the running state compared to the first state; or

[0013] The energy saving and carbon reduction amount is specifically the energy saving and carbon reduction amount of the hardware object in the running state compared to the first state; or

[0014] The power consumption level is specifically the power consumption of the hardware object in the running state compared to the power consumption in the second state; or

[0015] The carbon consumption is specifically the carbon consumption of the hardware object in the running state compared to the second state;

[0016] The first state is the highest power consumption state of the hardware object (also referred to as non-energy-saving state power consumption), and the second state is the lowest power consumption state of the hardware object.

[0017] In one possible implementation, the operating status is an energy-saving measure enabled on the hardware object; obtaining the information of the digital product passport DPP of the hardware object based on the operating status of the hardware object includes: obtaining the energy-saving amount of the hardware object based on the energy-saving amount generated by the enabled energy-saving measure.

[0018] In one possible implementation, the operating state is the actual power consumption of the hardware object;

[0019] The energy-saving health is calculated based on the actual power consumption and the minimum power consumption at which the hardware object can operate when in the operating state.

[0020] In one possible implementation, the energy savings generated by enabling each energy-saving measure can be predetermined, and then a mapping relationship between the energy-saving measure and the corresponding energy savings can be established. This mapping relationship can be in the form of a table, a graph, a function mapping, an AI model, etc., which is not limited in the embodiments of this application. After obtaining the energy-saving measures enabled for the hardware object, the energy savings of the enabled energy-saving measures can be obtained.

[0021] In one possible implementation, the operating status is the actual power consumption of the hardware object; and obtaining the digital product passport DPP information of the hardware object based on the operating status of the hardware object includes: obtaining the digital product passport DPP information of the hardware object based on the actual power consumption and the minimum power consumption or maximum power consumption at which the hardware object can operate when in the operating state.

[0022] In a possible implementation, the energy saving and carbon reduction amount is calculated based on the power consumption saving amount and the carbon emission coefficient; or, the carbon consumption amount is calculated based on the power consumption level and the carbon emission coefficient.

[0023] The carbon emission coefficient is calculated by matching energy consumption with carbon dioxide emissions. Different energy sources have different carbon emission coefficients. For example, coal has a higher carbon emission coefficient, while natural gas has a lower carbon emission coefficient.

[0024] In one possible implementation, the hardware object includes multiple electronic devices, the energy-saving health is the aggregation of the energy-saving health of the multiple electronic devices; the energy saving amount is the aggregation of the energy saving amount of the multiple electronic devices; and the energy consumption is the aggregation of the energy consumption of the multiple electronic devices.

[0025] In one possible implementation, the method further includes:

[0026] Present information about the Digital Product Passport (DPP); or

[0027] Storing information of the digital product passport DPP; or,

[0028] Send the information of the digital product passport DPP to the server or terminal device; or,

[0029] When the energy-saving health is lower than a first threshold, the energy-saving amount is lower than a second threshold, or the energy consumption is higher than a third threshold, an energy-saving operation is performed on the hardware object.

[0030] In a possible implementation, before obtaining the first information, the method further includes: receiving an instruction, where the instruction is used to instruct generation or query of information of a digital product passport DPP of the hardware object.

[0031] In a second aspect, the present application provides a data processing device, comprising:

[0032] The acquisition module is used to obtain the operating status of the hardware object;

[0033] A processing module is configured to obtain information of a digital product passport (DPP) of the hardware object according to the operating state of the hardware object, wherein the information of the digital product passport (DPP) includes at least one of the following target information:

[0034] The energy-saving health of the hardware object, wherein the energy-saving health is used to measure the energy efficiency level of the hardware object in the operating state;

[0035] The energy saving amount of the hardware object, the energy saving amount is used to measure at least one of the power consumption saving amount and the energy saving and carbon reduction amount of the hardware object in the operating state; or

[0036] The energy consumption of the hardware object is used to measure at least one of a power consumption level and a carbon consumption of the hardware object in the operating state.

[0037] In a possible implementation manner, the information of the digital product passport DPP specifically includes the identifier of the hardware object and the mapping relationship of the target information.

[0038] In a possible implementation manner, the power consumption saving is specifically the power consumption saving of the hardware object in the running state compared to the first state; or

[0039] The energy saving and carbon reduction amount is specifically the energy saving and carbon reduction amount of the hardware object in the running state compared to the first state; or

[0040] The power consumption level is specifically the power consumption of the hardware object in the running state compared to the power consumption in the second state; or

[0041] The carbon consumption is specifically the carbon consumption of the hardware object in the running state compared to the second state;

[0042] The first state is a highest power consumption state of the hardware object, and the second state is a lowest power consumption state of the hardware object.

[0043] In a possible implementation, the operating state is an energy-saving measure enabled on the hardware object;

[0044] The processing module is specifically used to:

[0045] The energy saving amount of the hardware object is obtained according to the energy saving amount generated by the enabled energy saving measures.

[0046] In a possible implementation, the operating state is the actual power consumption of the hardware object;

[0047] The processing module is specifically used to:

[0048] According to the actual power consumption and the lowest power consumption or the highest power consumption at which the hardware object can operate when in the operating state, information on the digital product passport DPP of the hardware object is obtained.

[0049] In this way, based on the actual power consumption of the hardware object and the minimum power consumption or maximum power consumption when in the operating state, the energy-saving state used to measure the energy-saving level of the hardware object can be calculated, thereby realizing real-time online evaluation of the energy-saving level of the hardware object.

[0050] In a possible implementation, the energy saving and carbon reduction amount is calculated based on the power consumption saving amount and the carbon emission coefficient; or, the carbon consumption amount is calculated based on the power consumption level and the carbon emission coefficient.

[0051] In one possible implementation, the hardware object includes multiple electronic devices, the energy-saving health is the aggregation of the energy-saving health of the multiple electronic devices; the energy saving amount is the aggregation of the energy saving amount of the multiple electronic devices; and the energy consumption is the aggregation of the energy consumption of the multiple electronic devices.

[0052] In a possible implementation, the device further includes:

[0053] A presentation module, configured to present information of the digital product passport DPP;

[0054] Or, the processing module is further configured to store information of the digital product passport DPP;

[0055] Or, a transceiver module, used to send the information of the digital product passport DPP to a server or a terminal device;

[0056] Alternatively, the processing module is further configured to execute an energy-saving operation on the hardware object when the energy-saving health is lower than a first threshold, the energy-saving amount is lower than a second threshold, or the energy consumption is higher than a third threshold.

[0057] In a possible implementation, the transceiver module is further configured to receive an instruction before obtaining the first information, where the instruction is used to instruct generation or query of information of the digital product passport DPP of the hardware object.

[0058] In a third aspect, the present application provides a data processing system, which includes a processor, a memory and a display (it should be understood that the display is optional). The processor and the memory communicate with each other. The processor is used to execute instructions stored in the memory so that the data processing system performs the data processing method in the first aspect or any one of the implementations of the first aspect. It should be noted that the memory can be integrated into the processor or can be independent of the processor. The data processing system may also include a bus. The processor is connected to the memory via a bus. The memory may include a readable memory and a random access memory.

[0059] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computing device, the computing device executes the operating steps of the data processing method described in the first aspect or any implementation of the first aspect.

[0060] In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a computing device, enables the computing device to execute the operational steps of the data processing method described in the first aspect or any one of the implementations of the first aspect.

[0061] Based on the implementation methods provided in the above aspects, this application can also be further combined to provide more implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] FIG1A is a schematic diagram of an exemplary application scenario provided by this application;

[0063] FIG1B is a schematic diagram of an exemplary application scenario provided by this application;

[0064] FIG1C is a schematic diagram of an exemplary application scenario provided by this application;

[0065] FIG2 is a flow chart of a data processing method provided by the present application;

[0066] FIG3 is a schematic diagram of calculating the energy-saving health of a device based on the energy-saving health of an electronic device provided by the present application;

[0067] FIG4 is a flow chart of the method for training an AI model provided in this application;

[0068] FIG5 is a schematic diagram of determining the minimum power consumption in each historical operating state by state projection provided by the present application;

[0069] FIG6 is a schematic diagram of a curve showing a change in the energy-saving health of a hardware object provided by this application;

[0070] FIG7 is a schematic structural diagram of a data processing device provided by the present application;

[0071] FIG8 is a schematic structural diagram of a data processing system provided by this application. DETAILED DESCRIPTION

[0072] The following describes the embodiments of the present application in conjunction with the accompanying drawings. The terms used in the implementation methods of the present application are only used to explain the specific embodiments of the present application and are not intended to limit the present application.

[0073] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0074] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, and this is merely a way of distinguishing the objects of the same attributes when describing them in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0075] As used herein, the terms "substantially," "about," and similar terms are used as terms of approximation, not as terms of degree, and are intended to take into account the inherent deviations in measurements or calculations that one of ordinary skill in the art would recognize. Furthermore, the use of "may" when describing embodiments of the present application refers to "one or more possible embodiments." As used herein, the terms "use," "using," and "used" may be considered synonymous with the terms "utilize," "utilizing," and "utilized," respectively. Additionally, the term "exemplary" is intended to refer to an example or illustration.

[0076] The technical solution in this application will be described below in conjunction with the drawings of this application.

[0077] See Figure 1A, which is a schematic diagram of an exemplary application scenario provided by the present application. In the application scenario shown in Figure 1A, there are multiple levels of hardware objects. The levels of hardware objects can be divided, for example, according to the granularity of the product form to which the hardware object belongs in the actual application scenario, or can be divided according to the size of the service scope of the object, etc., and this is not limited. Figure 1A is illustrated by taking three levels of hardware objects as an example, wherein the hardware objects of the first level are electronic devices, such as the central processing unit (CPU) 101, hard disk 102, fan 103, etc. shown in Figure 1A. Different electronic devices can communicate through a bus, such as through a compute express link (CXL) bus, a peripheral component interconnect express (PCIE) bus, an inter-integrated circuit (I2C) bus, a unified bus (UB or Ubus), etc. One or more buses communicate, etc. The second-level hardware objects are devices, such as device 201, device 202, and device 203 shown in Figure 1A. The devices can be computing servers, storage servers, or terminals, etc. The second-level hardware objects can include multiple first-level hardware objects, such as device 201 can include CPU 101, hard disk 102, fan 103, etc. Different devices can communicate through a wired network or a wireless network. The third-level hardware objects are clusters, such as cluster 200 shown in Figure 1A, which can include multiple devices. Figure 1A uses cluster 200 including device 201, device 202, and device 203 as an example for illustrative purposes. In actual application, cluster 200 can be a data center including multiple computing devices, or it can be an availability zone (AZ) including multiple computing devices, or it can be a partition (region) including multiple computing devices, etc. This embodiment is not limited to this. Each AZ includes a data center or multiple geographically close data centers, and generally a region can include multiple AZs. When the third-level hardware objects include multiple clusters, different clusters can communicate through a wired network or a wireless network.

[0078] With the rapid development of energy-saving and emission-reduction technologies in the IT industry, driven by policies promoting digitalization, intelligence, and green development, users are increasingly focusing on energy conservation, electricity cost reduction, environmental protection, recycling, and carbon reduction assessments for hardware (such as IT equipment). Understanding the precise energy-saving status of hardware allows users to manage and improve equipment, or determine whether hardware needs to be retired or replaced.

[0079] Therefore, there is an urgent need for a method for identifying the energy-saving state of a hardware object.

[0080] Based on this, the present application adds a data processing device 300 in the application scenario shown in Figure 1A. The data processing device 300 can calculate the energy-saving status of the hardware object (such as energy-saving health or energy-saving amount) based on the information of the hardware object (such as information related to power consumption), and write the energy-saving status into the information of the digital product passport (DPP). Users can obtain the energy-saving status of the hardware device by reading the DPP information.

[0081] The hardware object can be an object at any level in FIG1A . Furthermore, the data processing apparatus 300 utilizes information about the hardware object (e.g., information related to power consumption) to evaluate its energy-saving status without imposing additional load on the hardware object. This effectively reduces the time required to evaluate the energy-saving status and the resource usage of the hardware object, thereby effectively reducing the time cost and resource consumption required for data processing.

[0082] Exemplarily, the data processing device 300 can be implemented by software, for example, by at least one of a virtual machine, a container, and a computing engine. Alternatively, the data processing device 300 can be implemented by a physical device including a processor, wherein the processor can be a CPU, an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), a system on chip (SoC), a software-defined infrastructure (SDI) chip, an artificial intelligence (AI) chip, a data processing unit (DPU), or any combination thereof. Furthermore, the number of processors included in the data processing device 300 can be one or more, and the types of processors included can be one or more. The number and type of processors can be set according to the business requirements of the actual application, and this embodiment does not limit this.

[0083] It is worth noting that the application scenario shown in FIG1A is merely an example. In actual application, the above data processing method can also be applied to other application scenarios. For example, in other possible application scenarios, device 101 may include a larger number of electronic devices, or cluster 200 may include a larger number of devices, or device 101 may be a terminal device such as a smartphone or an intelligent terminal (such as an iPad), and this embodiment is not limited to this.

[0084] Next, the relationship between the data processing device 300 and the deployment location of the hardware object is introduced:

[0085] Referring to FIG1B , FIG1B is a schematic diagram of an architecture, in which the data processing device 300 can be deployed in a hardware object or deployed on the end side in an associated manner. For example, the data processing device 300 can be a software module of the hardware object, or the data processing device 300 can be deployed separately from the hardware object.

[0086] For example, a user can interact with a hardware object (or directly with the data processing device 300) to generate instructions to trigger the data processing device 300 to calculate the energy-saving status of the hardware object (such as energy-saving health or energy-saving amount) based on the information of the hardware object (such as information related to power consumption), and write the energy-saving status into the information of the digital product passport (DPP). The hardware object can present the DPP information, store the DPP information, or send the DPP information to the cloud side.

[0087] 1C , which is a schematic diagram of an architecture, wherein the data processing device 300 may be deployed on the cloud side, for example, as a software module of a cloud side server.

[0088] For example, a user can trigger the hardware device to interact with the cloud-side server, so that the cloud-side server generates instructions and triggers the data processing device 300 to calculate the energy-saving status of the hardware object (such as energy-saving health or energy-saving amount) based on the information of the hardware object (such as information related to power consumption), and write the energy-saving status into the information of the digital product passport (DPP). The server can transmit the DPP information to the end side or store the DPP information.

[0089] For ease of understanding, an embodiment of the data processing method provided in this application is described below in conjunction with the accompanying drawings.

[0090] Referring to FIG. 2 , FIG. 2 is a flow chart illustrating a data processing method provided in an embodiment of the present application. This method can be applied to the application scenarios shown in FIG. 1A to FIG. 1C , or can be applied to other applicable application scenarios. The hardware object whose energy efficiency level is being evaluated can be the electronic device, equipment, or cluster shown in FIG. 1A .

[0091] The data processing method shown in FIG2 may be executed by the data processing device 300 in FIG1A , and the method may specifically include:

[0092] S201: Obtain the operating status of the hardware object.

[0093] In one possible implementation, step 201 may be triggered by a user. For example, the user may trigger the generation of an instruction for instructing the generation or query of information about the digital product passport (DPP) for the hardware object. Furthermore, the data processing device may obtain the operating status of the hardware object based on the received instruction.

[0094] The operating state may be a state related to the power consumption of the hardware object.

[0095] In a possible implementation, the operating state may be at least one energy-saving measure enabled on the hardware object.

[0096] Among them, the energy-consuming devices of the hardware objects can have certain energy-saving measures (as shown in Table 1, which shows the main energy-consuming devices of the server and the corresponding energy-saving measures). For example, for the CPU, energy saving can be achieved by frequency modulation, sleep or core shutdown.

[0097] Table 1

[0098] After turning on each energy-saving measure, a certain amount of energy saving can be generated for the hardware object. The data processing device 300 can obtain the energy saving generated by the energy-saving measures turned on by the hardware object. The so-called energy saving here can be the energy saving generated when the energy-saving measure is turned on compared to when the energy-saving measure is not turned on (the status of other energy-saving measures of the hardware object remains unchanged), for example, the energy saving generated when the CPU is in sleep mode compared to when it is not in sleep mode.

[0099] It should be understood that the energy saving here may also be related to some operating states of the hardware object (for example, but not limited to one or more parameters such as CPU utilization, memory utilization, memory read and write rates, hard disk read and write rates, device sensor temperature, etc.). For example, for hardware objects in different operating states, the energy saving amount generated by enabling the same energy-saving measure may be different.

[0100] Next, we will introduce how to obtain the energy savings generated by turning on energy-saving measures.

[0101] In one possible implementation, the energy saving amount generated by enabling each energy-saving measure can be determined in advance and then a mapping relationship between the energy-saving measure and the corresponding energy saving amount can be constructed. The mapping relationship can be in the form of a table, a graph, a function mapping, an AI model, etc., and the embodiments of the present application are not limited thereto.

[0102] After the energy-saving measures enabled by the hardware object are acquired, the energy-saving amount of the enabled energy-saving measures may be acquired.

[0103] In a possible implementation, the operating state of the hardware object may be actual power consumption, such as by using a sensor to collect data such as actual power consumption.

[0104] The actual power consumption of the hardware object may be, for example, the power when the hardware object is running, or the voltage and current when the hardware object is running, or other data used to characterize the power consumption of the hardware object.

[0105] In one possible implementation, the minimum power consumption or maximum power consumption at which the hardware object can operate can be determined based on the operating state of the hardware object. The minimum power consumption or maximum power consumption can be obtained by reasoning based on the operating state of the hardware object through a target model, and the target model is obtained using the operating state and historical power consumption data of the hardware object in a historical time period. For example, the target model can be an artificial intelligence (AI) model or can be obtained by fitting based on the operating state and historical power consumption data in a historical time period.

[0106] Next, we will take the lowest power consumption as an example to illustrate:

[0107] After obtaining the operating state of the hardware object, the data processing device 300 can further obtain the minimum power consumption at which the hardware object can operate in the operating state, that is, the minimum power consumption that can be achieved, which can also be called ideal power consumption. The minimum power consumption that the hardware object can achieve in different operating states may be different. For example, the minimum power consumption that the hardware object can achieve in operating state 1 is 100w (watts), while the minimum power consumption that can be achieved in operating state 2 is 150w. Alternatively, the minimum power consumption that the hardware object can achieve in some operating states may be the same. For example, among the 10 different operating states that the hardware object may be in, the minimum power consumption that the hardware object can achieve in the first operating state is the same as the minimum power consumption that the hardware object can achieve in the tenth operating state, but is different from the minimum power consumption that the hardware object can achieve in the remaining eight operating states. For ease of understanding, this embodiment provides the following implementation examples for determining ideal power consumption.

[0108] In a first possible implementation, the data processing device 300 can determine the ideal power consumption corresponding to the hardware object in the current operating state based on the power consumption data of the hardware object in a historical time period. That is, the power consumption of the hardware object in the past period of time can be used to guide the minimum power consumption that the hardware object can achieve.

[0109] For example, the data processing device 300 can obtain the ideal power consumption through AI model reasoning. In specific implementation, the data processing device 300 can obtain a trained AI model, which can be, for example, a model built based on a neural network model, such as a model built based on a recurrent neural network (RNN), a deep neural network (DNN), etc.; or, the AI ​​model can be a regression tree model, a support vector machine (SVM) model, etc. The specific implementation method of the AI ​​model is not limited in this embodiment. Among them, the training sample of the AI ​​model can be, for example, the lowest power consumption achieved by the hardware object in various operating states during a historical time period. For the specific implementation process of training the AI ​​model, please refer to the description below and will not be repeated here. After obtaining the operating status of the hardware object, the data processing device 300 can input the obtained operating status into the AI ​​model, use the AI ​​model to perform reasoning based on the input operating status, and obtain the ideal power consumption corresponding to the operating state output by the AI ​​model.

[0110] Furthermore, the data processing device 300 can also obtain the energy-saving control parameters of the hardware object in this operating state, and input the operating state and energy-saving control parameters of the hardware object into the AI ​​model, which then infers the ideal power consumption of the hardware object. In this way, reasoning based on multiple dimensions of data such as the operating state and energy-saving control parameters can further improve the accuracy of the determined ideal power consumption, thereby further improving the accuracy of subsequent evaluations of the hardware object's energy efficiency level.

[0111] In a second possible implementation, the data processing device 300 may be configured with a mapping relationship between the operating status of a hardware object and its ideal power consumption. For example, this mapping relationship may be pre-configured in the data processing device 300 by a technician. Thus, after obtaining the operating status of the hardware object, the data processing device 300 can determine the ideal power consumption corresponding to this operating status by searching this mapping relationship. For example, taking the hardware object as a CPU, the operating status of the hardware object may be, for example, CPU utilization. Furthermore, the data processing device 300 may be configured with a mapping relationship between CPU utilization and ideal power consumption, such as configuring an ideal power consumption of 100W when the CPU utilization is 10% and 300W when the CPU utilization is 50%. Thus, after obtaining the current CPU utilization, the data processing device 300 can determine the ideal power consumption corresponding to this CPU utilization by searching this configured mapping relationship.

[0112] Exemplarily, the mapping relationship in the data processing device 300 can be determined based on the lowest power consumption achieved by the hardware object in various operating states during a historical time period. Taking the hardware object as a CPU as an example, assuming that in the past 30 days, the CPU utilization rate at multiple times was 10%, but the power consumption generated by the CPU at different times during these multiple times was 100w, 150w, and 300w, respectively, then the lowest power consumption (100w) can be determined from the multiple power consumptions, and a mapping relationship between the CPU utilization rate (10%) and the lowest power consumption (100w) can be established. In actual application, the mapping relationship in the data processing device 300 can also be determined by other means, and this embodiment does not limit this.

[0113] The above-mentioned implementation methods of determining the ideal power consumption are merely exemplary. In other embodiments, the data processing device 300 may also adopt other methods to determine the ideal power consumption of the object in the running state.

[0114] S202: Obtaining information of the digital product passport DPP of the hardware object according to the operating status of the hardware object.

[0115] The information of the digital product passport DPP may include target information, and the target information may include the energy-saving health of the hardware object, where the energy-saving health is used to measure the energy efficiency level of the hardware object in the operating state.

[0116] Illustratively, the data processing device 300 may calculate the energy-saving health of the hardware object based on actual power consumption and ideal power consumption (ie, minimum power consumption). The energy-saving health is used to measure the energy efficiency level of the hardware object.

[0117] It can be understood that the ideal power consumption indicates the lowest power consumption that the hardware object can achieve in the current operating state. The lowest power consumption is the actual power consumption in the historical time period, that is, the lowest power consumption actually achieved by the hardware object during operation in the past time period is taken as the theoretical value; and the actual power consumption is the power consumption actually generated by the hardware object in the current operating state. Therefore, based on the actual power consumption and ideal power consumption of the hardware object, the energy efficiency level of the hardware object in the current operating state can be reflected. Specifically, when the deviation between the actual power consumption and the ideal power consumption is small, it indicates that the current power consumption of the hardware object is small and it is in a better energy-saving state; accordingly, the energy efficiency level of the hardware object is currently in a higher state. When the deviation between the actual power consumption and the ideal power consumption is large, the actual power consumption is usually much greater than the ideal power consumption. At this time, the current power consumption of the hardware object is too high and there is a lot of energy waste; accordingly, the energy efficiency level of the hardware object is currently in a lower state.

[0118] In this embodiment, the energy-saving health level can be used to measure the energy efficiency level of a hardware object. The energy-saving health level is used to indicate the energy-saving effect of the hardware object, that is, it can be used to indicate the energy efficiency level of the hardware object. The greater the energy-saving health level, the higher the energy efficiency level of the hardware object; the smaller the energy-saving health level, the lower the energy efficiency level of the hardware object.

[0119] As an implementation example, the data processing device 300 may calculate the energy-saving health of the hardware object based on the following formula (1).

[0120] Among them, h1 is the energy-saving health of the hardware object; p is the actual power consumption; p ★ is the ideal power consumption.

[0121] Alternatively, the data processing device 300 may also calculate the energy-saving health of the hardware object based on the following formula (2).

[0122] In this way, the data processing device 300 can calculate the energy-saving health degree and realize online real-time evaluation of the energy efficiency level of the hardware object.

[0123] In one possible implementation, the data processing device 300 can calculate the energy saving degree of the hardware object based on the actual power consumption and the maximum power consumption, and the energy saving amount is used to measure at least one of the power consumption saving and energy saving and carbon reduction of the hardware object in the operating state.

[0124] The power consumption saving amount may be the power consumption saving amount of the hardware object in the running state compared to the power consumption in the first state, where the first state is the highest power consumption state of the hardware object.

[0125] The energy saving and carbon reduction amount is specifically the energy saving and carbon reduction amount of the hardware object in the running state compared with the first state, and the first state is the highest power consumption state of the hardware object.

[0126] Taking the energy saving amount as an example to measure the power saving amount of the hardware object in the running state, the difference between the actual power consumption and the non-energy-saving state power consumption can describe the power saving amount, and the accumulation of the power saving amount over time can be used to calculate the power saving amount.

[0127] For example, the data processing device 300 may calculate the power saving amount of the hardware object based on the following formulas (3) and (4).

[0128] Where h2 is the power saving of the hardware object; p i is the actual power consumption; is the non-energy-saving state power consumption, and T is the time. For example, T can be the number of days in a year: 365*24.

[0129] In one possible implementation, the energy saving amount generated by the energy-saving measures enabled by the hardware object can be obtained, and then the fusion of the energy saving amounts generated by the enabled energy-saving measures (for example, the sum of the results) can describe the power consumption savings, that is, the power consumption savings can be determined by the energy-saving benefits brought by the energy-saving measures enabled by the hardware object.

[0130] For example, the power savings generated by the energy-saving measures enabled by the hardware object can be obtained, and the power consumption savings can be calculated by accumulating the power savings over time.

[0131] For example, the data processing device 300 may calculate the power saving amount of the hardware object based on the following formula (3).

[0132] Where h2 is the power saving of the hardware object; P k The aggregation (e.g., summation) of the energy savings resulting from the activated energy-saving measures.

[0133] Taking the energy saving amount as an example of measuring the energy saving and carbon reduction amount of the hardware object in the operating state, the difference between the actual power consumption and the non-energy-saving state power consumption can describe the power saving amount, and the accumulation of power saving amount over time can be used to calculate the power consumption saving amount, and the power saving amount can be mapped to the energy saving and carbon reduction amount. For example, the energy saving and carbon reduction amount can be calculated based on the power consumption saving amount and the carbon emission coefficient.

[0134] For example, the data processing device 300 can calculate the energy saving and carbon reduction amount of the hardware object based on the following formula (5).

[0135] Among them, h3 is the energy saving and carbon reduction of the hardware object; P k is the power saving, N is the local carbon emission coefficient of the equipment, and the weight of carbon dioxide emitted per kilowatt-hour of electricity used.

[0136] In a possible implementation, the data processing device 300 may calculate the energy consumption of the hardware object based on the actual power consumption and the minimum power consumption, where the energy consumption is used to measure at least one of the power consumption level and carbon consumption of the hardware object in the operating state.

[0137] The power consumption level may be the power consumption of the hardware object in the running state compared to the power consumption in the second state, and the second state is the lowest power consumption state of the hardware object.

[0138] The carbon consumption is specifically the carbon consumption of the hardware object in the running state compared to the carbon consumption in the second state, and the second state is the lowest power consumption state of the hardware object.

[0139] Taking the energy consumption as an example of measuring the power consumption level of the hardware object in the operating state, the difference between the actual power consumption and the ideal power consumption (ie, minimum power consumption) can describe the power consumption level, and the power consumption level can be calculated by accumulating the power consumption over time.

[0140] For example, the data processing device 300 may calculate the power consumption level of the hardware object based on the following formulas (6) and (7).

[0141] Among them, h4 is the power consumption level of the hardware object; p i is the actual power consumption; is the ideal power consumption (that is, the minimum power consumption), and T is the time. For example, T can be the number of days in a year: 365*24.

[0142] Taking the energy consumption as an example of being used to measure the carbon consumption of the hardware object in the operating state, the difference between the actual power consumption and the ideal power consumption (that is, the minimum power consumption) can describe the power consumption. The accumulation of power consumption over time can be used to calculate the power consumption level, and the power consumption level can be mapped to carbon consumption. For example, the carbon consumption can be calculated based on the power consumption level and the carbon emission coefficient.

[0143] For example, the data processing device 300 may calculate the carbon consumption of the hardware object based on the following formula (8).

[0144] Among them, h5 is the carbon consumption of the hardware object; P k is the power consumption, N is the carbon emission coefficient of the local area where the equipment is used, and the weight of carbon dioxide emitted per kilowatt-hour of electricity.

[0145] After obtaining the target information, the target information can be written into the information of the digital product passport (DPP). The DPP information can include the identification of the hardware object and the mapping relationship between the second information. The data processing device 300 can also provide a reading interface for users and support remote periodic upload to the cloud for aggregation and management.

[0146] A schematic diagram of DPP information can be found in Table 2:

[0147] Table 2

[0148] In the embodiments of the present application, the energy-saving status of the hardware object is written into the information of the digital product passport. This can provide energy-saving status monitoring and energy-saving and carbon-reduction calculation of the hardware object during actual operation, allowing users (such as operation and maintenance personnel) to accurately identify the energy-saving status of the hardware object, supporting the design and optimization of operation and maintenance energy-saving measures. At the same time, it can provide technical support for the management of the computing power and efficiency of IT equipment in data centers and supercomputing centers. In addition, the energy-saving and carbon-reduction indicators can provide basic data for the carbon emissions and carbon reduction during the operation phase of the user's computing equipment, facilitating the calculation of annual indicators.

[0149] Furthermore, the data processing device 300 may further perform the following steps:

[0150] Present information about the Digital Product Passport (DPP); or

[0151] Storing information of the digital product passport DPP; or,

[0152] Send the information of the digital product passport DPP to the server or terminal device; or,

[0153] When the energy-saving health is lower than a first threshold, the energy-saving amount is lower than a second threshold, or the energy consumption is higher than a third threshold, an energy-saving operation is performed on the hardware object.

[0154] In this way, users (such as operation and maintenance personnel of the hardware object) can learn the energy-saving status of the hardware object based on the presented energy-saving status of the hardware object, so that users can understand the energy-saving situation of the hardware object during operation.

[0155] In this way, when the energy-saving state of a hardware object is lower than a threshold, the data processing device 300 may perform an energy-saving operation for the hardware object.

[0156] Taking energy-saving health as an example, it can be understood that when the energy-saving health of a hardware object is greater than or equal to a threshold (such as 85%), it indicates that the hardware object is in a good energy-saving state and no further energy-saving operations are required for the hardware object. When the energy-saving health of a hardware object is less than the threshold, it indicates that the hardware object is in a poor energy-saving state, that is, there is a lot of energy waste during the operation of the hardware object. In this case, the data processing device 300 can perform energy-saving operations on the hardware object based on the energy-saving health to reduce the energy consumption generated by the hardware object during operation and improve the energy efficiency level of the hardware object.

[0157] In specific implementation, the data processing device 300 can obtain the energy-saving control parameters of the hardware object, generate new energy-saving control parameters according to the energy-saving health and the energy-saving control parameters, and perform energy-saving operations on the hardware object based on the energy-saving control parameters.

[0158] For example, assume the hardware object is a CPU, the CPU's energy-saving health is 60%, the energy-saving control parameter is the CPU frequency, and the CPU's current operating frequency is 3 GHz (gigahertz). Because the CPU's energy-saving health is below 85% (the threshold), the data processing device 300 can calculate, based on the energy-saving health and the current CPU frequency of 3 GHz, that the CPU's desired frequency after frequency reduction is 2 GHz. Finally, the data processing device 300 reduces the CPU's frequency to the calculated 2 GHz. The CPU's operating frequency indicates the number of synchronization pulses generated by the CPU in one second and determines the CPU's computing speed. Generally, a higher CPU frequency indicates a faster CPU computing speed and, accordingly, higher CPU energy consumption. Conversely, a lower CPU frequency indicates a slower CPU computing speed and lower CPU energy consumption. For another example, if the CPU's energy-saving health is 80%, the data processing device 300 can reduce the CPU's frequency to 2.8 GHz, based on the energy-saving health and the current CPU frequency of 3 GHz.

[0159] In this way, the data processing device 300 can avoid missing energy-saving points during the operation of the hardware object by performing real-time evaluation of the hardware object and automatically executing corresponding energy-saving operations, and can perform energy-saving operations on the hardware object in a timely manner, thereby improving the energy-saving effect on the hardware object and ensuring that the energy efficiency level of the hardware object is always maintained at a high level.

[0160] It is worth noting that when the hardware object is specifically a second-level device or a third-level cluster, the data processing device 300 can also calculate the energy-saving health corresponding to the device or the energy-saving status corresponding to the cluster based on a similar method as mentioned above, so as to realize real-time evaluation of the energy-saving level of the device or cluster.

[0161] In a specific implementation, the data processing device 300 can calculate the energy-saving states corresponding to the multiple electronic devices included in a single device of the second level based on the process described in the embodiment shown in FIG2 above, assuming that a single device includes N electronic devices (N is a positive integer). Taking the calculation of energy-saving health as an example, for example, as shown in FIG3 , the device may include multiple electronic devices such as a CPU, memory, hard disk, fan, and power supply unit (PSU). Moreover, during the operation of the device, the multiple electronic devices in the device are in operation and generate energy consumption. Therefore, the data processing device 300 can calculate the energy-saving state of each electronic device according to the operation state of each electronic device.

[0162] Then, the data processing device 300 performs a weighted summation on the energy-saving states corresponding to the N electronic devices to calculate the energy-saving state of the entire device, taking the energy-saving health as an example, as shown in Figure 3. For example, the data processing device 300 can calculate the energy-saving health of the entire device based on the following formula (9).

[0163] Where h2 is the energy-saving health of a single device; N is the number of electronic devices that generate energy consumption included in the device; h i is the energy-saving health of the i-th electronic device; w i is the weight corresponding to the i-th electronic component. The weights corresponding to each electronic component can be pre-set by a technician based on actual application needs, such as the importance or energy consumption percentage of each electronic component, and configured in the data processing device 300 so that the data processing device 300 can calculate the energy efficiency health of the entire device based on the weights and energy efficiency health of each electronic component. In this way, based on the energy efficiency health of the electronic components within each device, the energy efficiency health of each device at the second level can be calculated, enabling real-time assessment of the energy efficiency level of each device.

[0164] Furthermore, after obtaining the energy-saving health of each device, the data processing device 300 may also perform corresponding energy-saving operations on each device to improve the energy efficiency of each device. In specific implementation, for each device, the data processing device 300 may compare the energy-saving health of the device with a pre-set threshold. Furthermore, when the energy-saving health is greater than or equal to the threshold, the data processing device 300 may not perform the energy-saving operation. When the energy-saving health is less than the threshold, the data processing device 300 may perform energy-saving operations on each electronic component within the device based on the energy-saving health or the energy-saving health of each electronic component within the device. For example, if the device includes electronic components such as a CPU, memory, hard drive, fan, and PSU, the data processing device 300 can perform energy-saving operations such as dynamic voltage and frequency scaling (DVFS) on the CPU, hibernate, or shut down the processor core; reduce the memory refresh rate, such as from 2000MHz to 1300MHz; reduce the hard drive head speed, or switch the hard drive's operating mode to hibernation mode; reduce the fan speed; and switch the PSU's power supply mode, such as from load balancing mode to active-standby mode. In actual testing scenarios, performing energy-saving operations on devices based on energy-saving health can improve the device's energy efficiency by an average of more than 10%.

[0165] Since a third-level cluster typically includes one or more devices, the energy consumption of the cluster is the sum of the energy consumption of the one or more devices within the cluster. Therefore, the data processing device 300 can further calculate the energy-saving health of the cluster based on the calculated energy-saving health of each device.

[0166] Exemplarily, the data processing device 300 may calculate the energy-saving health of the cluster based on the following formula (10).

[0167] Among them, h3 is the energy-saving health of the cluster; M is the number of energy-generating devices included in the cluster; h j is the energy-saving health of the jth device; w j is the weight corresponding to the jth device. The weights corresponding to each device can be pre-set by technical personnel based on actual application needs, such as the importance of each device or the proportion of energy consumption. These weights can then be configured in data processing device 300 so that data processing device 300 can calculate the energy efficiency health of the entire cluster by weighted summation based on the weights of each device and the energy efficiency health. In this way, data processing device 300 can achieve real-time assessment of the energy efficiency level of the cluster.

[0168] After determining the energy-saving health of the cluster, the data processing device 300 can also perform corresponding energy-saving operations on the cluster to improve the cluster's energy efficiency. For example, the data processing device 300 can shut down some devices in the cluster or adjust some devices in the cluster from a running state to a dormant state, thereby reducing the overall energy consumption of the cluster.

[0169] In the embodiment shown in FIG2 above, it is introduced that the data processing device 300 determines the non-energy-saving power consumption of the hardware object and determines the energy-saving health or energy-saving amount based on the non-energy-saving power consumption, as well as determines the energy consumption based on the ideal power consumption. Among them, the data processing device 300 can use the pre-trained AI model to infer the ideal power consumption or non-energy-saving power consumption of the hardware object in the current operating state. Below, the process of training the AI ​​model is introduced in detail. Among them, the AI ​​model can be trained by the data processing device 300, or the AI ​​model can be obtained by training by other devices and then provided to the data processing device 300. For the convenience of description, the following ideal power consumption and the training process of the AI ​​model executed by the data processing device 300 are used as examples for exemplary explanation.

[0170] 4 , which shows a flow chart of a method for training an AI model by a data processing device 300. As shown in FIG4 , the method includes:

[0171] S401: The data processing device 300 constructs an AI model.

[0172] Exemplarily, the AI ​​model may be a model constructed based on a neural network model, such as a model constructed based on RNN, DNN, etc.; or, the AI ​​model may be a regression tree model, an SVM model.

[0173] In other embodiments, the AI ​​model may be constructed by the user and then input into the data processing device 300 , which is not limited here.

[0174] S402: The data processing device 300 collects historical operation data of the hardware object, where the historical operation data includes a historical operation state of the hardware object within a historical time period and power consumption generated by the hardware object in the historical operation state.

[0175] Furthermore, the historical operation data collected by the data processing device 300 may also include energy-saving control parameters adopted by the hardware object in the historical operation state, such as CPU frequency, memory frequency, disk speed, fan speed, power supply mode, etc.

[0176] Among them, the historical time period refers to a period of time in the past, such as the past 15 days, 30 days, 180 days, etc.

[0177] In one possible implementation, a hardware object may generate a corresponding log during operation. The log is used to record relevant parameters of the hardware object during operation, such as operating status, operating power (and energy-saving control parameters), etc. The data processing device 300 may then obtain the log generated by the hardware object during a historical time period and read the historical operating data of the hardware object from the log.

[0178] In another possible embodiment, the data processing device 300 may record relevant parameters of the hardware object during its operation, and when the amount of recorded data reaches a preset threshold or when the recording duration reaches a preset duration, the data processing device 300 stops recording data and uses the recorded data as historical operation data of the hardware object.

[0179] S403: The data processing device 300 generates a training sample based on the collected historical operation data. The training sample includes multiple historical operation states of the hardware object within a historical time period and the historical minimum power consumption corresponding to each of the multiple historical operation states.

[0180] This embodiment provides the following implementation examples for generating training samples.

[0181] In a first implementation example, the data processing device 300 can traverse historical operation data to determine multiple historical operation states that the hardware object was in during a historical time period, and further determine one or more power consumptions generated by the hardware object in each historical operation state. Different power consumptions correspond to multiple moments in the historical time period. For example, the power consumption of the hardware object at time A is 100W, the power consumption at time B is 150W, and the power consumption at time C is 300W, and these multiple energy consumptions are generated by the hardware object under the control of multiple sets of energy-saving control parameters. Then, for each historical operation state, the data processing device 300 determines the minimum power consumption that the hardware object can achieve in the historical operation state. For example, the data processing device 300 can compare the power consumption of the hardware object at time A, time B, and time C, and determine that the minimum power consumption that the hardware object can achieve in the historical operation state is 100W. In this way, the data processing device 300 can determine the minimum power consumption corresponding to each historical operation state of the hardware object. Next, the data processing device 300 uses the multiple historical operating states as model input, uses the lowest power consumption corresponding to each historical operating state as a training label of the AI ​​model, and generates training samples for the AI ​​model.

[0182] When the historical operating data obtained by the data processing device 300 also includes energy-saving control parameters, the data processing device 300 can also determine the energy-saving control parameters corresponding to the minimum power consumption after determining the minimum power consumption corresponding to each historical operating state. Thus, the data processing device 300 uses the multiple historical operating states and the energy-saving control parameters corresponding to each historical operating state as model inputs, uses the minimum power consumption corresponding to each historical operating state as a training label for the AI ​​model, and generates training samples for the AI ​​model.

[0183] In the second implementation example, each historical operation data acquired by the data processing device 300 includes a historical operation state and an energy-saving control parameter, as shown in the following formula (11).

[0184] Among them, s i is the i-th historical running data; is the i-th historical running state; is the i-th energy-saving control parameter.

[0185] Then, the data processing device 300 may project the historical operation data having the same historical operation state to obtain a projected state, as shown in the following formula (12).

[0186] in, It is the historical running data of the projection status.

[0187] Next, the data processing device 300 can traverse and compare multiple power consumptions with the same projection state based on the following formula (13) to determine the lowest power consumption in each projection state, that is, to determine the lowest power consumption that the hardware object can achieve in each historical operating state, as shown in FIG5 .

[0188] in, is the lowest power consumption that can be achieved in the i-th historical operating state. In this embodiment, the power consumption of the hardware object is characterized by the power of the hardware object. In other embodiments, it can also be characterized by parameters such as voltage and current, which are not limited here.

[0189] Finally, the data processing device 300 can determine the minimum power consumption corresponding to each historical operating state of the hardware object, and use the multiple historical operating states as model inputs, and use the minimum power consumption corresponding to each historical operating state as the training label of the AI ​​model to generate training samples for the AI ​​model. Alternatively, multiple historical operating states and the energy-saving control parameters corresponding to each historical operating state can be used as model inputs, and the minimum power consumption corresponding to each historical operating state can be used as the training label of the AI ​​model to generate training samples for the AI ​​model.

[0190] In this embodiment, the data processing device 300 generates training samples based on the historical operating data of the hardware object as an example. In other embodiments, the data processing device 300 may also generate training samples based on other data or based on other methods. For example, training samples may be generated based on test data, or the ideal power consumption of the hardware object in each operating state may be set by a technician based on experience.

[0191] S404: The data processing device 300 trains the constructed AI model using training samples.

[0192] In specific implementation, the data processing device 300 can input the historical operating status (and energy-saving control parameters) in the training sample into the AI ​​model, and the AI ​​model will infer based on the sample input and output the ideal power consumption. Then, the data processing device 300 can compare the ideal power consumption output by the AI ​​model with the minimum power consumption in the training sample, and adjust the parameters in the AI ​​model according to the deviation between the ideal power consumption and the minimum power consumption, so as to realize the training of the AI ​​model. In this way, based on multiple groups of historical operating status and minimum power consumption data in the training sample, the AI ​​model is trained multiple times until the AI ​​model completes the training termination conditions, such as the AI ​​model converges or the number of training times reaches a preset number.

[0193] In this way, the data processing device 300 can train the AI ​​model based on the above steps S401 to S404. In this way, the data processing device 300 can use the AI ​​model to achieve real-time evaluation of the energy efficiency level of the hardware object.

[0194] It is worth noting that when the hardware object is specifically an electronic device, the data processing device 300 can train different AI models for different electronic devices, such as training AI model 1 for the CPU and AI model 2 for the memory, so as to use different AI models to infer the ideal power consumption of different electronic devices.

[0195] Furthermore, when the training samples of the AI ​​model are generated based on the historical operating data of the hardware object within a historical time period, the minimum power consumption of the hardware object in some historical operating states may not be the minimum power consumption that the hardware object can actually achieve in these historical operating states. Therefore, when the data processing device 300 uses the AI ​​model to infer the ideal power consumption of the hardware object in the current operating state, it can also correct the ideal power consumption output by the AI ​​model, as shown in the following formula (14).

[0196] p ★★ =p ★ +δ Formula (14)

[0197] Among them, p ＊＊ is the corrected ideal power consumption; p ★ is the ideal power consumption output by the AI ​​model, that is, the ideal power consumption before correction; δ is the correction amount, and the value of the correction amount corresponding to different operating states may be different.

[0198] Accordingly, the energy-saving health of the hardware object can be calculated based on the following formula (15).

[0199] The correction amount δ can be set by a technician.

[0200] Alternatively, the correction amount δ may be dynamically set by the data processing device 300 according to the energy-saving health of the hardware object over a period of time.

[0201] In specific implementation, the data processing device can continuously monitor the energy-saving health of the hardware object, and when the energy-saving health of the hardware object is greater than or equal to the threshold, it indicates that the energy-saving state of the hardware object is good, and there is no need to perform energy-saving operations to further improve the energy efficiency level of the hardware object. However, when the energy-saving health of the hardware object is greater than or equal to the threshold for a duration greater than the first duration, that is, when the energy-saving health of the hardware object continues to be in the high energy-saving health range for a long time, it may be that the ideal power consumption output by the AI ​​model is too high, that is, higher than the minimum power consumption that the hardware object can actually achieve. At this time, the data processing device 300 can reduce the correction amount to reduce the value of the ideal power consumption, so that the value of the ideal power consumption used to calculate the energy-saving health is closer to the minimum power consumption that the hardware object can actually achieve, thereby improving the accuracy of the calculated energy-saving health.

[0202] When the energy-saving health of a hardware object is less than a threshold value, it indicates that the energy-saving state of the hardware object is poor. At this time, the data processing device 300 can perform corresponding energy-saving operations on the hardware object according to the energy-saving health to improve the energy efficiency level of the hardware object and achieve further energy saving of the hardware object. However, when the energy-saving health of the hardware object is less than the threshold value for a duration greater than the second duration, that is, when the energy-saving health of the hardware object continues to be in the low energy-saving health interval for a long time, it may be that the ideal power consumption output by the AI ​​model is too low, that is, lower than the minimum power consumption that the hardware object can actually achieve. At this time, the data processing device 300 can increase the correction amount to increase the value of the ideal power consumption, so that the value of the ideal power consumption used to calculate the energy-saving health is closer to the minimum power consumption that the hardware object can actually achieve, thereby improving the accuracy of the calculated energy-saving health.

[0203] In this way, during the operation of the hardware object, after the data processing device 300 continuously adjusts the ideal power consumption output by the AI ​​model, the energy-saving health change curve of the hardware object calculated based on the continuously adjusted ideal power consumption can be shown in Figure 6.

[0204] In actual application scenarios, the adjustment amount of the correction value by the data processing device 300 can be gradually reduced as the monitoring duration of the energy-saving health of the hardware object increases, until it reaches 0, so that the ideal power consumption value determined by the data processing device 300 converges. In this case, the accuracy of the energy-saving health calculated by the data processing device 300 based on the converged ideal power consumption can be maintained at a continuously high level, that is, the accuracy of the energy-saving health level assessment of the hardware object can be maintained at a stable and high level.

[0205] Furthermore, the data processing device 300 can also use the corrected ideal power consumption and the current operating state of the hardware object (as well as the energy-saving control parameters used) to update the AI ​​model, thereby improving the accuracy of the ideal power consumption output by the AI ​​model based on the operating state. In this way, using the updated AI model to infer the ideal power consumption of the hardware object in various operating states can further improve the accuracy of the energy-saving health calculated based on the ideal power consumption, thereby achieving an effective assessment of the energy efficiency level of the hardware object.

[0206] In this embodiment, the data processing device 300 uses the operating power consumption of the hardware object in the historical time period to not only train an AI model for determining the ideal power consumption of the hardware object, so as to use the AI ​​model to realize real-time evaluation of the energy efficiency level of the hardware object, but also improves the reliability and credibility of the ideal power consumption inferred by the AI ​​model. Thus, the energy-saving health calculated by the data processing device 300 based on the ideal power consumption output by the AI ​​model can more accurately reflect the energy efficiency level of the hardware object.

[0207] In addition, the data processing device 300 can further dynamically adjust the correction amount of the ideal power consumption, which can make the determined ideal power consumption more reliable and reduce the interference of some factors (such as the operating power consumption of the hardware object in the historical time period not reaching the actual minimum power consumption that can be achieved, etc.) in determining the ideal power consumption. This can further improve the accuracy of the energy-saving health degree finally calculated by the data processing device 300, thereby improving the accuracy of measuring the energy efficiency level of the hardware object.

[0208] It is worth noting that other reasonable step combinations that can be thought of by those skilled in the art based on the above description also fall within the scope of protection of this application. Secondly, those skilled in the art should also be familiar with that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this application.

[0209] The data processing method provided in the embodiment of the present application is introduced above with reference to Figures 1A to 6. Next, the functions of the data processing device provided in the embodiment of the present application and the data processing system for implementing the data processing device are introduced with reference to the accompanying drawings.

[0210] 7 , which shows a schematic structural diagram of a data processing device. The data processing device 700 includes:

[0211] Acquisition module 701, used to obtain the operating status of the hardware object;

[0212] The processing module 702 is configured to obtain information of a digital product passport (DPP) of the hardware object according to the operating status of the hardware object, where the information of the digital product passport (DPP) includes at least one of the following target information:

[0213] The energy-saving health of the hardware object, wherein the energy-saving health is used to measure the energy efficiency level of the hardware object in the operating state;

[0214] The energy saving amount of the hardware object, the energy saving amount is used to measure at least one of the power consumption saving amount and the energy saving and carbon reduction amount of the hardware object in the operating state; or

[0215] The energy consumption of the hardware object is used to measure at least one of a power consumption level and a carbon consumption of the hardware object in the operating state.

[0216] In a possible implementation manner, the information of the digital product passport DPP specifically includes the identifier of the hardware object and the mapping relationship of the target information.

[0217] In a possible implementation manner, the power consumption saving is specifically the power consumption saving of the hardware object in the running state compared to the first state; or

[0218] The energy saving and carbon reduction amount is specifically the energy saving and carbon reduction amount of the hardware object in the running state compared to the first state; or

[0219] The power consumption level is specifically the power consumption of the hardware object in the running state compared to the power consumption in the second state; or

[0220] The carbon consumption is specifically the carbon consumption of the hardware object in the running state compared to the second state;

[0221] The first state is a highest power consumption state of the hardware object, and the second state is a lowest power consumption state of the hardware object.

[0222] In a possible implementation, the operating state is an energy-saving measure enabled on the hardware object;

[0223] The processing module 702 is specifically configured to:

[0224] The energy saving amount of the hardware object is obtained according to the energy saving amount generated by the enabled energy saving measures.

[0225] In a possible implementation, the operating state is the actual power consumption of the hardware object;

[0226] The processing module 702 is specifically configured to:

[0227] According to the actual power consumption and the lowest power consumption or the highest power consumption at which the hardware object can operate when in the operating state, information on the digital product passport DPP of the hardware object is obtained.

[0228] In one possible implementation, the operating state is the actual power consumption of the hardware object;

[0229] The energy-saving health is calculated based on the actual power consumption and the minimum power consumption at which the hardware object can operate when in the operating state.

[0230] In a possible implementation manner, the energy saving and carbon reduction amount is calculated based on the power consumption saving amount and the carbon emission coefficient; or,

[0231] The carbon consumption is calculated based on the power consumption level and the carbon emission coefficient.

[0232] In one possible implementation, the hardware object includes multiple electronic devices, the energy-saving health is the aggregation of the energy-saving health of the multiple electronic devices; the energy saving amount is the aggregation of the energy saving amount of the multiple electronic devices; and the energy consumption is the aggregation of the energy consumption of the multiple electronic devices.

[0233] In a possible implementation, the apparatus 700 further includes:

[0234] A presentation module, configured to present information of the digital product passport DPP;

[0235] Alternatively, the processing module 702 is further configured to store information of the digital product passport DPP;

[0236] Or, a transceiver module, used to send the information of the digital product passport DPP to a server or a terminal device;

[0237] Alternatively, the processing module 702 is further configured to execute an energy-saving operation for the hardware object when the energy-saving health is lower than a first threshold, the energy-saving amount is lower than a second threshold, or the energy consumption is higher than a third threshold.

[0238] In a possible implementation, the transceiver module is further configured to receive an instruction before obtaining the first information, where the instruction is used to instruct generation or query of information of the digital product passport DPP of the hardware object.

[0239] Since the data processing device 700 shown in FIG7 corresponds to the method shown in FIG2 , the specific implementation of the data processing device 700 shown in FIG7 and its technical effects can be found in the relevant descriptions in the aforementioned embodiments and will not be repeated here.

[0240] Figure 8 is a schematic diagram of a data processing system 800 provided in this application. The data processing system 800 shown in Figure 8 can be used to implement the method steps performed by the data processing device 300 in the embodiment shown in Figure 2 . In actual applications, the data processing system 800 can be, for example, a standalone card, a server, or a processor within a server, though this embodiment is not limiting. For ease of understanding, the hardware structure of the data processing system 800 will be described below using the server as an example.

[0241] As shown in Figure 8, the data processing system 800 includes a processor 801, a memory 802, and a communication interface 803. The processor 801, the memory 802, and the communication interface 803 communicate via a bus 804, and communication can also be achieved through other means such as wireless transmission. The memory 802 is used to store instructions, and the processor 801 is used to execute the instructions stored in the memory 802. Furthermore, the data processing system 800 may also include a memory unit 805, which may be connected to the processor 801, the storage medium 802, and the communication interface 803 via the bus 804.

[0242] The memory 802 stores program code, and the processor 801 can call the program code stored in the memory 802 to perform the following operations:

[0243] Get the operating status of the hardware object;

[0244] According to the running state of the hardware object, information of the digital product passport DPP of the hardware object is obtained, where the information of the digital product passport DPP includes at least one of the following target information:

[0245] The energy-saving health of the hardware object, wherein the energy-saving health is used to measure the energy efficiency level of the hardware object in the operating state;

[0246] The energy saving amount of the hardware object, the energy saving amount is used to measure at least one of the power consumption saving amount and the energy saving and carbon reduction amount of the hardware object in the operating state; or

[0247] The energy consumption of the hardware object is used to measure at least one of a power consumption level and a carbon consumption of the hardware object in the operating state.

[0248] It should be understood that in the embodiment of the present application, the processor 801 may be a CPU, or may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete device components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0249] The memory 802 may include a read-only memory and a random access memory, and provides instructions and data to the processor 801. The memory 802 may also include a non-volatile random access memory. For example, the memory 802 may also store device type information.

[0250] The memory 802 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0251] The communication interface 803 is used to communicate with other devices connected to the data processing system 800. In addition to the data bus, the bus 804 may also include a power bus, a control bus, and a status signal bus. However, for the sake of clarity, various buses are labeled as bus 804 in the figure.

[0252] It should be understood that the data processing system 800 according to the embodiment of the present application may correspond to the data processing device 700 in the embodiment of the present application, and may correspond to the data processing device 300 that executes the method shown in Figure 2 in the embodiment of the present application, and the above-mentioned and other operations and / or functions implemented by the data processing system 800 are respectively for implementing the corresponding processes of the method in Figure 2. For the sake of brevity, they will not be repeated here.

[0253] The present application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center that contains one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the above-mentioned data processing method.

[0254] The present application also provides a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the computer program product fully or partially generates the process or function described in the present application.

[0255] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0256] The computer program product may be a software installation package. When any of the aforementioned data processing methods is required, the computer program product may be downloaded and executed on a computing device.

[0257] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0258] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A data processing method, characterized in that, The method includes: Obtaining the operating state of the hardware object; Based on the operating state of the hardware object, obtaining information of the Digital Product Passport (DPP) of the hardware object, where the information of the Digital Product Passport (DPP) includes at least one of the following target information: The energy-saving health degree of the hardware object, which is used to measure the energy efficiency level of the hardware object in the operating state; The energy-saving amount of the hardware object, which is used to measure at least one of the power consumption savings amount and the energy-saving and carbon-reduction amount of the hardware object in the operating state; or, The energy consumption amount of the hardware object, which is used to measure at least one of the power consumption level and the carbon consumption amount of the hardware object in the operating state.

2. The method according to claim 1, characterized in that, The information of the Digital Product Passport (DPP) specifically includes the mapping relationship between the identifier of the hardware object and the target information.

3. The method according to claim 1 or 2, wherein The power consumption savings amount is specifically the power consumption savings amount of the hardware object in the operating state compared to that in the first state; or, The energy-saving and carbon-reduction amount is specifically the energy-saving and carbon-reduction amount of the hardware object in the operating state compared to that in the first state; or, The power consumption level is specifically the power consumption amount of the hardware object in the operating state compared to that in the second state; or, The carbon consumption amount is specifically the carbon consumption amount of the hardware object in the operating state compared to that in the second state; The first state is the highest power consumption state of the hardware object, and the second state is the lowest power consumption state of the hardware object.

4. The method according to claim 1, wherein The operating state is the energy-saving measure enabled on the hardware object; The obtaining the information of the Digital Product Passport (DPP) of the hardware object based on the operating state of the hardware object includes: Based on the energy-saving amount generated by the enabled energy-saving measure, obtaining the energy-saving amount of the hardware object.

5. The method according to any one of claims 1 to 3, characterized in that, The operating state is the actual power consumption of the hardware object; The obtaining the information of the Digital Product Passport (DPP) of the hardware object based on the operating state of the hardware object includes: Based on the actual power consumption and the lowest or highest power consumption at which the hardware object can operate in the operating state, obtaining the information of the Digital Product Passport (DPP) of the hardware object.

6. The method according to any one of claims 1 to 5, characterized in that The energy-saving and carbon-reduction amount is calculated based on the power consumption savings amount and the carbon emission coefficient; or, The carbon consumption amount is calculated based on the power consumption level and the carbon emission coefficient.

7. The method according to any one of claims 1 to 6, characterized in that The operating state is the actual power consumption of the hardware object; The energy-saving health degree is calculated based on the actual power consumption and the lowest power consumption at which the hardware object can operate in the operating state.

8. The method according to any one of claims 1 to 7, characterized in that The hardware object includes multiple electronic devices, and the energy-saving health degree is the aggregation of the energy-saving health degrees of the multiple electronic devices; the energy-saving amount is the aggregation of the energy-saving amounts of the multiple electronic devices; the energy consumption amount is the aggregation of the energy consumption amounts of the multiple electronic devices.

9. The method according to any one of claims 1 to 8, characterized in that The method further includes: Presenting the information of the Digital Product Passport (DPP); or, Storing the information of the Digital Product Passport (DPP); or, Send the information of the digital product passport (DPP) to a server or a terminal device; or, When the energy-saving health degree is lower than a first threshold, the energy-saving amount is lower than a second threshold, or the energy consumption amount is higher than a third threshold, perform An energy-saving operation for the hardware object.

10. The method according to any one of claims 1 to 9, characterized in that, Before obtaining the first information, the method further includes: Receiving an instruction for instructing to generate or query the information of the digital product passport (DPP) of the hardware object.

11. A data processing device, characterized in that, The data processing device includes: An obtaining module, configured to obtain the operating state of a hardware object; A processing module, configured to obtain the information of the digital product passport (DPP) of the hardware object according to the operating state of the hardware object, where the information of the digital product passport (DPP) includes at least one of the following target information: The energy-saving health degree of the hardware object, where the energy-saving health degree is used to measure the energy efficiency level of the hardware object in the operating state; The energy-saving amount of the hardware object, where the energy-saving amount is used to measure at least one of the power consumption savings amount and the energy-saving and carbon-reduction amount of the hardware object in the operating state; or, The energy consumption amount of the hardware object, where the energy consumption amount is used to measure at least one of the power consumption level and the carbon consumption amount of the hardware object in the operating state.

12. A data processing system, characterized in that, Including a processor and a memory; The processor is configured to execute the instructions stored in the memory, so that the data processing system executes the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, Including instructions, when running on a computing device, causing the computing device to execute the steps of the method according to any one of claims 1 to 10.

14. A computer program product, characterized in that, Including instructions, when running on a computing device, causing the computing device to execute the steps of the method according to any one of claims 1 to 10.

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