Data processing method, device and system and related equipment

Abstract

The data processing method comprises the following steps: acquiring a running state of a hardware object; information of a digital product passport DPP of the hardware object is obtained according to the running state of the hardware object, and the information of the digital product passport DPP comprises at least one of the following target information: the energy-saving health degree of the hardware object, the energy-saving amount of the hardware object or the energy consumption amount of the hardware object; the energy saving amount is used for measuring at least one of the power consumption saving amount and the energy saving and carbon reduction amount of the hardware object in the running state, and the energy consumption amount is used for measuring at least one of the power consumption level and the carbon consumption amount of the hardware object in the running state. According to the method, the energy-saving state of the hardware object is written into the information of the DPP, so that a user (such as operation and maintenance personnel) can accurately identify the energy-saving state of the hardware object in real time by using the DPP.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a data processing method, apparatus, system, and related devices. Background Art

[0002] A Digital Product Passport (DPP) is used to record relevant information about a product, so as to electronically register, process, and share product-related information among supply chain enterprises, regulatory agencies, and consumers.

[0003] Users can manage and improve the energy efficiency of a device by understanding the accurate energy-saving status of a hardware object, or determine whether the hardware needs to be phased out and replaced.

[0004] Therefore, there is an urgent need for a method for identifying the energy-saving status of a hardware object. Summary of the Invention

[0005] This application provides a data processing method. By writing the energy-saving status of a hardware object into the information of a digital product passport, it is possible to provide monitoring of the energy-saving status and identification of energy-saving opportunity points of the hardware object during actual operation, enabling users (such as operation and maintenance personnel) to accurately identify the energy-saving status of the hardware object. In addition, this application also provides corresponding devices, data processing systems, computer-readable storage media, and computer program products.

[0006] In a first aspect, this application provides a data processing method. Specifically, it includes: obtaining the operating state of a hardware object; according to the operating state of the hardware object, obtaining the information of the digital product passport DPP of the hardware object, and 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.

[0007] In the embodiments of the present application, writing the energy-saving status of the hardware object into the information of the digital product passport can provide monitoring of the energy-saving status of the hardware object during actual operation and calculation of the energy-saving and carbon-reduction amount, enabling 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 calculation and management of the computing power and efficiency of IT devices in data centers and supercomputing centers. In addition, the energy-saving and carbon-reduction indicators can provide basic data for calculating the carbon emissions and carbon-reduction amount during the operation stage of user computing devices, facilitating the calculation of annual indicators.

[0008] Among them, the carbon emissions can refer to the amount of carbon dioxide emissions per unit time per unit area or per unit energy.

[0009] In a possible implementation, the information of the digital product passport DPP specifically includes the mapping relationship between the identifier of the hardware object and the target information.

[0010] In a possible implementation, the power consumption savings is specifically the power consumption savings of the hardware object in the operating state compared to the power consumption in the first state; or,

[0011] 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 the energy-saving and carbon-reduction amount in the first state; or,

[0012] The power consumption level is specifically the power consumption of the hardware object in the operating state compared to the power consumption in the second state; or,

[0013] The carbon consumption is specifically the carbon consumption of the hardware object in the operating state compared to the carbon consumption in the second state;

[0014] The first state is the highest power consumption state of the hardware object (which can also be called the non-energy-saving power consumption), and the second state is the lowest power consumption state of the hardware object.

[0015] In a possible implementation, the operating state is the energy-saving measure enabled on the hardware object; obtaining the information of the digital product passport DPP of the hardware object according to the operating state of the hardware object includes: obtaining the energy savings of the hardware object according to the energy savings generated by the enabled energy-saving measure.

[0016] In a possible implementation, the operating state is the actual power consumption of the hardware object;

[0017] The energy-saving health degree is calculated according to the actual power consumption and the lowest power consumption at which the hardware object can operate in the operating state.

[0018] In a possible implementation, the energy savings generated by enabling each energy-saving measure can be determined in advance, and then a mapping relationship between the energy-saving measures and the corresponding energy savings can be constructed. This mapping relationship can be in the form of a table, graph, function mapping, AI model, etc., which is not limited in the embodiments of the present application. After obtaining the energy-saving measures enabled by the hardware object, the energy savings of the enabled energy-saving measures can be obtained.

[0019] In a possible implementation, the operating state is the actual power consumption of the hardware object; obtaining the information of the digital product passport DPP of the hardware object according to the operating state of the hardware object includes: obtaining the information of the digital product passport DPP of the hardware object according to the actual power consumption and the minimum or maximum power consumption that can be run when the hardware object is in the operating state.

[0020] In a possible implementation, the energy-saving and carbon-reduction amount is calculated according to the power consumption savings and the carbon emission coefficient; or, the carbon consumption amount is calculated according to the power consumption level and the carbon emission coefficient.

[0021] The calculation of the carbon emission coefficient is obtained by corresponding the consumption of energy with the emissions of carbon dioxide. There are differences in the carbon emission coefficients of different energy sources. For example, the carbon emission coefficient of coal is relatively high, while the carbon emission coefficient of natural gas is relatively low.

[0022] In a possible implementation, 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 savings is the aggregation of the energy savings of the multiple electronic devices; the energy consumption is the aggregation of the energy consumptions of the multiple electronic devices.

[0023] In a possible implementation, the method further includes:

[0024] Presenting the information of the digital product passport DPP; or,

[0025] Storing the information of the digital product passport DPP; or,

[0026] Sending the information of the digital product passport DPP to the server or the terminal device; or,

[0027] When the energy-saving health degree is lower than the first threshold, the energy savings is lower than the second threshold, or the energy consumption is higher than the third threshold, an energy-saving operation for the hardware object is performed.

[0028] In a possible implementation, before obtaining the first information, the method further includes: receiving an instruction for indicating to generate or query the information of the digital product passport DPP of the hardware object.

[0029] Second aspect, the present application provides a data processing device, the data processing device includes:

[0030] An acquisition module, configured to acquire the operating state of a hardware object;

[0031] A processing module, configured to obtain information on the Digital Product Passport (DPP) of the hardware object according to the operating state of the hardware object, and the information on the Digital Product Passport (DPP) of the hardware object includes at least one of the following target information:

[0032] 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;

[0033] 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,

[0034] 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.

[0035] In a possible implementation manner, the information on the Digital Product Passport (DPP) specifically includes the mapping relationship between the identifier of the hardware object and the target information.

[0036] In a possible implementation manner, 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,

[0037] 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,

[0038] 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,

[0039] 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;

[0040] 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.

[0041] In a possible implementation manner, the operating state is the energy-saving measure enabled on the hardware object;

[0042] The processing module is specifically configured to:

[0043] Obtain the energy-saving amount of the hardware object according to the energy-saving amount generated by the enabled energy-saving measure.

[0044] In a possible implementation manner, the operating state is the actual power consumption of the hardware object;

[0045] The processing module is specifically configured to:

[0046] Obtain information on the Digital Product Passport (DPP) of the hardware object according to the actual power consumption and the lowest or highest power consumption at which the hardware object can operate in the operating state.

[0047] In this way, according to the actual power consumption of the hardware object and the lowest or highest power consumption in the operating state, the energy-saving state for measuring the energy-saving level of the hardware object can be calculated, so as to realize real-time online evaluation of the energy-saving level of the hardware object.

[0048] In a possible implementation manner, the energy-saving and carbon-reduction amount is calculated according to the power-saving amount and the carbon emission coefficient; or, the carbon consumption amount is calculated according to the power consumption level and the carbon emission coefficient.

[0049] In a possible implementation manner, the hardware object includes a plurality of electronic devices, and the energy-saving health degree is the aggregation of the energy-saving health degrees of the plurality of electronic devices; the energy-saving amount is the aggregation of the energy-saving amounts of the plurality of electronic devices; the energy consumption amount is the aggregation of the energy consumption amounts of the plurality of electronic devices.

[0050] In a possible implementation manner, the device further includes:

[0051] A presentation module, configured to present the information on the Digital Product Passport (DPP);

[0052] Or, the processing module is further configured to store the information on the Digital Product Passport (DPP);

[0053] Or, a transceiver module, configured to send the information on the Digital Product Passport (DPP) to a server or a terminal device;

[0054] Or, the processing module is further configured to perform an energy-saving operation on the hardware object 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.

[0055] In a possible implementation manner, the transceiver module is further configured to receive an instruction before obtaining the first information, where the instruction is used to indicate generating or querying the information on the Digital Product Passport (DPP) of the hardware object.

[0056] 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 configured to execute instructions stored in the memory, so that the data processing system executes the data processing method in the first aspect or any implementation manner of the first aspect. It should be noted that the memory may be integrated into the processor or independent of the processor. The data processing system may further include a bus. Among them, the processor is connected to the memory through the bus. Among them, the memory may include a readable memory and a random access memory.

[0057] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions are stored. When it runs on a computing device, the computing device is caused to execute the operation steps of the data processing method described in the first aspect or any implementation manner of the first aspect.

[0058] In a fifth aspect, the present application provides a computer program product containing instructions. When it runs on a computing device, the computing device is caused to execute the operation steps of the data processing method described in the first aspect or any implementation manner of the first aspect.

[0059] Based on the implementation manners provided in the above aspects of the present application, further combinations can be made to provide more implementation manners. Description of the Drawings

[0060] Figure 1A It is a schematic diagram of an exemplary application scenario provided by the present application;

[0061] Figure 1B It is a schematic diagram of an exemplary application scenario provided by the present application;

[0062] Figure 1C It is a schematic diagram of an exemplary application scenario provided by the present application;

[0063] Figure 2 It is a schematic flowchart of a data processing method provided by the present application;

[0064] Figure 3 It is a schematic diagram for calculating the energy-saving health degree of a device based on the energy-saving health degree of an electronic device provided by the present application;

[0065] Figure 4 It is a schematic flowchart of a method for training an AI model provided by the present application;

[0066] Figure 5 It is a schematic diagram for determining the minimum power consumption at each historical operating state through state projection provided by the present application;

[0067] Figure 6 A schematic diagram of the energy saving health change curve of the hardware object provided in this application;

[0068] Figure 7 A schematic diagram of the structure of a data processing device provided in this application;

[0069] Figure 8 A schematic diagram of the structure of a data processing system provided for this application. DETAILED DESCRIPTION

[0070] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section 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.

[0071] 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.

[0072] 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 need not be 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, which is only to describe the distinction mode adopted by the objects of the same attributes when describing 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.

[0073] As used herein, the terms "substantially," "about," and the like are used as terms of approximation, not as terms of degree, and are intended to take into account the inherent deviations of measurements or calculations that one of ordinary skill in the art would know. In addition, the use of "may" when describing embodiments of the present application refers to "possible one or more embodiments." As used herein, the terms "use," "using," and "used" may be considered synonymous with the terms "utilize," "utilizing," and "utilized," respectively. In addition, the term "exemplary" is intended to refer to an example or illustration.

[0074] The technical solution in this application will be described below in conjunction with the accompanying drawings of this application.

[0075] See alsoFigure 1A , which is a schematic diagram of an exemplary application scenario provided for this application. In Figure 1A the application scenario shown, 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 here. Figure 1A Here, an example of hardware objects including three levels is used for illustration. Among them, the hardware objects at the first level are electronic devices, such as Figure 1A the central processing unit (CPU) 101, hard disk 102, fan 103, etc. shown. Different electronic devices can communicate through a bus, such as through a compute express link (CXL) bus, peripheral component interconnect express (PCIE) bus, inter-integrated circuit (I2C) bus, unified bus (UB or Ubus), or a combination of one or more of these buses for communication. The hardware objects at the second level are devices, such as Figure 1A the devices 201, 202, and 203 shown. The device can be a computing server, a storage server, or a terminal, etc. The hardware objects at the second level can include multiple hardware objects at the first level. For example, the device 201 can include a CPU 101, a hard disk 102, a fan 103, etc. Different devices can communicate through a wired network or through a wireless network. The hardware objects at the third level are clusters, such as Figure 1A the cluster 200 shown, which can include multiple devices. Figure 1A Here, an example is used to illustrate that the cluster 200 includes devices 201, 202, and 203. In actual application, the cluster 200 can be a data center including multiple computing devices, or can be an availability zone (AZ) including multiple computing devices, or can be a region including multiple computing devices, etc. This embodiment does not limit this. Among them, each AZ includes one data center or multiple geographically close data centers, and generally a region can include multiple AZs. When the hardware objects at the third level include multiple clusters, different clusters can communicate through a wired network or a wireless network.

[0076] Users can manage and improve the energy efficiency of devices by understanding the accurate energy-saving status of hardware objects, or determine whether the hardware needs to be phased out and replaced.

[0077] Therefore, there is an urgent need for a method for identifying the energy-saving state of hardware objects.

[0078] Based on this, in the Figure 1A application scenario shown, a data processing device 300 is added. The data processing device 300 can calculate the energy-saving state of the hardware object (such as energy-saving health or energy-saving amount) according to the information of the hardware object (such as information related to power consumption), and write the energy-saving state into the information of the digital product passport (DPP). Users can obtain the energy-saving state of the hardware device by reading the information of the DPP.

[0079] Among them, the hardware object can be Figure 1A an object at any hierarchical level. Moreover, the data processing device 300 uses the information of the hardware object (such as information related to power consumption) to evaluate its energy-saving state without applying an additional load to the hardware object. Therefore, the time consumption for evaluating the energy-saving state and the resource occupancy of the hardware object can be effectively reduced, thereby effectively reducing the time cost and resource consumption required for data processing.

[0080] Exemplarily, the data processing device 300 can be implemented by software, for example, it can be implemented by at least one of virtual machines, containers, computing engines, etc. Alternatively, the data processing device 300 can be implemented by a physical device including a processor, where the processor can be a CPU, and any one of 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. Moreover, 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. Specifically, the number and types of processors can be set according to the business requirements of the actual application, and this embodiment does not limit this.

[0081] It should be noted that the Figure 1A application scenarios shown above are only for exemplary illustration. In actual applications, the above data processing method can also be applied to other application scenarios. For example, in other possible application scenarios, the device 101 can include a greater number of electronic devices, or the cluster 200 can include a greater number of devices, etc. Or, the device 101 can be a terminal device such as a smart phone, a smart terminal (such as an iPad), etc. This embodiment does not limit this.

[0082] Next, the relationship between the deployment locations of the data processing device 300 and the hardware object will be introduced:

[0083] Referring to Figure 1B , Figure 1B as an architecture schematic, where the data processing device 300 can be deployed in the hardware object or deployed in an associated manner on the edge side. 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.

[0084] For example, the user can interact with the hardware object (or directly with the data processing device 300) to generate an instruction, thereby triggering 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.

[0085] Referring to Figure 1C , Figure 1C is an architectural schematic. Among them, the data processing device 300 can be deployed on the cloud side. For example, it can be a software module of the cloud side server.

[0086] For example, the user can trigger the hardware device to interact with the cloud side server, so that the cloud side server generates an instruction 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 transfer the DPP information to the terminal side or store the DPP information.

[0087] For ease of understanding, the embodiments of the data processing method provided in this application will be described below with reference to the accompanying drawings.

[0088] See Figure 2 , Figure 2 is a schematic flowchart of a data processing method provided in an embodiment of this application. This method can be applied to Figures 1A to 1C the application scenario shown, or can be applied to other applicable application scenarios. Among them, the hardware object whose energy efficiency level is to be evaluated can be Figure 1A the electronic devices, equipment or clusters in

[0089] Among them, Figure 2 the data processing method shown can be executed by the data processing device 300 in Figure 1A . Specifically, this method can include:

[0090] S201: Obtain the operating status of the hardware object.

[0091] In a possible implementation, the trigger of step 201 can be implemented by the user. For example, the user can generate an instruction through a trigger, and the instruction is used to indicate the generation or query of the information of the digital product passport DPP of the hardware object. Furthermore, the data processing device can obtain the operating status of the hardware object based on the received instruction.

[0092] Among them, the operating state can be a state related to the power consumption of the hardware object.

[0093] In a possible implementation, the operating state can be at least one energy-saving measure enabled on the hardware object.

[0094] Among them, the energy-consuming devices of the hardware object can have certain energy-saving measures (as shown in Table 1, Table 1 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 through frequency modulation, sleep, or core shutdown.

[0095] Table 1

[0096]

[0097] After enabling each energy-saving measure, a certain amount of energy savings can be generated for the hardware object. The data processing device 300 can obtain the energy savings generated by the energy-saving measures enabled on the hardware object. Here, the so-called energy savings can be the energy savings generated when enabling the energy-saving measure compared to not enabling the energy-saving measure (the states of other energy-saving measures of the hardware object remain unchanged). For example, the energy savings generated when the CPU is in sleep mode compared to when it is not in sleep mode.

[0098] It should be understood that the energy savings here can also be related to some operating states of the hardware object (such as, but not limited to, one or more of parameters such as CPU utilization rate, memory utilization rate, memory read / write rate, hard disk read / write rate, device sensing temperature, etc.). For example, for hardware objects in different operating states, the energy savings generated by enabling the same energy-saving measure may be different.

[0099] Next, it is introduced how to obtain the energy savings generated by enabling the energy-saving measure.

[0100] In a possible implementation, the energy savings 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 savings 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 do not limit this.

[0101] After obtaining the energy-saving measures enabled on the hardware object, the energy savings of the enabled energy-saving measures can be obtained.

[0102] In a possible implementation, the operating state of the hardware object can be the actual power consumption, such as collecting data such as actual power consumption using sensors.

[0103] The actual power consumption of the hardware object can be, for example, the power when the hardware object is running, or it can be the voltage and current when the hardware object is running, or it can be other data used to characterize the power consumption of the hardware object.

[0104] In a possible implementation, the minimum power consumption or the maximum power consumption at which the hardware object can operate may be determined based on the operating state of the hardware object. Among them, the minimum power consumption or the maximum power consumption may be obtained by reasoning through a target model according to the operating state of the hardware object, and the target model is obtained by using the operating state and historical power consumption data of the hardware object in a historical time period. For example, the target model may be an artificial intelligence (AI) model or obtained by fitting based on the operating state and historical power consumption data in a historical time period.

[0105] Next, an example will be given with the minimum power consumption:

[0106] After the data processing device 300 obtains the operating state of the hardware object, it can further obtain the minimum power consumption at which the hardware object can operate in this operating state, that is, the minimum power consumption that can be achieved, which can also be called the ideal power consumption. Among them, 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 100 W (watts), while the minimum power consumption that can be achieved in operating state 2 is 150 W. Or, 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 8 operating states. For ease of understanding, the following implementation examples for determining the ideal power consumption are provided in this embodiment.

[0107] In the first possible implementation manner, the data processing device 300 may determine the ideal power consumption corresponding to the current operating state of the hardware object according to the power consumption data of the hardware object in a historical time period, that is, the power consumption situation of the hardware object in the past period of time may be used to guide the minimum power consumption that the hardware object can achieve.

[0108] For example, the data processing device 300 can infer the ideal power consumption through an AI model. In specific implementation, the data processing device 300 can obtain the trained AI model. The AI model can be, for example, a model constructed based on a neural network model, such as a model constructed 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. In this embodiment, the specific implementation manner of the AI model is not limited. Among them, the training samples of the AI model can be, for example, the minimum power consumption reached by the hardware object in each operating state during the historical time period. Regarding the specific implementation process of training the AI model, reference can be made to the description later, and details are not elaborated here. After the data processing device 300 obtains the operating state of the hardware object, it can input the obtained operating state into the AI model, and use the AI model to infer according to the input operating state to obtain the ideal power consumption corresponding to this operating state output by the AI model.

[0109] 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 of the hardware object and the energy-saving control parameters into the AI model together. The AI model infers the ideal power consumption of the hardware object. In this way, inferring based on multiple-dimensional data such as the operating state and the energy-saving control parameters can further improve the accuracy of the determined ideal power consumption, and thus can further improve the accuracy of subsequent evaluation of the energy efficiency level of the hardware object.

[0110] In the second possible implementation manner, a mapping relationship between the operating state of the hardware object and the ideal power consumption can be configured in the data processing device 300. For example, the technical personnel can pre-configure this mapping relationship in the data processing device 300, etc. In this way, after the data processing device 300 obtains the operating state of the hardware object, by looking up this mapping relationship, it can determine the ideal power consumption corresponding to this operating state. Taking the hardware object as a CPU specifically, the operating state of the hardware object can be, for example, the CPU utilization rate. And a mapping relationship between the CPU utilization rate and the ideal power consumption can be configured in the data processing device 300. For example, when the CPU utilization rate is configured to be 10%, the ideal power consumption is 100w; when the CPU utilization rate is 50%, the ideal power consumption is 300w, etc. In this way, after the data processing device 300 obtains the current CPU utilization rate, it can determine the ideal power consumption corresponding to this CPU utilization rate by looking up the configured mapping relationship.

[0111] Exemplarily, the mapping relationship in the data processing device 300 can be determined, for example, according to the minimum power consumption achieved by the hardware object in each operating state within a historical time period. Taking the hardware object as a CPU specifically, assuming that within the past 30 days, the utilization rate of the CPU is 10% at multiple moments, but the power consumption generated by the CPU at different moments among these multiple moments is 100w, 150w, and 300w respectively, then the minimum power consumption (100w) can be determined from the multiple power consumptions, and a mapping relationship between the CPU utilization rate (10%) and the minimum power consumption (100w) can be established. In actual applications, the mapping relationship in the data processing device 300 can also be determined by other means, and this embodiment does not limit this.

[0112] The above implementation manner for determining the ideal power consumption is only for some exemplary descriptions. In other embodiments, the data processing device 300 can also use other methods to determine the ideal power consumption of the hardware object in the operating state.

[0113] S202: Obtain the information of the digital product passport DPP of the hardware object according to the operating state of the hardware object.

[0114] Among them, the information of the digital product passport DPP can include target information, and the target information can include the energy-saving health degree of the hardware object, and the energy-saving health degree is used to measure the energy efficiency level of the hardware object in the operating state.

[0115] Exemplarily, the data processing device 300 can calculate the energy-saving health degree of the hardware object according to the actual power consumption and the ideal power consumption (that is, the minimum power consumption), and the energy-saving health degree is used to measure the energy efficiency level of the hardware object.

[0116] It can be understood that the ideal power consumption indicates the minimum power consumption that the hardware object can achieve in the current operating state, and the minimum power consumption is the actual power consumption within the historical time period, that is, the minimum power consumption actually achieved during the operation of the hardware object in the past time period is used as the theoretical value; while 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 the ideal power consumption of the hardware object, the high or low 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 good energy-saving state; correspondingly, the energy efficiency level of the hardware object is currently in a high state. When the deviation between the actual power consumption and the ideal power consumption is large, usually the actual power consumption is 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; correspondingly, the energy efficiency level of the hardware object is currently in a low state.

[0117] In this embodiment, the energy-saving health degree can be used to measure the energy efficiency level of a hardware object. The energy-saving health degree is used to indicate the energy-saving effect of the hardware object, that is, it can also be used to indicate the level of the energy efficiency of the hardware object. Among them, the larger the energy-saving health degree, the higher the energy efficiency level of the hardware object is characterized; the smaller the energy-saving health degree, the lower the energy efficiency level of the hardware object is characterized.

[0118] As an implementation example, the data processing device 300 can calculate the energy-saving health degree of the hardware object based on the following formula (1).

[0119]

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

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

[0122]

[0123] In this way, the data processing device 300 can calculate the energy-saving health degree and realize the online real-time evaluation of the energy efficiency level of the hardware object.

[0124] In a possible implementation, the data processing device 300 can calculate the energy-saving degree of the hardware object according to the actual power consumption and the maximum power consumption. The energy-saving amount is used to measure at least one of the power-saving amount and the energy-saving and carbon-reduction amount of the hardware object in the operating state.

[0125] Among them, the power-saving amount may be the power-saving amount of the hardware object in the operating state compared with that in the first state, and the first state is the maximum power consumption state of the hardware object.

[0126] Among them, 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 with that in the first state, and the first state is the maximum power consumption state of the hardware object.

[0127] Taking the energy-saving amount as an example to measure the power-saving 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 cumulative value of the power-saving amount over time can be used to calculate the power-saving amount.

[0128] For example, the data processing device 300 can calculate the power-saving amount of the hardware object based on the following formulas (3) and (4).

[0129]

[0130]

[0131] Among them, h2 is the power saving amount of the hardware object; p i is the actual power consumption; is the power consumption in the non-energy-saving state, T is time, for example, T can be the number of days included in a year: 365 * 24.

[0132] In a possible implementation, the energy saving amount generated by the energy saving measures enabled by the hardware object can be obtained, and then the fusion (such as the summation result) of the energy saving amounts generated by the enabled energy saving measures can describe the power saving amount. That is, the power saving amount can be determined by the energy saving benefits brought by the energy saving measures enabled by the hardware object.

[0133] For example, the power saving amount generated by the energy saving measures enabled by the hardware object can be obtained, and the cumulative power saving amount over time can be calculated to obtain the power consumption saving amount.

[0134] For example, the data processing device 300 can calculate the power saving amount of the hardware object based on the following formula (3).

[0135]

[0136] Among them, h2 is the power saving amount of the hardware object; P k is the fusion (such as the summation result) of the energy saving amounts generated by the enabled energy saving measures.

[0137] Taking the energy saving amount as a measure of the energy saving and carbon reduction amount of the hardware object in the operating state as an example, the difference between the actual power consumption and the non-energy-saving state power consumption can describe the power saving amount. The cumulative power saving amount over time can be calculated to obtain 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 according to the power consumption saving amount and the carbon emission coefficient.

[0138] 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).

[0139]

[0140] Among them, h3 is the energy saving and carbon reduction amount of the hardware object; P k is the power saving amount, N is the carbon emission coefficient of the local area where the device is used, the weight of carbon dioxide emitted per kilowatt-hour of electricity.

[0141] In a possible implementation, the data processing device 300 may calculate the energy consumption of a hardware object according to the actual power consumption and the minimum power consumption, and the energy consumption is used to measure at least one of the power consumption level and the carbon consumption of the hardware object in the operating state.

[0142] Wherein, the power consumption level may be the power consumption amount of the hardware object in the operating state compared to that in the second state, and the second state is the minimum power consumption state of the hardware object.

[0143] Wherein, the carbon consumption is specifically the carbon consumption of the hardware object in the operating state compared to that in the second state, and the second state is the minimum power consumption state of the hardware object.

[0144] Taking the energy consumption as an example to measure the power consumption level of the hardware object in the operating state, the difference between the actual power consumption and the ideal power consumption (i.e., the minimum power consumption) can describe the power consumption level, and the cumulative power consumption amount over time can be used to calculate the power consumption level.

[0145] 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).

[0146]

[0147]

[0148] Wherein, h4 is the power consumption level of the hardware object; p i is the actual power consumption; is the ideal power consumption (i.e., the minimum power consumption), T is time, for example, T can be the number of days included in a year: 365 * 24.

[0149] Taking the energy consumption as an example 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 (i.e., the minimum power consumption) can describe the power consumption amount, and the cumulative power consumption amount over time can be used to calculate the power consumption level, and the power consumption level can be mapped to the carbon consumption. For example, the carbon consumption can be calculated according to the power consumption level and the carbon emission coefficient.

[0150] For example, the data processing device 300 may calculate the carbon consumption of the hardware object based on the following formula (8).

[0151]

[0152] Wherein, h5 is the carbon consumption of the hardware object; P k is the power consumption amount, N is the carbon emission coefficient of the local area where the device is used, the weight of carbon dioxide emitted per kilowatt-hour of electricity.

[0153] After obtaining the target information, the target information can be written into the information of the digital product passport DPP. Among them, the information of the DPP can include the identification of the hardware object and the mapping relationship of the second information. The data processing device 300 can also provide a reading interface for the user and support remote periodic uploading to the cloud for summarization and management.

[0154] A schematic of the information of the DPP can be referred to Table 2:

[0155] Table 2

[0156]

[0157] In the embodiments of the present application, writing the energy-saving state of the hardware object into the information of the digital product passport can provide energy-saving state monitoring and energy-saving and carbon reduction calculation during the actual operation of the hardware object, so that users (such as operation and maintenance personnel) can accurately identify the energy-saving state of the hardware object, support the design and optimization of operation and maintenance energy-saving measures. At the same time, it can provide technical support for the IT device computing power and efficiency management of data centers and supercomputing centers. In addition, the energy-saving and carbon reduction indicators can provide basic data for calculating the carbon emissions and carbon reduction amounts during the operation stage of the user's computing device, facilitating the calculation of annual indicators.

[0158] Furthermore, the data processing device 300 can also perform the following steps:

[0159] Present the information of the digital product passport DPP; or,

[0160] Store the information of the digital product passport DPP; or,

[0161] Send the information of the digital product passport DPP to the server or the terminal device; or,

[0162] When the energy-saving health degree is lower than the first threshold, the energy-saving amount is lower than the second threshold, or the energy consumption is higher than the third threshold, perform an energy-saving operation on the hardware object.

[0163] In this way, users (such as operation and maintenance personnel of hardware objects, etc.) can know the energy-saving state of the hardware object according to the presented energy-saving state of the hardware object, so that users can understand the energy-saving situation of the hardware object during operation.

[0164] In this way, when the energy-saving state of the hardware object is lower than the threshold, the data processing device 300 can perform an energy-saving operation on the hardware object.

[0165] 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 value (such as 85% etc.), it indicates that the current energy-saving state of the hardware object is good, and no further energy-saving operation needs to be performed on the hardware object. When the energy-saving health of the hardware object is less than this threshold value, it indicates that the current energy-saving state of the hardware object is poor, that is, there is more energy waste during the operation of the hardware object. At this time, the data processing device 300 can perform an energy-saving operation on the hardware object according to this energy-saving health to reduce the energy consumption generated during the operation of the hardware object and improve the energy efficiency level of the hardware object.

[0166] Specifically, the data processing device 300 can obtain the energy-saving control parameters of the hardware object, and generate new energy-saving control parameters according to this energy-saving health and the energy-saving control parameters, and perform an energy-saving operation on the hardware object based on the energy-saving control parameters.

[0167] For example, assume that the hardware object is specifically a CPU, the energy-saving health of the CPU is 60%, the energy-saving control parameter is the CPU frequency, and the frequency of the CPU in the current operating state is 3 GHz (gigahertz). Since the energy-saving health of the CPU is lower than 85% (the threshold value), the data processing device 300 can calculate that the frequency that the CPU should reach after downscaling the CPU according to this energy-saving health and the CPU frequency of 3 GHz in the current operating state is 2 GHz; finally, the data processing device 300 reduces the frequency of the CPU to the calculated 2 GHz. Among them, the frequency when the CPU operates indicates the number of synchronous pulses that occur within 1 second of the CPU, and can determine the computing speed of the CPU. Generally, the larger the CPU frequency, the faster the computing speed of the CPU, and correspondingly, the higher the energy consumption of the CPU; on the contrary, the smaller the CPU frequency, the slower the computing speed of the CPU, and the lower the energy consumption of the CPU. Another example is that when the energy-saving health of the CPU is 80%, the data processing device 300 can reduce the frequency of the CPU to 2.8 GHz etc. according to this energy-saving health and the CPU frequency of 3 GHz in the current operating state.

[0168] In this way, by performing real-time evaluation on the hardware object and automatically performing corresponding energy-saving operations, the data processing device 300 can avoid missing the energy-saving point during the operation of the hardware object, and perform energy-saving operations on the hardware object in a timely manner, thereby improving the energy-saving effect on the hardware object and keeping the energy efficiency level of the hardware object at a relatively high level all the time.

[0169] It should be noted that when the hardware object is specifically a device at the second level or a cluster at the third level, the data processing device 300 can also calculate the energy-saving health corresponding to the device or the energy-saving state corresponding to the cluster based on a similar method as above, to realize real-time evaluation of the energy-saving level of the device or the cluster.

[0170] In specific implementation, the data processing device 300 may, based on the process described in the foregoing Figure 2 illustrated embodiment, calculate the energy-saving states corresponding to the multiple electronic devices included in a single device at the second level. Assume that a single device includes N electronic devices (N is a positive integer). Taking the calculation of the energy-saving health degree as an example, for instance, as Figure 3 illustrated, the device may include multiple electronic devices such as a CPU, a memory, a hard disk, a fan, and a power supply unit (PSU). Moreover, during the operation of the device, the multiple electronic devices in the device are all in an operating state and consume energy. Therefore, the data processing device 300 may calculate the energy-saving states of the respective electronic devices according to the operating states of the respective electronic devices.

[0171] Then, the data processing device 300 performs a weighted sum 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 degree as an example, as Figure 3 illustrated. Exemplarily, the data processing device 300 may calculate the energy-saving health degree of the entire device based on the following formula (9).

[0172]

[0173] where h2 is the energy-saving health degree of a single device; N is the number of energy-consuming electronic devices included in the device; h i is the energy-saving health degree of the i-th electronic device; w i is the weight corresponding to the i-th electronic device. Among them, the weights corresponding to the respective electronic devices may be preset by technicians according to the actual application needs, such as setting according to the importance degree or energy consumption ratio of the respective electronic devices, etc., and configuring them in the data processing device 300 so that the data processing device 300 can perform a weighted sum according to the weights and energy-saving health degrees of the respective electronic devices to obtain the energy-saving health degree of the whole machine. Thus, according to the energy-saving health degrees of the electronic devices in each device, the energy-saving health degrees of the respective devices at the second level can be calculated, realizing the real-time evaluation of the energy efficiency levels of the respective devices.

[0174] Moreover, after obtaining the energy-saving health degrees of each device, the data processing device 300 may further perform corresponding energy-saving operations on each device to improve the energy efficiency level of each device. Specifically, in implementation, for each device, the data processing device 300 may compare the energy-saving health degree of the device with a preset threshold. Moreover, when the energy-saving health degree is greater than or equal to the threshold, the data processing device 300 may not perform energy-saving operations. When the energy-saving health degree is less than the threshold, the data processing device 300 may perform energy-saving operations on each electronic component in the device according to the energy-saving health degree or the energy-saving health degrees of each electronic component in the device. For example, the device may include electronic components such as a CPU, a memory, a hard disk, a fan, and a PSU. Then, the data processing device 300 may perform energy-saving operations such as dynamic voltage and frequency scaling (DVFS), sleeping, or turning off processor cores on the CPU; reducing the refresh rate of the memory, such as reducing the refresh rate of the memory from 2000 MHz (megahertz) to 1300 MHz; reducing the head rotation speed of the hard disk, or switching the working mode of the hard disk to the sleep mode; reducing the rotation speed of the fan; switching the power supply mode of the PSU, such as switching the power supply mode of the PSU from the load balancing mode to the primary and standby mode, etc. In an actual test scenario, performing energy-saving operations on the device based on the energy-saving health degree can increase the energy efficiency level of the device by more than 10% on average.

[0175] Since the third-level cluster usually includes one or more devices, the energy consumption of the cluster is the sum of the energy consumptions generated by one or more devices in the cluster. Therefore, the data processing device 300 may further calculate the energy-saving health degree of the cluster according to the calculated energy-saving health degrees of each device.

[0176] Exemplarily, the data processing device 300 may calculate the energy-saving health degree of the cluster based on the following formula (10).

[0177]

[0178] where h3 is the energy-saving health degree of the cluster; M is the number of devices that generate energy consumption included in the cluster; h j is the energy-saving health degree of the jth device; w j is the weight corresponding to the jth device. Among them, the weights corresponding to each device may be preset by technicians according to actual application needs, such as setting according to the importance or energy consumption ratio of each device, etc., and configuring them in the data processing device 300 so that the data processing device 300 can perform weighted summation according to the weights and energy-saving health degrees of each device to obtain the energy-saving health degree of the entire cluster. In this way, the data processing device 300 can realize real-time evaluation of the energy efficiency level of the cluster.

[0179] After obtaining the energy-saving health degree of the cluster, the data processing device 300 can also perform corresponding energy-saving operations on the cluster to improve the energy efficiency level of the cluster. For example, the data processing device 300 can turn off some devices in the cluster, or adjust some devices in the cluster from the running state to the sleep state, etc., so as to reduce the overall energy consumption of the cluster.

[0180] The above Figure 2 In the above-described embodiments, the data processing device 300 determines the non-energy-saving state power consumption of the hardware object and determines the energy-saving health degree or the energy-saving amount according to the non-energy-saving state power consumption, and determines the energy consumption amount according to the ideal power consumption and 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 the non-energy-saving state power consumption of the hardware object in the current running state. Next, the process of training the AI model will be described in detail. Among them, the AI model can be trained by the data processing device 300, or the AI model can be trained by other devices and then provided to the data processing device 300. For the sake of description, the following ideal power consumption and the training process of the AI model executed by the data processing device 300 will be used as an example for illustrative explanation.

[0181] See Figure 4 , which shows a schematic flowchart of the method for the data processing device 300 to train the AI model. As Figure 4 shown, the method includes:

[0182] S401: The data processing device 300 constructs an AI model.

[0183] Exemplarily, the AI model can be, for example, a model constructed based on a neural network model, such as a model constructed based on RNN, DNN, etc.; or, the AI model can be a regression tree model, an SVM model.

[0184] In other embodiments, the AI model can also be constructed by the user and then input into the data processing device 300, which is not limited herein.

[0185] S402: The data processing device 300 collects the historical operation data of the hardware object, and the historical operation data includes the historical operation state of the hardware object in the historical time period and the power consumption generated by the hardware object in the historical operation state.

[0186] Furthermore, the historical operation data collected by the data processing device 300 can also include the energy-saving control parameters adopted by the hardware object in the historical operation state, such as CPU frequency, memory frequency, disk rotation speed, fan rotation speed, power supply mode, etc.

[0187] 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.

[0188] In a possible implementation manner, during the operation of the hardware object, corresponding logs can be generated. The logs are used to record relevant parameters of the hardware object during operation, such as the operating state, operating power (and energy-saving control parameters), etc. Then, the data processing device 300 can obtain the logs generated by the hardware object during the historical time period and read the historical operation data of the hardware object from the logs.

[0189] In another possible implementation manner, the data processing device 300 can record relevant parameters of the hardware object during 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 the historical operation data of the hardware object.

[0190] S403: The data processing device 300 generates training samples based on the collected historical operation data. The training samples include multiple historical operation states of the hardware object during the historical time period and the historical minimum power consumption corresponding to each historical operation state in the multiple historical operation states.

[0191] This embodiment provides the following implementation examples of generating training samples.

[0192] In the first implementation example, the data processing device 300 can traverse the historical operation data to determine multiple historical operation states of the hardware object during the 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 during the historical time period. For example, the power consumption of the hardware object at moment A is 100w, at moment B is 150w, and at moment C is 300w in the same historical operation state, and the multiple energy consumptions are generated by the hardware object under the control of multiple groups of energy-saving control parameters respectively. Then, for each historical operation state, the data processing device 300 determines the lowest power consumption that the hardware object can reach in the historical operation state. For example, the data processing device 300 can compare the power consumptions of the hardware object at moment A, moment B, and moment C and determine that the lowest power consumption that the hardware object can reach in this historical operation state is 100w. In this way, the data processing device 300 can determine the lowest power consumption corresponding to each historical operation state of the hardware object respectively. Then, the data processing device 300 uses the multiple historical operation states as the model input and the lowest power consumption corresponding to each historical operation state as the training label of the AI model to generate training samples for the AI model.

[0193] When the historical operation data obtained by the data processing device 300 further includes energy-saving control parameters, after determining the minimum power consumption corresponding to each historical operation state, the data processing device 300 can further determine the energy-saving control parameter corresponding to the minimum power consumption. Thus, the data processing device 300 uses the multiple historical operation states and the energy-saving control parameter corresponding to each historical operation state as model inputs, and uses the minimum power consumption corresponding to each historical operation state as the training label of the AI model to generate a training sample for the AI model.

[0194] In the second implementation example, each piece of historical operation data obtained by the data processing device 300 includes a historical operation state and an energy-saving control parameter, as shown in the following formula (11).

[0195]

[0196] where s i is the i-th piece of historical operation data; is the i-th historical operation state; is the i-th energy-saving control parameter.

[0197] Then, the data processing device 300 can project the historical operation data with the same historical operation state to obtain a projection state, as shown in the following formula (12).

[0198]

[0199] where is the historical operation data of the projection state.

[0200] 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 minimum power consumption in each projection state, that is, to determine the minimum power consumption that the hardware object can reach in each historical operation state, as Figure 5 shown.

[0201]

[0202] where is the minimum power consumption that can be reached in the i-th historical operation 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 herein.

[0203] Finally, the data processing device 300 may determine the minimum power consumption corresponding to the hardware object in each historical operating state, use the multiple historical operating states as model inputs, use the minimum power consumption corresponding to each historical operating state as training labels for the AI model, and generate training samples for the AI model. Alternatively, the multiple historical operating states and the energy-saving control parameters corresponding to each historical operating state may be used as model inputs, and the minimum power consumption corresponding to each historical operating state may be used as training labels for the AI model to generate training samples for the AI model.

[0204] In this embodiment, the data processing device 300 generates training samples based on the historical operation 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 in other ways. For example, training samples may be generated based on test data, or a technician may set the ideal power consumption of the hardware object in each operating state according to experience.

[0205] S404: The data processing device 300 trains the constructed AI model using the training samples.

[0206] Specifically, the data processing device 300 may input the historical operating states (and energy-saving control parameters) in the training samples into the AI model, and the AI model infers and outputs the ideal power consumption according to the sample inputs. Then, the data processing device 300 may compare the ideal power consumption output by the AI model with the minimum power consumption in the training samples, and adjust the parameters in the AI model according to the deviation between the ideal power consumption and the minimum power consumption, thereby realizing the training of the AI model. In this way, based on the multiple sets of historical operating states and the data of the minimum power consumption in the training samples, the AI model is trained multiple times until the training termination condition of the AI model is met, such as the AI model converges or the number of training times reaches a preset number, etc.

[0207] In this way, the data processing device 300 can implement the training of 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 realize the real-time evaluation of the energy efficiency level of the hardware object.

[0208] It should be noted that when the hardware object is specifically an electronic device, for different electronic devices, the data processing device 300 may train different AI models respectively. For example, an AI model 1 is trained for the CPU, an AI model 2 is trained for the memory, etc., so as to infer the ideal power consumption of different electronic devices using different AI models.

[0209] Further, when the training samples of the AI model are generated based on the historical operation data of the hardware object within a historical time period, the minimum power consumption of the hardware object in some historical operation states may not be the actual minimum power consumption that the hardware object can achieve in these historical operation 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 operation state, it can also correct the ideal power consumption output by the AI model, as shown in the following formula (14).

[0210] p ＊＊ = p ＊ + δ Formula (14)

[0211] Wherein, 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 values of the correction amounts corresponding to different operation states may vary.

[0212] Correspondingly, the energy-saving health degree of the hardware object can be calculated based on the following formula (15).

[0213]

[0214] Wherein, the correction amount δ can be set by a technician.

[0215] Alternatively, the correction amount δ can be dynamically set by the data processing device 300 according to the energy-saving health degree of the hardware object within a period of time.

[0216] In specific implementation, the data processing device can continuously monitor the energy-saving health degree of the hardware object. And when the energy-saving health degree of the hardware object is greater than or equal to the threshold value, 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 continuous duration of the energy-saving health degree of the hardware object being greater than or equal to the threshold value is greater than the first duration, that is, when the duration of the energy-saving health degree of the hardware object continuously being in the high energy-saving health degree range is relatively long, it may be that the ideal power consumption output by the AI model is too high, that is, higher than the actual minimum power consumption that the hardware object can achieve. At this time, the data processing device 300 can reduce the correction amount to lower the value of the ideal power consumption, so that the value of the ideal power consumption used to calculate the energy-saving health degree is closer to the actual minimum power consumption that the hardware object can achieve, thereby improving the accuracy of the calculated energy-saving health degree.

[0217] When the energy-saving health degree of the hardware object is less than the 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 degree to improve the energy efficiency level of the hardware object and achieve further energy saving for the hardware object. However, when the duration for which the energy-saving health degree of the hardware object is less than the threshold value is greater than the second duration, that is, when the duration for which the energy-saving health degree of the hardware object continuously remains in the low energy-saving health degree range is relatively long, 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 degree is closer to the minimum power consumption that the hardware object can actually achieve, thereby improving the accuracy of the calculated energy-saving health degree.

[0218] 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 change curve of the energy-saving health degree of the hardware object calculated based on the continuously adjusted ideal power consumption can be as Figure 6 shown.

[0219] In an actual application scenario, the magnitude of the adjustment amount of the correction amount by the data processing device 300 can gradually decrease as the monitoring duration of the energy-saving health degree of the hardware object increases until it becomes 0, so that the value of the ideal power consumption determined by the data processing device 300 converges. At this time, the accuracy of the energy-saving health degree calculated by the data processing device 300 based on the converged ideal power consumption can be continuously maintained at a high level, that is, the evaluation accuracy of the energy efficiency level of the hardware object can be stably maintained at a high level.

[0220] Furthermore, the data processing device 300 can also update the AI model by using the corrected ideal power consumption, the current operating state of the hardware object (and the energy-saving control parameters adopted), so as to improve the accuracy of the ideal power consumption output by the AI model according to the operating state. In this way, by using the updated AI model to infer the ideal power consumption of the hardware object in various operating states, the accuracy of the energy-saving health degree calculated according to the ideal power consumption can be further improved, and an effective evaluation of the energy efficiency level of the hardware object can be achieved.

[0221] In this embodiment, the data processing device 300 can not only train an AI model for determining the ideal power consumption of the hardware object by using the operating power consumption of the hardware object in the historical time period, so as to use the AI model to achieve real-time evaluation of the energy efficiency level of the hardware object, but also improve the reliability and credibility of the ideal power consumption inferred by the AI model. Therefore, the energy-saving health degree 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.

[0222] Moreover, 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 not reaching the lowest power consumption that can actually be achieved during the historical time period) on the determination of the ideal power consumption. Thus, the accuracy of the energy-saving health degree finally calculated by the data processing device 300 can be further improved, and the accuracy of measuring the energy efficiency level of the hardware object can be improved.

[0223] It should be noted that other reasonable combinations of steps that can be thought of by those skilled in the art based on the above description also fall within the protection scope of this application. Secondly, those skilled in the art should also be familiar that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential for this application.

[0224] The above combines Figures 1A to 6 to introduce the data processing method provided by the embodiments of this application. Next, the functions of the data processing device provided by the embodiments of this application and the data processing system for implementing this data processing device will be introduced with reference to the accompanying drawings.

[0225] See Figure 7 , which shows a schematic structural diagram of a data processing device. The data processing device 700 includes:

[0226] An acquisition module 701, configured to acquire the operating state of a hardware object;

[0227] A processing module 702, configured to obtain information of the Digital Product Passport (DPP) of the hardware object according to the operating state of the hardware object. The information of the Digital Product Passport (DPP) of the hardware object includes at least one of the following target information:

[0228] 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;

[0229] 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,

[0230] 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.

[0231] In a possible implementation manner, the information of the Digital Product Passport (DPP) of the hardware object specifically includes the mapping relationship between the identifier of the hardware object and the target information.

[0232] In a possible implementation, the power consumption savings is specifically the power consumption savings of the hardware object in the operating state compared to the first state; or,

[0233] The energy conservation and carbon reduction amount is specifically the energy conservation and carbon reduction amount of the hardware object in the operating state compared to the first state; or,

[0234] The power consumption level is specifically the power consumption amount of the hardware object in the operating state compared to the second state; or,

[0235] The carbon consumption amount is specifically the carbon consumption amount of the hardware object in the operating state compared to the second state;

[0236] 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.

[0237] In a possible implementation, the operating state is an energy-saving measure enabled on the hardware object;

[0238] The processing module 702 is specifically configured to:

[0239] Obtain the energy savings amount of the hardware object according to the energy savings amount generated by the enabled energy-saving measure.

[0240] In a possible implementation, the operating state is the actual power consumption of the hardware object;

[0241] The processing module 702 is specifically configured to:

[0242] Obtain the information of the digital product passport DPP of the hardware object according to the actual power consumption and the minimum or maximum power consumption at which the hardware object can operate in the operating state.

[0243] In a possible implementation, the operating state is the actual power consumption of the hardware object;

[0244] The energy-saving health degree is calculated according to the actual power consumption and the minimum power consumption at which the hardware object can operate in the operating state.

[0245] In a possible implementation, the energy conservation and carbon reduction amount is calculated according to the power consumption savings amount and the carbon emission coefficient; or,

[0246] The carbon consumption amount is calculated according to the power consumption level and the carbon emission coefficient.

[0247] In a possible implementation, the hardware object includes a plurality of electronic devices, and the energy-saving health degree is the aggregation of the energy-saving health degrees of the plurality of electronic devices; the energy-saving amount is the aggregation of the energy-saving amounts of the plurality of electronic devices; the energy consumption amount is the aggregation of the energy consumption amounts of the plurality of electronic devices.

[0248] In a possible implementation, the device 700 further includes:

[0249] A presentation module, configured to present the information of the digital product passport DPP;

[0250] Or, the processing module 702 is further configured to store the information of the digital product passport DPP;

[0251] Or, a transceiver module, configured to send the information of the digital product passport DPP to a server or a terminal device;

[0252] Or, the processing module 702 is further configured to perform an energy-saving operation on the hardware object 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.

[0253] 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 indicate generating or querying the information of the digital product passport DPP of the hardware object.

[0254] Since Figure 7 the data processing device 700 shown corresponds to Figure 2 the method shown, therefore Figure 7 for the specific implementation manners of the data processing device 700 shown and the technical effects thereof, reference may be made to the relevant descriptions in the foregoing embodiments, and details are not described herein again.

[0255] Figure 8 This is a schematic diagram of a data processing system 800 provided by this application. Figure 8 The data processing system 800 shown can be used to implement Figure 2 the method steps executed by the data processing device 300 in the embodiment shown. In actual application, the data processing system 800 can be, for example, an independently operable card, a server, a processor in a server, etc., and this embodiment does not limit this. For ease of understanding, the hardware structure of the data processing system 800 is introduced below by taking the data processing system 800 as a server as an example.

[0256] As Figure 8As shown, the data processing system 800 includes a processor 801, a memory 802, and a communication interface 803. Among them, the processor 801, the memory 802, and the communication interface 803 communicate through a bus 804, and can also communicate 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. Further, the data processing system 800 may further include a memory unit 805, and the memory unit 805 can be connected to the processor 801, the storage medium 802, and the communication interface 803 through the bus 804.

[0257] Among them, 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:

[0258] Obtain the operating status of the hardware object;

[0259] According to the operating status of the hardware object, obtain the information of the digital product passport DPP of the hardware object, and the information of the digital product passport DPP includes at least one of the following target information:

[0260] 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;

[0261] The energy-saving amount of the hardware object, which is used to measure at least one of the power consumption savings and the energy-saving and carbon-reduction amount of the hardware object in the operating state; or,

[0262] The energy consumption 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.

[0263] It should be understood that in the embodiments of the present application, the processor 801 may be a CPU, and the processor 801 may also 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. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0264] The memory 802 may include a read-only memory and a random access memory, and provide instructions and data to the processor 801. The memory 802 may further include a non-volatile random access memory. For example, the memory 802 may also store information about the device type.

[0265] The memory 802 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0266] The communication interface 803 is used to communicate with other devices connected to the data processing system 800. In addition to including a data bus, the bus 804 can also include a power bus, a control bus, a status signal bus, etc. However, for the sake of clarity, all kinds of buses are labeled as the bus 804 in the figure.

[0267] It should be understood that the data processing system 800 according to the embodiments of the present application can correspond to the data processing device 700 in the embodiments of the present application, and can correspond to the data processing device 300 that executes the method shown in the embodiments of the present application. And the above 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 are not described in detail here. Figure 2

[0268] ​An embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium may be any available medium that can be stored by a computing device or a data storage device such as a data center including one or more available media. The available medium may 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), etc. The computer-readable storage medium includes instructions that direct the computing device to execute the above data processing method.

[0269] An embodiment of 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, they generate, in whole or in part, the processes or functions described in the embodiments of the present application.

[0270] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.).

[0271] The computer program product may be a software installation package. In the case where any method of the foregoing data processing method is needed, the computer program product can be downloaded and executed on a computing device.

[0272] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. 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 wired (such as coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0273] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or replacements, and these modifications or replacements should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A data processing method, characterized in that, The method includes: Obtaining the operating state of a 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 and the energy-saving and carbon-reduction amount of the hardware object in the operating state; or, The energy consumption 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 is specifically the power consumption savings 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 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 is the aggregation of the energy consumptions 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 on 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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