An upgrading method of an electronic device and the electronic device

Abstract

The application discloses an upgrading method of an electronic device and the electronic device. The method comprises the following steps: obtaining hardware configuration information of a first electronic device; determining running capability information of the first electronic device based on the hardware configuration information and based on a preset running dimension; the running capability information represents a running task type scene to which the first electronic device is preferentially adapted based on the hardware configuration information; selecting target hardware configuration information from hardware configuration information of a plurality of second electronic devices based on the running capability information; the hardware specification of the second electronic device is higher than that of the first electronic device; and the hardware configuration information of the second electronic device is collected according to an upgrade target that can be selected by a user.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more particularly to an upgrade method for an electronic device and the electronic device itself. Background Technology

[0002] Currently, when selecting and upgrading electronic devices, recommendations are often based on power consumption metrics. However, relying solely on power consumption ignores the actual application scenarios of users, leading to discrepancies between recommended configurations and users' real needs. Summary of the Invention

[0003] The technical solution provided in this application is as follows:

[0004] The first aspect of this application provides a method for upgrading an electronic device, comprising:

[0005] Obtain the hardware configuration information of the first electronic device;

[0006] Based on the hardware configuration information, the operating capability information of the first electronic device is determined based on a preset operating dimension; the operating capability information represents the operating task type scenario that the first electronic device preferentially adapts to based on the hardware configuration information.

[0007] From the hardware configuration information of multiple second electronic devices, target hardware configuration information is selected based on the operational capability information; the hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected according to the upgrade targets available to the user.

[0008] The step of determining the operational capability information of the first electronic device based on the hardware configuration information and a preset operational dimension includes:

[0009] Based on the hardware configuration information, a plurality of first performance characterization values ​​are determined for the first electronic device; the first performance characterization values ​​characterize the performance of the first electronic device when performing a running task in a corresponding running dimension;

[0010] A first proportional relationship is determined among the plurality of first performance characterization values, and the first proportional relationship is determined as the operating capability information of the electronic device.

[0011] The step of selecting target hardware configuration information that matches the operational capability information from the hardware configuration information of multiple second electronic devices includes:

[0012] Based on the hardware configuration information of each second electronic device, multiple second performance characterization values ​​are determined for each second electronic device; the second performance characterization values ​​characterize the performance of the second electronic device when performing running tasks in the corresponding running dimension;

[0013] Determine a second proportional relationship among multiple second performance characterization values ​​for each of the second electronic devices;

[0014] Determine the similarity between the second proportion relationship and the first proportion relationship for each of the second electronic devices;

[0015] The hardware configuration information of the second electronic device whose similarity meets the similarity threshold is determined as the target hardware configuration information.

[0016] Determining the first proportional relationship among the plurality of first performance characterization values ​​includes:

[0017] The plurality of first performance characterization values ​​are normalized respectively to obtain a plurality of normalized first performance characterization values; the plurality of normalized first performance characterization values ​​represent a first proportion relationship among the plurality of first performance characterization values;

[0018] Determining the second proportional relationship among the plurality of second performance characterization values ​​for each of the second electronic devices includes:

[0019] The multiple second performance characterization values ​​of each second electronic device are normalized to obtain multiple normalized second performance characterization values ​​for each second electronic device; the multiple normalized second performance characterization values ​​represent a second proportional relationship among the multiple second performance characterization values.

[0020] Determining the similarity between the second proportion relationship and the first proportion relationship for each of the second electronic devices includes:

[0021] Determine the Euclidean distance between the normalized multiple second performance characterization values ​​of each second electronic device and the normalized multiple first performance characterization values; wherein, the smaller the Euclidean distance, the higher the corresponding similarity.

[0022] The plurality of first performance characterization values ​​include at least two of the following:

[0023] The first computing performance characterization value characterizes the performance of the first electronic device when performing a running task in the computing processing dimension;

[0024] The first storage performance characterization value characterizes the performance of the first electronic device when performing running tasks in the storage dimension;

[0025] The first network interaction representation value represents the performance of the first electronic device when performing running tasks in the network interaction dimension;

[0026] The first graphics processing performance characterization value characterizes the performance of the first electronic device when performing a running task in the graphics processing dimension.

[0027] The acquisition of the hardware configuration information of the first electronic device includes at least one of the following:

[0028] Obtain the hardware configuration parameters predefined by the hardware manufacturer of the first electronic device;

[0029] Obtain hardware configuration data actively entered by the user;

[0030] Obtain historical hardware configuration data with the same model as the first electronic device from the historical transaction hardware configuration records.

[0031] The method for upgrading the electronic device further includes:

[0032] Obtain reference data corresponding to the target hardware configuration information; the reference data is used to assist the user in selecting the target hardware configuration information to upgrade the first electronic device;

[0033] Output the reference data.

[0034] The reference data includes at least one of the following:

[0035] The first reference data represents the predicted power consumption of the second electronic device corresponding to the target hardware configuration information.

[0036] The second reference data characterizes the carbon emission level of the second electronic device corresponding to the target hardware configuration information;

[0037] The third reference data represents the device price of the second electronic device corresponding to the target hardware configuration information;

[0038] The fourth reference data represents the comprehensive usage cost of the second electronic device corresponding to the target hardware configuration information from the procurement stage to the scrapping stage.

[0039] After determining multiple first performance characterization values ​​of the first electronic device based on the hardware configuration information, the method further includes:

[0040] In response to an adjustment command input by the user, at least one of the plurality of first performance characterization values ​​is modified;

[0041] Determining the first proportional relationship among the plurality of first performance characterization values ​​includes:

[0042] Determine the first proportion relationship among the modified first performance characterization values.

[0043] Another aspect of this application provides an electronic device, comprising:

[0044] Memory, used to store computer programs;

[0045] A processor is configured to execute the computer program to enable the electronic device to perform the following method steps:

[0046] Obtain the hardware configuration information of the first electronic device;

[0047] Based on the hardware configuration information, the operating capability information of the first electronic device is determined based on a preset operating dimension; the operating capability information represents the operating task type scenario that the first electronic device preferentially adapts to based on the hardware configuration information.

[0048] From the hardware configuration information of multiple second electronic devices, target hardware configuration information is selected based on the operational capability information; the hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected according to the upgrade targets available to the user. Attached Figure Description

[0049] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0050] Figure 1 A schematic flowchart illustrating an electronic device upgrade method provided in Embodiment 1 of this application;

[0051] Figure 2 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 2 of this application;

[0052] Figure 3 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 3 of this application;

[0053] Figure 4 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 5 of this application;

[0054] Figure 5 This is a schematic flowchart of an electronic device upgrade method provided in Embodiment 6 of this application;

[0055] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0056] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0057] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0058] The terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of units is not necessarily limited to those units, but may include other units not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0059] In the embodiments of this application, reference is made to Figure 1 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 1 of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps:

[0060] Step S101: Obtain the hardware configuration information of the first electronic device.

[0061] The first electronic device can be a server, personal computer (PC), laptop, workstation, or other device that needs to be upgraded. Hardware configuration information can include hardware parameters such as the model, specifications, and quantity of each component that makes up the first electronic device, such as the model and clock speed of the central processing unit (CPU), the capacity and frequency of the memory, the type and capacity of the hard drive, the port speed and number of ports of the network card, and the model and computing power of the graphics processing unit (GPU).

[0062] There are various ways to obtain the hardware configuration information of the first electronic device, and this application does not limit this method.

[0063] Step S102: Based on the hardware configuration information, determine the operating capability information of the first electronic device based on a preset operating dimension; the operating capability information represents the operating task type scenario that the first electronic device preferentially adapts to based on the hardware configuration information.

[0064] The hardware configuration information of a first electronic device includes hardware parameters of multiple components. However, the hardware parameters of a single component can only reflect the performance of the first electronic device in a certain local capability and cannot directly characterize the types of tasks that the first electronic device can adapt to in actual business. If upgrade selection and matching are directly based on the absolute values ​​of the hardware parameters of each component, the matching result will be highly dependent on the model and generation of the specific component, and will not reflect the business orientation of the device in terms of the overall resource configuration structure. For example, even if processors of different generations have similar clock speeds, differences in their microarchitecture, cache hierarchy, instruction set support, etc., will lead to significant differences in actual computing power; even if the specifications of the same type of hardware from different manufacturers are similar, their design goals and optimization directions may be completely different. All of the above factors will affect the capability distribution structure of the device in various operating dimensions, making it impossible to accurately judge the business adaptability of the device based solely on the absolute values ​​of hardware parameters.

[0065] To this end, this embodiment presupposes multiple operational dimensions, including but not limited to computational processing, storage, network interaction, and graphics processing dimensions. These operational dimensions can be determined based on the functional requirements of each hardware component when the electronic device undertakes various operational tasks, thus establishing a correlation between hardware parameters and operational task types and scenarios. Through these operational dimensions, the evaluation of device capabilities can be expanded from the specification level to the operational task adaptation level.

[0066] The operational capability information of the first electronic device, determined based on the hardware configuration information and the preset operational dimensions, is not a simple list of the operational capabilities of the first electronic device in each preset operational dimension. Rather, it includes the relative strengths and weaknesses of the operational capabilities of the first electronic device in each operational dimension, that is, in which operational dimensions the first electronic device is stronger, in which operational dimensions it is weaker, and the gap between the strengths and weaknesses.

[0067] Different types of tasks have different emphases on capabilities across various operational dimensions. The bottleneck for compute-intensive tasks lies primarily in the processor's computing power, thus requiring the highest level of computational processing capability. The bottleneck for storage-intensive tasks lies primarily in data read / write speed and access capacity, thus requiring the highest level of storage capability. The bottleneck for graphics-intensive tasks lies primarily in parallel computing and graphics rendering capabilities, thus requiring the highest level of graphics processing capability. Therefore, each type of task corresponds to a specific distribution of capability requirements, indicating which dimensions have high requirements, which have low requirements, and the differences between the requirements of each dimension.

[0068] When the relative strengths and weaknesses of a first electronic device across its preset operational dimensions align with the capability requirements of a particular type of task across those dimensions, the device's hardware resource configuration matches the optimal resource allocation structure required to execute that type of task. Therefore, the device is preferentially suited for that type of task. For example, if a device's capabilities in the graphics processing dimension are significantly stronger than in other dimensions, and the gap between its capabilities and those in other dimensions is large, then the device is preferentially suited for graphics-intensive tasks. If the gaps between capabilities across dimensions are small and relatively balanced, then the device is more suitable for general-purpose scenarios involving a mix of tasks.

[0069] Step S103: Select target hardware configuration information from the hardware configuration information of multiple second electronic devices based on the operational capability information; the hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected according to the upgrade targets available for user selection.

[0070] The second electronic device can be an upgrade target device with higher hardware specifications than the first electronic device, which is available to the user, such as a new generation of server models or a higher-configuration workstation. In this embodiment, a database of device configurations can be pre-established and maintained. The database can store the hardware configuration information of multiple second electronic devices, as well as the operational capability information of each second electronic device calculated based on the same preset operational dimensions. This hardware configuration information of the second electronic devices can come from various channels, such as product specifications released by hardware manufacturers, device configuration data from historical sales records, and alternative device information actively entered by users.

[0071] In this embodiment, the operational capability information of the first electronic device can be compared one by one with the operational capability information of each second electronic device in the database to calculate the similarity between the two. The similarity calculation is based on the strength and gap between the operational capabilities in each dimension included in the operational capability information. That is, it compares which dimensions the first electronic device and each second electronic device have stronger capabilities in each preset operational dimension, which dimensions they have weaker capabilities in, and whether the gaps between the strengths and weaknesses are similar. For example, if the operational capability information of the first electronic device shows that its graphics processing capability is significantly stronger than other dimensions and the gap is large, then the performance database will be searched for a second electronic device that also shows a significant advantage in graphics processing capability and a large gap in other dimensions.

[0072] After similarity calculation, the hardware configuration information of one or more second electronic devices with the highest similarity can be identified as the target hardware configuration information. Since the hardware specifications of the second electronic device are higher than those of the first electronic device, and its operational capabilities are highly similar to those of the first electronic device, this target hardware configuration information can provide a higher overall performance level while maintaining the original device's business characteristics, thus achieving precise upgrade selection based on business scenarios. Users can conduct further evaluation and decision-making based on the recommended target hardware configuration information without having to compare hardware parameters and performance indicators one by one among a large number of candidate models.

[0073] In this embodiment, by acquiring the hardware configuration information of the first electronic device to be upgraded, and determining the operational capability information, which includes the strength and gap between the operational capabilities of each dimension, based on preset operational dimensions, the target hardware configuration information that matches the operational capability information is then matched in the second electronic device with higher hardware specifications. This makes the upgrade selection no longer dependent on a single power consumption index or user subjective experience, but starts from the actual operational task types and scenarios that the device is adapted to. This achieves a precise alignment between the selection results and business requirements, ensuring that the selected target hardware configuration information has higher hardware specifications while maintaining the original business orientation. This ensures that the upgraded device can achieve an overall performance improvement while maintaining business adaptability, thereby reducing the risk of wasted hardware resources or insufficient performance due to improper selection.

[0074] At the same time, it avoids the tedious process of users comparing hardware parameters one by one among a large number of candidate models, significantly improving the selection efficiency, thus providing users with an intelligent, efficient and accurate electronic device upgrade selection solution.

[0075] As another optional embodiment of this application, refer to Figure 2 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 2 of this application. This embodiment is mainly an implementation of step S102 in Embodiment 1, such as... Figure 2 As shown, the specific steps may include, but are not limited to, the following:

[0076] Step S1021: Based on the hardware configuration information, determine a plurality of first performance characterization values ​​for the first electronic device; the first performance characterization values ​​characterize the performance of the first electronic device when performing a running task in the corresponding running dimension.

[0077] In this embodiment, the hardware configuration information of the first electronic device can be processed according to the performance calculation method corresponding to each preset operating dimension to obtain the first performance characterization value of the first electronic device in each preset operating dimension.

[0078] Hardware configuration information inherently includes the hardware parameters of each component, which determine the performance level that the device can achieve in the corresponding dimension. For example, the CPU's clock speed and number of cores determine computing power, memory frequency and capacity determine storage access capabilities, disk IOPS determine data read / write capabilities, and network card port speed determines network interaction capabilities. Therefore, the first performance characterization value calculated based on hardware configuration information is essentially a quantitative expression of the inherent performance level of the first electronic device in the operational dimension, determined by the hardware parameters according to the corresponding performance calculation method. It directly reflects the performance of the first electronic device when performing tasks in the corresponding operational dimension.

[0079] This embodiment does not limit the specific method for determining the first performance characterization value, as long as it can objectively reflect the performance of the first electronic device when performing a running task in the corresponding operating dimension.

[0080] Step S1022: Determine the first proportional relationship among the plurality of first performance characterization values, and determine the first proportional relationship as the operating capability information of the electronic device.

[0081] Performance metrics across different operational dimensions reflect a device's absolute capabilities in areas such as CPU, memory, disk, network, and GPU. However, hardware from different generations and manufacturers naturally exhibits differences in absolute values. For example, a 4th generation CPU from the same brand generally has a higher performance metric than a 3rd generation CPU. Even if performance metrics are similar between different brands, their hardware architectures and designs may be completely different. These architectural differences lead to varying resource allocation efficiencies and capability focuses when performing the same task, thus affecting their proportion across different operational dimensions.

[0082] If subsequent matching comparisons are based solely on the absolute values ​​of each primary performance indicator, results may emerge where the absolute values ​​are similar but the business inclinations differ. This is because similar absolute values ​​do not necessarily indicate similar strengths or weaknesses in each dimension. The strengths and weaknesses in each dimension are the fundamental factors determining the type of task a device is suited for. For example, one device might excel in both computational and graphics processing but be relatively weak in storage; another device might be weak in both but relatively strong in storage. While the absolute values ​​of these two devices' performance indicators may be similar, their strengths and weaknesses in each dimension are entirely different, resulting in different business inclinations. The goal of upgrade selection is to improve overall hardware specifications while maintaining the existing device's business inclinations, rather than seeking a device with similar absolute values ​​but vastly different strengths and weaknesses in each dimension.

[0083] In contrast, the proportion relationship preserves the relative position and proportion between the capabilities of each operational dimension, rather than the specific numerical value of each dimension. The proportion relationship reflects the percentage of each dimension's capability within the overall capability distribution; that is, which dimensions are stronger, which are weaker, and the gap between the strong and weaker ones, thus characterizing whether the device is computationally oriented, graphics-oriented, or balanced. This proportion relationship typically remains stable during hardware upgrades because it is determined by the device's design positioning and business adaptation direction, rather than by hardware generations. As long as the device's business orientation remains unchanged, the proportion relationship between its dimensions remains essentially constant, while the absolute values ​​will increase overall with hardware iterations.

[0084] In other words, the essence of upgrade matching lies in matching the strengths and weaknesses of capabilities across different dimensions, rather than matching the absolute magnitude of capabilities in each dimension. Two devices may have significantly different absolute performance levels, but as long as the proportions of each dimension are consistent—that is, which dimensions are stronger, which are weaker, and the gaps between them are consistent—they are suitable for handling the same type of operational tasks. Conversely, two devices may have similar absolute performance levels, but if the proportions are different—that is, which dimensions are stronger, which are weaker, and the gaps between them are different—their business orientations differ, and a forced upgrade may result in a mismatch between the new equipment and business needs.

[0085] In this embodiment, by comparing the highs and lows and the differences between the various first performance characterization values, it can be determined which dimensions have relatively high first performance characterization values, which dimensions have relatively low first performance characterization values, and the degree of difference between high and low values, as the first proportion relationship.

[0086] In this embodiment, by determining multiple first performance characterization values ​​and further determining the first proportion relationship between them, and using this proportion relationship as operational capability information, the incomparability caused by differences in measurement units and magnitudes between different hardware generations and different manufacturers is eliminated. This allows devices with different hardware generations and overall differences in absolute performance values ​​to be identified as devices with the same business orientation as long as their capability distribution structure is similar, thus achieving cross-generational and cross-manufacturer capability comparability.

[0087] Meanwhile, since the first proportion relationship is determined by the design positioning and business adaptation direction of the equipment, rather than by the generation of hardware, when upgrading hardware, as long as the business orientation of the equipment remains unchanged, the proportion relationship between its various dimensions remains basically stable. This allows for more accurate screening of the second electronic device that is consistent with the business orientation of the first electronic device, avoiding incorrect matching caused by similar absolute performance values ​​but different capability structures, thereby improving the accuracy of business orientation matching in upgrade selection.

[0088] As an optional implementation, the corresponding operational dimension may include, but is not limited to, computational processing dimension, storage dimension, network interaction dimension, and graphics processing dimension. Accordingly, the plurality of first performance characterization values ​​may include at least two of the following:

[0089] The first computing performance characterization value characterizes the performance of the first electronic device when performing a running task in the computing processing dimension;

[0090] The first storage performance characterization value characterizes the performance of the first electronic device when performing running tasks in the storage dimension;

[0091] The first network interaction representation value represents the performance of the first electronic device when performing running tasks in the network interaction dimension;

[0092] The first graphics processing performance characterization value characterizes the performance of the first electronic device when performing a running task in the graphics processing dimension.

[0093] The specific methods for determining the above performance characterization values ​​can be based on the hardware components and their specifications corresponding to each operational dimension, according to preset calculation rules. The calculation of the performance characterization values ​​for each operational dimension is explained below using a specific implementation method:

[0094] (a) First computational processing performance characterization value.

[0095] As one implementation method, the adjusted peak performance (APP) data can be used as the first computational processing performance characterization value.

[0096] APP is a standardized processor performance evaluation metric that comprehensively considers factors such as CPU clock speed, core count, and architectural efficiency, and can accurately reflect the processor's theoretical peak performance in computing tasks. This data is usually provided by processor manufacturers or third-party testing organizations and can be obtained directly by looking up a table based on the CPU model.

[0097] (ii) First storage performance characterization value.

[0098] The storage dimension includes two parts: the memory sub-dimension and the disk sub-dimension. The performance characteristics of the storage dimension can be determined by combining memory performance and disk performance.

[0099] Determining memory performance depends on both the memory's operating frequency and the configuration of the memory channels. Specifically, a performance factor can be determined based on the number of memory modules installed in a memory channel and the channel's occupancy. Multiplying the memory's operating frequency by this factor yields the memory performance value.

[0100] The configuration of memory channels includes two aspects: the number of memory modules per channel and the channel occupancy.

[0101] Memory Modules Per Channel (DPC): This refers to the number of memory modules installed on each memory channel, including 1 module per channel (1DPC) and 2 modules per channel (2DPC).

[0102] Channel occupancy status: This refers to the percentage of memory channels that are occupied, including three scenarios: Full, Half, and Others. Full means all available memory channels are occupied, Half means half of the available memory channels are occupied, and Others indicates a situation between Full and Half or a non-standard configuration.

[0103] The combination of the number of memory modules per channel and channel occupancy determines the memory performance factor. Different combinations correspond to different factor values, reflecting the combined impact of factors such as signal integrity, electrical load, and memory access interleaving on memory performance in a multi-channel memory architecture. Specifically, when one memory module is installed per channel and the channel is fully utilized, the memory operates at its highest efficiency, with a factor of 1. When two memory modules are installed per channel or the channel occupancy is insufficient, memory performance will be affected to varying degrees, and the factor will decrease accordingly.

[0104] The rules for determining memory performance are shown in the table below:

[0105]

[0106] The corresponding calculation formula is: Performance Score = frequency × factor (Memory performance score = frequency × impact factor).

[0107] The following table illustrates a specific calculation example. In one example, assuming the memory operating frequency is 6400MHz, the capacity of a single memory module is 32GB, a full 1DPC configuration corresponds to 16 memory modules (16×DIMMs), and a full 2DPC configuration corresponds to 32 memory modules (32×DIMMs), the memory performance calculations for each channel configuration are as follows:

[0108]

[0109] As shown in the table above, the performance impact factor is 0.95 when 2DPC is fully configured, and 1 when 1DPC is fully configured. This indicates that at the same operating frequency, the memory performance of a fully configured 1DPC is higher than that of a fully configured 2DPC.

[0110] Determining disk performance is related to the disk type and RAID configuration. Specifically, the base IOPS value can be obtained from the disk's specifications. This base value reflects the random read and write performance of a single disk with 4KB block sizes. Then, the corresponding RAID performance impact factor (RAID policy) is determined based on the RAID level used by the disk. Multiplying the base IOPS value by this factor yields the disk performance characterization value.

[0111] That is: Disk performance rating = Specification IOPS (4KB block read / write) × RAID strategy impact coefficient.

[0112] Among them, RAID policy will impact disk performance. Different RAID levels have different impacts on read and write performance, and the impact coefficients on read and write performance are different.

[0113] Specifically, RAID levels can include RAID 0, RAID 1, RAID 5, RAID 6, RAID 10, etc. The read / write speed of a single disk (in MB / s) is determined by the disk specifications. N represents the number of disks in the array, R represents the single disk read speed (MB / s), and W represents the single disk write speed (MB / s). The read / write performance calculations for each RAID level are shown in the table below:

[0114]

[0115] In one example, assuming that the read speed (R) and write speed (W) of a single disk are both 100 MB / s (i.e., a single disk represents 100% performance), and N=4, the read and write performance corresponding to each RAID level is calculated as follows:

[0116]

[0117] It should be noted that RAID 5 and RAID 6 suffer from a write penalty due to the need to calculate parity information, resulting in a significant reduction in write speed. The percentage values ​​mentioned above represent multiples of the performance of a single disk.

[0118] The above memory performance and disk performance values ​​reflect the storage capabilities of the first electronic device from the two sub-dimensions of memory and disk, respectively.

[0119] As an optional implementation, the first storage performance characterization value can be both the memory performance characterization value and the disk performance characterization value, that is, the storage dimension capability is jointly characterized by the two sub-dimensions of memory and disk.

[0120] As another alternative implementation, the first storage performance characterization value can also be a single comprehensive value obtained by comprehensively processing the memory performance characterization value and the disk performance characterization value, such as the sum of the two, or a weighted average, etc.

[0121] This application does not impose specific limitations on this, as long as it can objectively reflect the performance of the first electronic device when performing tasks in the storage dimension.

[0122] (iii) First network interaction representation value.

[0123] As one implementation method, the first network interaction representation value can be calculated according to the following relationship:

[0124] Network=Port rate×port number

[0125] Wherein, Network represents the first network interaction characteristic value, Port rate represents the port rate of the network interface, and port number represents the number of ports.

[0126] For example, a dual-port 10 Gigabit Ethernet card has a first network interaction performance value of 10Gbps × 2 = 20Gbps.

[0127] (iv) First graphics processing performance characterization value.

[0128] As one implementation method, the peak double-precision floating-point performance (Peak FP64) and peak single-precision floating-point performance (Peak FP32) of the graphics processor can be obtained based on its specifications, and the two can be added together to obtain the first graphics processing performance characterization value.

[0129] The performance characterization values ​​for each of the above operational dimensions are calculated based on the hardware configuration information of the first electronic device, and do not depend on the real-time load status of the device during operation. Therefore, it is possible to make horizontal comparisons between different devices under the same benchmark, ensuring the consistency and effectiveness of subsequent determination of proportion relationships and similarity matching. Furthermore, for the same operational dimension, each electronic device uses the same calculation rules and standards to determine the performance characterization value, thereby ensuring the comparability of the performance characterization values ​​of each device in the same dimension.

[0130] It should be noted that the above-mentioned division of operational dimensions and the corresponding determination of performance characterization values ​​are merely illustrative examples, and this application does not impose any specific limitations on them. In actual applications, they can be flexibly adjusted according to the device type and application scenario.

[0131] As another optional embodiment of this application, refer to Figure 3 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 3 of this application. This embodiment is mainly an implementation of step S1031 in Embodiment 2, such as... Figure 3 As shown, the specific steps may include, but are not limited to, the following:

[0132] Step S1031: Based on the hardware configuration information of each second electronic device, determine multiple second performance characterization values ​​for each second electronic device; the second performance characterization values ​​characterize the performance of the second electronic device when performing a running task in the corresponding running dimension.

[0133] For each second electronic device in the database, the hardware configuration information of the second electronic device can be processed according to the performance calculation method corresponding to each preset operating dimension, in the same way as step S1021 in Embodiment 2, to obtain the second performance characterization value of the second electronic device in each preset operating dimension.

[0134] For example, for the computational processing dimension, the APP data can be determined based on the CPU model and specifications of the second electronic device; for the storage dimension, memory performance characterization values ​​and disk performance characterization values ​​can be calculated based on the memory configuration and disk configuration of the second electronic device, respectively; for the network interaction dimension, network performance characterization values ​​can be calculated based on the network card port speed and number of ports of the second electronic device; for the graphics processing dimension, graphics processing performance characterization values ​​can be calculated based on the GPU model and specifications of the second electronic device. Through the above methods, multiple second performance characterization values ​​corresponding to each second electronic device across multiple operational dimensions can be obtained.

[0135] Step S1032: Determine the second proportion relationship among the multiple second performance characterization values ​​of each of the second electronic devices.

[0136] The second proportional relationship corresponds to the first proportional relationship in meaning, and will not be repeated here. The method for determining the second proportional relationship is the same as that for determining the first proportional relationship, and will not be repeated here.

[0137] Step S1033: Determine the similarity between the second proportion relationship and the first proportion relationship for each of the second electronic devices.

[0138] As an optional implementation, the first and second proportion relationships can be represented as vectors or sequences of the relative strengths of capabilities in each dimension, and their similarity can be measured by calculating the distance or angle between them. The closer the first and second proportion relationships are, the higher the similarity between the two electronic devices in terms of capability distribution structure in each dimension, that is, the more consistent the types of operational tasks they are preferentially suited for.

[0139] This application does not limit the specific calculation method of similarity, as long as it can objectively reflect the degree of similarity between the two proportion relationships.

[0140] Step S1034: Determine the hardware configuration information of the second electronic device whose similarity meets the similarity threshold as the target hardware configuration information.

[0141] In this embodiment, the similarity threshold can be set or adjusted according to the actual application scenario, for example, it can be set to 60%, 70% or 80%.

[0142] As one implementation method, when the similarity between the second proportion relationship and the first proportion relationship of a certain second electronic device is greater than or equal to a preset similarity threshold, it is determined that the second electronic device has the same business orientation as the first electronic device, and its hardware configuration information is identified as the target hardware configuration information. When multiple second electronic devices meet the similarity threshold condition, they can be sorted in descending order of similarity, and the hardware configuration information of one or more second electronic devices with the highest similarity can be provided to the user as the recommendation result.

[0143] As another implementation, when the similarity of all second electronic devices fails to reach the preset similarity threshold, a prompt message can be sent to the user, suggesting that the user manually adjust the selection parameters or expand the search range to ensure the rationality and usability of the selection results.

[0144] In this embodiment, by determining multiple second performance characterization values ​​and their second proportion relationships for each second electronic device, and comparing the similarity of each second proportion relationship with the first proportion relationship, the matching process in the upgrade selection no longer relies on the absolute values ​​of each hardware parameter, but is based on the proportion relationships that reflect the distribution structure of device capabilities. Since the proportion relationships are determined by the device's design positioning and business adaptation direction, and do not undergo structural changes with the overall performance improvement of hardware generations, this matching method can establish an effective horizontal comparison benchmark between devices of different generations and from different manufacturers.

[0145] When the similarity between the second and first proportion relationships meets a threshold condition, it means that the capability distribution of the second electronic device across various operational dimensions is basically consistent with that of the first electronic device. This means both devices are preferentially suited to the same type of operational task scenarios. Furthermore, since the hardware specifications of the second electronic device are generally superior to those of the first, this matching result can achieve performance upgrades while maintaining business adaptability. Therefore, matching devices through proportion relationship comparison effectively avoids mismatches caused by similar absolute values ​​but different capability distribution structures, improving the accuracy and reliability of business-oriented matching in upgrade selection. Simultaneously, by setting similarity thresholds and ranking mechanisms, flexible and adjustable recommendation results tailored to specific business scenarios can be provided to users.

[0146] In an optional implementation, determining the first proportional relationship among the plurality of first performance characterization values ​​in step S1022 above may include:

[0147] Step S10221: Normalize the plurality of first performance characterization values ​​respectively to obtain a plurality of normalized first performance characterization values; the plurality of normalized first performance characterization values ​​represent a first proportion relationship among the plurality of first performance characterization values.

[0148] In this embodiment, since the performance representation values ​​of different operating dimensions have different units of measurement and magnitudes—for example, the performance representation value of the CPU dimension may be in GFLOPS, the performance representation value of the disk dimension may be in IOPS, and the performance representation value of the GPU dimension may be in TFLOPS—these raw values ​​cannot be directly compared and calculated. The purpose of normalization is to eliminate the incomparability between different operating dimensions caused by different units of measurement and magnitudes, and to map the performance representation values ​​of each dimension to the same scale.

[0149] Specifically, normalization refers to converting each first performance characterization value into a normalized value within the same numerical range according to a preset transformation rule. As an optional implementation, the Min-Max normalization method can be used to map each first performance characterization value to the [0,1] interval, with the sum of all normalized values ​​being 1. Thus, each normalized value represents the proportion of the performance characterization value of its corresponding operating dimension in the total performance of all dimensions, i.e., the proportion of that dimension. The proportions of each dimension collectively constitute a normalized numerical sequence. The relative magnitudes of the values ​​in this sequence directly reflect the relative strength of each dimension's capabilities. Therefore, the multiple first performance characterization values ​​after normalization represent the first proportion relationship among these multiple first performance characterization values.

[0150] For example, suppose an electronic device has first-order performance values ​​of (400, 200, 100, 300) in the dimensions of computing processing, storage, network interaction, and graphics processing, respectively. Normalizing these values ​​involves first calculating the sum: 400 + 200 + 100 + 300 = 1000. Then, dividing the performance value of each dimension by the sum yields normalized values ​​of (0.4, 0.2, 0.1, 0.3). The sum of these values ​​is 1, and each value represents the proportion of the corresponding operational dimension in the overall performance. Therefore, the device has the highest proportion in the computing processing dimension (0.4) and the lowest proportion in the network interaction dimension (0.1). The difference in proportions between dimensions reflects the device's capability distribution across these operational dimensions. Thus, the normalized numerical sequence constitutes a proportional relationship reflecting the capability distribution of each dimension.

[0151] Step S1032 above may include, but is not limited to:

[0152] Step S10321: Normalize the multiple second performance characterization values ​​of each second electronic device to obtain multiple normalized second performance characterization values ​​for each second electronic device; the multiple normalized second performance characterization values ​​characterize the second proportion relationship among the multiple second performance characterization values.

[0153] In this embodiment, the multiple second performance characterization values ​​can be normalized using the same normalization method as in step S10221. Specifically, for each second electronic device, the same normalization method is used to map the second performance characterization values ​​of each dimension to the same numerical range, and the sum of each normalized value is 1. In this way, each second electronic device obtains a set of normalized numerical sequences, where each value in the numerical sequence represents the capability proportion of the second electronic device in each operating dimension, thus constituting the second proportion relationship of the second electronic device.

[0154] Step S1033 above may include, but is not limited to:

[0155] Step S10331: Determine the Euclidean distance between the normalized multiple second performance characterization values ​​of each second electronic device and the normalized multiple first performance characterization values; wherein, the smaller the Euclidean distance, the higher the corresponding similarity.

[0156] In this embodiment, the normalized multiple first performance characterization values ​​can be regarded as a vector in a multidimensional space, and the normalized multiple second performance characterization values ​​can be regarded as another vector in the same multidimensional space. The Euclidean distance between the two vectors is calculated. The smaller the Euclidean distance, the closer the two vectors are in the multidimensional space, that is, the more similar the capability distribution of the two devices in each operating dimension, and therefore the higher the similarity.

[0157] For example, if the preset operating dimensions are four, and the normalized numerical sequence of the first electronic device is (0.2, 0.2, 0.2, 0.4), while the normalized numerical sequence of a second electronic device is (0.3, 0.1, 0.2, 0.4), then the Euclidean distance between them can be obtained by calculating the square root of the sum of the squares of the differences in each corresponding dimension. If the normalized numerical sequence of another second electronic device is (0.3, 0.3, 0.3, 0.1), then its Euclidean distance with the first electronic device is relatively large. Therefore, the Euclidean distance can reflect the similarity in capability distribution between two devices from the perspective of the overall distribution across all dimensions, thus serving as a quantitative basis for similarity.

[0158] After calculating the Euclidean distance between each second electronic device and the first electronic device, the multiple second electronic devices can be sorted according to the magnitude of the Euclidean distance. The second electronic device with the smallest Euclidean distance is determined as the candidate upgrade target with the highest similarity to the first electronic device, or recommended to the user in order of increasing Euclidean distance.

[0159] By using Euclidean distance as a quantitative indicator, we can objectively and accurately measure the degree of matching between each candidate device and the first electronic device in terms of business orientation, thereby providing a quantitative reference for upgrade selection decisions.

[0160] In this embodiment, a performance database for device configuration can be pre-established and maintained. This database stores hardware configuration information, performance characterization values ​​for various operational dimensions, service categories, and device information for multiple second electronic devices. An exemplary performance database table is provided below for illustration:

[0161]

[0162] In this example database, each row represents a second electronic device, and each column stores the device's performance characteristics across five operational dimensions: CPU, memory, disk, network, and GPU, along with the corresponding business category and device model information. The values ​​in the CPU, memory, disk, network, and GPU columns are the raw performance characteristics determined according to the aforementioned performance calculation methods for each operational dimension; these raw performance characteristics have not yet been normalized. When performing similarity matching with the first electronic device, these raw performance characteristics need to be normalized to obtain the normalized proportions for each dimension, thereby determining the second proportion relationship between the second electronic devices. The business category identifies the business scenarios that the device is suitable for in actual applications, such as AI learning or general computing services. The device model information identifies the device's model or generation, such as "4th generation" or "3rd generation."

[0163] It should be noted that the second proportion relationship (i.e., the strength of each dimension and the gap between the strengths) obtained after normalizing the original performance characterization values ​​of each second electronic device corresponds to the business classification. Specifically, each second proportion relationship corresponds to a business classification, meaning that the performance tendency of the device determines the type of task it is best suited for. For example, although Sample 1 and Sample 2 differ in absolute values ​​of CPU, memory, disk, and network, their GPU performance characterization values ​​are significantly higher than those of other dimensions, indicating that their performance tendency is dominated by the graphics processing dimension. Therefore, they are both classified as AI learning business. Sample 3, on the other hand, has relatively balanced performance characterization values ​​across all dimensions, with no single dimension showing significant dominance. Therefore, it is classified as "ordinary computing business".

[0164] During similarity matching, the matching is based on the first proportion relationship of the first electronic device and the second proportion relationship of each second electronic device, without explicitly relying on the business classification field. That is, the matching is based on the similarity between the first and second electronic devices in terms of capability distribution across various dimensions, rather than the consistency of business classification labels. However, business classification can be presented to users as supplementary information to help them intuitively understand the business suitability direction of the recommendation results, facilitating their selection decisions.

[0165] As another optional embodiment of this application, this embodiment provides an upgrade method for an electronic device according to Embodiment 4 of this application. This embodiment is mainly an implementation of step S101 in Embodiment 1, and may include, but is not limited to, at least one of the following:

[0166] Step S1011: Obtain the hardware configuration parameters predefined by the hardware manufacturer of the first electronic device.

[0167] When electronic devices leave the factory, hardware manufacturers typically record the device's hardware configuration parameters, such as processor model and clock speed, memory capacity and frequency, hard drive capacity and type, and network interface information, in the device's Basic Input / Output System (BIOS) or Unified Extensible Firmware Interface (UEFI). This information is stored in the device's System Management BIOS (SMBIOS) data. By reading the SMBIOS data from the BIOS / UEFI, the standard hardware configuration parameters of the first electronic device can be obtained.

[0168] The advantage of this method is that the data obtained comes directly from the underlying hardware, has high accuracy and reliability, and does not require manual input from the user, thus improving the automation of information collection.

[0169] Step S1012: Obtain the hardware configuration data actively entered by the user.

[0170] When users have a clear understanding of the hardware components of their devices, they can manually input or select various hardware configuration parameters through a human-computer interaction interface. For example, users can input or select information such as CPU model, memory capacity and frequency, hard drive type and capacity, network card speed and number of ports, GPU model and computing power through a graphical user interface or command-line interface to collect hardware configuration information.

[0171] The advantage of this method lies in its high flexibility, making it suitable for scenarios where hardware information cannot be read automatically, such as when the device is powered off or the operating system fails to boot. Furthermore, this method can also meet the customization needs of users who wish to specify particular configuration parameters.

[0172] Step S1013: Obtain historical hardware configuration data with the same model as the first electronic device from the historical transaction hardware configuration records.

[0173] When users cannot directly obtain the detailed configuration of the current device, they can query the configuration information of the same model device in historical sales records or device management databases based on the machine model or serial number of the first electronic device. For example, enterprise asset management systems or sales systems usually record the configuration information of sold or deployed devices. Users can retrieve the corresponding hardware configuration parameters from these systems by entering the device model or serial number, which serves as the hardware configuration information of the first electronic device.

[0174] The advantage of this method is that it does not rely on the device's own operating status, making it suitable for situations where the device cannot be powered on or the user cannot access the device's BIOS. At the same time, it can utilize existing data resources to reduce information collection costs.

[0175] The three acquisition methods described above can be used individually or in combination. For example, as a preferred implementation, step S1011 can be used to obtain hardware configuration information by reading BIOS / UEFI data. If the reading fails or the data is incomplete, the user is prompted to manually enter the information in step S1012. If the user cannot provide complete configuration data, step S1013 is used to query and supplement the data from historical transaction records. By combining these methods, the success rate and completeness of hardware configuration information acquisition can be improved, adapting to the information collection needs of different application scenarios, thereby ensuring the smooth execution of subsequent steps.

[0176] As another optional embodiment of this application, refer to Figure 4 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 5 of this application. Figure 4 As shown, the method may include, but is not limited to, the following steps:

[0177] Step S201: Obtain the hardware configuration information of the first electronic device.

[0178] Step S202: Based on the hardware configuration information, determine the operating capability information of the first electronic device based on a preset operating dimension; the operating capability information represents the operating task type scenario that the first electronic device preferentially adapts to based on the hardware configuration information.

[0179] Step S203: Select target hardware configuration information from the hardware configuration information of multiple second electronic devices based on the operational capability information; the hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected according to the upgrade targets available for user selection.

[0180] For a detailed description of steps S201-S203, please refer to the relevant description of steps S101-S103 in Example 1, which will not be repeated here.

[0181] Step S204: Obtain reference data corresponding to the target hardware configuration information; the reference data is used to assist the user in selecting the target hardware configuration information to upgrade the first electronic device.

[0182] In this embodiment, the reference data can be used to provide users with one or more dimensions of auxiliary information related to upgrade selection decisions, helping users to fully understand the characteristics of the recommended configuration from multiple perspectives, so that users can make the final upgrade selection decision based on their own needs and constraints.

[0183] In one alternative implementation, the reference data may include, but is not limited to, at least one of the following:

[0184] The first reference data represents the predicted power consumption of the second electronic device corresponding to the target hardware configuration information, that is, the estimated power consumption level of the second electronic device under typical operating load. This data can be estimated based on the power consumption specifications of each hardware component of the second electronic device and the typical workload model.

[0185] The second reference data characterizes the carbon emission level of the second electronic device corresponding to the target hardware configuration information, that is, the carbon emission expected to be generated by the second electronic device during operation. This data can be calculated based on the predicted power consumption and the preset carbon emission conversion coefficient, and is used to assist users in assessing the environmental impact of the upgrade plan.

[0186] The third reference data represents the equipment price of the second electronic device corresponding to the target hardware configuration information, that is, the hardware procurement cost of the second electronic device. This data can come from the official pricing of the hardware manufacturer, historical transaction prices, or real-time market quotations.

[0187] The fourth reference data represents the comprehensive usage cost of the second electronic device corresponding to the target hardware configuration information from the procurement stage to the scrapping stage. This comprehensive usage cost may include the total cost of ownership over the entire equipment life cycle, such as equipment procurement cost, operating energy consumption cost, maintenance cost, and scrapping disposal cost, and is used to assist users in evaluating upgrade solutions from a long-term economic perspective.

[0188] It should be noted that the types and sources of the reference data mentioned above are merely illustrative examples. This application does not limit the specific types, acquisition methods, or calculation methods of the reference data. In practical applications, the types and contents of the reference data can be increased, decreased, or adjusted according to specific needs.

[0189] Step S205: Output the reference data.

[0190] In this embodiment, the reference data can be output to the user. The output method may include, but is not limited to, presenting the data on a display interface as a list, chart, or comparison report, or notifying the user via voice, email, or other means. By outputting the reference data, users can intuitively understand the differences in power consumption, carbon emissions, equipment price, and overall operating costs among various candidate hardware configurations. This allows them to make a more reasonable and comprehensive upgrade selection decision from multiple candidate solutions that match their business needs, taking into account their budget constraints, environmental requirements, and business requirements.

[0191] In this embodiment, by acquiring and outputting reference data corresponding to the target hardware configuration information, it is possible not only to provide users with upgrade selection solutions that match their business preferences, but also to provide users with multi-faceted auxiliary decision-making information, making upgrade selection decisions more transparent, comprehensive and efficient, and further reducing the risk of decision-making errors caused by ignoring factors such as power consumption and cost.

[0192] As another optional embodiment of this application, refer to Figure 5 This is a flowchart illustrating an electronic device upgrade method provided in Embodiment 6 of this application. This embodiment is mainly an implementation of step S102 in Embodiment 1, such as... Figure 5 As shown, the specific steps may include, but are not limited to, the following:

[0193] Step S1023: Based on the hardware configuration information, determine a plurality of first performance characterization values ​​for the first electronic device; the first performance characterization values ​​characterize the performance of the first electronic device when performing a running task in the corresponding running dimension.

[0194] For a detailed description of step S1023, please refer to the relevant description of step S1021 in Example 2, which will not be repeated here.

[0195] Step S1024: In response to the adjustment command input by the user, at least one of the plurality of first performance characterization values ​​is modified.

[0196] In this embodiment, after automatically determining multiple first performance characterization values, the user can also be provided with the option of manual adjustment. Specifically, the user can input adjustment commands through a human-computer interaction interface to modify the performance characterization value of at least one dimension among the multiple first performance characterization values.

[0197] In practical applications, the automatically determined first performance characterization value is a theoretical value calculated based on hardware configuration information according to preset rules. However, users' actual business scenarios may have unique characteristics. For example, users may prefer to focus on the performance of a certain dimension, or the actual workload of users may require higher capabilities in certain dimensions than reflected by the theoretical values. In this case, users can manually adjust the performance characterization values ​​of one or more operating dimensions according to their actual business needs, so that the adjusted performance characterization values ​​better reflect actual requirements.

[0198] Through the above method, this embodiment can give users the ability to flexibly adjust based on automatically determined performance characterization values, so that subsequent upgrade selection results can better meet the user's personalized needs.

[0199] Step S1025: Determine the first proportion relationship among the modified multiple first performance characterization values, and determine the first proportion relationship as the operating capability information of the electronic device.

[0200] In this embodiment, based on the modified first performance characterization values, a first proportional relationship among the modified first performance characterization values ​​can be determined in the same manner as in the aforementioned embodiments, and this first proportional relationship can be determined as the operational capability information of the first electronic device. In subsequent steps, upgrade selection and matching can be performed based on the modified operational capability information to obtain target hardware configuration information that matches the user's adjusted needs.

[0201] Furthermore, as an optional implementation, this embodiment can also record the user's adjustment instructions, along with corresponding device and scenario information, and store this information in a database. When performing upgrade selections again, the database can be searched for matching historical adjustment records based on the current device's characteristics and scenario information. If similar business scenarios exist, the user's historically adjusted data can be directly referenced as the basis for recommendations, eliminating the need for the user to repeat the adjustment process and improving the intelligence and efficiency of the selection process. Simultaneously, the user's adjusted data reflects their actual business needs. Including this data in the database enriches the capability type samples within the database, especially when data is incomplete during the initial database construction phase. The user's historical adjustment data can serve as an effective supplement, continuously improving the accuracy and adaptability of the selection recommendations.

[0202] In this embodiment, by modifying the first performance characterization value in response to user-inputted adjustment commands, the upgrade selection method provided in this application can be adjusted based on the user's personalized needs in addition to automatic recommendations, thereby improving the flexibility of the selection results and user satisfaction. Simultaneously, by recording the user's historical adjustment data and feeding it back to the database, the accuracy of the selection recommendations can be continuously optimized, achieving self-improvement and iterative upgrades of the selection system.

[0203] The following section describes the upgrade apparatus for the electronic device provided in this application. The upgrade apparatus for the electronic device described below can be referred to in correspondence with the upgrade method for the electronic device described above.

[0204] Upgrade devices for electronic equipment may include:

[0205] The first acquisition module is used to acquire the hardware configuration information of the first electronic device;

[0206] The first determining module is used to determine the operating capability information of the first electronic device based on the hardware configuration information and a preset operating dimension; the operating capability information represents the operating task type scenario that the first electronic device preferentially adapts to based on the hardware configuration information.

[0207] The selection module is used to select target hardware configuration information from the hardware configuration information of multiple second electronic devices based on the operational capability information; the hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected according to the upgrade targets available for user selection.

[0208] In this embodiment, the first determining module can specifically be used for:

[0209] Based on the hardware configuration information, a plurality of first performance characterization values ​​are determined for the first electronic device; the first performance characterization values ​​characterize the performance of the first electronic device when performing a running task in a corresponding running dimension;

[0210] A first proportional relationship is determined among the plurality of first performance characterization values, and the first proportional relationship is determined as the operating capability information of the electronic device.

[0211] In this embodiment, the selection module can specifically be used for:

[0212] Based on the hardware configuration information of each second electronic device, multiple second performance characterization values ​​are determined for each second electronic device; the second performance characterization values ​​characterize the performance of the second electronic device when performing running tasks in the corresponding running dimension;

[0213] Determine a second proportional relationship among multiple second performance characterization values ​​for each of the second electronic devices;

[0214] Determine the similarity between the second proportion relationship and the first proportion relationship for each of the second electronic devices;

[0215] The hardware configuration information of the second electronic device whose similarity meets the similarity threshold is determined as the target hardware configuration information.

[0216] In this embodiment, the first determining module determines a first proportional relationship among the plurality of first performance characterization values, which may specifically include:

[0217] The plurality of first performance characterization values ​​are normalized respectively to obtain a plurality of normalized first performance characterization values; the plurality of normalized first performance characterization values ​​represent a first proportion relationship among the plurality of first performance characterization values;

[0218] Determining the second proportional relationship among the plurality of second performance characterization values ​​for each of the second electronic devices includes:

[0219] The multiple second performance characterization values ​​of each second electronic device are normalized to obtain multiple normalized second performance characterization values ​​for each second electronic device; the multiple normalized second performance characterization values ​​represent a second proportional relationship among the multiple second performance characterization values.

[0220] Determining the similarity between the second proportion relationship and the first proportion relationship for each of the second electronic devices includes:

[0221] Determine the Euclidean distance between the normalized multiple second performance characterization values ​​of each second electronic device and the normalized multiple first performance characterization values; wherein, the smaller the Euclidean distance, the higher the corresponding similarity.

[0222] In this embodiment, the plurality of first performance characterization values ​​may include at least two of the following:

[0223] The first computing performance characterization value characterizes the performance of the first electronic device when performing a running task in the computing processing dimension;

[0224] The first storage performance characterization value characterizes the performance of the first electronic device when performing running tasks in the storage dimension;

[0225] The first network interaction representation value represents the performance of the first electronic device when performing running tasks in the network interaction dimension;

[0226] The first graphics processing performance characterization value characterizes the performance of the first electronic device when performing a running task in the graphics processing dimension.

[0227] The first acquisition module acquires the hardware configuration information of the first electronic device, which may include at least one of the following:

[0228] Obtain the hardware configuration parameters predefined by the hardware manufacturer of the first electronic device;

[0229] Obtain hardware configuration data actively entered by the user;

[0230] Obtain historical hardware configuration data with the same model as the first electronic device from the historical transaction hardware configuration records.

[0231] In this embodiment, the upgrade device for the electronic device may further include:

[0232] The second acquisition module is used to acquire reference data corresponding to the target hardware configuration information; the reference data is used to assist the user in selecting the target hardware configuration information to upgrade the first electronic device.

[0233] The output module is used to output the reference data.

[0234] In this embodiment, the reference data may include at least one of the following:

[0235] The first reference data represents the predicted power consumption of the second electronic device corresponding to the target hardware configuration information.

[0236] The second reference data characterizes the carbon emission level of the second electronic device corresponding to the target hardware configuration information;

[0237] The third reference data represents the device price of the second electronic device corresponding to the target hardware configuration information;

[0238] The fourth reference data represents the comprehensive usage cost of the second electronic device corresponding to the target hardware configuration information from the procurement stage to the scrapping stage.

[0239] In this embodiment, after the first determining module determines multiple first performance characterization values ​​of the first electronic device based on the hardware configuration information, it can also be used to:

[0240] In response to an adjustment command input by the user, at least one of the plurality of first performance characterization values ​​is modified.

[0241] The first determining module determines a first proportional relationship among the plurality of first performance characterization values, which may specifically include:

[0242] Determine the first proportion relationship among the modified first performance characterization values.

[0243] In another embodiment of this application, an electronic device is provided, referring to Figure 6 Electronic devices may include:

[0244] Memory 100 is used to store computer programs;

[0245] Processor 200 is configured to execute the computer program to enable the electronic device to perform the following method steps:

[0246] Obtain the hardware configuration information of the first electronic device;

[0247] Based on the hardware configuration information, the operating capability information of the first electronic device is determined based on a preset operating dimension; the operating capability information represents the operating task type scenario that the first electronic device preferentially adapts to based on the hardware configuration information.

[0248] From the hardware configuration information of multiple second electronic devices, target hardware configuration information is selected based on the operational capability information; the hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected according to the upgrade targets available to the user.

[0249] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0250] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0251] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0252] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

Claims

1. A method for upgrading an electronic device, comprising: Obtain the hardware configuration information of the first electronic device; Based on the hardware configuration information, the operating capability information of the first electronic device is determined based on a preset operating dimension; The operational capability information represents the operational task type scenarios that the first electronic device preferentially adapts to based on the hardware configuration information; Select target hardware configuration information from the hardware configuration information of multiple second electronic devices based on the operational capability information; The hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected based on the upgrade targets available to the user.

2. The method for upgrading an electronic device according to claim 1, wherein determining the operational capability information of the first electronic device based on the hardware configuration information and a preset operational dimension includes: Based on the hardware configuration information, a plurality of first performance characterization values ​​of the first electronic device are determined; The first performance characterization value characterizes the performance of the first electronic device when performing a running task in the corresponding running dimension; A first proportional relationship is determined among the plurality of first performance characterization values, and the first proportional relationship is determined as the operating capability information of the electronic device.

3. The method for upgrading an electronic device according to claim 2, wherein selecting target hardware configuration information that matches the operational capability information from the hardware configuration information of a plurality of second electronic devices includes: Based on the hardware configuration information of each second electronic device, multiple second performance characterization values ​​are determined for each second electronic device. The second performance characterization value characterizes the performance of the second electronic device when performing a running task in the corresponding running dimension; Determine a second proportional relationship among multiple second performance characterization values ​​for each of the second electronic devices; Determine the similarity between the second proportion relationship and the first proportion relationship for each of the second electronic devices; The hardware configuration information of the second electronic device whose similarity meets the similarity threshold is determined as the target hardware configuration information.

4. The method for upgrading an electronic device according to claim 3, wherein determining the first proportional relationship among the plurality of first performance characterization values ​​includes: The plurality of first performance characterization values ​​are normalized respectively to obtain the plurality of normalized first performance characterization values. The normalized first performance characterization values ​​represent a first proportional relationship among the multiple first performance characterization values. Determining the second proportional relationship among the plurality of second performance characterization values ​​for each of the second electronic devices includes: The multiple second performance characterization values ​​of each second electronic device are normalized to obtain multiple normalized second performance characterization values ​​for each second electronic device; the multiple normalized second performance characterization values ​​represent a second proportional relationship among the multiple second performance characterization values. Determining the similarity between the second proportion relationship and the first proportion relationship for each of the second electronic devices includes: Determine the Euclidean distance between the normalized multiple second performance characterization values ​​of each second electronic device and the normalized multiple first performance characterization values; wherein, the smaller the Euclidean distance, the higher the corresponding similarity.

5. The method for upgrading an electronic device according to claim 2, wherein the plurality of first performance characterization values ​​includes at least two of the following: The first computing performance characterization value characterizes the performance of the first electronic device when performing a running task in the computing processing dimension; The first storage performance characterization value characterizes the performance of the first electronic device when performing running tasks in the storage dimension; The first network interaction representation value represents the performance of the first electronic device when performing running tasks in the network interaction dimension; The first graphics processing performance characterization value characterizes the performance of the first electronic device when performing a running task in the graphics processing dimension.

6. The method for upgrading an electronic device according to claim 1, wherein obtaining the hardware configuration information of the first electronic device includes at least one of the following: Obtain the hardware configuration parameters predefined by the hardware manufacturer of the first electronic device; Obtain hardware configuration data actively entered by the user; Obtain historical hardware configuration data with the same model as the first electronic device from the historical transaction hardware configuration records.

7. The method for upgrading an electronic device according to claim 1, further comprising: Obtain reference data corresponding to the target hardware configuration information; The reference data is used to assist the user in selecting the target hardware configuration information to upgrade the first electronic device; Output the reference data.

8. The method for upgrading an electronic device according to claim 6, wherein the reference data includes at least one of the following: The first reference data represents the predicted power consumption of the second electronic device corresponding to the target hardware configuration information. The second reference data characterizes the carbon emission level of the second electronic device corresponding to the target hardware configuration information; The third reference data represents the device price of the second electronic device corresponding to the target hardware configuration information; The fourth reference data represents the comprehensive usage cost of the second electronic device corresponding to the target hardware configuration information from the procurement stage to the scrapping stage.

9. The method for upgrading an electronic device according to claim 2, further comprising, after determining a plurality of first performance characterization values ​​of the first electronic device based on the hardware configuration information: In response to an adjustment command input by the user, at least one of the plurality of first performance characterization values ​​is modified; Determining the first proportional relationship among the plurality of first performance characterization values ​​includes: Determine the first proportion relationship among the modified first performance characterization values.

10. An electronic device, comprising: Memory, used to store computer programs; A processor is configured to execute the computer program to enable the electronic device to perform the following method steps: Obtain the hardware configuration information of the first electronic device; Based on the hardware configuration information, the operating capability information of the first electronic device is determined based on a preset operating dimension; The operational capability information represents the operational task type scenarios that the first electronic device preferentially adapts to based on the hardware configuration information; Select target hardware configuration information from the hardware configuration information of multiple second electronic devices based on the operational capability information; The hardware specifications of the second electronic device are higher than those of the first electronic device; the hardware configuration information of the second electronic device is collected based on the upgrade targets available to the user.

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