Equipment hardware configuration method, electronic equipment and storage medium

Abstract

The invention discloses a hardware configuration method of a device, an electronic device and a storage medium, and the method comprises the steps: carrying out the filtering selection of hardware in a database for many times through a method flow containing a hardware resource database (a preset hardware knowledge base), customer demands (configuration condition information of a to-be-configured device), and an optimal assembly scheme (a target configuration strategy); and the final selection of hardware is carried out through a mode of comprehensively considering the enterprise inventory priority, the performance matching degree and the cost coefficient, so that rich hardware information basis and accurate customer requirements can be provided, and finally, the optimal assembly scheme considering both the performance and the cost can be generated through the selected target hardware. According to the method and the device, balance and optimization of different factors are realized, so that the technical problem of how to quickly determine a configuration scheme considering performance and cost in the hardware configuration process of the server can be solved, and the technical effect of efficiently realizing intelligent generation and optimization of a hardware assembly scheme of equipment is achieved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a hardware configuration method for a device, an electronic device, and a storage medium. Background Art

[0002] With the rapid development of information technology, servers are increasingly being used in various fields. Different customers have diverse requirements for server performance, functionality, and cost. Therefore, different servers require different hardware configurations. Existing server hardware configuration methods typically rely on manual configuration.

[0003] However, manual configuration is difficult to fully consider various factors, and it is easy to cause unreasonable assembly, resulting in the server's performance not being fully utilized or the cost being too high. Therefore, how to quickly determine a configuration plan that takes into account both performance and cost during the server hardware configuration process is an urgent problem that needs to be solved. Summary of the Invention

[0004] The present application provides a hardware configuration method, an electronic device, and a storage medium for a device, to at least solve the problem in the related art of how to quickly determine a configuration solution that takes into account both performance and cost during the hardware configuration process of a server.

[0005] This application provides a hardware configuration method for a device, including:

[0006] Selecting hardware from a preset hardware knowledge base according to the configuration condition information of the device to be configured to obtain a plurality of first hardware corresponding to the configuration condition information;

[0007] Performing hardware selection on the plurality of first hardware according to a preset device hardware selection mode to obtain a plurality of second hardware;

[0008] Selecting the plurality of second hardware items according to a predetermined rule to obtain a plurality of third hardware items;

[0009] Selecting the plurality of third hardware items according to inventory priorities, performance matching degrees, and cost coefficients corresponding to the plurality of third hardware items to obtain a plurality of target hardware items;

[0010] The configuration strategy is constructed for the target hardware according to the preset strategy generation rules to obtain the target configuration strategy corresponding to the device to be configured.

[0011] This application also provides a hardware configuration device for a device, including:

[0012] A first selection unit is configured to select hardware in a preset hardware knowledge base according to the configuration condition information of the device to be configured, and obtain a plurality of first hardware corresponding to the configuration condition information;

[0013] A second selection unit, configured to select a plurality of first hardware items according to a preset device hardware selection mode to obtain a plurality of second hardware items;

[0014] a third selection unit, configured to select the plurality of second hardware according to a predetermined rule to obtain a plurality of third hardware;

[0015] a fourth selection unit, configured to select the plurality of third hardware items according to the inventory priority, performance matching degree, and cost coefficient corresponding to each of the plurality of third hardware items to obtain a plurality of target hardware items;

[0016] The construction unit is used to construct a configuration policy for the target hardware according to the preset policy generation rules to obtain a target configuration policy corresponding to the device to be configured.

[0017] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any of the above-mentioned hardware configuration methods for the device when executing the computer program.

[0018] The present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the hardware configuration method of any of the above-mentioned devices are implemented.

[0019] The present application also provides a computer program product, including a computer program, which implements the steps of the hardware configuration method of any of the above devices when the computer program is executed by a processor.

[0020] Through the hardware configuration method, electronic device and storage medium of the equipment provided by this application, through a method flow covering the hardware resource database (pre-set hardware knowledge base), customer needs (configuration condition information of the equipment to be configured), and the optimal assembly plan (target configuration strategy), the hardware in the database is filtered and selected multiple times, and the final selection of the hardware is made by comprehensively considering the enterprise inventory priority, performance matching and cost coefficient. It can provide a rich hardware information base and accurate customer needs. Finally, the optimal assembly plan that takes into account both performance and cost can be generated through the selected target hardware, achieving the balance and optimization of different factors. Therefore, it can solve the technical problem of how to quickly determine the configuration plan that takes into account both performance and cost during the hardware configuration process of the server, and achieve the technical effect of efficiently realizing the intelligent generation and optimization of the hardware assembly plan of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 A flowchart of a method for hardware configuration of a device provided in an embodiment of the present application;

[0023] Figure 2 A flowchart of a hardware configuration method for another device provided in an embodiment of the present application;

[0024] Figure 3 A schematic diagram of the structure of a hardware configuration device of a device provided in an embodiment of the present application;

[0025] Figure 4 A schematic diagram of the structure of a hardware configuration device of another device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] It should be noted that, in the description of this application, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. The terms "first," "second," etc., in this application are used to distinguish similar objects, and are not used to describe a particular order or sequence.

[0028] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0029] Figure 1 A flowchart of a method for hardware configuration of a device provided in an embodiment of the present application is provided, and the method is described in detail in conjunction with the execution process of the method for hardware configuration of the device.

[0030] like Figure 1 As shown, the hardware configuration method of the device includes:

[0031] Step 101 : Select hardware from a preset hardware knowledge base according to configuration condition information of a device to be configured, and obtain a plurality of first hardware corresponding to the configuration condition information.

[0032] In an embodiment of the present application, the configuration condition information identifies a set of demand parameters such as performance indicators, budget ranges, application scenario types, etc. proposed by the customer, such as specific requirements for the main frequency of the central processing unit (CPU), memory capacity, or storage throughput. The preset hardware knowledge base refers to a database that integrates the company's self-developed hardware parameters and the hardware catalog of the cooperative manufacturer, which includes but is not limited to: performance data, physical specifications, compatibility rules, inventory status and other information of various types of hardware. Through preliminary screening of the hardware in the preset hardware knowledge base, a plurality of first hardware that matches the configuration condition information is obtained, and the first hardware refers to a set of candidate hardware components that meet the customer's basic performance threshold and budget constraints.

[0033] The preset hardware knowledge base includes at least:

[0034] 1) Storage of enterprise-owned hardware parameters: The enterprise-owned hardware parameter storage records in detail the performance data (such as the computing power of self-developed artificial intelligence (AI) accelerator cards), physical specifications (such as the dimensions of customized chassis), and exclusive compatibility rules of the enterprise's self-developed hardware, providing accurate basic data for subsequent hardware matching and assembly.

[0035] 2) Integration of strategic partner hardware catalogs and application programming interface (API): Integrate the hardware catalogs of strategic partner hardware and achieve real-time connection with the manufacturer's system through the API interface, so as to obtain the latest information of the manufacturer's hardware in a timely manner, including but not limited to: performance parameters, physical specifications, compatibility requirements, etc., to ensure that external resources can be fully utilized when generating assembly plans and enrich the range of optional hardware.

[0036] Step 102 : performing hardware selection on a plurality of first hardware according to a preset device hardware selection mode to obtain a plurality of second hardware.

[0037] In an embodiment of the present application, the preset device hardware selection mode includes at least the policy selection mechanisms of the enterprise resource priority mode and the open ecological mode. The enterprise resource priority mode refers to giving priority to the hardware products developed by the enterprise (that is, the hardware in the first database position in the preset hardware knowledge base) in the hardware selection process to improve the autonomy rate, while the open ecological mode is to introduce the hardware of the cooperative manufacturer (that is, the hardware in the second database position in the preset hardware knowledge base) to supplement the capabilities when the hardware products developed by the enterprise cannot meet the needs. Through the selection of the preset device hardware selection mode, multiple second hardware that meet the configuration condition information are further screened out from the first hardware. The second hardware refers to the candidate hardware set filtered by the preset device hardware selection mode, which not only retains the characteristics of meeting customer needs, but also reflects the enterprise's tendency to use resources.

[0038] It should be noted that the default device hardware selection mode is at least dual mode, including:

[0039] Enterprise Resource Priority: In this model, the company maximizes the use of its own hardware components, increasing the proportion of its own products. This helps promote its own products, increasing product exposure and market share, while also providing better control over product quality and supply chain.

[0040] Open Ecosystem Model: When a company's own hardware cannot fully meet customer needs or has shortcomings, it will integrate external hardware resources to supplement its own deficiencies. By collaborating with strategic partners, appropriate external hardware is introduced to ensure that the resulting assembly solution fully meets the customer's high-performance or specialized needs.

[0041] Step 103: Select the plurality of second hardware items according to a predetermined rule to obtain a plurality of third hardware items.

[0042] In an embodiment of the present application, after obtaining the second hardware set, the system performs in-depth optimization processing on it according to predetermined rules. The predetermined rules include but are not limited to: compatibility verification rules and inventory availability verification rules. The compatibility verification rules refer to the adaptability detection mechanism for the hardware combination based on the enterprise hardware design specifications (i.e., preset hardware design specifications), such as: interface standards, power supply parameters, physical dimensions, etc., and the constraints of the cooperative manufacturer (i.e., preset hardware constraints), for example: checking the matching of the motherboard slot and the CPU interface type, the compatibility of the chassis space and the hard disk size, etc.

[0043] Inventory availability verification rules rely on real-time interaction with the Enterprise Resource Planning (ERP) system to obtain the inventory status of secondary hardware, eliminating those with insufficient inventory or excessively long supply cycles. This screening process then outputs multiple sets of tertiary hardware candidates, which are those that have passed compatibility verification and have inventory status sufficient to support the solution.

[0044] Step 104 : performing hardware selection on the plurality of third hardware according to the inventory priorities, performance matching degrees, and cost coefficients corresponding to the plurality of third hardware to obtain a plurality of target hardware.

[0045] In an embodiment of the present application, a comprehensive evaluation is performed based on the inventory priority, performance matching and cost coefficient corresponding to the third hardware. The inventory priority refers to the promotion weight set by the enterprise for different third hardware, which is a numerical indicator that is usually dynamically adjusted according to the product gross profit level, inventory backlog or strategic importance; the performance matching refers to the degree of fit between the actual performance parameters of the third hardware and customer needs, which can be converted into a quantifiable score through a normalization algorithm; the cost coefficient is an economic indicator calculated based on the proportional relationship between the procurement cost of the third hardware and the customer budget. The lower the cost, the higher the coefficient.

[0046] The above three dimensions, namely inventory priority, performance matching and cost coefficient, are comprehensively calculated through a weighted algorithm to generate the target weight coefficient of the third hardware, and finally multiple target hardware are screened out. The target hardware refers to the set of hardware components that achieves the optimal balance under multiple constraints such as enterprise strategy, performance requirements and cost control, that is, the optimal set of hardware components that meets the configuration condition information.

[0047] Step 105 : constructing a configuration policy for the target hardware according to the preset policy generation rules to obtain a target configuration policy corresponding to the device to be configured.

[0048] In the embodiments of this application, the preset policy generation rules refer to combined policies generated based on the topological relationships between target hardware, assembly specifications, and enterprise recommendation logic. For example, these policies prioritize the use of target hardware of the same brand to reduce compatibility risks and optimize target hardware layout based on heat dissipation requirements. By simulating assembly scenarios and resource scheduling paths, the target configuration policy corresponding to the device to be configured is output. The target configuration policy is a complete configuration guide that includes specific hardware models, assembly plans, cost lists, and inventory reservation information.

[0049] Through the hardware configuration method, electronic device and storage medium of the equipment provided by this application, through a method flow covering the hardware resource database (pre-set hardware knowledge base), customer needs (configuration condition information of the equipment to be configured), and the optimal assembly plan (target configuration strategy), the hardware in the database is filtered and selected multiple times, and the final selection of the hardware is made by comprehensively considering the enterprise inventory priority, performance matching and cost coefficient. It can provide a rich hardware information base and accurate customer needs. Finally, the optimal assembly plan that takes into account both performance and cost can be generated through the selected target hardware, achieving the balance and optimization of different factors. Therefore, it can solve the technical problem of how to quickly determine the configuration plan that takes into account both performance and cost during the hardware configuration process of the server, and achieve the technical effect of efficiently realizing the intelligent generation and optimization of the hardware assembly plan of the equipment.

[0050] In one implementable manner of an embodiment of the present application, when selecting hardware from a preset hardware knowledge base, the following manner may also be employed but is not limited thereto: hardware matching is performed in the preset hardware knowledge base according to the target performance conditions and target budget conditions of the device to be configured included in the configuration condition information to obtain a hardware matching result; when it is determined according to the hardware matching result that the hardware performance of the hardware to be matched meets the target performance conditions, and the hardware budget of the hardware to be matched meets the target budget conditions, the hardware to be matched is determined to be hardware that meets the configuration condition information, wherein the hardware to be matched is any hardware in the preset hardware knowledge base; when the hardware performance of the hardware to be matched does not meet the target performance conditions, and / or the hardware budget of the hardware to be matched does not meet the target budget conditions, the hardware to be matched is determined to be hardware data that does not meet the configuration condition information; and multiple hardware that meet the configuration condition information are determined to be multiple first hardware.

[0051] In an embodiment of the present application, the process of selecting hardware from a preset hardware knowledge base based on the configuration condition information of the device to be configured may specifically include, but is not limited to, the following methods: executing hierarchical hardware screening logic by parsing customer needs and resource constraints. Among them, the configuration condition information at least includes the technical indicators and economic requirements clearly stated by the customer in the configuration request, that is, two types of parameters: target performance conditions and target budget conditions. The target performance conditions include the customer's quantitative requirements for the functional attributes of the server hardware, such as: CPU main frequency threshold, memory capacity lower limit or storage throughput standard; the target budget conditions include the customer's limit range on the total cost of the hardware configuration, for example: the assembly cost of a single server does not exceed a specific amount. The target performance conditions and the target budget conditions together constitute the basic input parameters for hardware screening, ensuring that the first hardware meets both the technical performance requirements and the economic constraints.

[0052] When selecting hardware from a pre-defined hardware knowledge base, the configuration requirements are matched against the hardware attributes in the knowledge base to generate hardware matching results. Hardware matching results are a list of candidate hardware components derived through algorithmic comparison, where each piece of hardware is validated against both performance and budget. Hardware to be matched refers to any hardware component in the knowledge base that participates in the matching process, such as a specific CPU model, memory module, or hard drive.

[0053] Specifically, the logic for selecting hardware from the preset hardware knowledge base includes but is not limited to the following two dimensions: positive verification and negative exclusion:

[0054] When the hardware performance of the matching hardware (i.e., the actual performance parameters of the component) meets or exceeds the target performance conditions and its hardware budget (i.e., the procurement cost of the component) is within the target budget conditions, the matching hardware is determined to be a valid candidate component that meets the configuration conditions. For example, if a customer requires a CPU frequency ≥ 3.0GHz and a unit cost ≤ ￥5000, and a self-developed CPU in the knowledge base has a frequency of 3.2GHz and a cost of ￥4800, it will pass verification and be included in the candidate list.

[0055] If the performance of the matching hardware does not meet the requirements (e.g., the main frequency is only 2.8GHz), or the cost exceeds the budget limit (e.g., the unit price is ￥5500), or both the performance and cost do not meet the requirements, it will be automatically marked as hardware data that does not meet the configuration conditions and removed from the candidate list. For example, if a partner's memory stick has a capacity of 64GB (lower than the customer's required 128GB), it will be excluded even if the cost meets the budget.

[0056] Through a dual dynamic verification mechanism of performance and budget, the system can quickly locate candidate components that meet customers' core needs from a vast hardware resource library; standardized data comparison based on the knowledge base avoids subjective errors in manual screening; the combined use of forward verification and reverse exclusion ensures the integrity and accuracy of the screening results, providing a reliable hardware pool for multi-strategy configuration.

[0057] In one implementable method of an embodiment of the present application, when performing hardware selection for multiple first hardwares, the following method can also be used but is not limited to: determining the hardware library information corresponding to each of the multiple first hardwares, wherein the hardware library information is used to determine the hardware library position of the first hardware in the preset hardware knowledge base, and the hardware library position includes at least the first hardware library position and the second hardware library position; when determining that the preset device hardware selection mode is a resource priority mode, preferentially select the first hardware whose hardware library information is the first hardware library position as the second hardware, wherein the preset device hardware selection mode includes at least the resource priority mode and the open ecological mode; when determining that the preset selection mode is an open ecological mode, perform hardware selection for the multiple first hardwares based on the first hardware performance and the first hardware budget corresponding to each of the multiple first hardwares to obtain the second hardware.

[0058] In an embodiment of the present application, the directional optimization of the hardware selection strategy is achieved through the linkage of the analysis of the hardware library information and the mode strategy. Among them, the hardware library information includes metadata for identifying the resource category to which the hardware belongs, which is generated by the classification rules defined in the preset hardware knowledge base. The hardware library location refers to the storage area identification divided according to the source of the hardware. The first hardware library location specifically refers to the exclusive library area for storing the company's self-developed hardware, and the second hardware library location refers to the extended library area for storing the hardware of the cooperative manufacturer. Through the identification of the hardware library location, the source attributes of the first hardware component can be accurately distinguished, providing a classification basis for policy selection.

[0059] In specific implementation, when it is determined that the preset device hardware selection mode is the resource priority mode, the priority screening mechanism for the company's self-developed hardware will be activated. The resource priority mode refers to a strategy with the core goal of improving the utilization rate of the company's own products. It identifies the hardware library information and gives priority to selecting the components located in the first hardware library position (that is, the company's self-developed library area) from the first hardware set as the second hardware. For example: If the candidate CPU includes self-developed model A (stored in the first hardware library position) and manufacturer model B (stored in the second hardware library position), the system will automatically include model A in the second hardware set, even if manufacturer model B has higher performance or lower cost. This mode ensures the market penetration and inventory turnover efficiency of the company's core technology products by strengthening the recommendation weight of self-developed hardware.

[0060] When the current mode is determined to be an open ecological mode, a comprehensive screening strategy that balances performance and budget is adopted. The open ecological mode refers to a strategy that allows the introduction of hardware from external manufacturers to make up for the shortcomings of self-developed product capabilities on the premise of meeting the core needs of customers. In this mode, the first hardware will be evaluated twice based on the first hardware performance (ie, the actual performance parameters of the hardware) and the first hardware budget (ie, the hardware procurement cost). Specifically, the same type of hardware is cross-sorted in descending order of performance and ascending order of cost, and components with performance that meets the standards and has the best cost ratio are given priority. For example: in the memory selection scenario, if the self-developed memory capacity is 128GB (cost ￥2000) and the cooperative manufacturer's memory capacity is 256GB (cost ￥3500), the system will dynamically select the best based on the actual needs of the customer (such as: whether scalability is required) and budget space, rather than simply relying on the hardware source attributes.

[0061] Through the coupling mechanism of hardware library location and selection mode, the system can adjust the hardware screening direction in real time according to corporate strategies; the resource-priority mode strengthens the promotion of self-developed products, and the open ecosystem mode improves the technical competitiveness of the solution by introducing external resources; the coexistence of dual-mode strategies makes the hardware selection process both in line with corporate interests and able to flexibly respond to diverse customer needs, laying a strategic consistency foundation for subsequent configuration optimization.

[0062] In one implementable method of the embodiment of the present application, when performing hardware selection for multiple second hardware, the following methods may also be used but are not limited to: performing compatibility verification on the multiple second hardware according to preset hardware design specifications and preset hardware constraints to obtain verification results, and performing hardware filtering on the second hardware according to the verification results to obtain multiple filtered second hardware; determining the hardware quantity corresponding to each of the multiple filtered second hardware in the preset hardware knowledge base, wherein the hardware quantity is the number of the multiple filtered second hardware in the first database position in the preset hardware knowledge base; performing hardware filtering on the multiple filtered second hardware according to the hardware quantity to obtain multiple filtered second hardware; performing hardware filtering on the multiple filtered second hardware according to the benefit data corresponding to each of the multiple filtered second hardware to obtain multiple third hardware.

[0063] In an embodiment of the present application, when performing compatibility checking, a dedicated compatibility checker (which quickly filters incompatible combinations based on hardware design specifications (interface type / power supply standard / physical size)) may be used, including:

[0064] 1) Built-in Enterprise Hardware Design Standards: Internal enterprise hardware design specifications and standards, such as power supply specifications and signal integrity requirements for custom backplanes, are embedded into the verifier. After the assembly plan is generated, the verifier rigorously verifies the plan against these standards, ensuring that the selected hardware components are fully compatible with the enterprise's existing systems and infrastructure in terms of physical connections and electrical characteristics.

[0065] 2) Dynamically Loading Hardware Constraints from Partners: In addition to the company's own design standards, the verifier can also dynamically load hardware constraints provided by partners. Constraints may include specific hardware installation requirements, compatibility restrictions, and more. By acquiring this information in real time, the verifier can fully consider the characteristics of external hardware during the verification process, ensuring the reliability and stability of the entire assembly solution.

[0066] When selecting hardware based on hardware quantity and benefit data, this can be achieved by synchronizing real-time inventory status with the company's hardware promotion strategy, including:

[0067] Connect with ERP systems to obtain hardware availability: Through seamless integration with ERP systems, the system obtains real-time information on the available quantities of various hardware components in the company's inventory. This allows the system to accurately consider inventory levels when generating assembly plans, avoiding unfeasible plans or delivery delays due to insufficient inventory.

[0068] Embedded enterprise hardware promotion strategies: When recommending configuration solutions, the system prioritizes high-profit, self-developed components. This approach not only allows companies to better promote their products, increasing sales and profits, but also maximizes economic benefits while meeting customer needs.

[0069] The process of selecting the third hardware from multiple second hardware according to predetermined rules is specifically manifested in the gradual elimination of the second hardware that does not meet the requirements through a triple filtering mechanism of compatibility verification, inventory verification and benefit evaluation. In this process, the preset hardware design specifications include a set of technical indicators such as hardware physical interface standards, electrical parameter requirements and assembly size restrictions formulated by the enterprise. For example, the motherboard slot type must be compatible with the CPU interface, and the power supply rated power must meet the power consumption requirements of the entire machine. The preset hardware constraints include special hardware installation requirements or compatibility restrictions provided by the cooperative manufacturer. For example, a certain model of CPU requires specific radiator support, and the purchased hard drive needs to adapt to the enterprise's customized backplane interface.

[0070] First, a compatibility check is performed on multiple second hardware, that is, the actual assembly scenario is simulated based on the topological relationship and interaction characteristics between the second hardware. During the verification process, the interface matching (such as version consistency), power supply capability adaptability (such as the balance between the total power supply power and component power consumption) and physical space compatibility (such as the conflict between the internal dimensions of the chassis and the hardware layout) in multiple second hardware are automatically detected. For example: when the length of a certain model of graphics card exceeds the maximum expansion slot space of the chassis, a verification result will be generated and marked as incompatible. Based on the verification results, the second hardware is filtered to eliminate hardware with compatibility risks, forming multiple filtered second hardware. Ensure that the filtered second hardware is assemblable at the technical level.

[0071] After completing the compatibility check, further resource availability screening is performed through inventory status verification. The first database location refers to the logical partition in the preset hardware knowledge base that identifies the storage area of the company's self-developed hardware, which is used to distinguish between self-developed components and external manufacturer components. The hardware quantity corresponding to the filtered second hardware is extracted from the knowledge base, that is, the inventory of self-developed hardware that can be immediately called in the current enterprise inventory. For example: the inventory of a self-developed model memory stick is marked as 50 pieces in the knowledge base, while the inventory status of the cooperative manufacturer's hard disk is displayed as "need to be purchased". The hardware is screened twice according to the inventory threshold (such as: minimum available quantity ≥ 1): If the inventory of a certain hardware is zero or below the safety threshold, it is marked as unavailable and eliminated, and finally multiple filtered second hardware are output. Ensure that all hardware in the configuration plan is available to avoid delivery delays due to inventory shortages.

[0072] Finally, benefit data is introduced to conduct an economic and strategic evaluation of the screened second hardware. Benefit data refers to a quantitative evaluation indicator generated by comprehensively considering dimensions such as hardware performance contribution, cost ratio, and inventory priority. For example, under the premise that a CPU meets performance requirements, the lower the proportion of its procurement cost to the budget and the more serious the inventory backlog, the higher the benefit score. By establishing a benefit evaluation model, the screened second hardware is sorted and optimized, and components with benefit scores below the set threshold are eliminated, and finally multiple third hardware are generated. For example: among multiple CPUs that meet compatibility and inventory conditions, models with serious inventory backlogs and the best cost-effectiveness ratio are given priority, rather than simply pursuing the highest performance. This ensures that hardware selection not only meets technical requirements, but also better fits enterprise resource optimization and business strategy goals.

[0073] Through the dual guarantees of compatibility verification and inventory validation, the system effectively avoids the technical and supply risks of hardware combinations; the dynamic evaluation model based on benefit data deeply integrates the company's inventory management strategy and cost control goals into the hardware selection process; the multi-level filtering mechanism significantly improves the quality of candidate hardware, so that the final generated third hardware set has both technical feasibility, resource availability and economic rationality, providing high-value input for the final generation of the configuration strategy.

[0074] In one implementable method of an embodiment of the present application, when selecting hardware for multiple third hardware, the following method may also be used but is not limited to: obtain the inventory priority, performance matching degree and cost coefficient corresponding to each of the multiple third hardware from a preset hardware knowledge base; determine the first weight corresponding to each inventory priority, the second weight corresponding to each performance matching degree and the third weight corresponding to each cost coefficient according to the preset weight coefficient; multiply each inventory priority by the corresponding first weight to obtain the first score corresponding to each of the multiple third hardware, multiply each performance matching degree by the corresponding second weight to obtain the second score corresponding to each of the multiple third hardware, multiply each cost coefficient by the corresponding third weight to obtain the third score corresponding to each of the multiple third hardware; add the first score, second score and third score corresponding to each of the multiple third hardware to obtain the target weight score corresponding to each of the multiple third hardware; select the multiple third hardware according to the target weight score to obtain multiple target hardware.

[0075] In the embodiment of the present application, when calculating the target weight score, the following formula (1) may be used but is not limited thereto:

[0076] Score = α × (enterprise inventory priority) + β × (performance matching) + γ × (cost coefficient) Formula (1)

[0077] Among them, α, β, γ are weight coefficients (α+β+γ=1), α is the first weight, β is the second weight, γ is the third weight, and Score is the target weight score.

[0078] The enterprise inventory priority reflects the hardware that the enterprise wants to consume first (high-gross-profit products are preferred, and then adjusted in combination with inventory backlogs). The performance matching degree measures the extent to which the hardware meets the customer's performance requirements. The lower the cost coefficient, the higher the score (for example: cost 1000→0.9, 1000→0.9, 2000→0.5).

[0079] By building an adjustable weighted scoring system, the quantitative execution of the hardware optimization strategy can be achieved. Among them, inventory priority refers to the resource utilization tendency index set by the enterprise for different hardware. Its value is dynamically adjusted according to the strategic value of the product, the degree of inventory backlog and the gross profit level. For example, high-gross-profit self-developed products are usually given a priority value of 0.8-1.0, while obsolete models that need to be cleared may be set to below 0.5. Performance matching refers to the quantitative value of the degree of fit between the actual hardware parameters and customer needs. The performance gap is converted into a score in the range of 0-1 through a normalization algorithm. For example: when the main frequency of a CPU reaches 120% of the customer's requirements, the matching degree is 1.0, and when it only meets 80% of the requirements, the matching degree is 0.6. The cost coefficient reflects the level of hardware economy, and its value is negatively correlated with the procurement cost. For example, the lower the cost ratio to the budget, the higher the coefficient. A nonlinear function is used to map the cost to the range of 0-1.

[0080] First, the above three indicator data corresponding to the third hardware are extracted from the preset hardware knowledge base, and then the preset weight coefficient is loaded for comprehensive score calculation. The preset weight coefficient refers to the allocation ratio parameter dynamically set by the enterprise strategy, including the first weight (corresponding to inventory priority), the second weight (corresponding to performance matching) and the third weight (corresponding to cost coefficient), and the sum of the three is constant to 1. For example: when the enterprise focuses on inventory digestion, the first weight can be set to 0.6, the second weight to 0.2, and the third weight to 0.2; if it switches to a cost-first strategy, it is adjusted to the first weight to 0.2, the second weight to 0.3, and the third weight to 0.5. This weight allocation mechanism enables the system to flexibly adapt to business goals at different stages.

[0081] In the specific calculation process, each third hardware is scored and the total score is synthesized:

[0082] The first score is calculated by multiplying the inventory priority by the first weight, reflecting the importance of the hardware in the enterprise resource strategy. For example, if a hard drive has a priority of 0.9 and the first weight is set to 0.5, its first score is 0.45.

[0083] The second score is generated by multiplying the performance match by the second weight, indicating how well the hardware meets the customer's technical requirements. For example, if a hard drive has a match of 0.95 and a second weight of 0.3, the second score is 0.285.

[0084] The third score is calculated by multiplying the cost coefficient by the third weight, reflecting the economic advantage of the solution. For example, if the cost coefficient of a hard drive is 0.8 and the third weight is 0.2, the third score is 0.16.

[0085] The target weighted score is calculated by adding the three scores together. This score comprehensively reflects the hardware's value across the three dimensions of enterprise strategy, performance compliance, and cost control. For example, the hard drive's total score is 0.45 + 0.285 + 0.16 = 0.895. The third hardware components are sorted in descending order based on their total scores, and the top-ranked components are selected as multiple target hardware components. For example, in a server configuration scenario, hardware with a total score above 0.8 is selected as a final candidate, while hardware with a score below 0.6 is eliminated.

[0086] By introducing configurable weight coefficients, the system can dynamically adjust the focus of hardware evaluation according to corporate strategies, achieving precise alignment between business goals and technical solutions; the sub-item scoring mechanism transforms abstract strategic guidance into a quantifiable calculation model, improving the transparency and traceability of the decision-making process; the total score synthesis algorithm effectively balances multiple constraints, ensuring that the final selected target hardware set not only meets the customer's core needs but also maximally meets the company's goals of resource optimization and economic efficiency improvement.

[0087] In one implementable method of an embodiment of the present application, when selecting hardware for multiple third hardware based on the target weight score, the following method can also be used but is not limited to: obtaining the hardware type corresponding to each of the multiple third hardware from a preset hardware knowledge base; performing pairwise comparison processing on the target weight scores corresponding to the third hardware of the same hardware type to obtain a score comparison result; based on the score comparison result, determining the third hardware with the highest target weight score among the third hardware of the same hardware type as the target hardware.

[0088] In an embodiment of the present application, horizontal selection of similar hardware can be achieved through hardware type classification and intra-group competition mechanism. Among them, hardware type refers to the component category divided according to the functional attributes of the hardware, such as: CPU, memory, hard disk, graphics card and other core component types, and its classification rules are determined by the standardized definition in the preset hardware knowledge base. By parsing the hardware metadata stored in the knowledge base, the third hardware is grouped by type to form a set of candidate components of the same hardware type, for example, all CPUs that meet the conditions are grouped into the same group for subsequent comparison.

[0089] After the grouping is complete, a pairwise comparison process is performed on hardware of the same type. The target weight score of each third hardware component in the group is numerically compared with other similar components one by one to generate a score comparison result. For example, if a CPU type group includes model A (target weight score 0.92), model B (0.85), and model C (0.78), the system will perform three comparisons: AB, AC, and BC, and record the relationship between the scores in each comparison. This full combination comparison method can accurately identify the relative advantages of each hardware in the group, providing data support for the final selection.

[0090] Based on the score comparison results, a competitive screening mechanism is initiated. For each hardware type group, the component with the highest score across all comparisons within the group is automatically selected as the target hardware. For example, in the CPU group, if model A maintains the highest score across all comparisons, it is selected as the target hardware for that type. If there is a tied highest score (e.g., model A and model D both have a score of 0.92), additional screening rules (e.g., prioritizing components with higher inventory priorities) are triggered for decision-making. This ensures that only one optimal component is ultimately retained for each hardware type, avoiding resource waste caused by redundant configurations.

[0091] In practice, hardware type-based grouping and isolation ensure the independence of selection for different functional units. For example, when simultaneously selecting CPUs and memory, each is separated into independent groups, undergoing intra-group comparison and optimization, ultimately generating a complete configuration list encompassing multiple target hardware types. This divide-and-conquer strategy not only reduces computational complexity but also ensures the optimality of each hardware unit is unaffected by interference from other components.

[0092] Through hardware type grouping and intra-group competition mechanisms, refined optimization of similar components is achieved, avoiding evaluation distortion caused by cross-type comparisons; pairwise comparison processing ensures that the relative advantages of each hardware are fully quantified, improving the fairness and accuracy of the selection process; the final target hardware set achieves the best comprehensive score in each category, making the overall configuration plan both technically adaptable, economically reasonable and strategically compliant, creating maximum value for enterprises and customers.

[0093] In one possible implementation method of the embodiment of the present application, after obtaining the target configuration policy corresponding to the device to be configured, the following methods can also be used but are not limited to: visually displaying the target configuration policy; responding to data optimization instructions based on the target configuration policy, updating the preset hardware knowledge base, preset selection mode, preset selection weight and preset policy generation rules according to the data optimization instructions.

[0094] In an embodiment of the present application, the configuration scheme is converted into an interactive graphical interface through a visualization module. Visualization display refers to the process of visually presenting information such as the topological structure, performance parameter distribution, cost structure and inventory source of the hardware configuration in the form of charts, three-dimensional models or heat maps. For example: generate a three-dimensional perspective view of the internal hardware layout of the server chassis, mark the model and connection relationship of each component; at the same time, use a ring chart to display the cost ratio of self-developed hardware and external hardware, and use a bar chart to compare the comprehensive score differences of different candidate schemes. This multi-dimensional visual expression enables customers to quickly understand the technical and economic characteristics of the configuration scheme, and provide a basis for subsequent optimization decisions.

[0095] When users submit data optimization instructions based on the visualization results, the system initiates a dynamic update mechanism. Data optimization instructions are policy adjustment requests triggered by users through the interactive interface. These include, but are not limited to, hardware replacement requests (such as specifying the use of a partner manufacturer's components), weight coefficient resets (such as increasing the cost weight to 0.7), and policy rule revisions (such as adding a cooling efficiency constraint).

[0096] The visual interactive interface significantly lowers the threshold for users to understand complex configuration plans, thereby improving decision-making efficiency. The closed-loop optimization mechanism based on real-time feedback enables the system to absorb user experience and wisdom, and continuously approach the optimal configuration strategy. The dynamic update function realizes the self-evolution of the system knowledge system and strategy logic, ensuring that the resource allocation plan always keeps pace with the actual needs of the enterprise and market changes.

[0097] In one possible implementation method of the embodiment of the present application, before obtaining multiple first hardware corresponding to the configuration condition information, the following method can also be used but is not limited to: storing the first data obtained from the first hardware library location in a preset hardware knowledge base; obtaining the second data from the second hardware library location through a preset program interface, and storing the second data in the preset hardware knowledge base.

[0098] In an embodiment of the present application, before starting the hardware screening process, the system needs to complete the initial construction of the preset hardware knowledge base, the core of which is to achieve the deep integration of the company's internal resources and external ecological data. The first hardware library location refers to the exclusive storage area for the company's self-developed hardware data. The first data stored therein contains the full life cycle information of the company's self-developed hardware, covering performance parameters (such as: the computing power index of the self-developed CPU), physical specifications (such as: the chassis size of the customized server), compatibility rules (such as the electrical standard of the backplane interface) and production status (such as inventory quantity, batch number).

[0099] The second hardware library location refers to the logical storage partition of the cooperative manufacturer's hardware resources, which realizes dynamic access to external data through a preset program interface. The preset program interface refers to a heterogeneous system docking module developed based on a standardized protocol, which can establish a secure connection with the data platforms of different manufacturers. The second data obtained by the system through this interface includes the manufacturer's hardware technical documents (such as: GPU heat dissipation requirements), supply chain information (such as minimum order quantity, delivery cycle) and certification certificates (such as compatibility test reports). For example: When a cooperative manufacturer releases a new model of solid-state drive, the system automatically captures its performance data and interface specifications through the API, and stores them in the second hardware library location of the knowledge base after format conversion.

[0100] Through preset program interfaces, seamless integration of internal and external data is achieved, building a knowledge base system covering the entire enterprise ecosystem of hardware resources; the data cleaning and verification mechanism effectively improves the accuracy and comparability of hardware information, laying a high-quality data foundation for subsequent intelligent screening; the dual-library partitioning design not only maintains the independence of self-developed hardware, but also enhances the system's flexibility to cope with complex demands through the introduction of external data, achieving a technical depth expansion of resource allocation capabilities.

[0101] Specifically, in order to facilitate understanding of the implementation process of this application, this application provides a flowchart of another device hardware configuration method, such as Figure 2 As shown, including:

[0102] 1. Start: The starting point of the entire process.

[0103] 2. Customer demand input: The customer enters performance requirements, budget and other information (i.e., enters configuration condition information of the equipment to be configured).

[0104] 3. Hardware resource database screening: Filter out hardware components that meet basic requirements from the enterprise hardware knowledge base (i.e., select hardware from the preset hardware knowledge base).

[0105] 4. Demand-resource matching engine: Generates a preliminary configuration plan (i.e., obtains multiple first hardware) based on customer needs and the selected hardware.

[0106] 5. Dual-mode selection (i.e., performing hardware selection on multiple first hardware devices according to a preset device hardware selection mode):

[0107] 1) Enterprise resource priority mode: maximize the use of self-developed hardware.

[0108] 2) Open ecological model: integrating external hardware to make up for capability shortcomings.

[0109] 6. Exclusive compatibility verification: Verify the compatibility of solutions based on corporate standards and partner manufacturer constraints, and quickly filter out invalid combinations.

[0110] 7. Real-time inventory status synchronization: Connect with the ERP system to obtain the available quantity of hardware and ensure the feasibility of the solution.

[0111] 8. Embed enterprise hardware promotion strategy: prioritize high-profit self-developed components to increase enterprise profits.

[0112] Among them, steps 6, 7, and 8 are steps of selecting hardware from multiple second hardware according to predetermined rules.

[0113] 9. Hardware selection weight algorithm calculation (i.e., calculation of the target weight scores corresponding to multiple third hardware): Use the Score formula to calculate the comprehensive score of each solution.

[0114] 10. Solution presentation and selection: Display the best performance and most cost-effective solution for customers to choose.

[0115] 11. Feedback and Optimization: Collect customer feedback, optimize the system, and prepare for the next process.

[0116] For a further understanding of the embodiments of the present application, the present application provides two examples for illustration, Example 1:

[0117] Customer requirements (configuration conditions): A database server is needed. Performance requirements: Support at least 1 million transactions per second (TPS). Budget limit: ￥150,000. Candidate hardware (third hardware after screening): The company has the following optional CPUs in stock:

[0118] 1) Self-developed CPU-A (inventory overstock); Performance: 1.2 million TPS; Cost: ￥80,000; Inventory priority: 0.9 (the enterprise hopes to consume it first)

[0119] 2) Partner CPU-B (purchased externally); Performance: 1.5 million TPS; Cost: ￥120,000; Inventory Priority: 0.3 (not a self-developed product)

[0120] 3) Self-developed CPU-C (stock in short supply); Performance: 1.1 million TPS; Cost: ￥60,000; Inventory priority: 0.6 (some inventory must be retained)

[0121] 4) Calculation process:

[0122] Step 1: Calculate performance matching

[0123] CPU-A: 120 / 100 = 1.2 → Normalized to 1 (if exceeding the requirement, the full score will be counted)

[0124] CPU-B: 150 / 100 = 1.5 → normalized to 1

[0125] CPU-C: 110 / 100 = 1.1 → normalized to 1

[0126] (Note: If the performance does not meet the standards, it will be calculated proportionally)

[0127] Step 2: Calculate the cost factor

[0128] CPU-A: ￥80k → Cost score = 1-(80000 / 150000) = 0.47

[0129] CPU-B:￥120k→1-(120000 / 150000)=0.2

[0130] CPU-C:￥60k→1-(60000 / 150000)=0.6

[0131] (Assuming linear normalization, nonlinear function optimization can actually be used)

[0132] Step 3: Set weights

[0133] According to the company's current strategy:

[0134] α=0.5 (focus on destocking)

[0135] β=0.3 (secondary to performance)

[0136] γ=0.2 (taking into account cost)

[0137] Step 4: Comprehensive calculation is shown in Table 1:

[0138] Table 1

[0139]

[0140] Interpretation of the results:

[0141] CPU-A has the highest score (0.844): Although its performance is only up to standard and its cost is relatively high, it is still the most preferred option because the company prioritizes destocking (α = 0.5).

[0142] PU-C ranks second (0.72): Although it has the lowest cost and acceptable inventory priority, it is limited by the weight distribution and does not surpass A.

[0143] CPU-B scored the lowest (0.49): outsourced hardware inventory has a low priority and lacks cost advantages.

[0144] Policy adjustment example:

[0145] If the enterprise strategy changes to cost priority (adjust weights to α = 0.2, β = 0.2, γ = 0.6)

[0146] CPU-C score: 0.2 × 0.6 + 0.2 × 1 + 0.6 × 0.6 = 0.68

[0147] CPU-A score: 0.2 × 0.9 + 0.2 × 1 + 0.6 × 0.47 = 0.632

[0148] At this time, CPU-C will become the most preferred option, reflecting the dominant role of the weight coefficient on the result.

[0149] Example 2: Performance requirements (configuration requirements): CPU frequency of at least 3.0 GHz, memory capacity of at least 128 GB, and storage space of at least 1 TB. Budget: Cost budget of 100,000 yuan.

[0150] The enterprise hardware inventory situation is shown in Table 2:

[0151] Table 2

[0152]

[0153] The weight coefficient is set as:

[0154] α=0.4 (Enterprise inventory priority)

[0155] β=0.4 (performance matching)

[0156] γ=0.2 (cost coefficient)

[0157] Solution generation: Based on customer needs and hardware inventory, the system generates the following two assembly solutions:

[0158] 1) Option 1 (Enterprise Resource Priority Model)

[0159] Hardware component selection:

[0160] CPU: Self-developed CPU A

[0161] Memory: Self-developed memory B

[0162] Hard disk: Self-developed hard disk C

[0163] Comprehensive score calculation:

[0164] CPU Score: 0.4×0.8+0.4×0.9+0.2×0.7=0.32+0.36+0.14=0.82

[0165] Memory Score: 0.4×0.7+0.4×0.8+0.2×0.6=0.28+0.32+0.12=0.72

[0166] Hard Drive Score: 0.4 × 0.6 + 0.4 × 0.7 + 0.2 × 0.5 = 0.24 + 0.28 + 0.10 = 0.62

[0167] 2) Option 2 (Open Ecosystem Model)

[0168] Hardware component selection:

[0169] CPU: Partner CPU D

[0170] Memory: Memory E from partner manufacturer

[0171] Hard disk: Hard disk F from cooperative manufacturer

[0172] Comprehensive score calculation:

[0173] CPU Score: 0.4×0.4+0.4×0.95+0.2×0.6=0.16+0.38+0.12=0.66

[0174] Memory Score: 0.4×0.3+0.4×0.85+0.2×0.5=0.12+0.34+0.10=0.56

[0175] Hard Drive Score: 0.4 × 0.5 + 0.4 × 0.75 + 0.2 × 0.4 = 0.20 + 0.30 + 0.08 = 0.58

[0176] Solution Comparison

[0177] Option 1: Give priority to the company's self-developed high-gross-profit products with higher overall scores.

[0178] Option 2: Combining with the hardware of the partner manufacturer, the performance match is higher, but the overall score is lower.

[0179] result:

[0180] If you want to achieve enterprise promotion, you need to choose Option 1 based on the customer's actual needs and preferences, because Option 1 not only meets performance and budget requirements, but also supports the company's promotion of high-gross-profit products, achieving a win-win situation for the company and the customer.

[0181] In summary, the embodiments of the present application can achieve the following technical effects:

[0182] 1. Through a method process covering the hardware resource database (preset hardware knowledge base), customer needs (configuration condition information of the equipment to be configured), and the optimal assembly plan (target configuration strategy), the hardware in the database is filtered and selected multiple times, and the final selection of hardware is made by comprehensively considering the company's inventory priority, performance matching and cost coefficient. This can provide a rich hardware information base and accurate customer needs. Ultimately, the selected target hardware can generate an optimal assembly plan that takes into account both performance and cost, achieving a balance and optimization of different factors. Therefore, it can solve the technical problem of how to quickly determine a configuration plan that takes into account both performance and cost during the server hardware configuration process, and achieve the technical effect of efficiently realizing the intelligent generation and optimization of the hardware assembly plan of the equipment.

[0183] 2. Solve the pain point that traditional configuration solutions rely on manual experience, are inefficient, and cannot quickly match customer needs with inventory hardware during manual configuration.

[0184] 3. By dynamically adjusting weights, companies can flexibly balance the three major goals of inventory digestion, performance satisfaction, and cost control, achieving a precise match between business strategies and technical solutions.

[0185] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method.

[0186] The embodiment of the present application also provides a hardware configuration device for a device, Figure 3 A schematic diagram of the hardware configuration device of a device provided for application, such as Figure 3 As shown, including:

[0187] A first selection unit 31 is configured to select hardware in a preset hardware knowledge base according to the configuration condition information of the device to be configured, and obtain a plurality of first hardware corresponding to the configuration condition information;

[0188] A second selection unit 32 is configured to select a plurality of first hardware items according to a preset device hardware selection mode to obtain a plurality of second hardware items;

[0189] A third selection unit 33 is configured to select the plurality of second hardware according to a predetermined rule to obtain a plurality of third hardware;

[0190] The fourth selection unit 34 is configured to select the plurality of third hardware items according to the inventory priority, performance matching degree, and cost coefficient corresponding to each of the plurality of third hardware items to obtain a plurality of target hardware items;

[0191] The construction unit 35 is configured to construct a configuration policy for the target hardware according to a preset policy generation rule, and obtain a target configuration policy corresponding to the device to be configured.

[0192] In one embodiment of the present application, the first selection unit 31 is further configured to:

[0193] According to the target performance conditions and target budget conditions of the device to be configured included in the configuration condition information, hardware matching is performed in a preset hardware knowledge base to obtain a hardware matching result;

[0194] If it is determined according to the hardware matching result that the hardware performance of the hardware to be matched meets the target performance condition and the hardware budget of the hardware to be matched meets the target budget condition, the hardware to be matched is determined to be hardware that meets the configuration condition information, wherein the hardware to be matched is any hardware in the preset hardware knowledge base;

[0195] When the hardware performance of the hardware to be matched does not meet the target performance condition, and / or the hardware budget of the hardware to be matched does not meet the target budget condition, determining that the hardware to be matched is hardware data that does not meet the configuration condition information;

[0196] A plurality of hardware items meeting the configuration condition information is determined as a plurality of first hardware items.

[0197] In one embodiment of the present application, the second selection unit 32 is further configured to:

[0198] Determining hardware library information corresponding to each of the plurality of first hardware items, wherein the hardware library information is used to determine a hardware library position of the first hardware item in a preset hardware knowledge base, wherein the hardware library position includes at least a first hardware library position and a second hardware library position;

[0199] When it is determined that the preset device hardware selection mode is the resource priority mode, first hardware whose hardware library information is the first hardware library location is preferentially selected as the second hardware, wherein the preset device hardware selection mode includes at least the resource priority mode and the open ecosystem mode;

[0200] When it is determined that the preset selection mode is the open ecological mode, hardware selection is performed on the plurality of first hardware according to the first hardware performance and the first hardware budget corresponding to each of the plurality of first hardware to obtain the second hardware.

[0201] In one embodiment of the present application, the third selection unit 33 is further configured to:

[0202] Performing compatibility verification on the plurality of second hardware according to preset hardware design specifications and preset hardware constraints to obtain verification results, and performing hardware filtering on the second hardware according to the verification results to obtain a plurality of filtered second hardware;

[0203] Determining the hardware quantity corresponding to each of the plurality of filtered second hardware in the preset hardware knowledge base, wherein the hardware quantity is the quantity of the plurality of filtered second hardware in the first database position in the preset hardware knowledge base;

[0204] Performing hardware screening on the plurality of filtered second hardware according to the hardware quantity to obtain a plurality of filtered second hardware;

[0205] The plurality of filtered second hardware items are hardware filtered according to the benefit data corresponding to each of the plurality of filtered second hardware items to obtain a plurality of third hardware items.

[0206] In one embodiment of the present application, the fourth selection unit 34 is further configured to:

[0207] Obtaining inventory priorities, performance matching degrees, and cost coefficients corresponding to multiple third hardware items from a preset hardware knowledge base;

[0208] Determine, based on preset weight coefficients, a first weight corresponding to each inventory priority, a second weight corresponding to each performance matching degree, and a third weight corresponding to each cost coefficient;

[0209] Multiplying each inventory priority by its corresponding first weight to obtain first scores corresponding to each of the plurality of third hardware items; multiplying each performance matching degree by its corresponding second weight to obtain second scores corresponding to each of the plurality of third hardware items; and multiplying each cost coefficient by its corresponding third weight to obtain third scores corresponding to each of the plurality of third hardware items.

[0210] respectively adding the first scores, the second scores, and the third scores corresponding to the plurality of third hardware items to obtain target weight scores corresponding to the plurality of third hardware items;

[0211] Hardware selection is performed on the plurality of third hardware according to the target weight scores to obtain a plurality of target hardware.

[0212] In one embodiment of the present application, the fourth selection unit 34 is further configured to:

[0213] Acquire hardware types corresponding to respective pieces of third hardware from a preset hardware knowledge base;

[0214] Perform pairwise comparison processing on the target weight scores corresponding to the third hardware of the same hardware type to obtain a score comparison result;

[0215] According to the score comparison result, the third hardware with the highest target weight score among the third hardware of the same hardware type is determined as the target hardware.

[0216] In one embodiment of the present application, Figure 4 As shown, the hardware configuration device of the device also includes:

[0217] A display unit 36 is used to visually display the target configuration strategy;

[0218] The optimization unit 37 is configured to respond to the data optimization instruction based on the target configuration strategy and update the preset hardware knowledge base, the preset selection mode, the preset selection weight and the preset strategy generation rule according to the data optimization instruction.

[0219] In one embodiment of the present application, Figure 4 As shown, the hardware configuration device of the device also includes:

[0220] A storage unit 38, configured to store the first data obtained from the first hardware library location in a preset hardware knowledge base;

[0221] The storage unit 38 is further configured to obtain second data from a second hardware library location through a preset program interface and store the second data in a preset hardware knowledge base.

[0222] For the description of the features in the embodiment corresponding to the hardware configuration device of the device, please refer to the relevant description of the embodiment corresponding to the hardware configuration method of the device, and no further details will be given here.

[0223] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any of the above-mentioned embodiments of the hardware configuration method for the device.

[0224] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above-mentioned device hardware configuration method embodiments when run.

[0225] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0226] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any of the above-mentioned hardware configuration method embodiments of the device are implemented.

[0227] An embodiment of the present application also provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the hardware configuration method embodiment of any of the above-mentioned devices are implemented.

[0228] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0229] The above is a detailed introduction to the hardware configuration method, electronic device and storage medium of a device provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core ideas of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for hardware configuration of a device, characterized in that: include: Selecting hardware from a preset hardware knowledge base according to the configuration condition information of the device to be configured to obtain a plurality of first hardware corresponding to the configuration condition information; Performing hardware selection on the plurality of first hardware according to a preset device hardware selection mode to obtain a plurality of second hardware; Selecting the plurality of second hardware items according to a predetermined rule to obtain a plurality of third hardware items; Selecting the plurality of third hardware items according to the inventory priorities, performance matching degrees, and cost coefficients corresponding to the plurality of third hardware items to obtain a plurality of target hardware items; A configuration policy is constructed for the target hardware according to a preset policy generation rule to obtain a target configuration policy corresponding to the device to be configured.

2. The hardware configuration method of the device according to claim 1, characterized in that: The selecting hardware in a preset hardware knowledge base according to the configuration condition information of the device to be configured to obtain a plurality of first hardware corresponding to the configuration condition information includes: Performing hardware matching in the preset hardware knowledge base according to the target performance condition and the target budget condition of the device to be configured included in the configuration condition information to obtain a hardware matching result; If it is determined according to the hardware matching result that the hardware performance of the hardware to be matched meets the target performance condition, and the hardware budget of the hardware to be matched meets the target budget condition, determining that the hardware to be matched is hardware that meets the configuration condition information, wherein the hardware to be matched is any hardware in the preset hardware knowledge base; If the hardware performance of the hardware to be matched does not meet the target performance condition, and / or the hardware budget of the hardware to be matched does not meet the target budget condition, determining that the hardware to be matched is hardware data that does not meet the configuration condition information; The plurality of hardware items satisfying the configuration condition information are determined as the plurality of first hardware items.

3. The hardware configuration method of the device according to claim 1, characterized in that: The step of selecting the plurality of first hardware items according to the preset device hardware selection mode to obtain the plurality of second hardware items comprises: Determining hardware library information corresponding to each of the plurality of first hardware items, wherein the hardware library information is used to determine a hardware library position of the first hardware item in the preset hardware knowledge base, the hardware library position including at least a first hardware library position and a second hardware library position; When it is determined that the preset device hardware selection mode is the resource priority mode, preferentially selecting the first hardware whose hardware library information is the first hardware library location as the second hardware, wherein the preset device hardware selection mode includes at least the resource priority mode and the open ecosystem mode; When it is determined that the preset selection mode is the open ecological mode, hardware selection is performed on the plurality of first hardware according to the first hardware performance and the first hardware budget corresponding to each of the plurality of first hardware to obtain the second hardware.

4. The hardware configuration method of the device according to claim 3, characterized in that: The step of selecting the plurality of second hardware according to a predetermined rule to obtain the plurality of third hardware comprises: Performing compatibility verification on the plurality of second hardware according to preset hardware design specifications and preset hardware constraints to obtain verification results, and performing hardware filtering on the second hardware according to the verification results to obtain a plurality of filtered second hardware; Determining the hardware quantity corresponding to each of the plurality of filtered second hardware in the preset hardware knowledge base, wherein the hardware quantity is the quantity of the plurality of filtered second hardware in the preset hardware knowledge base at the first database position; Performing hardware screening on the plurality of filtered second hardware according to the hardware quantity to obtain a plurality of filtered second hardware; The plurality of filtered second hardware are hardware-filtered according to the benefit data corresponding to each of the plurality of filtered second hardware to obtain the plurality of third hardware.

5. The hardware configuration method of the device according to claim 1, characterized in that: The selecting the plurality of third hardware items to obtain the plurality of target hardware items according to the inventory priorities, performance matching degrees, and cost coefficients corresponding to the plurality of third hardware items includes: Obtaining the inventory priority, the performance matching degree, and the cost coefficient corresponding to each of the plurality of third hardware from the preset hardware knowledge base; Determining, according to preset weight coefficients, a first weight corresponding to each inventory priority, a second weight corresponding to each performance matching degree, and a third weight corresponding to each cost coefficient; Multiplying each inventory priority by its corresponding first weight to obtain a first score corresponding to each of the plurality of third hardware items; multiplying each performance matching degree by its corresponding second weight to obtain a second score corresponding to each of the plurality of third hardware items; and multiplying each cost coefficient by its corresponding third weight to obtain a third score corresponding to each of the plurality of third hardware items; respectively adding the first scores, the second scores, and the third scores corresponding to the respective third hardware components to obtain target weight scores corresponding to the respective third hardware components; Hardware selection is performed on the plurality of third hardware according to the target weight scores to obtain the plurality of target hardware.

6. The hardware configuration method of the device according to claim 1, characterized in that: The selecting the plurality of third hardware according to the target weight scores to obtain the plurality of target hardware comprises: Acquire hardware types corresponding to the plurality of third hardware respectively from the preset hardware knowledge base; Performing pairwise comparison processing on the target weight scores corresponding to the third hardware of the same hardware type to obtain a score comparison result; According to the score comparison result, the third hardware with the highest target weight score among the third hardware of the same hardware type is determined as the target hardware.

7. The hardware configuration method of the device according to claim 1, characterized in that: After constructing a configuration policy for the target hardware according to the preset policy generation rule to obtain a target configuration policy corresponding to the device to be configured, the method further includes: Visually displaying the target configuration strategy; In response to a data optimization instruction based on the target configuration policy, the preset hardware knowledge base, the preset selection mode, the preset selection weight, and the preset policy generation rule are updated according to the data optimization instruction.

8. The hardware configuration method of the device according to claim 1, characterized in that: Before selecting hardware in a preset hardware knowledge base according to the configuration condition information of the device to be configured to obtain a plurality of first hardware corresponding to the configuration condition information, the method further includes: Storing the first data acquired at the first hardware library location in the preset hardware knowledge base; The second data is obtained from the second hardware library location through a preset program interface, and the second data is stored in the preset hardware knowledge base.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the hardware configuration method of the device according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the hardware configuration method of the device according to any one of claims 1 to 8.

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