Component recommendation method based on user preference and related device

Abstract

The application provides a component recommendation method based on user preferences and related equipment, the method comprises: obtaining a plurality of target function parameters, and obtaining a plurality of function parameters and a plurality of influence parameters corresponding to each alternative component; based on the plurality of target function parameters and the plurality of function parameters, screening a plurality of target alternative components from all alternative components; based on the plurality of influence parameters, determining a plurality of matching degrees corresponding to the target alternative components; based on the pre-selected preference conditions and the corresponding matching degrees, screening all target alternative components to obtain a plurality of final target alternative components; based on all matching degrees corresponding to the final target alternative components, calculating a comprehensive matching degree corresponding to the final target alternative components, and determining the final target alternative component with the maximum comprehensive matching degree as the recommended component, thereby solving the problems of poor quality and low efficiency in the prior art.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and related equipment for recommending components based on user preferences. Background Technology

[0002] Components are the foundation of a product's reliability, innovation, and advancement, and are a crucial factor in ensuring that a product can fulfill its intended functions. Given the vast variety and specialized nature of components, how users can quickly and accurately locate the required components from a massive amount of information to expedite component selection has become a pressing issue. Currently, a common component selection method involves multi-parameter searches and database filtering to find suitable components. However, this still requires expert experience to select the best components from the filtered pool. The lack of a fully integrated component lifecycle data chain, an unsystematic database, and the absence of technological means to fully integrate and deeply mine data value makes it difficult for users to comprehensively consider relevant component data during product design. This can lead to suboptimal component selection, negatively impacting the reliability, safety, and cost of the overall product. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a method and related equipment for recommending components based on user preferences, so as to overcome all or part of the shortcomings of the prior art.

[0004] To achieve the above objectives, this application provides a method for recommending components based on user preferences, comprising: acquiring multiple target functional parameters, and acquiring multiple functional parameters and multiple influence parameters corresponding to each candidate component; selecting multiple target candidate components from all candidate components based on the multiple target functional parameters and multiple functional parameters; determining multiple matching degrees corresponding to the target candidate components based on the multiple influence parameters, wherein the multiple matching degrees correspond one-to-one with a predetermined number of preference scenarios; filtering all target candidate components based on a pre-selected preference scenario and its corresponding matching degree to obtain multiple final target candidate components, wherein the pre-selected preference scenario is one of the multiple preference scenarios; calculating the comprehensive matching degree corresponding to the final target candidate components based on all matching degrees corresponding to the final target candidate components, and determining the final target candidate component with the highest comprehensive matching degree as the recommended component.

[0005] Optionally, the preference situation includes quality assurance situation; determining the multiple matching degrees corresponding to the target candidate component based on the multiple influence parameters includes: calculating the re-inspection screening element value, destructive physical analysis element value, batch rejection element value, failure analysis element value and quality zeroing element value corresponding to the target candidate component based on the influence parameters associated with the quality assurance situation; determining the first influence weights corresponding to the re-inspection screening element value, the destructive physical analysis element value, the batch rejection element value, the failure analysis element value and the quality zeroing element value according to the first predetermined requirements; and determining the matching degree corresponding to the quality assurance situation based on the re-inspection screening element value, the destructive physical analysis element value, the batch rejection element value, the failure analysis element value, the quality zeroing element value and the first influence weights.

[0006] Optionally, the preference situation includes basic assurance situation; determining multiple matching degrees corresponding to the target candidate component based on the multiple influence parameters includes: calculating the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, and long-term stable supply capability element value corresponding to the target candidate component based on the influence parameters associated with the basic assurance situation; determining the second influence weights corresponding to the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, and long-term stable supply capability element value according to the second predetermined requirements; and determining the matching degree corresponding to the basic assurance situation based on the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, long-term stable supply capability element value, and the second influence weights.

[0007] Optionally, the preference includes production applicability; determining multiple matching degrees corresponding to the target candidate component based on the multiple influencing parameters includes: calculating the production consumption factor value, component size applicability factor value, and component weight applicability factor value corresponding to the target candidate component based on the influencing parameters associated with the production applicability; determining the third influence weight corresponding to the production consumption factor value, the component size applicability factor value, and the component weight applicability factor value according to the third predetermined requirement; and determining the matching degree corresponding to the production applicability based on the production consumption factor value, the component size applicability factor value, the component weight applicability factor value, and the third influence weight.

[0008] Optionally, the step of selecting multiple target candidate components from all candidate components based on multiple target functional parameters and multiple functional parameters includes: determining the functional parameter similarity of each candidate component based on multiple target functional parameters and multiple functional parameters; and determining the candidate component as the target candidate component in response to determining that the functional parameter similarity is greater than or equal to a predetermined similarity threshold.

[0009] Optionally, the multiple target functional parameters include multiple first numerical parameters and multiple first character parameters, and the multiple functional parameters include multiple second numerical parameters and multiple second character parameters; determining the functional parameter similarity of each candidate component based on the multiple target functional parameters and multiple functional parameters includes: for each candidate component, calculating the first sub-similarity between the first numerical parameter and a second numerical parameter with the same parameter name according to a first predetermined rule, and calculating the second sub-similarity between the first character parameter and a second character parameter with the same parameter name according to a second predetermined rule; classifying all functional parameters of the candidate components according to a predetermined importance level, and finding the importance weight of the category corresponding to the functional parameter; determining the functional parameter similarity of the candidate components based on all first sub-similarity, all second sub-similarity, and all importance weights.

[0010] Optionally, the step of filtering all target candidate components based on pre-selected preferences and their corresponding matching degrees to obtain multiple final target candidate components includes: classifying the matching degrees of all target candidate components corresponding to the pre-selected preferences according to predetermined rules to obtain a matching level of the matching degree of each target candidate component corresponding to the pre-selected preferences, wherein the matching degree is proportional to the matching level; and taking the target candidate component associated with each matching degree corresponding to the highest matching level as the final target candidate component.

[0011] Based on the same inventive concept, this application also provides a component recommendation device based on user preferences, comprising: an acquisition module configured to acquire multiple target functional parameters, and to acquire multiple functional parameters and multiple influence parameters corresponding to each candidate component; a first filtering module configured to filter multiple target candidate components from all candidate components based on the multiple target functional parameters and multiple influence parameters; a first determining module configured to determine multiple matching degrees corresponding to the target candidate components based on the multiple influence parameters, wherein the multiple matching degrees correspond one-to-one with a predetermined number of preference states; a second filtering module configured to filter all target candidate components based on a pre-selected preference state and its corresponding matching degree to obtain multiple final target candidate components, wherein the pre-selected preference state is one of a number of preference states; and a second determining module configured to calculate the comprehensive matching degree corresponding to the final target candidate components based on all matching degrees corresponding to the final target candidate components, and determine the final target candidate component with the largest comprehensive matching degree as the recommended component.

[0012] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0013] Based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to perform the method described above.

[0014] As can be seen from the above, the component recommendation method and related equipment based on user preferences provided in this application include obtaining multiple target functional parameters, and obtaining multiple functional parameters and multiple influence parameters corresponding to each candidate component. Based on the multiple target functional parameters and multiple influence parameters, multiple target candidate components are screened from all candidate components, reducing the number of candidate components that need to be recommended and achieving the purpose of initially determining the components to be recommended. Based on the multiple influence parameters, multiple matching degrees corresponding to the target candidate components are determined, wherein the multiple matching degrees correspond one-to-one with multiple predetermined preference situations, and the preference situations are quantified to accurately determine the matching degree between the target candidate components and each preference situation. Based on the pre-selected preference situation and its corresponding matching degree, all target candidate components are screened to obtain multiple final target candidate components, wherein the pre-selected preference situation is one of multiple preference situations, reducing the number of target candidate components that need to be recommended and achieving the purpose of further determining the components to be recommended. Based on the total matching degree of the final target candidate components, the comprehensive matching degree of the final target candidate components is calculated, and the final target candidate components with the highest comprehensive matching degree are determined as recommended components. This improves the efficiency of determining recommended components and makes the recommended components more accurate. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the component recommendation method based on user preferences according to an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of a questionnaire for scoring application history elements according to an embodiment of this application;

[0018] Figure 3 This is a schematic diagram of a questionnaire for scoring the elements of a qualified supplier, as described in an embodiment of this application.

[0019] Figure 4 This is a schematic diagram of a questionnaire for scoring selected catalog elements according to an embodiment of this application;

[0020] Figure 5 This is a schematic diagram of a questionnaire for scoring negative information elements according to an embodiment of this application;

[0021] Figure 6 This is a schematic diagram of the structure of a component recommendation device based on user preferences according to an embodiment of this application;

[0022] Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] As described in the background section, electronic components are fundamental to the reliability, innovation, and advancement of a complete product, and are a crucial factor in ensuring that the product can perform its intended functions. In the past, many imported electronic components were frequently selected during product development. However, the transportation of imported components is subject to time constraints and supply uncertainties, which directly impacts the success or failure of product development and production, as well as subsequent maintenance and support. Therefore, appropriate component selection is essential for ensuring the long-term stability of the entire product, helping to reduce future maintenance risks and guarantee the product's availability throughout its entire lifecycle. Given the vast variety and specialized nature of electronic components, how users can quickly and accurately locate the required components from a massive amount of component information to rapidly complete the component selection for their products has become a pressing issue.

[0026] Currently, the most common method for component selection involves searching for components based on multiple parameters and filtering them in a database to find the required components. However, this still requires the expertise of specialists to make recommendations from the filtered component set. However, current database infrastructure is not yet systematic, the entire lifecycle data chain for components is not yet fully integrated, and there is a lack of technical means to fully integrate and deeply mine the value of data. This makes it difficult for users to comprehensively consider relevant component data during product design, potentially leading to suboptimal component selection and negatively impacting the reliability, safety, and cost of the overall product.

[0027] In view of this, embodiments of this application propose a method for recommending components based on user preferences, referring to... Figure 1 This includes the following steps:

[0028] Step 101: Obtain multiple target functional parameters, as well as multiple functional parameters and multiple influence parameters corresponding to each candidate component.

[0029] In this step, multiple target functional parameters are obtained. These parameters reflect the required functional parameters of the candidate components, and are set according to predetermined requirements. It should be noted that target functional parameters can also be the functional parameters of an ideal component, which is the target component and the component required by the user. If the functional parameters of an ideal component meet the predetermined requirements, but the ideal component cannot be obtained for various reasons, then the purpose of recommending components is to find alternative components to the ideal component. Multiple functional parameters and multiple influence parameters are obtained for each candidate component. The functional parameters of the candidate components reflect their functional performance, and the influence parameters allow for the calculation of the matching degree between the component and different preferences.

[0030] Step 102: Based on multiple target functional parameters and multiple functional parameters, select multiple target candidate components from all candidate components.

[0031] In this step, when the number of candidate components is relatively large, making recommendations for all candidate components would lead to low efficiency in determining the recommended components. Therefore, based on multiple target functional parameters and multiple functional parameters, all candidate components are screened to obtain multiple target candidate components, reducing the number of candidate components that need to be recommended and achieving the goal of initially determining the components to be recommended.

[0032] Step 103: Based on the multiple influencing parameters, determine multiple matching degrees corresponding to the target candidate components, wherein the multiple matching degrees correspond one-to-one with multiple predetermined preference conditions.

[0033] In this step, component recommendations consider not only the impact of functional parameters but also the influence of various preferences. These preferences reflect the user's specific needs regarding each component, including quality assurance, basic support, and production suitability. The correlation between component recommendations and user preferences is represented by a matching degree. Therefore, based on multiple influencing parameters for each preference, the matching degree for each preference corresponding to the target candidate component is determined. By quantifying these preferences, the matching degree between the target candidate component and each preference can be accurately determined.

[0034] For example, preference scenarios include quality assurance, basic assurance, and production suitability. The element weights and their quantified values ​​for different preference scenarios are shown in Table 1.

[0035] Table 1. Overview of Factors Affecting Different Selection Preferences

[0036]

[0037] When recommending components to users based on quality assurance, the component matching degree is calculated as follows:

[0038] F1 = 1 - Q1,

[0039] Where Q1 = W 11 *V 11 +W 12 *V 12 +W 13 *V 13 +W 14 *V 14 +W 15 *V 15 This value indicates the quality assurance status; the lower the better.

[0040] When recommending components to users based on basic support conditions, the component matching degree is calculated as follows:

[0041] F2 = 1 - Q2,

[0042] Where Q2 = W 21 *V 21 +W 22 *V 22 +W 23 *V 23 +W 24 *V 24 +W 25 *V 25 +W 26 *V 26 This represents the value of basic protection; the lower the better.

[0043] When recommending components to users based on their suitability for production, the component matching degree is calculated as follows:

[0044] F3 = 1 - Q3,

[0045] Where Q3 = W 31 *V 31 +W 32 *V 32 +W 33 *V 33 This indicates the applicable production conditions; the lower the better.

[0046] Step 104: Based on the pre-selected preference and its corresponding matching degree, all target candidate components are screened to obtain multiple final target candidate components, wherein the pre-selected preference is one of multiple preference conditions.

[0047] In this step, different users have different preferences. Therefore, when recommending components to users, they can pre-select their preferences. By using the user's pre-selected preferences and their corresponding matching degrees, all target candidate components are filtered to obtain multiple final target candidate components. This reduces the number of target candidate components that need to be judged for recommendation, thus achieving the goal of further determining the components to be recommended.

[0048] Step 105: Based on all matching degrees corresponding to the final target candidate components, calculate the comprehensive matching degree corresponding to the final target candidate components, and determine the final target candidate components with the largest comprehensive matching degree as the recommended components.

[0049] In this step, the user has multiple preferences for the final target candidate components, each with a corresponding matching degree. By using all the matching degrees for the final target candidate components, the overall matching degree of the final target candidate components can be calculated, thus quantifying the user's preferences. The calculation method for the overall matching degree is as follows: based on the matching degree corresponding to quality assurance, basic assurance, and production applicability in the preference categories for each final target candidate component, and sorting them in descending order according to the matching degree of each preference category, the ranking of each final target candidate component in these three categories is added together to obtain the component's recommended ranking. The above recommended ranking is inversely proportional to the overall matching degree; the higher the overall matching degree of the final target candidate component, the closer it is to the user's predetermined needs. Therefore, the optimal component with the highest overall matching degree is recommended to the user, i.e., the final target candidate component with the highest overall matching degree is determined as the recommended component. It should be noted that this application filters all target candidate components according to pre-selected preferences and their corresponding matching degrees, resulting in multiple final target candidate components. Then, the comprehensive matching degree of each final target candidate component is calculated, and the final target candidate component with the highest comprehensive matching degree is determined as the recommended component. This demonstrates that the recommended component, while ensuring the pre-selected preferences, is also likely to have good other preferences, thus improving the quality of component selection. Since the determination of recommended components based on expert opinions is influenced by the experts' own experience, this application eliminates the need for expert opinions to recommend components, solving the problems of poor component selection quality and low efficiency. It improves both the efficiency and accuracy of determining recommended components.

[0050] The above scheme obtains multiple target functional parameters, as well as multiple functional parameters and multiple influence parameters corresponding to each candidate component. Based on these multiple target functional parameters, multiple target candidate components are selected from all candidate components, reducing the number of candidate components requiring recommendation judgment and achieving the initial goal of determining the components to be recommended. Based on the multiple influence parameters, multiple matching degrees corresponding to the target candidate components are determined, wherein each matching degree corresponds one-to-one with a predetermined number of preference scenarios. The preference scenarios are quantified to accurately determine the matching degree between the target candidate components and each preference scenario. Based on the pre-selected preference scenarios and their corresponding matching degrees, all target candidate components are screened to obtain multiple final target candidate components. The pre-selected preference scenarios are one of multiple preference scenarios, further reducing the number of target candidate components requiring recommendation judgment and achieving the goal of further determining the components to be recommended. Based on the total matching degree of the final target candidate components, the comprehensive matching degree of the final target candidate components is calculated, and the final target candidate components with the highest comprehensive matching degree are determined as recommended components. This improves the efficiency of determining recommended components and makes the recommended components more accurate.

[0051] In some embodiments, the preference situation includes a quality assurance situation; determining multiple matching degrees corresponding to the target candidate component based on the multiple influence parameters includes: calculating, respectively, the re-inspection screening element value, destructive physical analysis element value, batch rejection element value, failure analysis element value, and quality zeroing element value corresponding to the target candidate component based on the influence parameters associated with the quality assurance situation; determining, respectively, a first influence weight corresponding to the re-inspection screening element value, the destructive physical analysis element value, the batch rejection element value, the failure analysis element value, and the quality zeroing element value according to a first predetermined requirement; and determining the matching degree corresponding to the quality assurance situation based on the re-inspection screening element value, the destructive physical analysis element value, the batch rejection element value, the failure analysis element value, the quality zeroing element value, and the first influence weight.

[0052] In this embodiment, quality assurance is measured by the ability of components to perform their specified functions under specified conditions and within a specified time. This embodiment evaluates quality assurance through the following five aspects: re-inspection screening element value, destructive physical analysis element value, batch rejection element value, failure analysis element value, and quality zeroing element value. (1) Re-inspection screening element: Re-inspection screening test refers to the re-screening when the screening test does not meet the specified items, stress, time, or other special technical conditions. The higher the re-inspection screening element value, the worse the quality assurance of the target candidate component. (2) Destructive physical analysis element: DPA (Destructive Physical Analysis) test randomly selects a small number of test samples from the same batch for dissection, testing, and analysis to verify whether the material, design, process quality, and structure of the component meet the requirements of the corresponding specifications. Quality assurance deteriorates as the destructive physical analysis element value increases. (3) Batch rejection element: Batch rejection refers to the entire batch being rejected due to the batch of equipment components not meeting the pass rate or the test conclusion being unqualified. A smaller batch rejection value indicates a better quality assurance status for the target candidate components. (4) Failure analysis elements: If critical equipment components experience fatal failures or serious parameter deviations during special tests, acceptance, arrival inspection, supplementary screening (secondary screening), storage and transfer, assembly, testing, and use, failure analysis should be performed. A smaller failure analysis element value corresponds to a better quality assurance status. (5) Quality zeroing element: This usually indicates whether, at a certain stage or process, the quality problems that occur after delivery of components produced by the component manufacturing unit can be resolved according to standard requirements, quality control measures can be re-evaluated and improved, batch quality problem information can be promptly notified to other relevant users, and corresponding measures can be taken. A smaller quality zeroing element value indicates a better quality assurance status for the target candidate components.

[0053] Statistical analysis methods were used to calculate the values ​​of re-inspection screening elements, destructive physical analysis elements, batch rejection elements, failure analysis elements, and quality zeroing elements.

[0054] (1) Retesting screening factor value: The following calculation is performed based on the test failure rate of the retesting screening:

[0055]

[0056] Where: A1 represents the value of the re-examination screening element; A 1j This represents the re-examination screening element value in year j; σ represents the mean of the normalized rejection rate after re-inspection and screening of a certain target candidate component in year j; 1j n represents the standard deviation of the failure rate of a target candidate component in the re-inspection and screening test in year j;1j This represents the total number of re-testing and screening tests conducted on a certain target candidate component in year j; a j As a time coefficient, it reflects the time-series decay situation, with a j The multiple is the attenuation coefficient, for example, a = 0.9; the value of j is the difference between the current year and the predetermined year. For example, if the current year is 2023, j = 0 for the predetermined year 2023, j = 1 for the predetermined year 2022, j = 2 for the predetermined year 2021, and j = 3 for the predetermined year 2020.

[0057] (2) Destructive Physical Analysis Element Values: Based on the DPA test results, the following calculations are performed: The results include three levels (1, 0.5, 0), where 1 corresponds to qualified, 0.5 corresponds to questionable, and 0 corresponds to unqualified. It should be noted that in the unqualified category, "unqualified" and "sample failed" are both recorded as "unqualified". In the qualified category, "qualified" and "sample passed" are both recorded as qualified.

[0058]

[0059] Where A2 represents the destructive physical analysis element value; A 2j Indicates the destructive physical analysis element value for year j; n 2j This represents the total number of DPA tests conducted on a certain target candidate component in year j. σ represents the mean of the results of DPA (Discretionary Approach) in year j for a certain target candidate component; 2j This represents the standard deviation of the DPA test results for a target candidate component in year j; 'a' is the time coefficient, consistent with the aforementioned retesting and screening element values.

[0060] (3) Approval and rejection element value: The task status of the re-examination screening test is calculated as follows. The task status includes two levels (1, 0), 1 corresponds to approval and rejection, and 0 corresponds to no approval and rejection.

[0061]

[0062] Where A3 represents the approval / rejection element value; A 3j Indicates the value of the approved / rejected element in year j; n 3j This represents the total number of retesting and screening tests conducted on a certain target candidate component in year j. σ represents the mean value corresponding to the re-inspection and screening task status of a certain target candidate component in year j; 3j denoted by x3, represents the standard deviation of the re-inspection and screening task for a certain target candidate component in year j; x3 represents the number of times the task is rejected; S is a constant; and a is a time coefficient, consistent with the aforementioned re-inspection and screening element value section.

[0063] (4) Failure analysis element values: The following calculations are made based on the classification of failure causes. The classification of failure causes includes three types: inherent failure, use failure, and others, each with a different score, which can be obtained through expert surveys.

[0064]

[0065] Where A4 represents the failure analysis element value; This represents the failure analysis element value corresponding to the k1th production batch; This indicates the number of times the component failed in the k1th production batch; This represents the mean value of the failure cause classification in the failure analysis of the k1th batch of components used in a certain equipment. The standard deviation represents the conclusion of the failure analysis of the k1th batch of a target candidate component; K1 represents the total number of batches for which failure analysis was performed.

[0066] (5) Quality zeroing factor value: The following calculation is performed by statistically classifying the secondary causes of quality zeroing. The secondary causes of quality zeroing correspond to batch failure, inherent quality problems and others. Each type corresponds to a different score, which can be obtained by expert survey.

[0067]

[0068] Where A5 represents the value of the quality zero element; This represents the quality zeroing element value for the k2th batch. This represents the number of times the quality of a certain target candidate component reaches zero in the k2th production batch; This represents the average of the categories of reasons why the quality of the k2th batch of a certain target candidate component is zero; This represents the standard deviation of the classification of reasons for the zero quality in the k2th batch of a target candidate component; K2 represents the total number of batches with zero quality.

[0069] All of the aforementioned factor values ​​are related to the calculation of quality assurance status, but the importance of each factor value varies. Therefore, according to the first predetermined requirement, the first influence weights corresponding to the re-inspection screening factor value, destructive physical analysis factor value, batch rejection factor value, failure analysis factor value, and quality zeroing factor value are determined respectively. The first predetermined requirement is determined based on user needs, which can be reflected in expert opinions. Based on the re-inspection screening factor value, destructive physical analysis factor value, batch rejection factor value, failure analysis factor value, quality zeroing factor value, and the first influence weights, the numerical value of the quality assurance status is determined, quantifying the quality assurance status of the target candidate components, thereby achieving the goal of accurately determining the matching degree of the quality assurance status of the target candidate components.

[0070] It should be noted that each preference can be decomposed into two levels: the upper level is the target level, and the lower level is the indicator level. The influence weight of the indicator level is solved through the following steps. Therefore, the first influence weight in this embodiment is determined according to the following method: (I) Constructing a judgment matrix: The judgment matrix compares all elements in each preference in the target candidate components pairwise, using a relative scale to minimize the difficulty of comparing different preference levels and improve accuracy. For example, as shown in Table 2, a 1-9 scale is used. To avoid the problem of strong subjectivity in the AHP (Analytic Hierarchy Process) model, representative experts are interviewed and asked to fill out a questionnaire, which reflects the first predetermined requirement in this embodiment. It should be noted that the second and third predetermined requirements below can also be reflected through expert opinions. The judgment matrix is ​​obtained by synthesis. If the indicator level has n elements, the constructed judgment matrix is ​​denoted as A = (a ij ) n*n .

[0071] Table 2 Definition of Matrix Scale

[0072]

[0073] (II) Impact Weight Calculation: First, calculate the product of the elements in each row of the judgment matrix. Secondly, calculate m i nth root Next, the root directional quantities obtained above... Standardization processing, i.e. Then w = [w1, w2, ..., w n ] T This is the eigenvector of the obtained judgment matrix. Finally: Calculate the largest eigenvalue of the eigenvector w. The first influence weight in this embodiment can then be obtained. It should be noted that the second or third influence weights for other preference scenarios can also be calculated using the above method. The consistency test determines whether there are significant differences between the means or variances at a certain significance level. This can be tested using the following index CI. The larger the CI, the higher the degree of inconsistency in the judgment matrix.

[0074]

[0075] The average random consistency index (degrees of freedom index RI) of the judgment matrix is ​​introduced to calculate whether the consistency of the judgment matrix is ​​acceptable. The consistency ratio CR is calculated as follows:

[0076]

[0077] When CR < 0.1, the consistency test of the judgment matrix of target layer A is considered acceptable.

[0078] For judgment matrices of order 1 to 9, the values ​​of RI are shown in Table 3:

[0079] Table 3 Average Random Consistency Index Values

[0080]

[0081] In some embodiments, the preference situation includes basic assurance situation; determining multiple matching degrees corresponding to the target candidate components based on the multiple influence parameters includes: calculating, based on the influence parameters associated with the basic assurance situation, the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, and long-term stable supply capability element value corresponding to the target candidate components; determining, according to a second predetermined requirement, the second influence weights corresponding to the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, and long-term stable supply capability element value; and determining the matching degree corresponding to the basic assurance situation based on the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, long-term stable supply capability element value, and the second influence weights.

[0082] In this embodiment, the basic assurance situation refers to the stability and reliability of the application and supply of components throughout their entire life cycle. If the components are used extensively in the product and can be reliably supplied according to product requirements, ensuring the smooth progress of the product production plan, then the basic assurance situation of the components is good. This embodiment evaluates the basic assurance situation through the following six aspects: (1) Application history element: mainly refers to the historical usage range of the components in the product. The richer the application history of the target candidate components, that is, the wider the usage range of the target candidate components in the product, the better the basic assurance situation. (2) Qualified supplier element: refers to the suppliers of components that are recognized and verified in the supply chain management as meeting certain standards and requirements, and can provide high-quality, reliable and compliant components to the product manufacturing unit. If the target candidate components are provided by qualified suppliers, then the basic assurance situation of the target candidate components is good. (3) Selection catalog element: a list of components that can be selected for product design, compiled and officially released in accordance with the prescribed requirements and procedures based on product usage experience and characteristics. (4) Negative information element: used to describe different situations in the research, development, production and assembly of components. (5) Self-sufficiency capability: Based on available R&D and production resources, the ability to independently carry out component equipment design, manufacturing and supply guarantee, and the ability to independently control and manage its technology R&D, manufacturing and supply chain. (6) Long-term stable supply capability: Refers to the ability of component suppliers to maintain stable supply capability over a long period of time, that is, to provide the components required for the product on time, in good quality and in sufficient quantity.

[0083] This embodiment employs a nine-level Likert scale and an expert questionnaire survey method to calculate the application resume element value, qualified supplier element value, selected catalog element value, negative information element value, self-sufficiency capability element value, and long-term stable supply capability element value. First, the correspondence between risk level and score in the questionnaire is pre-defined. Risk level is reflected by the score; the higher the risk level, the higher the score. For example, extremely low risk level - 1 point, very low risk level - 2 points, low risk level - 3 points, lower low risk level - 4 points, medium risk level - 5 points, higher risk level - 6 points, high risk level - 7 points, very high risk level - 8 points, and extremely high risk level - 9 points. Furthermore, the industry has already made preliminary hierarchical classifications of self-sufficiency capability and long-term stable supply capability levels. This patent, based on this, comprehensively uses the upper and lower limits of the levels to quantify the value of basic assurance status.

[0084] (1) Application history element values: determined based on the application scope of the target candidate components in the product, and the expert scoring questionnaire on the impact of the application scope, such as... Figure 2 As shown, the application resume element values ​​are calculated using the following formula:

[0085]

[0086] in, To apply resume element values, This represents the risk level score given by the nth expert to the indicator value with index number i1, where N represents the number of valid expert survey questionnaires collected. For example, an indicator value of index number i1 = 1 corresponds to the component being used in 4 or more products, an indicator value of index number i1 = 2 corresponds to the component being used in 3 or more products, an indicator value of index number i1 = 3 corresponds to the component being used in 2 or more products, an indicator value of index number i1 = 4 corresponds to the component being used in 1 or more products, and an indicator value of index number i1 = 5 corresponds to the component not being used in any product. The risk level score for the indicator value with index number i1 can be any positive integer value in the range of 1-9.

[0087] (2) Qualified Supplier Factors: This is determined based on whether the suppliers of the target candidate components are on the list of qualified suppliers. The expert scoring questionnaire on supply stability is as follows: Figure 3 As shown, the element score for qualified supplier elements is calculated using the following formula;

[0088]

[0089] in, For qualified supplier element values, This represents the risk level score given by the nth expert to case number i2, where N represents the number of experts participating in the scoring. For example, case number i2 indicates that the supplier of the component for case 1 is on the list of qualified suppliers, while case number i2 indicates that the supplier of the component for case 2 is not on the list of qualified suppliers. The risk level score for case number i2 is any positive integer value in the range of 1-9.

[0090] (3) Selection Catalog Element Values: Determined based on whether the target candidate components are within the selection catalog. The selection catalog refers to a list of components available for equipment design, compiled and officially released according to prescribed requirements and procedures, taking into account equipment usage experience and characteristics. Expert scoring questionnaires for the recommended components are as follows: Figure 4 As shown, the selected directory element value is calculated using the following formula:

[0091]

[0092] in, To select catalog element values, This represents the risk level score given by the nth expert to the i3th possible value, where N represents the number of experts who participated in the survey. For example, the component with the i3th possible value is rated "Preferred" for the first value, "Optional" for the second value, "Limited" for the third value, and not included in the selection list for the fourth value. The risk level score for the i3th possible value is any positive integer value in the range of 1-9.

[0093] (4) Negative Information Element Values: These describe different aspects of the research, development, production, and assembly of components. The expert scoring questionnaire for negative information elements is as follows: Figure 5 As shown, the negative information element value is calculated using the following formula:

[0094]

[0095] in, X represents the negative information element value. ni This represents the risk level score given by the nth expert to the indicator value with index number i4, where N represents the number of valid expert survey questionnaires collected. For example, an indicator value with index number i4 of 1 corresponds to a component that is domestically developed and produced; an indicator value with index number i4 of 2 corresponds to a component whose key technologies are limited by domestic research and development; an indicator value with index number i4 of 3 corresponds to a component whose key components are not domestically produced; and an indicator value with index number i4 of 4 corresponds to a component that is neither domestically developed nor domestically produced. The risk level score for the indicator value with index number i4 can be any positive integer value in the range of 1 to 9.

[0096] (5) Self-sufficiency capability element value: At present, the industry has made a preliminary hierarchical classification of self-sufficiency capability levels. Level A corresponds to 80-100 points, Level B corresponds to 70-79 points, Level C and C* correspond to 60-69 points, Level D corresponds to 50-59 points, and Level E corresponds to 0-50 points. The self-sufficiency capability element value is calculated using the following formula.

[0097]

[0098] in, For the self-sufficiency capability element values, i5 = 1, 2, 3, 4, 5 correspond to self-sufficiency capabilities A, B, C (C*), D, and E, respectively. and This indicates the upper and lower limits of the score when the level is i5.

[0099] (6) Long-term stable supply capacity factor value: The industry has made a preliminary classification of the level of long-term stable supply capacity. Excellent corresponds to 180-200 points, qualified corresponds to 120-179 points, and poor corresponds to 0-120 points. The long-term stable supply capacity factor value is calculated by the following formula.

[0100]

[0101] in, For long-term stable supply capacity, i6 = 1, 2, and 3 correspond to three levels (excellent, qualified, and poor) of the supplier's long-term stable supply capacity. and This represents the upper and lower limits of the score when the level is i6.

[0102] All of the aforementioned matching degrees are related to the calculation of basic assurance conditions, but the importance of each matching degree differs. Therefore, according to the second predetermined requirement, the second influence weights corresponding to the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency capability element value, and long-term stable supply capability element value are determined respectively. The second predetermined requirement is determined based on user needs, which can be reflected in expert opinions. Based on the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency capability element value, long-term stable supply capability element value, and second influence weights, the numerical values ​​of the basic assurance conditions are determined, quantifying the basic assurance conditions of the target candidate components, thereby achieving the goal of accurately determining the matching degree of the basic assurance conditions of the target candidate components.

[0103] In some embodiments, the preference includes production applicability; determining multiple matching degrees corresponding to the target candidate component based on the multiple influencing parameters includes: calculating the production consumption factor value, device size applicability factor value, and device weight applicability factor value corresponding to the target candidate component based on the influencing parameters associated with the production applicability; determining the third influence weight corresponding to the production consumption factor value, the device size applicability factor value, and the device weight applicability factor value according to a third predetermined requirement; and determining the matching degree corresponding to the production applicability based on the production consumption factor value, the device size applicability factor value, the device weight applicability factor value, and the third influence weight.

[0104] In this embodiment, production suitability mainly refers to the comprehensive performance of components in terms of production consumption, package size, and weight. Components with good production suitability should have reasonable production consumption, suitable package size, and appropriate weight, and can minimize the manufacturing and usage costs of the product while meeting product performance and function requirements. Production suitability includes: (1) Production consumption factors: The production consumption of components reflects the value of components in the market and can vary depending on various factors, including the type, brand, specifications, batch purchase quantity, supplier, and market demand of components. The production consumption of components can affect production suitability. Users who choose components with moderate production consumption correspond to better production suitability. Moderate production consumption determines that the value of components in the market is moderate. (2) Component size suitability factors: Package size plays an important role in the design and manufacturing process of components. The package size of components determines their layout on the circuit board and how they are assembled in electronic products. It is necessary to ensure that the size of the components is suitable for the given circuit board space and their arrangement inside the product casing. Users who choose appropriate package size correspond to better production suitability. (3) Applicable factors for component weight: Weight refers to the mass of the component itself, usually measured in milligrams (mg), grams (g), or kilograms (kg). As a basic characteristic of components, it directly affects the design, manufacturing, and use of products.

[0105] (1) Production consumption factor value: The market value of the target candidate components in the candidate set is sorted in ascending order. For example, they are divided into 5 levels according to 20%, 40%, 60%, and 80%, as shown in Table 4:

[0106] Table 4 Market Value Classification Table

[0107]

[0108] C1 = L P , where L P This indicates the level corresponding to the market value of the target candidate components.

[0109] (2) Applicable element value for device size: The similarity between the candidate device and the target device is calculated based on the Euclidean distance, as shown in the following formula:

[0110]

[0111] Where, x i y represents the package size value of the i-th dimension of the target component. i This represents the package size value of the target candidate component in the corresponding i-th dimension, and n represents the total number of dimensions of the target component's package size.

[0112] (3) Applicable element value for component weight: The weights of the target candidate components in the candidate set are sorted in ascending order. For example, they are divided into 5 levels according to 20%, 40%, 60%, and 80%, as shown in Table 5:

[0113] Table 5. Classification of Weight Element Levels

[0114]

[0115] C3 = L W , where L W This indicates the grade corresponding to the weight of the target candidate component.

[0116] All of the aforementioned matching degrees are related to the calculation of production applicability, but the importance of each matching degree differs. Therefore, according to the third predetermined requirement, the third influence weights corresponding to the production consumption factor value, the device size applicability factor value, and the device weight applicability factor value are determined respectively. The third predetermined requirement is determined based on user needs, which can be reflected in expert opinions. Based on the production consumption factor value, the device size applicability factor value, the device weight applicability factor value, and the third influence weight, the numerical value of the production applicability is determined, quantifying the production applicability of the target candidate components, thereby achieving the goal of accurately determining the matching degree of the production applicability of the target candidate components.

[0117] In some embodiments, the step of selecting multiple target candidate components from all candidate components based on multiple target functional parameters and multiple functional parameters includes: determining the functional parameter similarity of each candidate component based on multiple target functional parameters and multiple functional parameters; and determining the candidate component as the target candidate component in response to determining that the functional parameter similarity is greater than or equal to a predetermined similarity threshold.

[0118] In this embodiment, each target functional parameter is compared with its associated functional parameter to obtain the similarity between the target functional parameter and the associated functional parameter. Therefore, the similarity of each candidate component can be determined based on multiple target functional parameters and multiple functional parameters. If the functional parameter similarity is greater than or equal to a predetermined similarity threshold, the candidate component is determined as a target candidate component. The predetermined similarity threshold can be determined according to predetermined requirements; for example, the predetermined similarity threshold can be the median of all functional similarities. Candidate components are screened based on their functional parameters, and those with relatively large deviations from the target functional parameters are eliminated, achieving the initial recommendation of components.

[0119] In some embodiments, the multiple target functional parameters include multiple first numerical parameters and multiple first character parameters, and the multiple functional parameters include multiple second numerical parameters and multiple second character parameters. Determining the functional parameter similarity of each candidate component based on the multiple target functional parameters and the multiple functional parameters includes: for each candidate component, calculating a first sub-similarity between the first numerical parameter and a second numerical parameter with the same parameter name according to a first predetermined rule, and calculating a second sub-similarity between the first character parameter and a second character parameter with the same parameter name according to a second predetermined rule; classifying all functional parameters of the candidate components according to a predetermined importance level, and finding the importance weight of the category corresponding to the functional parameter; and determining the functional parameter similarity of the candidate components based on all first sub-similarity, all second sub-similarity, and all importance weights.

[0120] In this embodiment, the premise for effective component recommendation is that the recommended components meet the user's functional and performance requirements. This embodiment achieves accurate calculation of the functional and performance similarity of components by integrating various different performance parameters. Considering the significant differences in processing methods for different numerical types, performance parameters are divided into two types: numerical and character, and similarity calculations are performed separately for each type.

[0121] (I) Numerical Parameter Similarity Measurement

[0122] Considering that external factors may prevent the acquisition of all values ​​in the second numerical parameters of the target candidate components, the calculation of the first sub-similarity between the first numerical parameter and its second numerical parameter with the same parameter name includes the following cases:

[0123] Case 1: When the intersection of the first and second numeric parameters is the maximum, minimum, and typical value fields:

[0124] S1=(S 11 +S 12 ) / 2,

[0125] Among them, S 11 S is the similarity of the number of intervals formed by the maximum and minimum values. 12 This represents the similarity of typical values. For the maximum and minimum values, assume the two interval numbers are [a...]. L ,a U ],[b L ,b U ],S 11 The calculation is as follows:

[0126]

[0127] For typical values, let's assume the typical values ​​of the first and second numerical parameters are a, b, and S, respectively. 12 The calculation is as follows:

[0128]

[0129] Case 2: When the intersection of the first and second numeric parameters is the maximum value and typical value fields, the typical value is the minimum value by default, and S1 = S 11 .

[0130] Case 3: When the intersection of the first and second numeric parameters is the maximum and minimum value fields: S1 = S 11 .

[0131] Case 4: When the intersection of the first and second numeric parameters is the typical value and the minimum value fields, the default typical value is the maximum value: S1 = S 11 .

[0132] Case 5: When the intersection of the first and second numeric parameters is only the typical value field: S1 = S 12 .

[0133] Case 6: When the intersection of the first and second numeric parameters is only the minimum value field: Where a and b are both the minimum values ​​of the parameter.

[0134] Case 7: When the intersection of the first and second numeric parameters is only the maximum value field: Where a and b are both the maximum values ​​of the parameter.

[0135] Case 8: When the intersection of the first and second numerical parameters is empty, the following situations exist:

[0136] 1) When one of the first and second numerical parameters has both a maximum and a minimum value, while the other only has a typical value,

[0137]

[0138] Among them, a U and a L Let b be the maximum and minimum values, b be the typical value, and α be the penalty factor constant when the intersection is empty.

[0139] 2) If one of the first and second numerical parameters has a maximum and a typical value, and the other has only a minimum value, then if the typical value is less than the minimum value, S1 = 0; otherwise...

[0140]

[0141] Where a is a typical value, b L The minimum value is α, which is the penalty factor constant when the intersection is empty.

[0142] 3) If one of the first and second numerical parameters has a minimum and a typical value, and the other has only a maximum value, then if the typical value is greater than the maximum value, S1 = 0; otherwise...

[0143]

[0144] Where a is a typical value, b U The maximum value is α, which is the penalty factor constant when the intersection is empty.

[0145] 4) If one of the first and second numerical parameters has only a typical value and the other has only a maximum value, then if the typical value is greater than the maximum value, S1 = 0; otherwise...

[0146]

[0147] Where a is a typical value, b U The maximum value is α, which is the penalty factor constant when the intersection is empty.

[0148] 5) If one of the first and second numerical parameters has only a typical value and the other has only a minimum value, then if the typical value is less than the minimum value, S1 = 0; otherwise...

[0149]

[0150] Where a is a typical value, b L The maximum value is α, which is the penalty factor constant when the intersection is empty.

[0151] 6) If one of the first and second numerical parameters has only a maximum value and the other has only a minimum value, then if the maximum value is less than the minimum value, S1 = 0; otherwise...

[0152]

[0153] Among them, a L For typical values, b U The maximum value is α, which is the penalty factor constant when the intersection is empty.

[0154] (II) Similarity Measurement of Character-Type Parameters

[0155] When the function parameter is a character parameter, if the first character parameter and the second character parameter are exactly the same, S1 = 1; otherwise, S1 = 0.

[0156] In the component functional parameters, each component includes different critical and non-critical functional parameters. Critical functional parameters are those that significantly affect the component's functional performance, while non-critical functional parameters have a smaller impact. During component recommendation, all functional parameters of candidate components are categorized according to predetermined importance by identifying the sets of critical and non-critical parameters in the performance parameter library, and similarity is calculated based on their category. It should be noted that different categories have different importance weights; the more important the category, the greater the importance weight.

[0157] For the set of key function parameters, calculate the key function similarity S_key:

[0158]

[0159] Where N is the number of key functional parameters of the target candidate components, and N1 is the number of key functional parameters used when calculating the similarity between the first character-type parameter and the second character-type parameter with the same parameter name.

[0160] For the set of non-critical function parameters, calculate the non-critical function similarity S_no_key:

[0161]

[0162] Where M represents the number of non-critical functional parameters of the recommended target component, and M1 represents the number of non-critical functional parameters used when calculating the similarity between the first character parameter and the second character parameter with the same parameter name.

[0163] The formula for calculating the functional similarity of candidate components is: S = W1 * S_key + W2 * S_no_key.

[0164] Here, W1 and W2 represent the weights of key functional parameters and non-key functional parameters, respectively. The weights are determined based on the influence of the category corresponding to the functional parameter. The correspondence between the influence of the category corresponding to the functional parameter and the weight can be set in advance based on expert surveys.

[0165] In some embodiments, the step of filtering all target candidate components based on pre-selected preferences and their corresponding matching degrees to obtain multiple final target candidate components includes: classifying the matching degrees of all target candidate components corresponding to the pre-selected preferences according to predetermined rules to obtain a matching level of the matching degree of each target candidate component corresponding to the pre-selected preferences, wherein the matching degree is proportional to the matching level; and taking the target candidate component associated with each matching degree corresponding to the highest matching level as the final target candidate component.

[0166] In this embodiment, the user selects one of three preferences: quality assurance, basic assurance, and production applicability. The matching degree between each target candidate component and the preference is then categorized according to a predetermined rule to obtain a matching level for each target candidate component. A higher matching level value indicates a higher matching level. For example, the highest matching level corresponds to a matching level value of 4; a high matching level to 3; a medium matching level to 2; and a low matching level to 1. The greater the matching degree of the target candidate component, the higher the matching level value. For example, the predetermined rule is to sort the matching degrees corresponding to the preference in descending order and divide them into four matching levels based on 25%, 50%, and 75% according to the classification results, as shown in Table 6. Components with a matching level of 4 are selected as the final target candidate components, further narrowing down the range of recommended components.

[0167] Table 6. Component Preference Matching Level Classification Table

[0168]

[0169] In another embodiment provided in this application, an example of a personalized component selection recommendation method based on the analytic hierarchy process is shown below:

[0170] This embodiment collected 20 questionnaires, and the element weights for different preferences are shown in Table 7:

[0171] Table 7 Overview of Factors Affecting Different Selection Preferences

[0172]

[0173] Assume the recommended target functional parameters include: a high-speed CMOS differential 4-channel analog multiplexer / demultiplexer with TTL input, model CD74HCT4052E, with performance parameters (typical bandwidth 185, switching structure 4:1, typical turn-off drain capacitance 5, typical on-state capacitance 5, number of channels 2, maximum turn-off leakage current 1, maximum on-state leakage current 1, power supply type Single, Dual, typical on-resistance 40, minimum input high-level voltage 2, maximum input low-level voltage 0.8, maximum single supply voltage 5.5, minimum single supply voltage 4.5, maximum dual supply voltage range 0, minimum dual supply voltage range -6, function analog switch / multiplexer).

[0174] One example of a potential candidate component is as follows:

[0175] Table 8. Example data for target candidate components

[0176]

[0177] Calculations show that the overall similarity of all functional parameters of the candidate component to the target functional parameters with the same name is 0.90, the matching degree under the influence of quality assurance is 0.76, the matching degree under the influence of basic assurance is 0.92, and the matching degree under the influence of production applicability is 0.76.

[0178] Assuming the recommended candidate set includes 16 target candidate components, the matching situation of the 16 target candidate components is shown in Table 8. From the data in the table, it can be seen that if the user prefers better quality assurance, then component 6 is recommended; if the user prefers better basic assurance 1, then component 7 is recommended; if the user prefers better production applicability, then component 10 is recommended.

[0179] Table 9. Example data on matching situations for target candidate components.

[0180]

[0181] With a wide variety of electronic components available, and varying overall quality levels, accurately identifying suitable components and quickly selecting the necessary parts for a product is a pressing issue for users facing a vast amount of component information. This embodiment proposes an intelligent component recommendation method based on user selection preferences. First, a component functional performance similarity model is established based on mathematical statistical methods, effectively integrating various functional performance parameters to calculate the functional performance similarity of components, ensuring that recommended candidate components effectively meet product functional requirements. Second, three selection preferences—quality assurance, basic assurance, and production applicability—are established through literature review and expert interviews, along with the indicator elements considered for each preference. Likert quantification and mathematical analysis methods are used to quantify these elements. Then, the analytic hierarchy process (AHP) is used to calculate the weights of each element under different preferences, determining the degree of component matching under each preference. Finally, a specific method for component recommendation based on user selection preferences is proposed, comprehensively considering the other two aspects while satisfying user preferences, to provide the final recommendation result.

[0182] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0183] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0184] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a device for recommending components based on user preferences.

[0185] refer to Figure 6 The component recommendation device based on user preferences includes:

[0186] The acquisition module 10 is configured to acquire multiple target functional parameters, as well as multiple functional parameters and multiple influence parameters corresponding to each candidate component.

[0187] The first screening module 20 is configured to select multiple target candidate components from all candidate components based on multiple target function parameters and multiple function parameters.

[0188] The first determining module 30 is configured to determine multiple matching degrees corresponding to the target candidate components based on the multiple influencing parameters, wherein the multiple matching degrees correspond one-to-one with a predetermined multiple preference situations.

[0189] The second filtering module 40 is configured to filter all target candidate components based on pre-selected preferences and their corresponding matching degrees to obtain multiple final target candidate components, wherein the pre-selected preferences are one of multiple preferences.

[0190] The second determining module 50 is configured to calculate the comprehensive matching degree of the final target candidate component based on all matching degrees corresponding to the final target candidate component, and determine the final target candidate component with the largest comprehensive matching degree as the recommended component.

[0191] The aforementioned device acquires multiple target functional parameters, as well as multiple functional parameters and multiple influence parameters corresponding to each candidate component. Based on these multiple target functional parameters and multiple influence parameters, multiple target candidate components are selected from all candidate components, reducing the number of candidate components requiring recommendation judgment and achieving the initial goal of determining the components to be recommended. Based on the multiple influence parameters, multiple matching degrees corresponding to the target candidate components are determined, wherein each matching degree corresponds one-to-one with a predetermined number of preference scenarios. The preference scenarios are quantified to accurately determine the matching degree between the target candidate components and each preference scenario. Based on the pre-selected preference scenarios and their corresponding matching degrees, all target candidate components are screened to obtain multiple final target candidate components. The pre-selected preference scenarios are one of multiple preference scenarios, further reducing the number of target candidate components requiring recommendation judgment and achieving the goal of further determining the components to be recommended. Based on the total matching degree of the final target candidate components, the comprehensive matching degree of the final target candidate components is calculated, and the final target candidate components with the highest comprehensive matching degree are determined as recommended components. This improves the efficiency of determining recommended components and makes the recommended components more accurate.

[0192] In some embodiments, the first determining module 30 is further configured to: include a quality assurance situation in the preference situation; calculate, based on the influence parameters associated with the quality assurance situation, the re-inspection screening element value, destructive physical analysis element value, batch rejection element value, failure analysis element value, and quality zeroing element value corresponding to the target candidate component; determine, according to a first predetermined requirement, a first influence weight corresponding to the re-inspection screening element value, the destructive physical analysis element value, the batch rejection element value, the failure analysis element value, and the quality zeroing element value; and determine the matching degree corresponding to the quality assurance situation based on the re-inspection screening element value, the destructive physical analysis element value, the batch rejection element value, the failure analysis element value, the quality zeroing element value, and the first influence weight.

[0193] In some embodiments, the first determining module 30 is further configured to: include basic assurance conditions in the preference situation; calculate, based on the influence parameters associated with the basic assurance conditions, the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, and long-term stable supply capability element value corresponding to the target candidate components; determine, according to the second predetermined requirements, the second influence weights corresponding to the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, and long-term stable supply capability element value; and determine the matching degree corresponding to the basic assurance conditions based on the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value, long-term stable supply capability element value, and the second influence weights.

[0194] In some embodiments, the first determining module 30 is further configured to: include production applicability conditions in the preference conditions; calculate production consumption factor values, device size applicability factor values, and device weight applicability factor values ​​corresponding to the target candidate components based on the influence parameters associated with the production applicability conditions; determine third influence weights corresponding to the production consumption factor values, device size applicability factor values, and device weight applicability factor values ​​according to a third predetermined requirement; and determine the matching degree corresponding to the production applicability conditions based on the production consumption factor values, device size applicability factor values, device weight applicability factor values, and third influence weights.

[0195] In some embodiments, the first screening module 20 is further configured to determine the functional parameter similarity of each candidate component based on multiple target functional parameters and multiple functional parameters; and to determine the candidate component as the target candidate component in response to determining that the functional parameter similarity is greater than or equal to a predetermined similarity threshold.

[0196] In some embodiments, the first screening module 20 is further configured to include multiple target functional parameters, including multiple first numerical parameters and multiple first character parameters, and multiple functional parameters, including multiple second numerical parameters and multiple second character parameters; for each candidate component, according to a first predetermined rule, calculate the first sub-similarity between the first numerical parameter and a second numerical parameter with the same parameter name, and according to a second predetermined rule, calculate the second sub-similarity between the first character parameter and a second character parameter with the same parameter name; classify all functional parameters of the candidate components according to a predetermined importance level, and find the importance weight of the category corresponding to the functional parameter; and determine the functional parameter similarity of the candidate components based on all first sub-similarity, all second sub-similarity, and all importance weights.

[0197] In some embodiments, the second filtering module 40 is further configured to classify the matching degree of all target candidate components corresponding to the pre-selected preference according to a predetermined rule to obtain the matching level of the matching degree of each target candidate component corresponding to the pre-selected preference, wherein the matching degree is proportional to the matching level; and to take the target candidate component associated with each matching degree corresponding to the highest matching level as the final target candidate component.

[0198] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0199] The apparatus of the above embodiments is used to implement the corresponding component recommendation method based on user preferences in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0200] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the component recommendation method based on user preferences as described in any of the above embodiments.

[0201] Figure 7 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0202] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0203] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0204] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0205] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0206] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0207] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0208] The electronic devices described above are used to implement the corresponding user preference-based component recommendation method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0209] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the user preference-based component recommendation method as described in any of the above embodiments.

[0210] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0211] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the component recommendation method based on user preferences as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0212] Based on the same concept, corresponding to the methods of any of the above embodiments, this application also provides a computer program product, including computer program instructions, which, when run on a computer, cause the computer to execute the component recommendation method based on user preferences as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0213] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0214] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0215] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0216] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for recommending electronic components based on user preferences, characterized in that, include: Obtain multiple target function parameters, as well as multiple function parameters and multiple influence parameters corresponding to each candidate component; Based on multiple target functional parameters and multiple functional parameters, multiple target candidate components are selected from all candidate components. Based on the multiple influencing parameters, multiple matching degrees corresponding to the target candidate components are determined, wherein the multiple matching degrees correspond one-to-one with multiple predetermined preference situations; Based on the pre-selected preferences and their corresponding matching degrees, all target candidate components are screened to obtain multiple final target candidate components. The pre-selected preferences are one of multiple preferences. Based on all matching degrees corresponding to the final target candidate components, calculate the comprehensive matching degree corresponding to the final target candidate components, and determine the final target candidate components with the largest comprehensive matching degree as the recommended components; The step of filtering all target candidate components based on pre-selected preferences and their corresponding matching degrees to obtain multiple final target candidate components includes: classifying the matching degrees of all target candidate components corresponding to the pre-selected preferences according to predetermined rules to obtain a matching level of the matching degree of each target candidate component corresponding to the pre-selected preferences, wherein the matching degree is proportional to the matching level; and taking the target candidate component associated with each matching degree corresponding to the highest matching level as the final target candidate component.

2. The method according to claim 1, characterized in that, The preference criteria include quality assurance criteria; The determination of multiple matching degrees corresponding to the target candidate components based on the multiple influencing parameters includes: Based on the impact parameters associated with the quality assurance status, the re-inspection screening factor value, destructive physical analysis factor value, batch rejection factor value, failure analysis factor value and quality zeroing factor value corresponding to the target candidate components are calculated respectively. According to the first predetermined requirements, the first influence weights corresponding to the re-inspection screening element value, the destructive physical analysis element value, the batch rejection element value, the failure analysis element value, and the quality zeroing element value are determined respectively. Based on the re-inspection screening factor value, the destructive physical analysis factor value, the batch rejection factor value, the failure analysis factor value, the quality zeroing factor value, and the first influence weight, the matching degree corresponding to the quality assurance status is determined.

3. The method according to claim 1, characterized in that, The preferences include basic protection conditions; The determination of multiple matching degrees corresponding to the target candidate components based on the multiple influencing parameters includes: Based on the impact parameters associated with the basic support situation, the application history element value, qualified supplier element value, selection catalog element value, negative information element value, self-sufficiency element value and long-term stable supply capability element value corresponding to the target candidate components are calculated respectively. According to the second predetermined requirements, the second influence weights corresponding to the application history element value, the qualified supplier element value, the selection catalog element value, the negative information element value, the self-sufficiency element value, and the long-term stable supply capacity element value are determined respectively. Based on the application history element value, the qualified supplier element value, the selected catalog element value, the negative information element value, the self-sufficiency element value, the long-term stable supply capacity element value, and the second influence weight, the matching degree corresponding to the basic guarantee situation is determined.

4. The method according to claim 1, characterized in that, The preference criteria include production suitability; The determination of multiple matching degrees corresponding to the target candidate components based on the multiple influencing parameters includes: Based on the influence parameters associated with the production application conditions, calculate the production consumption factor value, device size application factor value, and device weight application factor value corresponding to the target candidate components, respectively. According to the third predetermined requirement, the third influence weights corresponding to the production consumption factor value, the device size applicable factor value, and the device weight applicable factor value are determined respectively; Based on the production consumption factor value, the device size applicable factor value, the device weight applicable factor value, and the third influence weight, the matching degree corresponding to the production applicable situation is determined.

5. The method according to claim 1, characterized in that, The process involves selecting multiple target candidate components from all available components based on multiple target functional parameters and multiple functional parameters, including: Based on multiple target functional parameters and multiple functional parameters, determine the functional parameter similarity of each candidate component; In response to determining that the similarity of the functional parameters is greater than or equal to a predetermined similarity threshold, the candidate component is determined as the target candidate component.

6. The method according to claim 5, characterized in that, The multiple target function parameters include multiple first numeric parameters and multiple first character parameters, and the multiple function parameters include multiple second numeric parameters and multiple second character parameters; The process of determining the functional parameter similarity of each candidate component based on multiple target functional parameters and multiple functional parameters includes: For each candidate component, according to a first predetermined rule, the first sub-similarity between the first numerical parameter and a second numerical parameter with the same parameter name is calculated, and according to a second predetermined rule, the second sub-similarity between the first character parameter and a second character parameter with the same parameter name is calculated. All functional parameters of the candidate components are classified according to a predetermined level of importance, and the importance weight of the corresponding category of each functional parameter is found. The functional parameter similarity of the candidate components is determined based on all first sub-similarity, all second sub-similarity, and all importance weights.

7. A component recommendation device based on user preferences, characterized in that, include: The acquisition module is configured to acquire multiple target functional parameters, as well as multiple functional parameters and multiple influence parameters corresponding to each candidate component; The first screening module is configured to select multiple target candidate components from all candidate components based on multiple target function parameters and multiple function parameters. The first determining module is configured to determine multiple matching degrees corresponding to the target candidate components based on the multiple influencing parameters, wherein the multiple matching degrees correspond one-to-one with multiple predetermined preference situations; The second filtering module is configured to filter all target candidate components based on pre-selected preferences and their corresponding matching degrees to obtain multiple final target candidate components, wherein the pre-selected preferences are one of multiple preferences. The second determining module is configured to calculate the comprehensive matching degree of the final target candidate component based on all matching degrees corresponding to the final target candidate component, and determine the final target candidate component with the largest comprehensive matching degree as the recommended component. The second determining module is further configured to classify the matching degree of all target candidate components corresponding to the pre-selected preference according to a predetermined rule to obtain the matching level of the matching degree of each target candidate component corresponding to the pre-selected preference, wherein the matching degree is proportional to the matching level; and to take the target candidate component associated with each matching degree corresponding to the highest matching level as the final target candidate component.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 6.

Images