Electronic component data intelligent recommendation method

Abstract

The invention discloses an electronic component data intelligent recommendation method, and belongs to the technical field of electronic components, and the method comprises the steps: S1, obtaining electronic component data, processing the electronic component data, and determining electronic component feature data; s2, optimizing the electronic component data intelligent recommendation model, and determining an optimal electronic component data intelligent recommendation model; s3, predicting and analyzing the user demand data, and intelligently recommending electronic components to the user; and S4, obtaining user evaluation feedback opinions, and continuously optimizing an electronic component data intelligent recommendation result. According to the invention, the problems of complex process, poor user experience, high electronic component selection cost, low electronic component recommendation efficiency and the like in the existing component selection method are solved. The electronic components can be quickly and accurately recommended for the user, the user experience is improved, the model selection cost of the electronic components is reduced, and the recommendation efficiency of the electronic components can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic components, and in particular to an intelligent recommendation method for electronic component data. Background Art

[0002] With the development of electronic technology, the information and electronic content of equipment is also increasing, and the proportion of electronic components used in them is increasing. According to the "Military Electronic Components Classification and Code" (GJB8118-2013), it covers 29 categories including microelectronic devices, optoelectronic devices, special components, general components and electromechanical components. In particular, the application of a large number of high-density and high-precision electronic components has promoted the change of equipment combat methods, making battlefield full-dimensional perception and coordinated strike technology, all-round detection and high-speed sighting technology possible under high-mobility conditions. It can be said that electronic components have become the core link of controlling equipment, directly determining combat effectiveness and battlefield survivability. In view of the huge role of electronic components in upgrading weapons and equipment, since 1996, military powers represented by the United States have gradually changed from the original certification of qualified manufacturers and the identification of qualified production lines (QPL) to focus on the approval of manufacturers' capabilities (QML) and trusted (TRUST) certification, and introduced the minimum requirements for upgrading and screening COTS devices suitable for military use to quickly support the needs of military applications and ensure the advantages in the field of key national defense technologies. In order to maximize the use of resources and technologies, avoid duplication of work, improve its efficiency and enhance rapid response capabilities, the European Space Agency (ESA) established the "European Space Component Coordination Center (ESCC)" in 2002 to obtain high-reliability electronic components for aerospace. Since the 1990s, China has been certifying component manufacturers and identifying components in accordance with the component quality certification procedure rules. Component products that have passed the certification and identification are included in the catalog of qualified products. Component production lines and management are certified, and component production lines that have passed the certification and identification are included in the catalog of qualified manufacturers. This has established a basic product entry threshold for electronic component products, and through quality management system certification, production process certification, and product identification and inspection, electronic components that meet the corresponding quality levels are provided to the entire military.

[0003] In recent years, the increase in the service life of equipment and the shortening of the research and development cycle of electronic components have resulted in the mixed use of various new and old components and a large gap in usage. In particular, due to low-cost requirements, equipment uses a large number of domestic industrial-grade components. There is an urgent need to implement a method of intelligent selection of electronic components based on actual selection conditions.

[0004] The Chinese patent with publication number CN116127267A discloses an electronic component design selection verification and evaluation system based on data analysis, including a data acquisition module, a data preprocessing module, a verification and evaluation model construction module, a storage module, and a visual display module; wherein the data acquisition module is used to collect and input the electronic component design selection list data file and parse it; the data preprocessing module preprocesses the input component design selection list file; the verification and evaluation model construction module establishes a verification and evaluation model; the storage module is used to store structured selection list data and verification and evaluation results, and provide the basic attribute dictionary of components and different dimensional quality information bases required by the data preprocessing module and the verification and evaluation model construction module; the visual display module displays the verification and evaluation results, and performs multi-dimensional statistical analysis on the design selection list; however, the patent has the following defects:

[0005] The existing component selection methods have complex processes, poor user experience, high electronic component selection costs, and low electronic component recommendation efficiency. Summary of the invention

[0006] The purpose of the present invention is to provide a method for intelligently recommending electronic component data, which can quickly and accurately recommend electronic components to users, improve user experience, reduce the cost of electronic component selection, improve the efficiency of electronic component recommendation, and solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An electronic component data intelligent recommendation method, comprising:

[0009] S1: acquiring electronic component data based on artificial intelligence, processing the electronic component data, and determining characteristic data of the electronic component based on artificial intelligence;

[0010] S2: construct an electronic component data intelligent recommendation model, optimize the electronic component data intelligent recommendation model, and determine the optimal electronic component data intelligent recommendation model;

[0011] S3: Collect user demand data, perform predictive analysis on user demand data based on the electronic component data intelligent recommendation model, and intelligently recommend electronic components to users;

[0012] S4: Obtain user evaluation feedback, continuously and adaptively update and improve the electronic component data intelligent recommendation model, and continuously optimize the electronic component data intelligent recommendation results.

[0013] Preferably, in S1, the electronic component data based on artificial intelligence is obtained by performing the following operations:

[0014] Collect basic information of electronic components in real time and obtain attribute data of electronic components;

[0015] Collect the performance parameters of electronic components in real time to obtain the performance data of electronic components;

[0016] Collect the application fields of electronic components in real time and obtain the application data of electronic components;

[0017] Among them, electronic component data based on artificial intelligence is determined based on electronic component attribute data, electronic component performance data and electronic component application data.

[0018] Preferably, in S1, the electronic component data is processed to perform the following operations:

[0019] Obtaining AI-based electronic component data;

[0020] Retrieve data of electronic components based on artificial intelligence based on sequential retrieval method;

[0021] Conduct consistency checks on electronic component data based on artificial intelligence;

[0022] Check whether the electronic component data meets the requirements according to the reasonable value range and mutual relationship of the electronic component data;

[0023] Remove inconsistent data that is beyond the normal range and logically unreasonable in electronic component data;

[0024] Process invalid and missing values ​​of electronic component data based on artificial intelligence;

[0025] Remove invalid data and missing data in electronic component data that are of no value to intelligent recommendation of electronic component data;

[0026] Determine the electronic component data that is valuable for intelligent recommendation of electronic component data;

[0027] After obtaining the search, the valuable electronic component data is intelligently recommended;

[0028] Based on the data normalization method, the valuable electronic component data for intelligent recommendation of electronic component data after retrieval is normalized to eliminate the dimension of electronic component data;

[0029] Determine standardized electronic component data based on artificial intelligence.

[0030] Preferably, in S1, the electronic component data is processed, and the following operations are further performed:

[0031] Obtain standardized electronic component data based on artificial intelligence;

[0032] Sort the standardized electronic component data based on the internal sorting method;

[0033] Determining electronic component data having a sorting order;

[0034] Acquire the electronic component data having a sorted order after sorting;

[0035] Extract features from the electronic component data with sorting order, and select features that are valuable for intelligent recommendation of electronic component data;

[0036] Determine the characteristic data of electronic components based on artificial intelligence.

[0037] Preferably, in S2, an intelligent recommendation model for electronic component data is constructed, and the following operations are performed:

[0038] Obtaining electronic component feature data based on artificial intelligence;

[0039] Divide the characteristic data of electronic components based on artificial intelligence;

[0040] Determine an electronic component training set and an electronic component test set;

[0041] According to the requirements of intelligent recommendation of electronic component data, select a neural network model framework suitable for intelligent recommendation of electronic component data;

[0042] Based on the electronic component training set, the selected neural network model framework is trained;

[0043] Determine an intelligent recommendation model for electronic components data based on artificial intelligence.

[0044] Preferably, in S2, the electronic component data intelligent recommendation model is optimized, and the following operations are performed:

[0045] Obtain an intelligent recommendation model for electronic component data based on artificial intelligence;

[0046] Based on the electronic component test set, the performance of the electronic component data intelligent recommendation model is tested;

[0047] Determine the performance test results based on the intelligent recommendation model of electronic component data;

[0048] Mining and analyzing the performance test results based on the intelligent recommendation model of electronic component data;

[0049] Determine the optimization adjustment plan based on the intelligent recommendation model of electronic component data;

[0050] Optimizing and adjusting the electronic component data intelligent recommendation model according to an optimization and adjustment scheme based on the electronic component data intelligent recommendation model;

[0051] Determine the optimal intelligent recommendation model for electronic components data.

[0052] Preferably, in S3, user demand data is collected and the following operations are performed:

[0053] Intelligently recommend requirements based on electronic component data;

[0054] Collect user history records and obtain user history record data;

[0055] Collect user semantic information and obtain user semantic information data;

[0056] Wherein, user demand data is determined based on user history record data and user semantic information data.

[0057] Preferably, in S3, the user demand data is predicted and analyzed, and the following operations are performed:

[0058] Obtain user demand data;

[0059] Input user demand data into the optimal electronic component data intelligent recommendation model;

[0060] Based on the intelligent recommendation model of electronic components data, predict and analyze user demand data;

[0061] Determine the intelligent recommendation results of electronic components data based on artificial intelligence.

[0062] Preferably, in S3, to intelligently recommend electronic components to the user, the following operations are performed:

[0063] Obtain intelligent recommendation results of electronic component data based on artificial intelligence;

[0064] Based on the electronic components database, compare and analyze the results of intelligent recommendation of electronic components data;

[0065] Determine the electronic component data that matches the electronic component data intelligent recommendation result;

[0066] Based on electronic component data, intelligently recommend electronic components to users.

[0067] Preferably, in S4, the electronic component data intelligent recommendation results are continuously optimized by performing the following operations:

[0068] Obtain user evaluation feedback on intelligently recommended electronic components;

[0069] Conduct in-depth mining and relevant analysis on user evaluation feedback;

[0070] Determine the adaptive update improvement plan based on the intelligent recommendation model of electronic component data;

[0071] According to the adaptive update and improvement plan, the electronic component data intelligent recommendation model is continuously adaptively updated and improved to continuously optimize the electronic component data intelligent recommendation results.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The present invention obtains electronic component data based on artificial intelligence, processes the electronic component data, determines electronic component feature data based on artificial intelligence, constructs an electronic component data intelligent recommendation model according to the electronic component data intelligent recommendation demand, optimizes the electronic component data intelligent recommendation model, determines the optimal electronic component data intelligent recommendation model, collects user demand data, predicts and analyzes the user demand data based on the electronic component data intelligent recommendation model, intelligently recommends electronic components to users, obtains user evaluation feedback, continuously and adaptively updates and improves the electronic component data intelligent recommendation model, and continuously optimizes the electronic component data intelligent recommendation results. This can quickly and accurately recommend electronic components to users, improve user experience, reduce the electronic component selection cost, and improve the efficiency of electronic component recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The figure is a flow chart of the electronic component data intelligent recommendation method of the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] In order to solve the problems of complex process, poor user experience, high cost of electronic component selection and low efficiency of electronic component recommendation in the existing component selection method, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0077] An electronic component data intelligent recommendation method, comprising:

[0078] S1: acquiring electronic component data based on artificial intelligence, processing the electronic component data, and determining characteristic data of the electronic component based on artificial intelligence;

[0079] In this embodiment, to obtain electronic component data based on artificial intelligence, the following operations are performed:

[0080] Collect basic information of electronic components in real time and obtain attribute data of electronic components;

[0081] Among them, the basic information of electronic components includes the name, model, manufacturer, size and price of the electronic components;

[0082] Collect the performance parameters of electronic components in real time to obtain the performance data of electronic components;

[0083] Among them, the performance parameters of electronic components include rated values, limit values ​​and quality parameters of electronic components;

[0084] Collect the application fields of electronic components in real time and obtain the application data of electronic components;

[0085] Among them, electronic component data based on artificial intelligence is determined based on electronic component attribute data, electronic component performance data and electronic component application data.

[0086] In this embodiment, the electronic component data is processed to perform the following operations:

[0087] Obtaining AI-based electronic component data;

[0088] Retrieve data of electronic components based on artificial intelligence based on sequential retrieval method;

[0089] Conduct consistency checks on electronic component data based on artificial intelligence;

[0090] Check whether the electronic component data meets the requirements according to the reasonable value range and mutual relationship of the electronic component data;

[0091] Remove inconsistent data that is beyond the normal range and logically unreasonable in electronic component data;

[0092] Process invalid and missing values ​​of electronic component data based on artificial intelligence;

[0093] Remove invalid data and missing data in electronic component data that are of no value to intelligent recommendation of electronic component data;

[0094] Determine the electronic component data that is valuable for intelligent recommendation of electronic component data;

[0095] After obtaining the search, the valuable electronic component data is intelligently recommended;

[0096] Based on the data normalization method, the valuable electronic component data for intelligent recommendation of electronic component data after retrieval is normalized to eliminate the dimension of electronic component data;

[0097] Determine standardized electronic component data based on artificial intelligence.

[0098] It should be noted that by searching the electronic component data and determining the valuable electronic component data for intelligent recommendation of the electronic component data, the subsequent processing accuracy and efficiency of the electronic component data can be improved. By normalizing the electronic component data and eliminating the dimension of the electronic component data, standardized electronic component data can be determined, which facilitates the subsequent sorting and feature extraction of the electronic component data.

[0099] In this embodiment, the electronic component data is processed, and the following operations are further performed:

[0100] Obtain standardized electronic component data based on artificial intelligence;

[0101] Sort the standardized electronic component data based on the internal sorting method;

[0102] Determining electronic component data having a sorting order;

[0103] Acquire the electronic component data having a sorted order after sorting;

[0104] Extract features from the electronic component data with sorting order, and select features that are valuable for intelligent recommendation of electronic component data;

[0105] Determine the characteristic data of electronic components based on artificial intelligence.

[0106] It should be noted that by sorting the standardized electronic component data, the electronic component data with a sorting order is determined, and by extracting features from the electronic component data with a sorting order, features that are valuable for intelligent recommendation of electronic component data are selected, and the electronic component feature data is determined, which facilitates the subsequent construction of an electronic component data intelligent recommendation model suitable for intelligent recommendation of electronic component data.

[0107] S2: construct an electronic component data intelligent recommendation model, optimize the electronic component data intelligent recommendation model, and determine the optimal electronic component data intelligent recommendation model;

[0108] In this embodiment, an intelligent recommendation model for electronic component data is constructed, and the following operations are performed:

[0109] Obtaining electronic component feature data based on artificial intelligence;

[0110] Divide the characteristic data of electronic components based on artificial intelligence;

[0111] Determine an electronic component training set and an electronic component test set;

[0112] According to the requirements of intelligent recommendation of electronic component data, select a neural network model framework suitable for intelligent recommendation of electronic component data;

[0113] Based on the electronic component training set, the selected neural network model framework is trained;

[0114] Determine an intelligent recommendation model for electronic components data based on artificial intelligence.

[0115] In this embodiment, the electronic component data intelligent recommendation model is optimized, and the following operations are performed:

[0116] Obtain an intelligent recommendation model for electronic component data based on artificial intelligence;

[0117] Based on the electronic component test set, the performance of the electronic component data intelligent recommendation model is tested;

[0118] Determine the performance test results based on the intelligent recommendation model of electronic component data;

[0119] Mining and analyzing the performance test results based on the intelligent recommendation model of electronic component data;

[0120] Determine the optimization adjustment plan based on the intelligent recommendation model of electronic component data;

[0121] Optimizing and adjusting the electronic component data intelligent recommendation model according to an optimization and adjustment scheme based on the electronic component data intelligent recommendation model;

[0122] Determine the optimal intelligent recommendation model for electronic components data.

[0123] S3: Collect user demand data, perform predictive analysis on user demand data based on the electronic component data intelligent recommendation model, and intelligently recommend electronic components to users;

[0124] In this embodiment, user demand data is collected and the following operations are performed:

[0125] Intelligently recommend requirements based on electronic component data;

[0126] Collect user history records and obtain user history record data;

[0127] Collect user semantic information and obtain user semantic information data;

[0128] Wherein, user demand data is determined based on user history record data and user semantic information data.

[0129] In this embodiment, the user demand data is predicted and analyzed, and the following operations are performed:

[0130] Obtain user demand data;

[0131] Input user demand data into the optimal electronic component data intelligent recommendation model;

[0132] Based on the intelligent recommendation model of electronic components data, predict and analyze user demand data;

[0133] Determine the intelligent recommendation results of electronic components data based on artificial intelligence.

[0134] In this embodiment, to intelligently recommend electronic components to the user, the following operations are performed:

[0135] Obtain intelligent recommendation results of electronic component data based on artificial intelligence;

[0136] Based on the electronic components database, compare and analyze the results of intelligent recommendation of electronic components data;

[0137] Determine the electronic component data that matches the electronic component data intelligent recommendation result;

[0138] Based on electronic component data, intelligently recommend electronic components to users.

[0139] S4: Obtain user evaluation feedback, continuously and adaptively update and improve the electronic component data intelligent recommendation model, and continuously optimize the electronic component data intelligent recommendation results.

[0140] In this embodiment, the electronic component data intelligent recommendation results are continuously optimized by performing the following operations:

[0141] Obtain user evaluation feedback on intelligently recommended electronic components;

[0142] Conduct in-depth mining and relevant analysis on user evaluation feedback;

[0143] Determine the adaptive update improvement plan based on the intelligent recommendation model of electronic component data;

[0144] According to the adaptive update and improvement plan, the electronic component data intelligent recommendation model is continuously adaptively updated and improved to continuously optimize the electronic component data intelligent recommendation results.

[0145] Therefore, by acquiring artificial intelligence-based electronic component data, processing the electronic component data, determining artificial intelligence-based electronic component feature data, building an electronic component data intelligent recommendation model based on the electronic component data intelligent recommendation needs, optimizing the electronic component data intelligent recommendation model, determining the optimal electronic component data intelligent recommendation model, collecting user demand data, and predicting and analyzing user demand data based on the electronic component data intelligent recommendation model, intelligently recommending electronic components to users, obtaining user evaluation feedback, and continuously adaptively updating and improving the electronic component data intelligent recommendation model, and continuously optimizing the electronic component data intelligent recommendation results, electronic components can be recommended to users quickly and accurately, thereby improving user experience, reducing the cost of electronic component selection, and improving the efficiency of electronic component recommendation.

[0146] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0147] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligently recommending electronic component data, characterized in that: include: S1: acquiring electronic component data based on artificial intelligence, processing the electronic component data, and determining characteristic data of the electronic component based on artificial intelligence; S2: construct an electronic component data intelligent recommendation model, optimize the electronic component data intelligent recommendation model, and determine the optimal electronic component data intelligent recommendation model; S3: Collect user demand data, perform predictive analysis on user demand data based on the electronic component data intelligent recommendation model, and intelligently recommend electronic components to users; S4: Obtain user evaluation feedback, continuously and adaptively update and improve the electronic component data intelligent recommendation model, and continuously optimize the electronic component data intelligent recommendation results.

2. The method for intelligently recommending electronic component data according to claim 1, characterized in that: In S1, the electronic component data based on artificial intelligence is obtained, and the following operations are performed: Collect basic information of electronic components in real time and obtain attribute data of electronic components; Collect the performance parameters of electronic components in real time to obtain the performance data of electronic components; Collect the application fields of electronic components in real time and obtain the application data of electronic components; Among them, electronic component data based on artificial intelligence is determined based on electronic component attribute data, electronic component performance data and electronic component application data.

3. The method for intelligently recommending electronic component data according to claim 2, characterized in that: In S1, the electronic component data is processed and the following operations are performed: Obtaining AI-based electronic component data; Retrieve data of electronic components based on artificial intelligence based on sequential retrieval method; Conduct consistency checks on electronic component data based on artificial intelligence; Check whether the electronic component data meets the requirements according to the reasonable value range and mutual relationship of the electronic component data; Remove inconsistent data that is beyond the normal range and logically unreasonable in electronic component data; Process invalid and missing values ​​of electronic component data based on artificial intelligence; Remove invalid data and missing data in electronic component data that are of no value to intelligent recommendation of electronic component data; Determine the electronic component data that is valuable for intelligent recommendation of electronic component data; After obtaining the search, the valuable electronic component data is intelligently recommended; Based on the data normalization method, the valuable electronic component data for intelligent recommendation of electronic component data after retrieval is normalized to eliminate the dimension of electronic component data; Determine standardized electronic component data based on artificial intelligence.

4. The method for intelligently recommending electronic component data according to claim 3, characterized in that: In S1, the electronic component data is processed, and the following operations are performed: Obtain standardized electronic component data based on artificial intelligence; Sort the standardized electronic component data based on the internal sorting method; Determining electronic component data having a sorting order; Acquire the electronic component data having a sorted order after sorting; Extract features from the electronic component data with sorting order, and select features that are valuable for intelligent recommendation of electronic component data; Determine the characteristic data of electronic components based on artificial intelligence.

5. The method for intelligently recommending electronic component data according to claim 4, characterized in that: In S2, an intelligent recommendation model for electronic component data is constructed, and the following operations are performed: Obtaining electronic component feature data based on artificial intelligence; Divide the characteristic data of electronic components based on artificial intelligence; Determine an electronic component training set and an electronic component test set; According to the requirements of intelligent recommendation of electronic component data, select a neural network model framework suitable for intelligent recommendation of electronic component data; Based on the electronic component training set, the selected neural network model framework is trained; Determine an intelligent recommendation model for electronic components data based on artificial intelligence.

6. The method for intelligently recommending electronic component data according to claim 5, characterized in that: In S2, the electronic component data intelligent recommendation model is optimized, and the following operations are performed: Obtain an intelligent recommendation model for electronic component data based on artificial intelligence; Based on the electronic component test set, the performance of the electronic component data intelligent recommendation model is tested; Determine the performance test results based on the intelligent recommendation model of electronic component data; Mining and analyzing the performance test results based on the intelligent recommendation model of electronic component data; Determine the optimization adjustment plan based on the intelligent recommendation model of electronic component data; Optimizing and adjusting the electronic component data intelligent recommendation model according to an optimization and adjustment scheme based on the electronic component data intelligent recommendation model; Determine the optimal intelligent recommendation model for electronic components data.

7. The method for intelligently recommending electronic component data according to claim 6, characterized in that: In S3, user demand data is collected and the following operations are performed: Intelligently recommend requirements based on electronic component data; Collect user history records and obtain user history record data; Collect user semantic information and obtain user semantic information data; Wherein, user demand data is determined based on user history record data and user semantic information data.

8. The method for intelligently recommending electronic component data according to claim 7, characterized in that: In S3, the user demand data is predicted and analyzed, and the following operations are performed: Obtain user demand data; Input user demand data into the optimal electronic component data intelligent recommendation model; Based on the intelligent recommendation model of electronic components data, predict and analyze user demand data; Determine the intelligent recommendation results of electronic components data based on artificial intelligence.

9. The method for intelligently recommending electronic component data according to claim 8, characterized in that: In S3, electronic components are intelligently recommended to the user by performing the following operations: Obtain intelligent recommendation results of electronic component data based on artificial intelligence; Based on the electronic components database, compare and analyze the results of intelligent recommendation of electronic components data; Determine the electronic component data that matches the electronic component data intelligent recommendation result; Based on electronic component data, intelligently recommend electronic components to users.

10. The method for intelligently recommending electronic component data according to claim 9, characterized in that: In S4, the electronic component data intelligent recommendation results are continuously optimized, and the following operations are performed: Obtain user evaluation feedback on intelligently recommended electronic components; Conduct in-depth mining and relevant analysis on user evaluation feedback; Determine the adaptive update improvement plan based on the intelligent recommendation model of electronic component data; According to the adaptive update and improvement plan, the electronic component data intelligent recommendation model is continuously adaptively updated and improved to continuously optimize the electronic component data intelligent recommendation results.

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