Intelligent Management Methods and Systems for Electronic Materials

Abstract

This application discloses an intelligent management method and system for electronic materials, relating to the field of material management, which is based on a 12-bit production coding standard in the SPYY.XBBBVVVV format. This standard defines the device category (X), capability value (BBB), and main parameter (VVVV) through the main code segment (XBBBVVVV); and defines the device form (S), specific package (P), and key characteristics (YY) such as accuracy and grade through the sub-code segment (SPYY). Based on this rule, the target material code is structurally parsed, and candidate materials are extracted from the material library using the parsed device type and package type as physical constraints. Subsequently, the candidate materials are parameterized, and multi-dimensional compatibility scoring and weighted calculations are performed with the target materials to achieve accurate quantitative evaluation. Finally, based on the scoring, the optimal result is recommended, effectively solving the selection and substitution problems caused by inconsistent coding, and significantly improving the response efficiency and accuracy of the supply chain.

Description

Technical Field

[0001] This application relates to the field of materials management, and more specifically, to an intelligent management method and system for electronic materials. Background Technology

[0002] In today's rapidly evolving electronics manufacturing industry, the stability and efficiency of the supply chain are crucial to a company's core competitiveness. With the accelerating pace of electronic product iteration and the increasing complexity of the global supply chain, component shortages and production stoppages are frequent occurrences, posing significant challenges to companies' production planning and cost control. Therefore, building an efficient and intelligent electronic materials management solution to achieve rapid material substitution and optimized selection has become a pressing pain point for the industry.

[0003] However, existing electronic material management (EMS) solutions fall short in addressing these challenges, primarily due to the widespread lack of unified and transparent material coding standards across the industry. Material coding rules vary wildly among different manufacturers, suppliers, and even different departments within a company, resulting in severe information silos. This directly leads to significant difficulties in subsequent selection and replacement. Traditional material management heavily relies on manual analysis and comparison of these obscure and inconsistent material codes. For a single procurement code, engineers need to manually consult multiple specification sheets and internal documents to decipher key parameters such as device type, package, voltage rating, and capacitance. This fragmented information and complex decoding process is not only time-consuming and labor-intensive but also highly error-prone, severely impacting the efficiency and accuracy of material replacement.

[0004] Furthermore, because the coding fails to effectively carry comparable parameter information, existing solutions lack a systematic, multi-dimensional evaluation mechanism when assessing material compatibility. For example, when searching for alternative materials, it's necessary to consider not only upward compatibility principles for parameters such as precision but also ensuring complete consistency in physical parameters such as packaging. This problem of inconsistent coding makes material searching and screening extremely inefficient. Purchasing personnel need to manually input multiple parameters for filtering in ERP systems, supplier websites, or material databases, involving multiple attempts throughout the process. If the company's internal material database is not indexed based on a standardized parameter system, automated searching and intelligent matching are out of the question.

[0005] Therefore, an optimized intelligent management solution for electronic materials is desired. Summary of the Invention

[0006] To overcome the problems of fragmented material information, complex compatibility judgment, and low search efficiency in existing technologies, according to one aspect of this application, an intelligent management method for electronic materials is provided, comprising: obtaining a target material code; performing structured parsing on the target material code to obtain target material structured parameters; extracting a candidate material code list from a material library using the device type and package type in the target material structured parameters as core physical constraints; parameterizing each candidate material code in the candidate material code list to obtain a parameterized candidate material code list; performing multi-dimensional compatibility scoring on the target material structured parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list; sorting the candidate material code list based on the compatibility score list to obtain a sorted final candidate list; and extracting the top N candidate material codes from the sorted final candidate list as recommendation results.

[0007] According to another aspect of this application, an intelligent management system for electronic materials is provided, comprising: a target material code acquisition module for acquiring target material codes; a target material code structure parsing module for performing structure parsing on the target material codes to obtain target material structure parameters; a candidate material code extraction module for extracting a candidate material code list from a material library using the device type and package type in the target material structure parameters as core physical constraints; a material parameterization module for parameterizing each candidate material code in the candidate material code list to obtain a parameterized candidate material code list; a compatibility scoring module for performing multi-dimensional compatibility scoring on the target material structure parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list; a candidate material code sorting module for sorting the candidate material code list based on the compatibility score list to obtain a sorted final candidate list; and a recommendation module for extracting the top N candidate material codes from the sorted final candidate list as recommendation results.

[0008] Compared with existing technologies, this application provides an intelligent management method and system for electronic materials, which firstly is based on the 12-bit production coding specification in the SPYY.XBBBVVVV format. This specification defines the device category (X), capability value (BBB, such as power, withstand voltage), and main parameters (VVVV, such as resistance, capacitance) through the main code segment (XBBBVVVV); and defines the device form (S), specific package (P), and key characteristics (YY) such as accuracy and grade through the sub-code segment (SPYY). Based on this rule, the method performs structured parsing of the target material code and extracts candidate materials from the material library using the parsed device type and package type as physical constraints. Subsequently, the candidate materials are parameterized, and multi-dimensional compatibility scoring and weighted calculations are performed with the target materials to achieve accurate quantitative evaluation. Finally, based on the scoring, the optimal result is recommended, effectively solving the selection and substitution problems caused by inconsistent coding, and significantly improving the response efficiency and accuracy of the supply chain. Attached Figure Description

[0009] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 This is a flowchart of an intelligent electronic material management method according to an embodiment of this application.

[0011] Figure 2 This is a schematic diagram of the data flow in the intelligent management method for electronic materials according to an embodiment of this application.

[0012] Figure 3 This is a flowchart of step S2 in the intelligent management method for electronic materials according to an embodiment of this application.

[0013] Figure 4 This is a block diagram of an intelligent electronic material management system according to an embodiment of this application. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] To address the limitations of existing technologies, this application proposes an intelligent management method for electronic materials. Figure 1 This is a flowchart of an intelligent electronic material management method according to an embodiment of this application. Figure 2 This is a schematic diagram of the data flow in the intelligent electronic material management method according to an embodiment of this application. Figure 1 and Figure 2 As shown, the intelligent management method for electronic materials according to an embodiment of this application includes: S1, obtaining a target material code; S2, performing structured parsing on the target material code to obtain target material structured parameters; S3, using the device type and package type in the target material structured parameters as core physical constraints, extracting a candidate material code list from the material library; S4, parameterizing each candidate material code in the candidate material code list to obtain a parameterized candidate material code list; S5, performing multi-dimensional compatibility scoring on the target material structured parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list; S6, sorting the candidate material code list based on the compatibility score list to obtain a sorted final candidate list; S7, extracting the top N candidate material codes from the sorted final candidate list as recommendation results.

[0016] In step S1, the target material code is obtained. It should be understood that in electronic material management, facing massive amounts of material information and complex coding rules, traditional manual identification and processing methods are inefficient and prone to errors. Especially when it is necessary to select alternatives for specific materials, it is first necessary to clarify the key parameters of the material, which are often hidden in lengthy and unstandardized material codes. If the detailed information of the target material cannot be accurately obtained and understood, subsequent material searches, compatibility assessments, and recommendations will be impossible. This is the core challenge brought about by the fragmentation of material information in existing technologies. Therefore, in order to perform subsequent intelligent analysis and management of the target material, it is necessary to first clarify the material object to be processed. To solve the above problems, this application clarifies the material object to be processed by obtaining the target material code.

[0017] Optionally, step S1 can be performed as follows: Target material codes can be obtained from multiple sources. For example, when engineers need to find a substitute for a specific component when designing circuits, or when the purchasing department needs to find a replacement for a material in the existing BOM when dealing with material shortages, users can manually enter the target material code through the graphical user interface (GUI) input box. Furthermore, this code can also be automatically imported in batches from the BOM or inventory records through interface integration with the enterprise's internal material management system, such as an ERP system or PLM system. During the import process, a preliminary data format verification will be performed to ensure that the obtained code conforms to the preset string format requirements.

[0018] In step S2, the target material code is structured and parsed to obtain the target material's structured parameters. Correspondingly, although the target material code has been obtained, these codes are strings designed for human recognition, containing a large amount of fragmented and unstructured information. For a machine, it is merely a string of characters, unable to directly understand the key attributes it represents, such as device type, package, and capacitance. This unstructured encoding format makes subsequent automated processing, material parameter comparison, and compatibility judgment extremely difficult, severely hindering the implementation of intelligent management solutions. To achieve efficient material search and intelligent recommendation, these human-readable codes need to be transformed into structured data that is understandable and processable by machines. Therefore, to transform the rich information contained in the target material code into a standardized, machine-readable format for subsequent automated processing and intelligent analysis, the target material code needs to be structured and parsed.

[0019] In particular, in one alternative embodiment of this application, Figure 3 This is a flowchart of step S2 in the intelligent electronic material management method according to an embodiment of this application. Figure 3 As shown, step S2, performing structured parsing on the target material code to obtain target material structured parameters, includes: S21, calling the code parser; S22, the code parser, according to the rules in the document, segmenting the target material code to obtain a sequence of target material code segments; S23, the code parser, performing code parsing on each target material code segment in the sequence of target material code segments to obtain a sequence of target material code segment parsing information as the target material structured parameters.

[0020] Step S2 can be performed as follows: First, in S21, the encoding parser is a pre-designed and trained software module that integrates the ability to understand and process various material coding rules.

[0021] Next, in S22, the parser is built upon a well-defined, hierarchical set of rules derived from coding standards defined in the documentation.

[0022] Specifically, this document defines a unified coding system for electronic component bills of materials (BOMs), aiming to achieve shared coding rules, interoperability of codes across different companies and supply chain stages, and facilitate engineers in quickly extracting key parameters from the codes. This coding system expresses and manages the complex attributes of electronic components through a structured coding system, clearly distinguishing between industry-wide common material codes and company-specific materials. Specifically, the coding system defines production codes as a combination of 12 digits and letters, further subdivided into a main code segment and a sub-code segment, separated by a "." symbol to improve readability. The overall structure of the production code is SPYY.XBBBVVVV. Here, SPYY is the sub-code segment, primarily representing the device's characteristic values, such as form factor, package, precision, or industrial grade; XBBBVVVV is the main code segment, primarily representing the device's category and main parameter values. In addition, to guide procurement and ensure unique material coding, a 2-4 digit variable manufacturer code prefix can be added before the production code, separated by periods (.), forming a complete procurement code, such as F1~F4.SPYY.XBBBVVVV. If the manufacturer code is 00, it indicates that the component manufacturer is undetermined. In some special cases, if the production codes are the same but have subtle differences that cannot be distinguished, a suffix of up to 2 digits, N1~N2, can be added after the production code.

[0023] Detailed rules for production coding segments: I. Meaning and values ​​of the sub-coding segment SPYY. The sub-coding segment SPYY mainly reflects the form and packaging of the device and distinguishes between industry-standard materials and company-specific materials. When the S field is a letter, it indicates that the material is a company-specific custom material, such as circuit boards, modules, packaging materials, etc. In this case, the coding rules for proprietary materials are different from those for general materials, and the meaning of SPYY.XBBBVVVV will be redefined. For example, S can represent major categories such as solution level, product level, bare complete machine, OEM, circuit board, self-developed module, electronic hardware, software, structural, and packaging materials. When the S field is a number, it indicates that the device is an industry-standard material. Different numbers distinguish the overall form or mounting method of the material, such as wire connection, through-hole, surface mount, hang-type package, BGA, etc. This design not only helps with further classification of the P field but also makes it easier for engineers to intuitively judge the complexity of the production process. In addition, the S code segment also distinguishes whether the material contains lead: S≥5 indicates lead, and S≤4 indicates lead-free. Specifically, S=0 represents wire-connected materials, which can only be manually soldered during production; S=1 represents common through-hole materials, which require wave soldering or manual soldering; S=3 represents specific packaged components, such as TO-92 and DO-41; S=7 represents non-standard components (pins), with pin spacing identified in 1mm units; S=9 represents non-standard components (shapes), with shape identified in 1mm units, such as Φ-type devices. For surface mount materials, S=2 represents common surface mount components, such as SOP for ICs, and conventional packages like 0402 and 0603 for discrete devices; S=4 represents specific packaged surface mount components, such as SOT and SOD; S=6 represents non-standard surface mount components (pins), such as QFN for ICs (semi-exposed packages), with pin spacing identified in 1mm units for discrete devices, and hangable packages can be dispensed with nozzles; S=8 represents non-standard surface mount components (shapes), such as BGA for ICs (non-exposed packages), with shape identified in 1mm units for discrete devices, and hangable packages cannot be dispensed with nozzles.

[0024] The P field represents the specific package of the device, and its meaning needs to be combined with the S field value and X code segment value. For devices with clearly defined package details, P directly represents the specific package designation, such as 0603 for resistors and capacitors. For devices without clearly defined package details, P can represent the external dimensions or device height. For example, for resistors, capacitors, and semiconductor devices, P takes the larger of the length or width; for terminals (the main parameters are the number of pins and pin spacing), P takes the height. When there are too many package types, P can be used in conjunction with distinguishing code points. For example, when resistors and capacitors have the same parameters and the same P designation (such as 1206 and 1210), they can be distinguished by changing the S value, such as 2C referring to 1206, 4C referring to 1210, etc. The document provides a detailed table of the meanings of P for common packages of discrete components (as shown in Table 1) and a table of the meanings of P for common packages of IC devices (as shown in Table 2), listing the specific packages or dimensions corresponding to the P field under different S values.

[0025] Table 1: Meaning of P in Common Packages for Discrete Components

[0026]

[0027] Table 2: Meaning of P in Common IC Device Packages

[0028]

[0029] The YY field is used to represent key characteristics of the device, such as accuracy, industrial grade, or secondary classification. It consists of two digits, Y1 and Y2, which can use a mix of numbers and letters. For accuracy-sensitive devices (such as resistors, capacitors, inductors, and crystal oscillators), the Y1 field indicates the device's accuracy, using letters as values. For example, J represents ±5%, and F represents ±1%. The documentation provides a table of Y1 field values ​​for accuracy (as shown in Table 3). For devices such as integrated circuits that are not accuracy-sensitive but are sensitive to industrial grade, the Y1 field uses letters to represent the device's industrial grade, measured by the application environment temperature. For example, C represents commercial grade (0℃~+70℃), E represents industrial grade (-40℃~+85℃), and M represents military grade (-55℃~+125℃). Additionally, for electrolytic capacitors, Y1 may indicate height; for connectors, Y1 can be a distinguishing digit, such as row number, serial number, or bend / straightness. The Y2 field is mainly used for secondary classification of devices; the meaning of Y2 differs for different major categories of devices. For example, the Y2 field for resistors (X=R) can represent the material (e.g., W for potentiometer, J for metal film resistor) or special properties (e.g., 0 for ordinary thick film resistor, 8 for thin film resistor). The Y2 field for capacitors (X=C) can represent the material (e.g., A for tantalum capacitor, D for aluminum electrolytic capacitor) or special properties (e.g., 0 for C0G / X7R, 5 for X5R). The Y2 field for inductors (X=L) can represent the material (e.g., G for multilayer inductor, H for wire-wound inductor) or special application scenarios (e.g., Y for ferrite inductor, T for ceramic inductor). The Y1 and Y2 fields for transistors (X=Q, D) are used together for secondary classification; for example, N represents NPN / N-channel, P represents PNP / P-channel, E represents Schottky diode, and R represents fast recovery diode. The Y2 field for integrated circuits (X=U) directly references the national standard device classification and adds commonly used types, such as M for memory, U for microprocessor circuits, and G for gate circuits. The Y1Y2 fields of the protective device (X=F) are used together for secondary classification. Y1 represents the application type (e.g., F represents fuse, P represents PTC), and Y2 represents the specific classification or number of terminals (e.g., U represents unidirectional two-terminal TVS, B represents bidirectional two-terminal TVS).For other devices, such as crystal oscillators (X=X), Y2 indicates active / passive (P represents passive at both ends, A represents active); for energy devices (X=G), Y2 indicates battery type (B represents button cell); for relays (X=K), Y2 indicates type (P represents power relay, S represents signal relay); for transformers (X=T), Y2 indicates type (C represents current transformer, L represents isolation transformer); for common-mode inductors (X=N), Y2 indicates shape (L represents toroidal, F represents flat); mechanical... For switches (X=S), Y2 indicates the type (R represents push-button switch, T represents toggle switch); for connectors (X=J), Y1 indicates straight, bent, or angled, and Y2 indicates male / female or specific type (e.g., T represents terminal block, K represents card slot); for sensors (X=H), Y2 indicates the type (T represents temperature sensor, H represents magnetic sensor); for board-level modules (X=A), Y2 indicates the type (P represents power module, L represents isolation module); for display devices (X=V), Y2 indicates the type (L represents LCD screen). When these special meanings cannot be distinguished, Y1 or Y2 can be encoded as a number as a serial number to distinguish the position. If both digits are numbers, it indicates a special serial number that does not have a specific meaning.

[0030] Table 3: Values ​​for Y1 field when representing precision:

[0031]

[0032] II. Meaning and Values ​​of the Main Encoding Segment XBBBVVVV. The main encoding segment XBBBVVVV represents the specific device category and the main parameter value of the device. The X field is a single letter used to distinguish the major category of the device, directly referring to commonly used symbols in schematic design or industry-standard expressions. For example, R represents resistor, C represents capacitor, L represents inductor, U represents integrated circuit, D represents diode, Q represents transistor, X represents crystal oscillator, F represents protection device, J represents connector, S represents mechanical switch, T represents transformer, H represents sensor, A represents board-level module, Z represents filter, B represents electroacoustic device, G represents energy device (battery), K represents relay, Y represents mechanical device, M represents motor, P represents test equipment, and E represents miscellaneous.

[0033] The BBBVVVV field consists of 7 digits, representing the two most important values ​​of the device. The BBB field represents the device's capabilities, such as resistor power, capacitor voltage rating, and inductor rated current, among other secondary parameters. The BBB value must be numeric, using a numerical representation: the first two digits are significant figures, and the last digit is the exponent. For example, a power rating of 250 milliwatts is coded as 251 (25 multiplied by 10 to the power of 1). For parameters with a finite number of integer values ​​(such as the number of connector pins), all three digits are significant figures. If the actual significant figure is greater than two digits, it can be rounded down by one; for example, an inductor with a rated current of 1.36 amps has a BBB code of 1.3 amps, or 132. The VVVV field represents the device's main parameters, such as resistor value, capacitor value, inductor value, and connector pin spacing. VVVV uses a uniform 4-digit representation, with the first three digits being significant figures and the last digit being the exponent. For resistors, the unit is uniformly converted to ohms when coding. For example, a 12.3 kΩ resistor is converted to 12300 ohms, and the code is 1232 (123 multiplied by 10 to the power of 2). For capacitors, the unit varies depending on the type. Non-polarized capacitors (such as ceramic capacitors) are measured in picofarads, polarized capacitors (such as electrolytic capacitors) are measured in microfarads, and supercapacitors are measured in farads. For example, a 1.5 nanofarad capacitor is represented as 1501 (150 multiplied by 10 to the power of 1 picofarads). For inductors, the unit is mostly microhenries. For example, a 1.5 millihenry inductor is represented as 1501 (150 multiplied by 10 to the power of 1 microhenries).

[0034] In special cases, the entire BBBVVVV range of a crystal oscillator (X=X) is used as the entire digital representation, with the first 6 digits being significant figures and the last digit being the exponent, used to indicate the frequency. For connectors (X=J), the BBB segment indicates the number of pins, and VVVV is used for pin spacing or serial number. For LEDs, BBB indicates the forward voltage, and the first letter of VVVV indicates the primary color, while the last three digits represent the brightness (MCD) value. For photoresistors, VVVV indicates the minimum light resistance. For resistor arrays, BBB indicates the number of arrays, and VVVV indicates the resistance value. For photodiodes, BBB indicates the photocurrent, and VVVV indicates the peak wavelength. For transistors, BBB indicates the cutoff voltage, and VVVV indicates the current. For fuses, BBB indicates the rated voltage, and VVVV indicates the rated current. For MOVs (varistors), BBB indicates the surge current, and VVVV indicates the varistor voltage. For NTCs (thermistors), BBB indicates the resistance at 25°C, and the first three digits of the B value indicate the resistance at 25°C. In batteries, BBB indicates voltage and VVVV indicates capacity. For some devices (such as integrated circuits, transformers, mechanical switches, and relays), BBBVVVV may directly represent a serial number.

[0035] Taking the target material code AV.24K0.C5001003 as an example, the code parser first identifies its top-level structure. According to preset rules, this code is broken down into two main parts: the manufacturer prefix and the production code. AV is identified as the manufacturer prefix, representing AVX company. 24K0.C5001003 is identified as the production code. This segmentation is based on predefined separators such as "." and the fixed length or specific character patterns of each segment. For example, the rules might specify that the production code consists of SPYY.XBBBVVVV, where "." is an explicit separator. In this way, the complex overall code is decomposed into smaller, more easily processed logical units, forming an ordered sequence of code segments.

[0036] Finally, in S23, following the output of S22, the parser further analyzes the production code 24K0.C5001003. Based on the detailed rules defined above, the parser identifies: S segment is 2, inferring its meaning as a common surface mount material according to the general material S (digital segment) meaning table; P segment is 4, indicating a package of 0402 according to the S=2 column in the common package P meaning table for discrete components; Y1 segment is K, indicating a precision of ±10% according to the Y1 field precision value table; Y2 segment is 0, indicating X7R material according to the capacitor secondary classification (Y2) meaning table; X segment is C, indicating a capacitor according to the device category (X) value table; and BBB segment is 500, indicating a withstand voltage of 50V (50*10^0=50V) according to the BBB code segment meaning table for various device capability values ​​and the BBB multiplier and common value unit correspondence table. The VVVV segment is 1003. According to the VVVV code segment meaning table for various device parameters and the VVVV multiplier and common value unit correspondence table, its capacitance value is 100nF (100*10^3=100000pF). All this parsed information, including device type, package type, withstand voltage, capacitance value, accuracy, material, etc., will be aggregated into a structured data object, namely the target material structured parameters, for example: {"Manufacturer prefix": "AV", "Device type": "Capacitor", "Package type": "0402", "Accuracy": "±10%": "Material": "X7R", "Withstand voltage": "50V", "Capacitance value": "100nf"}.

[0037] In step S3, the device type and package type in the structured parameters of the target material are used as core physical constraints to extract a candidate material code list from the material library. It should be understood that after the detailed parameters of the target material are structured and parsed, the next step is to find potential substitutes from the vast material library. However, the material library may contain millions or even hundreds of millions of material records. If the search scope is not effectively limited, directly performing a full library scan or fuzzy matching based on all parameters will result in extremely low query efficiency, consuming a large amount of computing resources and time, and the returned results may include a large number of irrelevant materials, making subsequent screening work extremely laborious. Especially for electronic components, the device type and package type are the most fundamental constraints determining their physical compatibility and basic functional attributes. If these two do not match, no matter how close other parameters are, the material cannot be used as an effective substitute. Therefore, in order to quickly and accurately locate physically feasible candidate materials in a massive amount of materials, thereby significantly improving search efficiency and reducing invalid results, it is necessary to use the device type and package type as core physical constraints to extract a preliminary candidate material code list from the material library.

[0038] Specifically, in an optional embodiment of this application, step S3, using the device type and package type in the target material structure parameters as core physical constraints, extracts a candidate material code list from the material library, including: S31, constructing an SQL query statement based on the device type and package type in the target material structure parameters; S32, executing the SQL query statement to return the candidate material code list from the material library.

[0039] Step S3 can be performed as follows: First, in S31, the two core physical constraints of "device type: capacitor" and "package type: 0402" are extracted from the target material's structured parameters. The extraction process involves accessing predefined key-value pairs in the structured parameter object, such as obtaining the corresponding values ​​through the target material's structured parameter ['device type'] and target material's structured parameter ['package type']. These constraints are the most basic and uncompromising physical matching conditions when performing material substitution, because materials of different device types and packages cannot physically substitute for each other. For example, a capacitor cannot replace a resistor, and a 0402 packaged device cannot be directly mounted onto a 1206 packaged pad. Based on these extracted values, the program dynamically constructs an SQL query string, such as: SELECT Material Code FROM Material Library WHERE Device Type = 'Capacitor' AND Package Type = '0402'. This SQL query aims to initially filter candidate materials that physically match the target material from the vast material library.

[0040] Next, in S32, the constructed SQL query is sent to the background material database for execution. The execution process involves database connection pool management; the program obtains an available database connection from the pool and then executes the SQL query through that connection. The material database is a centralized repository containing detailed information on all entered electronic materials, where each material code is associated with its corresponding device type, package type, and other detailed parameters. Upon receiving the SQL query, the Database Management System (DBMS) performs an efficient data retrieval in the material database based on the query criteria. For example, for the above SQL query, the database scans all material records and returns all material codes for devices of type capacitor and package type 0402. These returned material codes are presented in the format manufacturer number.SPYY.XBBBVVVV, where SPYY.XBBBVVVV is a 12-digit combination of numbers and letters. The result will be a list of multiple candidate material codes, for example: ["MUT.24K0.C5001002, "SAM.24K0.C1601003", "TD.24J0.C2501002"]. This list is the preliminary list of candidate material codes, where MUT, SAM, and TD represent the manufacturers Murata, Samsung, and TDK, respectively.

[0041] In step S4, each candidate material code in the candidate material code list is parameterized to obtain a parameterized candidate material code list. Correspondingly, although step S3 has already filtered out a preliminary candidate material code list from the material library based on device type and package type, these codes are still in raw, unstructured string form. While they may be physically compatible, their specific electrical parameters, accuracy, grade, and other detailed information are still hidden within the code, making them unsuitable for subsequent precise compatibility comparisons and scoring. Without converting these candidate material codes into unified, machine-readable structured parameters, detailed multi-dimensional comparisons cannot be performed, nor can their compatibility with the target material be quantified, significantly reducing the accuracy and effectiveness of intelligent recommendations. Therefore, in order to transform the initially filtered candidate material codes from unstructured form into structured data containing detailed parameters for subsequent multi-dimensional compatibility scoring and ranking, it is necessary to parameterize each candidate material code in the candidate material code list to obtain a parameterized candidate material code list.

[0042] In particular, in an optional embodiment of this application, step S4, which parameterizes each candidate material code in the candidate material code list to obtain a parameterized candidate material code list, includes: inputting each candidate material code in the candidate material code list into the code parser to obtain the parameterized candidate material code list.

[0043] Step S4 can be performed as follows: This process iterates through each material code in the candidate material code list. The program accesses each candidate material code one by one. For each candidate material code in the list, it is used as input to call the code parser used in step S2. The code parser first segments the candidate material code according to the preset rules described in step 2 to obtain the manufacturer prefix and production code.

[0044] Next, the parser will further analyze each segment of the production code. All this parsed information, including device type, package type, precision, withstand voltage, capacitance, etc., will be aggregated into a structured data object, namely, a parameterized candidate material code.

[0045] It's worth noting that supplementary information not directly reflected in the parameterized candidate material codes, such as inventory quantity and unit price, is also retrieved from the material database. This is accomplished by performing an additional query on the material database, with the query condition being the complete code of the current candidate material. The retrieved supplementary information is then appended to the corresponding parameterized candidate material code object, making it more complete and comprehensive. All parameterized candidate material code objects, after parsing and obtaining supplementary information, are then stored one by one in a list, forming the final parameterized candidate material code list. The final output parameterized candidate material code list will contain detailed structured parameters for all candidate materials, for example: [{"Manufacturer prefix": "MUT", "Device type": "Capacitor", "Package type": "0402", "Accuracy": "±10%", "Material": "X7R", "Voltage rating": "50V", "Capacitance": "10nF", "Inventory quantity": 5000, "Unit price": 0.01},{"Manufacturer prefix": "SAM", "Device type": "Capacitor", "Package type": "0402", "Accuracy": "±10%", "Material": "X7R", "Voltage rating": "16V", "Capacitance": "100nF", "Inventory quantity": 2000, "Unit price": 0.02}, {"Manufacturer prefix": "TD", "Device type": "Capacitor", "Package type": "0402", "Accuracy": "±5%", "Material": "X7R", "Voltage rating": "25V", "Capacity": "10nF", "Inventory Quantity": 8000, "Unit Price": 0.015}).

[0046] In step S5, a multi-dimensional compatibility score is performed on the target material's structured parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list. It should be understood that although both the target material and candidate materials have been converted into structured parameters, and physically compatible materials have been initially screened, relying solely on device type and package type for screening is far from sufficient. Material compatibility is a complex, multi-dimensional concept, requiring consideration of not only physical dimensions but also electrical performance (such as capacitance and accuracy), reliability (such as grade), and many other factors. Traditional compatibility assessments heavily rely on engineers' experience and manual datasheet consultation, which is inefficient and prone to overlooking crucial details, leading to misjudgments. Therefore, to comprehensively, quantitatively, and objectively evaluate the compatibility between candidate and target materials, thereby providing a reliable basis for subsequent intelligent recommendations, a multi-dimensional compatibility score is required on the target material's structured parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list.

[0047] Specifically, in an optional embodiment of this application, step S5, performing multi-dimensional compatibility scoring on the target material structured parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list, includes: S51, traversing each parameterized candidate material code in the parameterized candidate material code list and comparing and scoring it with the target material structured parameters dimension by dimension to obtain multiple dimension comparison score values; S52, extracting the weight values ​​of each dimension from the compatibility strategy configuration; S53, calculating the weighted sum of the multiple dimension comparison score values ​​based on the weight values ​​of each dimension to obtain the compatibility score of each parameterized candidate material code.

[0048] Step S5 can be performed as follows: First, in step S51, each parameterized candidate material code in the parameterized candidate material code list is traversed, and each code is compared and scored with the target material structured parameters dimension by dimension to obtain multiple dimension comparison score values. Specifically, in an optional embodiment of this application, the multiple dimension comparison score values ​​include a packaging matching score value, a comparison capacitance score value, a comparison withstand voltage score value, a comparison accuracy score value, and a comparison grade score value.

[0049] It is understandable that the above-mentioned calculation of the weighted sum of binary scores may lead to insufficient discrimination in compatibility scores. This application introduces a more granular scoring mechanism. When multiple candidate materials all show compatibility under binary scoring, it is difficult to effectively distinguish their merits, thus affecting the accuracy of the final recommendation and the efficiency of material selection. To address this issue and provide a more insightful compatibility assessment, this application refines the scoring across each dimension to obtain more granular scores.

[0050] Specifically, in one optional embodiment of this application, the calculation of the multiple-dimensional comparison scores includes: calculating the sum-difference ratio of each structured parameter in the target material structured parameter and the parameterized candidate material code to obtain an initial comparison capacity score, an initial comparison withstand voltage score, an initial comparison accuracy score, and an initial comparison grade score; and performing phase-compatible normalization on the initial comparison capacity score, the initial comparison withstand voltage score, the initial comparison accuracy score, and the initial comparison grade score based on the packaging matching score to obtain the comparison capacity score, the comparison withstand voltage score, the comparison accuracy score, and the comparison grade score.

[0051] In other words, a simple yes-or-no judgment cannot capture the subtle differences between material parameters. For example, the capacitance values ​​of two candidate capacitors may both be within the accuracy range of the target material, but one may be closer to the target value. To quantify this closeness and convert it into a continuous numerical value, this solution uses a sum-difference ratio. This calculation method converts the relative difference between parameters into a continuous score between -1 and 1, where a score closer to 1 indicates a higher degree of matching, and a score closer to -1 indicates a greater difference. This transforms discrete compatibility judgments into continuous scores that reflect the degree of relative difference between parameters, thus providing richer numerical information for subsequent weighted summation and enhancing the scoring's discriminative power. Furthermore, considering that if the material's packaging type is incompatible, regardless of how well its capacitance, withstand voltage, accuracy, or grade meets the requirements, the material cannot be directly substituted. Traditional normalization methods may not effectively reflect this threshold effect. Therefore, this application uses phase-compatible normalization to ensure that when packaging is incompatible, the scores of other dimensions can be effectively zeroed out or significantly penalized, thus ensuring that the overall scoring logic remains consistent with the hard constraints in actual application scenarios. Furthermore, when the packaging matches, this normalization process can further amplify smaller differences in the scores, resulting in a more significant numerical improvement for highly compatible materials, making them easier to distinguish and identify in the final ranking. In other words, it unifies the scores across all dimensions to a consistent numerical basis and relative order, ensuring that the validity of other dimension scores is reset to zero when the packaging is mismatched, while simultaneously improving the score differentiation of highly compatible materials.

[0052] Specifically, therefore, a more granular score is desired. First, the initial package fit score, initial comparison capacitance score, initial comparison withstand voltage score, initial comparison accuracy score, and initial comparison grade score are relativized. For example, for the initial comparison capacitance score... The strategy yields: ;in, It is the tolerance value of the candidate material. It is the target material tolerance value. It is the absolute value. It is the target material precision value. It is the initial comparison tolerance score, that is, the score. The larger the value, the further away from the tolerance limit, and the higher the score.

[0053] Similarly, the initial comparison pressure rating value Compare with the initial accuracy score for: ;in, It is the pressure resistance value of the candidate material. It is the pressure resistance value of the target material. This is the initial comparison pressure resistance score. This is the precision value of the candidate material. It is the target material precision value. This is the initial comparison accuracy score. It is the material grade value of the candidate material. It is the material grade value of the target material. This is the initial comparative rating score.

[0054] Here, we can see that the strategy is... In the case of scores All The form of this is due, on the one hand, to the uncertain relationship between the threshold and the variable, thus requiring a unified approach based on the symmetry of the relationship; on the other hand, it addresses the different situations of the upper and lower thresholds. When used as the lower threshold, excessive deviation relative to the lower threshold is penalized by reducing the growth slope, for example, for initial comparative rating values. In order to ensure In this case, Exceeding The more, the higher the initial comparative rating value. The smaller the growth slope.

[0055] It is understandable that different material parameters (such as capacitance and pressure resistance) have different dimensions and numerical ranges, and their relationship with compatibility thresholds may also differ. The sum-difference ratio form can unify these differences in parameters of different natures into a relatively symmetrical numerical range, thus making the initial scores of different dimensions comparable and avoiding the undue amplification or reduction of certain parameters in the scoring due to differences in numerical magnitude. This form of calculation can achieve refined penalties for deviations based on different upper and lower threshold conditions. For example, for initial comparison grade scoring, when the candidate material grade is higher than the target material grade, the slope of the score increase is reduced to reflect the subtle differences that may be overly standardized but are upwardly compatible, rather than a simple linear score, thus more accurately reflecting compatibility considerations in practical applications. After calculation using the sum-difference ratio form, the obtained... These are preliminary scores, already normalized to some extent across their respective dimensions. These scores quantify the degree of matching between candidate materials and target materials across various key parameters, and reflect specific compatibility strategies (e.g., capacitance values ​​must be within the accuracy range, withstand voltage must be greater than or equal to the target value, and accuracy and grade must be better than or equal to the target value). For example, for a target capacitance of 100nF (±10%) and a candidate capacitance of 10nF, A low negative value or a value close to 0 will be calculated, indicating incompatibility; for both the target withstand voltage of 50V and the candidate withstand voltage of 50V, A value of 0 will be calculated, indicating that the condition is exactly met; for target accuracy ±10% and candidate accuracy ±5%, A positive value will be calculated, indicating better precision.

[0056] The encapsulation compatibility score can be directly expressed as a numerical value. To represent. And, as mentioned above, it can be seen that when the strategy is... In the case of scores Both are in the form of higher-order relativity, therefore requiring a relative encapsulation matching score. To perform normalization, that is, due to its compatibility score with the packaging... There are differences in relative order, and therefore differences in numerical range, for example... Closer to 0, and Closer to 1, therefore, relative to the numerical value Perform phase-compatible normalization: ;in, It is the initial comparison tolerance score. Initial comparative pressure resistance score Initial comparison accuracy score Comparison rating with initial rating The Middle Each rating value The encapsulation matching score is 1. It is a fixed numerical amplification factor, which is adjusted empirically based on historical data or by using optimization algorithms (e.g., it can be preset). To determine, It is the first among the comparison of capacitance rating, voltage withstand rating, accuracy rating, and grade rating. A compensation score. Specifically, if... If the value is 1, then The default value is 1, therefore, through Phase modulation, replacing simple digital normalization modulation, such as direct calculation. This is done to compensate for relative order differences, not just numerical differences, thereby improving the applicability of comparison scores across various dimensions while maintaining consistency. After phase-compatible normalization, The rating was converted to a... The scores are calculated on a unified scale with similar numerical ranges and relativity. This allows the scores from each dimension to participate more fairly and accurately in the final weighted sum, thereby improving the applicability and reliability of the multi-dimensional comparison scores in the overall compatibility assessment. The final outputs are the comparison capacitance score, comparison withstand voltage score, comparison accuracy score, and comparison grade score, all processed using the unified scale. These will serve as inputs for subsequent calculations of the weighted sum of the compatibility scores. It is worth noting that in other instances, the comparison capacitance score and comparison withstand voltage score can be other scores, and this is not limited to this application.

[0057] Next, in step S52, the weight values ​​for each dimension are extracted from the compatibility strategy configuration. To reflect the varying importance of different parameters in material substitution, a weight value needs to be set for each dimension. These weight values ​​are pre-configured based on actual application scenarios and engineers' experience and stored in the compatibility strategy configuration file. For example, in some applications, capacitance is the most critical functional parameter of a capacitor. If the capacitance value of the substitute material is outside the range of the target material (considering accuracy), its function in the circuit will be completely incompatible, potentially causing the circuit to malfunction or even be damaged. Therefore, the capacitance value comparison score may be the most critical, with a weight of 0.3. Withstand voltage is a critical safety and reliability parameter for capacitors. Using devices with insufficient withstand voltage can lead to breakdown, causing circuit failure or even safety accidents; therefore, the withstand voltage comparison score has the next highest weight of 0.25. Accuracy determines the precision of the device parameters. In many circuits, especially in precision measurement, filtering, or timing circuits, accuracy is crucial; therefore, the accuracy comparison score has a weight of 0.2. Material grades (such as X7R and X8R) affect implicit parameters of capacitor components such as temperature characteristics, ESR, and lifespan; therefore, the weight of the comparison grade score is 0.15. Complete consistency of physical parameters such as packaging is the most basic and uncompromising physical matching condition; therefore, the weight of the packaging matching score is 0.1. These weight values ​​can be adjusted according to different product lines or application requirements to meet specific compatibility preferences.

[0058] Finally, in step S53, a weighted sum of the comparison scores for each dimension is calculated based on the weight values ​​of each dimension to obtain the compatibility score of each parameterized candidate material code. For each candidate material, the comparison score for each dimension is multiplied by the extracted corresponding weight value, and then all product results are summed to obtain the final compatibility score for the candidate material. In this way, each candidate material will obtain a quantified compatibility score, ultimately forming a compatibility score list.

[0059] In step S6, the candidate material code list is sorted based on the compatibility score list to obtain a sorted final candidate list. That is, step S5 has already quantified the multi-dimensional compatibility between each candidate material and the target material, resulting in a list containing material codes and corresponding scores; however, this list is usually unordered. For engineers or purchasing personnel, directly selecting the optimal alternative from an unordered list when faced with numerous candidate materials with different compatibility scores is still a tedious and time-consuming task. They need to manually compare each score and find the material that best meets their needs, which not only reduces decision-making efficiency but may also lead to overlooking better options due to information overload. Therefore, in order to transform the results of the multi-dimensional compatibility scores into an intuitive and easy-to-understand sort, thereby helping users quickly identify the most suitable alternative to the target material and significantly improving the efficiency and accuracy of material selection and substitution, it is necessary to sort the candidate material code list based on the compatibility score list to obtain a sorted final candidate list.

[0060] Optionally, step S6 can be performed as follows: The program receives the compatibility score list generated in step S5 as input. Each element in this list contains a material code and its corresponding compatibility score. The core logic of sorting is to arrange the elements in descending order based on their compatibility score values; that is, materials with higher scores are listed first, indicating better compatibility with the target material and thus being recommended first. The sorting process can use standard sorting algorithms, such as quicksort, mergesort, or heapsort. These algorithms can efficiently rearrange the elements in the list. After sorting, the program generates a sorted final candidate list. This list not only contains the material code but also retains its corresponding compatibility score, so that users can intuitively understand the matching degree of each alternative material when viewing the recommendation results.

[0061] In step S7, the top N candidate material codes are extracted from the sorted final candidate list as the recommendation results. It should be understood that presenting multiple alternative options at once can lead to information overload for users, making it difficult for them to make a quick decision. Users only need a few of the best and most relevant alternatives to make a final evaluation and selection within a limited time. Too many choices not only increase the cognitive burden on users but may also dilute truly high-value recommendations. Therefore, in order to transform the comprehensively evaluated and sorted candidate material list into a refined and efficient recommendation result, thereby avoiding information overload, helping users quickly focus on the best alternative, and improving decision-making efficiency and user experience, it is necessary to extract the top N candidate material codes from the sorted final candidate list as the recommendation results.

[0062] Optionally, step S7 can be performed as follows: receiving a generated sorted final candidate list as input, in which the material codes have been sorted from high to low according to the compatibility score.

[0063] The program will truncate the list based on a preset number N of recommended items. N is a configurable parameter, and its value is usually set according to the actual application scenario and user experience requirements. For example, N can be set to 3, meaning that only the top 3 materials with the best compatibility will be recommended. This value of N can be configured by the administrator in the backend, or dynamically adjusted according to user roles, query complexity, etc. The program will start from the top of the sorted final candidate list and select the first N elements. If the number of materials in the list is less than N, all available materials will be selected. For example, if N is set to 2, the result extracted from the above list will be: ["SAM.24K0.C1601003","TD.24J0.C2501002"].

[0064] Ultimately, the top N extracted candidate material codes and their compatibility scores will be output as recommendations. These recommendations can be directly displayed on the user interface, for example, as a list of alternative materials, clearly showing the code of each recommended material and its matching degree with the target material. This allows for rapid final business or technical decisions and greatly simplifies the material substitution process.

[0065] In summary, the intelligent management method for electronic materials based on the embodiments of this application is explained. It firstly uses a 12-bit production coding standard in the SPYY.XBBBVVVV format. This standard defines the device category (X), capability value (BBB, such as power, withstand voltage), and main parameters (VVVV, such as resistance, capacitance) through the main code segment (XBBBVVVV); and defines the device form (S), specific package (P), and key characteristics (YY) such as accuracy and grade through the sub-code segment (SPYY). Based on this rule, the method performs structured parsing of the target material code and extracts candidate materials from the material library using the parsed device type and package type as physical constraints. Subsequently, the candidate materials are parameterized, and multi-dimensional compatibility scoring and weighted calculations are performed with the target materials to achieve accurate quantitative evaluation. Finally, based on the scoring ranking, the optimal result is recommended, effectively solving the selection and substitution problems caused by inconsistent coding, and significantly improving the response efficiency and accuracy of the supply chain.

[0066] Figure 4 This is a block diagram of an intelligent electronic material management system according to an embodiment of this application. Figure 4As shown, the electronic material intelligent management system 100 according to an embodiment of this application includes: a target material code acquisition module 110, used to acquire target material codes; a target material code structure parsing module 120, used to perform structure parsing on the target material codes to obtain target material structure parameters; a candidate material code extraction module 130, used to extract a candidate material code list from a material library using the device type and package type in the target material structure parameters as core physical constraints; a material parameterization module 140, used to perform material parameterization on each candidate material code in the candidate material code list to obtain a parameterized candidate material code list; a compatibility scoring module 150, used to perform multi-dimensional compatibility scoring on the target material structure parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list; a candidate material code sorting module 160, used to sort the candidate material code list based on the compatibility score list to obtain a sorted final candidate list; and a recommendation module 170, used to extract the top N candidate material codes from the sorted final candidate list as recommendation results.

[0067] Here, those skilled in the art will understand that the specific operations of each step in the above-mentioned intelligent electronic material management system have been referenced above. Figures 1 to 3 The description of the intelligent management method for electronic materials is detailed here, and therefore, its repeated description will be omitted.

Claims

1. A method for intelligent management of electronic materials, characterized in that, include: Obtain the target material code; The target material code is structured and parsed to obtain the target material structure parameters; Using the device type and package type in the target material's structured parameters as core physical constraints, a candidate material code list is extracted from the material library. Each candidate material code in the candidate material code list is parameterized to obtain a parameterized candidate material code list. Multi-dimensional compatibility scoring is performed on the target material's structured parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list. The candidate material code list is sorted based on the compatibility score list to obtain a sorted final candidate list. The top N candidate material codes are extracted from the sorted final candidate list as recommendation results. The process of performing structured parsing on the target material code to obtain target material structured parameters includes: calling an encoding parser; the encoding parser segmenting the target material code according to the rules in the document to obtain a sequence of target material code segments; and the encoding parser performing encoding parsing on each target material code segment in the sequence of target material code segments to obtain a sequence of target material code segment parsing information as the target material structured parameters.

2. The intelligent management method for electronic materials according to claim 1, characterized in that, Using the device type and package type in the target material's structured parameters as core physical constraints, a candidate material code list is extracted from the material library, including: constructing an SQL query statement based on the device type and package type in the target material's structured parameters; and executing the SQL query statement to return the candidate material code list from the material library.

3. The intelligent management method for electronic materials according to claim 2, characterized in that, Parameterizing each candidate material code in the candidate material code list to obtain a parameterized candidate material code list includes: inputting each candidate material code in the candidate material code list into the code parser to obtain the parameterized candidate material code list.

4. The intelligent management method for electronic materials according to claim 1, characterized in that, A multi-dimensional compatibility score is performed on the target material structured parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list. This includes: traversing each parameterized candidate material code in the parameterized candidate material code list and comparing and scoring it with the target material structured parameters dimension by dimension to obtain multiple dimension comparison score values; extracting the weight values ​​of each dimension from the compatibility strategy configuration; and calculating the weighted sum of the multiple dimension comparison score values ​​based on the weight values ​​of each dimension to obtain the compatibility score of each parameterized candidate material code.

5. The intelligent management method for electronic materials according to claim 4, characterized in that, The multiple dimensions of the comparison score include the package matching score, the comparison capacitance score, the comparison withstand voltage score, the comparison accuracy score, and the comparison grade score.

6. An intelligent management system for electronic materials, characterized in that, include: The target material code acquisition module is used to acquire the target material code; The target material code structure parsing module is used to perform structure parsing on the target material code to obtain the target material structure parameters; The candidate material code extraction module is used to extract a list of candidate material codes from the material library, using the device type and packaging type in the target material's structured parameters as the core physical constraints. The material parameterization module is used to parameterize each candidate material code in the candidate material code list to obtain a parameterized candidate material code list. The compatibility scoring module is used to perform multi-dimensional compatibility scoring on the target material structure parameters and each parameterized candidate material code in the parameterized candidate material code list to obtain a compatibility score list. The candidate material code sorting module is used to sort the candidate material code list based on the compatibility score list to obtain the sorted final candidate list; The recommendation module is used to extract the top N candidate material codes from the sorted final candidate list as recommendation results; The process of performing structured parsing on the target material code to obtain target material structured parameters includes: calling an encoding parser; the encoding parser segmenting the target material code according to the rules in the document to obtain a sequence of target material code segments; and the encoding parser performing encoding parsing on each target material code segment in the sequence of target material code segments to obtain a sequence of target material code segment parsing information as the target material structured parameters.

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