Electronic component product information processing method and system based on data dictionary tool

Abstract

The invention discloses an electronic component product information processing method and system based on a data dictionary tool, and the method comprises the following steps: obtaining the product data of a to-be-processed electronic component product, wherein the product data comprises the basic information, parameter information and a product picture of the product; and inputting the product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components. And matching the product data by using the electronic component data dictionary tool, and outputting the standard data of the matched standard electronic component. And replacing the product data of the electronic component product with the standard data of the standard electronic component.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing, and in particular to an electronic component product information processing method and system based on a data dictionary tool. Background Art

[0002] Electronic components are the basic building blocks of electronic devices and systems, including components such as complex integrated circuits (ICs), semiconductor devices, etc. They play key roles in electronic devices, such as signal processing, power management, data storage and transmission, and are widely used in consumer electronics, communications, computers, automotive electronics, industrial control, medical equipment and other fields.

[0003] Due to the particularity of the electronic components industry (diversity, fast iteration), the content structure of electronic component product information is relatively complex, and product updates and iterations are also very rapid, making it difficult to achieve standardized and refined management of product information. The non-standardization of product information has brought many challenges to enterprises. At present, enterprises rely on self-established product information management systems to manage product information, and product information management between enterprises is different. Due to the lack of unified product information standards, it is impossible to constrain or require other companies in the supply chain; this leads to different product information for the same electronic component product in different companies in the supply chain.

[0004] Different companies upstream and downstream in the supply chain often use different product information management, resulting in different product information for the same product. In the process of enterprise production and operation, the product information in the order, delivery, transportation, warehousing, sales and other links will be inconsistent. This requires manual verification and checking in each link, which increases a lot of labor costs, delays the collaborative efficiency of the supply chain, and affects the operating efficiency of the enterprise. On the other hand, the same product may be registered repeatedly due to different product information, resulting in chaotic inventory management and duplicate inventory. This not only increases inventory costs, but also may affect the accuracy of production plans, and thus affect the operating efficiency of the enterprise. Summary of the invention

[0005] In order to overcome the above technical defects, the present invention provides an electronic component product information processing method and system based on a data dictionary tool.

[0006] In order to solve the above problems, the present invention is implemented according to the following technical solutions: In a first aspect, the present invention provides an electronic component product information processing method based on a data dictionary tool, comprising the following steps: Acquire product data of the electronic component product to be processed, wherein the product data includes basic product information, parameter information and product pictures; Inputting product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components; Use electronic component data dictionary tools to match product data and output standard data of matching standard electronic components; The product data of the electronic component product is replaced with the standard data of the standard electronic component.

[0007] In combination with the first aspect, the present invention further provides a first specific implementation of the first aspect, specifically, using an electronic component data dictionary tool to match product data, specifically including: Identify the product model and product category in the basic product information; Input the product model and product category into the electronic component data dictionary tool for matching, and output a first matching result that matches the product model and product category; Inputting the parameter information into an electronic component data dictionary tool for matching, and outputting a second matching result in which the parameter information matches; Inputting the product image into an electronic component data dictionary tool for matching, and outputting a third matching result that matches the product image; According to the first matching result, the second matching result and the third matching result, standard data of the standard electronic component that is finally matched is output.

[0008] In combination with the first aspect, the present invention further provides a second specific implementation of the first aspect, specifically, inputting the parameter information into an electronic component data dictionary tool for matching, specifically including: Identify the number of parameter items in the parameter information, input the number of parameter items into an electronic component data dictionary tool for matching, and output a number of candidate matching results; Identify a parameter field of each parameter item of the parameter information; Match the parameter field of each parameter item with several candidate matching results one by one, and sort the candidate matching results according to the matching degree; The candidate matching result with the highest matching degree is output as the second matching result.

[0009] In combination with the first aspect, the present invention further provides a third specific implementation of the first aspect, specifically, inputting the product image into an electronic component data dictionary tool for matching, specifically including: Perform image matching between the product image and electrical component images of multiple standard electronic component data to obtain a first candidate matching result with the highest matching degree; When the product image is identified to contain text information, the text information is input into the electronic component data dictionary tool for matching, and a second candidate matching result matching the text information is output; Apply the Canny algorithm to detect product edges in the product image, the first candidate matching result, and the second candidate matching result, respectively, and output a product edge image, a first candidate edge image, and a second candidate edge image, respectively; Performing contour tracking on the product edge image, the first candidate edge image, and the second candidate edge image, and outputting the final product edge image, the first candidate edge image, and the second candidate edge image including the pin edge contour; The product edge image is compared with the first candidate edge image and the second candidate edge image respectively to obtain a third matching result.

[0010] In combination with the first aspect, the present invention further provides a fourth specific implementation of the first aspect, which specifically includes: before applying the Canny algorithm to detect product edges in the product image, the first candidate matching result, and the second candidate matching result, respectively: Convert the image data of the product image, the first candidate matching result, and the second candidate matching result into grayscale images and output them respectively; A guided filter is used to enhance the edge information in each grayscale image.

[0011] In combination with the first aspect, the present invention further provides a fifth specific implementation of the first aspect, specifically, outputting standard data of the final matching standard electronic component according to the first matching result, the second matching result and the third matching result, specifically including: When the first matching result, the second matching result and the third matching result are all the same, outputting the standard data of the uniquely corresponding standard electronic component; When the first matching result, the second matching result and the third matching result contain standard data of at least two different standard electronic components, a push message for manual screening and confirmation is generated; When there are invalid matching results among the first matching results, the second matching results and the third matching results, push information for manual screening and confirmation is generated.

[0012] In combination with the first aspect, the present invention further provides a sixth specific implementation of the first aspect, specifically, outputting standard data of the final matching standard electronic component according to the first matching result, the second matching result and the third matching result, specifically including: When the first matching result, the second matching result and the third matching result are all invalid matching results; Using product data of electronic components to conduct online search, a number of candidate search results are obtained; Generate push information for manual screening and confirmation, wherein the push information is used to manually select and confirm a number of candidate search results; The candidate search results confirmed by manual selection are used as standard data of standard electronic components and stored in an electronic component data dictionary tool.

[0013] In a second aspect, the present invention further provides an electronic component product information processing system based on a data dictionary tool, comprising: An acquisition module, which is used to acquire product data of the electronic component product to be processed, wherein the product data includes basic product information, parameter information and product pictures; A data processing module, which is used to input the product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components; A matching module, which uses an electronic component data dictionary tool to match product data and outputs standard data of matching standard electronic components; A replacement module replaces the product data of the electronic component product with the standard data of the standard electronic component.

[0014] In combination with the second aspect, the present invention further provides a first specific implementation of the second aspect, specifically, the data processing module matches the product data using an electronic component data dictionary tool, specifically including: Identify the product model and product category in the basic product information; Input the product model and product category into the electronic component data dictionary tool for matching, and output a first matching result that matches the product model and product category; Inputting the parameter information into an electronic component data dictionary tool for matching, and outputting a second matching result in which the parameter information matches; Inputting the product image into an electronic component data dictionary tool for matching, and outputting a third matching result that matches the product image; According to the first matching result, the second matching result and the third matching result, standard data of the standard electronic component that is finally matched is output.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an electronic component product information processing method based on a data dictionary tool, comprising the following steps: obtaining product data of an electronic component product to be processed, wherein the product data includes basic product information, parameter information, and product images. Inputting the product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components. Using the electronic component data dictionary tool to match the product data, outputting the standard data of the matching standard electronic component. Replacing the product data of the electronic component product with the standard data of the standard electronic component.

[0016] The present invention provides an automated analysis and effective and feasible technical solution, which can standardize and uniformly manage the product information of electronic components through a data dictionary tool, with the purpose of eliminating the differences in product information within an enterprise and between different enterprises in the supply chain. Through standardized product information management, enterprises can avoid duplicate inventory problems caused by inconsistent product information. The workload of manual verification and checking can be reduced in the order, delivery, transportation, warehousing, sales and other links.

[0017] On the other hand, the present invention is based on the basic product information, parameter information and product pictures of electronic components, and uses electronic data dictionary tools to achieve multi-dimensional matching, thereby improving the matching accuracy and reliability of electronic components and standard electronic components. Multi-dimensional matching is more accurate than single-dimensional matching, and can effectively avoid incorrect matching caused by incomplete or ambiguous information. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein: Figure 1 The present invention is a flowchart of an electronic component product information processing method based on a data dictionary tool. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0020] Due to the particularity of the electronic components industry (diversity, fast iteration), the content structure of electronic component product information is relatively complex, and product updates and iterations are also very rapid, making it difficult to achieve standardized and refined management of product information. The non-standardization of product information has brought many challenges to enterprises. At present, enterprises rely on self-established product information management systems to manage product information, and product information management between enterprises is different. Due to the lack of unified product information standards, it is impossible to constrain or require other companies in the supply chain; this leads to different product information for the same electronic component product in different companies in the supply chain.

[0021] Different companies upstream and downstream in the supply chain often use different product information management, resulting in different product information for the same product. In the process of enterprise production and operation, the product information in the order, delivery, transportation, warehousing, sales and other links will be inconsistent. This requires manual verification and checking in each link, which increases a lot of labor costs, delays the collaborative efficiency of the supply chain, and affects the operating efficiency of the enterprise. On the other hand, the same product may be registered repeatedly due to different product information, resulting in chaotic inventory management and duplicate inventory. This not only increases inventory costs, but also may affect the accuracy of production plans, and thus affect the operating efficiency of the enterprise.

[0022] It can be seen that it is difficult to carry out standardized management of product information in the field of electronic components, which leads to the following problems: 1. Internal problems of the enterprise: leading to duplicate registration and duplicate inventory of products of the same model; generating redundant inventory, affecting production plans and operating efficiency.

[0023] 2. Problems in cooperation between enterprises: The cooperation process between supply chain enterprises is prone to errors. From ordering, shipping, transportation, warehousing, sales and other chains, the product information of enterprises with different roles is different. Each link must be manually verified and checked, which is bound to generate a lot of labor costs, delay the collaborative efficiency of the work supply chain, and affect operating efficiency.

[0024] 3. External issues of the enterprise: It affects the analysis effect of the enterprise on the social or field market of the products it operates. Since the same non-standard situation also exists in the market, it is not easy for competing products to obtain market acceptance of the products from third-party platforms, which will affect the enterprise's decision-making on product operations.

[0025] To this end, the present invention provides an electronic component product information processing method based on a data dictionary tool, which mainly solves the problem of non-standard product information among enterprises in the current electronic component field. Through the data dictionary tool of the present invention, automatic standardized management of electronic component information is realized, providing standardized management of product information for individual enterprises and enterprise supply chain systems in the electronic component field.

[0026] For this purpose, refer to Figure 1 The present invention provides an electronic component product information processing method based on a data dictionary tool. The method can be executed by an electronic component product information processing system. The system can be implemented in the form of hardware and / or software. The system can be configured in a computer. Figure 1 As shown, the method includes: S100: Acquire product data of the electronic component product to be processed, wherein the product data includes basic product information, parameter information and product pictures; S200: inputting product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components; S300: Use the electronic component data dictionary tool to match product data and output standard data of matching standard electronic components; S400: replacing product data of the electronic component product with standard data of the standard electronic component.

[0027] The present invention provides an automated analysis and effective and feasible technical solution, which can standardize and uniformly manage the product information of electronic components through a data dictionary tool, with the purpose of eliminating the differences in product information within an enterprise and between different enterprises in the supply chain. Through standardized product information management, enterprises can avoid duplicate inventory problems caused by inconsistent product information. The workload of manual verification and checking can be reduced in the order, delivery, transportation, warehousing, sales and other links.

[0028] On the other hand, the present invention is based on the basic product information, parameter information and product pictures of electronic components, and uses electronic data dictionary tools to achieve multi-dimensional matching, thereby improving the matching accuracy and reliability of electronic components and standard electronic components. Multi-dimensional matching is more accurate than single-dimensional matching, and can effectively avoid incorrect matching caused by incomplete or ambiguous information. Specifically, this embodiment provides a detailed description of each step.

[0029] S100: Acquire product data of an electronic component product to be processed, wherein the product data includes basic product information, parameter information, and product images.

[0030] In a specific implementation, the basic product information includes the product model, product type, brand, manufacturer, and production date. The parameter information may involve the electrical characteristics and physical characteristics of the product. For example: the resistance value, accuracy, and power of a resistor; the capacity, withstand voltage, and dielectric type of a capacitor; the number of pins, operating voltage, and operating frequency of a chip.

[0031] It should be noted that the product picture is the front view of the product. It is a photograph of the appearance. You can apply for a side view, stereoscopic view, etc. The picture should clearly show the shape, size, logo and other features of the product to facilitate subsequent comparison and identification.

[0032] In a specific implementation, such basic product information, parameter information and product pictures are usually obtained from product documents provided by suppliers.

[0033] S200: Inputting product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components.

[0034] In the present invention, the electronic component data dictionary tool is a pre-established tool that stores a large amount of standard data of standard electronic components. The standard data includes standard models, standard parameters, standard pictures (product front view), etc., which are used for comparison with the input product data.

[0035] In specific implementation, the source of standard data can be found in the detailed technical documents provided by the manufacturers (original manufacturers) of electronic components for their products, including datasheets, specifications, technical guides, etc. These documents are the most authoritative source of standard data because they come directly from the designers and manufacturers of the products. You can download the datasheets through the manufacturer's official website.

[0036] Among them, the data dictionary tool exists in the form of a database or data table, which can be accessed through a software interface or a data interface. On the other hand, the data dictionary tool can be a software system that runs on a server and supports multi-user access and data interaction.

[0037] In a specific implementation, the product data (preferably in JSON format) acquired in step S100 is sent to the data dictionary tool through an API interface. After receiving the data, the data dictionary tool stores it in a temporary buffer.

[0038] S300: Use the electronic component data dictionary tool to match product data and output standard data of matching standard electronic components.

[0039] The core principle of the present invention is to find matching standard electronic components by comparing the input product data with the standard data in the data dictionary, and output its standard data. In the specific implementation, the electronic component data dictionary tool is used to match the product data, which specifically includes: S310: Identify the product model and product category in the basic product information.

[0040] In the specific implementation, the product model and product category are extracted from the basic product information. This information usually exists in text form. For example, the product model is STM32F407VET6 and the product category is ARM microcontroller - MCU.

[0041] S320: Input the product model and product type into the electronic component data dictionary tool for matching, and output a first matching result where the product model and product type match.

[0042] In the specific implementation, the matching strategy is: search the data dictionary for standard data that completely matches the input product model and type. If the data dictionary supports fuzzy matching, a certain error range can be allowed, such as case insensitivity of letters in the model.

[0043] In a specific implementation, if a match is found, an identifier (such as ID) of the matched standard data and related information are output as a first matching result. If no match is found, a first matching result of "no match" is output.

[0044] In specific implementation, the present invention provides the following specific implementation modes: (1) Input data preparation Before matching, you need to ensure that the entered product model and product category data is accurate and formatted. This includes the following steps: Data extraction: Extract product model and product category from basic product information. This information usually exists in text form, such as "Model: 103J" and "Category: Resistors".

[0045] Data cleaning: remove extra spaces, unify letter case (such as converting "103J" to "103j"), correct spelling errors, etc.

[0046] Data formatting: Convert the extracted data into a uniform format for comparison with standard data in the data dictionary. For example, the model number may need to have its prefix or suffix removed, and the type may need to be converted to standardized terms (such as "resistor" to "resistor").

[0047] (2) Construction and maintenance of data dictionary The data dictionary is the core of the matching operation, and the quality of its construction and maintenance directly affects the accuracy of the matching. The data dictionary should contain the following: Related information: The standard parameters, image paths, and other information associated with the product model and product category of each standard data for use in subsequent steps. Fuzzy matching rules: For possible spelling errors or non-standard inputs, the data dictionary should support fuzzy matching rules, such as allowing the letters in the model to be case-insensitive or ignoring certain common spelling errors.

[0048] (3) Matching algorithm design The matching algorithm is the key to matching product models and types. The following matching strategies are adopted: Exact match: Directly compare the model and type of the input to see if they are completely consistent with the standard data in the data dictionary. This is the simplest and most efficient matching method, but it may not be able to handle spelling errors or non-standard input.

[0049] Fuzzy matching: Use fuzzy matching algorithms to solve possible spelling errors or irregular inputs. For example: Levenshtein Distance: Calculates the minimum number of editing operations (insertion, deletion, replacement) between two strings and sets a threshold (such as the edit distance is less than 3) to determine whether it matches.

[0050] Regular expression matching: Use regular expressions to match common model formats or category names. For example, the model "103J" can be matched by the regular expression ^103[jJ]$.

[0051] Multi-level matching: First perform an exact match, and if no match is found, try a fuzzy match. This strategy can improve matching flexibility while ensuring matching efficiency.

[0052] (4) Matching result output: After the matching is completed, the corresponding information needs to be output based on the matching results.

[0053] S330: Input the parameter information into an electronic component data dictionary tool for matching, and output a second matching result in which the parameter information matches.

[0054] In a specific implementation, the present invention provides the following implementation modes: (1) Extraction and preprocessing of parameter information Objective: Extract parameter information from product data and format it into a unified format for matching. Parameter extraction: Extract parameter information from the input product data. This information may exist in a structured (such as JSON, XML) or unstructured (such as text description) form. If the parameter information is unstructured text, use natural language processing (NLP) technology or regular expressions to extract key parameters. For example, from the text "The resistance value is 10kΩ and the tolerance is ±5%", extract resistance = 10kΩ and tolerance = ±5%.

[0055] Unit Normalization: Convert all parameter values ​​to a uniform unit. For example: Resistance value: Convert 10kΩ to 10000Ω. Capacitance value: Convert 1μF to 0.000001F. Power: Convert 0.25W to 0.25 (in watts). Use the predefined unit conversion table to complete this operation.

[0056] Formatting: Separate the parameter value and unit to ensure that the value is in floating point or integer format to facilitate subsequent numerical comparison. Example: Convert 10kΩ to {"value": 10000, "unit": "Ω"}.

[0057] Error handling: If some parameters are missing or in incorrect format, the error message is recorded and the user is prompted to supplement or correct them. For missing parameters, you can choose to skip or mark them as "unmatched".

[0058] (2) Parameter matching strategy Goal: Match the extracted and formatted parameter information with the standard parameters in the data dictionary.

[0059] Exact match: Parameter information (such as model, standard level, etc.) must be completely consistent for a successful match.

[0060] Matching algorithm optimization: For complex parameter matching, machine learning algorithms (such as decision trees and support vector machines) can be introduced to improve the accuracy and efficiency of matching.

[0061] (3) Matching result output: Output the matching results of parameter information according to the matching strategy.

[0062] In a specific implementation, the present invention provides the following implementation mode, wherein the parameter information is input into an electronic component data dictionary tool for matching, specifically including: S331: Identify the number of parameter items in the parameter information, input the number of parameter items into an electronic component data dictionary tool for matching, and output a number of candidate matching results.

[0063] Specific, detailed implementation instructions 1. Identify the number of parameter items: Determine the number of parameter items contained in the input parameter information. Parse the parameter information and count the number of parameter items. For example, the parameter information may include multiple parameter items such as resistance value, tolerance, and power. Example: The parameter information is { "resistance": "10kΩ", "tolerance": "±5%", "power_rating": "0.25W"}, then the number of parameter items is 3.

[0064] 2. Preliminary matching: Preliminary screening of possible matching standard data based on the number of parameter items. Input the number of parameter items into the data dictionary tool to search for standard data with the same or similar number of parameter items. Example: Among the standard data stored in the data dictionary, only some standard data with 3 items are selected as candidate matching results.

[0065] 3. Output candidate matching results: Generate a preliminary candidate matching list for subsequent detailed matching. Operation: Output several candidate matching results, each of which contains the identifier of the standard data (such as ID) and partial matching information.

[0066] S332: Identify the parameter field of each parameter item of the parameter information.

[0067] In the specific implementation, parameter field identification: parse the specific fields of each parameter item, including parameter name and parameter value. Parse the input parameter information item by item to extract the parameter name and parameter value. Example: Parameter name: resistance, parameter value: 10kΩ. Parameter name: tolerance, parameter value: ±5%. Parameter name: power_rating, parameter value: 0.25W Parameters need to be formatted: Convert parameter values ​​to a uniform format for easy matching. For example, convert the resistance value from "10kΩ" to "10000Ω". Convert the tolerance from "±5%" to the decimal form "0.05". Ensure that the units and formats of all parameter values ​​are consistent.

[0068] S333: Match the parameter field of each parameter item with a number of candidate matching results one by one, and sort the candidate matching results according to the matching degree.

[0069] Detailed implementation instructions, item-by-item matching: Perform detailed matching on each parameter item. For each candidate matching result, compare the matching degree of its standard parameters with the input parameters one by one. Use the exact matching algorithm to quantify the matching degree of each candidate matching result.

[0070] S334: Output the candidate matching result with the highest matching degree as the second matching result.

[0071] Specifically, the result with the highest matching degree is selected from the sorted candidate matching results, and the candidate matching result with the highest matching degree is selected as the final second matching result.

[0072] By subdividing the matching process of parameter information into S331 to S334, the present invention can more accurately complete the matching task of parameter information. This gradually refined method not only improves the accuracy of matching, but also ensures the reliability and efficiency of the matching process by sorting and selecting the best matching results.

[0073] S340: Input the product image into an electronic component data dictionary tool for matching, and output a third matching result that matches the product image.

[0074] In a specific implementation of the present invention, the product image is input into an electronic component data dictionary tool for matching, specifically including: S341: Perform image matching on the product image and electrical component images of a plurality of standard electronic component data to obtain a first candidate matching result with the highest matching degree.

[0075] In the specific implementation of the present invention, the product pictures and the electrical component pictures of multiple standard electronic component data are all front views of the product, that is, after the electronic component is placed horizontally, it is photographed from directly above the electronic component, including the main body of the electronic component and multiple pins distributed on the side wall of the electronic component body.

[0076] In the specific implementation, the structural similarity index (SSIM) or feature matching algorithm (such as SIFT, SURF) is used to calculate the similarity between the product image and each standard image. The standard image with the highest matching degree is selected as the first candidate matching result.

[0077] S342: When the product image is identified to contain text information, the text information is input into the electronic component data dictionary tool for matching, and a second candidate matching result matching the text information is output.

[0078] In the specific implementation, the text information in the product image is extracted through optical character recognition (OCR) technology, and matched with the text information in the data dictionary to find the matching text information as the second candidate matching result. Use OCR tools (such as Tesseract) to extract text information from product images. Format the extracted text to remove extra spaces or special characters. Find standard data in the data dictionary that fully matches or fuzzily matches the extracted text information, and output the text information with the highest matching degree and its corresponding standard image.

[0079] S343: Apply the Canny algorithm to detect product edges in the product image, the first candidate matching result, and the second candidate matching result, respectively, and output the product edge image, the first candidate edge image, and the second candidate edge image, respectively.

[0080] In the specific implementation, the Canny edge detection algorithm performs preliminary edge positioning on the product image to be detected, the product image in the first candidate matching result, and the product image in the second candidate matching result, respectively, to obtain its edge point set. Specifically, the specific steps of the Canny operator of the Canny edge detection algorithm are as follows: 1. Gaussian smoothing of the image to obtain the smoothed image ; 2. Use the first-order partial derivative to obtain the grayscale gradient gx and gy in the horizontal and vertical directions of the image, where the gradient amplitude is With the gradient direction It can be expressed as:

[0081] 3. Perform non-maximum suppression on the obtained gradient amplitude; 4. Use the hysteresis threshold method to screen all possible edge points. Define two thresholds Max and Min for the gradient amplitude. If it is less than Min, the edge point is eliminated; if it is greater than Max, the point is a definite edge and is retained; if it is between max and min, if the point is connected to the definite edge, the point is retained, otherwise the point is discarded.

[0082] Therefore, the Canny operator obtains the edge contours of electronic component products with continuous edges, excludes discontinuous edge points, and also discards false edge points.

[0083] Before applying the Canny algorithm to detect product edges in the product image, the first candidate matching result, and the second candidate matching result, the following steps are specifically included: S343-1: Convert the image data of the product image, the first candidate matching result and the second candidate matching result into grayscale images respectively and output them.

[0084] In the present invention, the converted grayscale image is output for use in subsequent steps. The grayscale image reduces the amount of data, thereby reducing the computational complexity.

[0085] S343-2: Use a guided filter to enhance edge information in each grayscale image.

[0086] In the specific implementation, a guided filter is applied to process the grayscale image. The filter can adjust the filter strength according to the local statistical characteristics of the image and enhance the edge information. The input of the guided filter includes the grayscale image and the edge weight image, and the output is the enhanced image. The guided filter can maintain the sharpness of the edge while reducing the noise in the non-edge area, which is very important for subsequent edge detection and contour segmentation.

[0087] S344: Perform contour tracing on the product edge image, the first candidate edge image and the second candidate edge image, and output the final product edge image, the first candidate edge image and the second candidate edge image including the pin edge contour.

[0088] In the present invention, the edge obtained by using pixel-level operators such as Canny is at the pixel level, which is a continuous edge composed of several single pixels connected. Subsequent processing needs to convert discrete edges into ordered edge storage, so it is necessary to track the contour of the edge image.

[0089] In the specific implementation, the contour tracking algorithm of the electronic component product is designed based on the eight-neighborhood method. The specific steps of contour tracking are as follows: 1) Based on pixel-level operators, discrete edge points are obtained.

[0090] 2) According to the image characteristics of the pins of electronic components, the lower boundary edge point of the image is set as the starting point of the contour.

[0091] 3) Search for the next edge point in a clockwise direction using the eight neighborhoods around the starting point.

[0092] 4) The termination condition is that there are no edge points in the eight neighborhoods.

[0093] S345: Compare the product edge image with the first candidate edge image and the second candidate edge image respectively to obtain a third matching result.

[0094] In a specific implementation, the standard outline of a standard electronic component product in standard data is used as a reference, and the product edge image is compared with the first candidate edge image and the second candidate edge image respectively.

[0095] In a specific implementation, an image comparison algorithm, such as template matching, feature point matching or geometric comparison method, is used to compare the detected product edge image with the first candidate edge image and the second candidate edge image, respectively. The differences between the product edge image and the first candidate edge image and the second candidate edge image are analyzed, including deviations in shape, number of pins, pin positions, etc.

[0096] In another specific implementation, the contour matching algorithm uses dynamic time warping (DTW) or Hausdorff distance to measure the similarity between two contours. This method can determine the degree of contour matching by calculating the minimum distance between the two.

[0097] Hausdorff distance: Calculate the maximum and minimum distances between the product edge image and the first candidate edge image and the second candidate edge image. If the distance is less than the preset threshold, it is considered a match. Output the matched candidate edge image and the corresponding standard data of the standard electronic component as the third matching result.

[0098] It is understandable that by combining image matching (based on visual similarity), text matching (based on text information extracted by OCR) and contour matching (based on shape and pin details), products can be verified from multiple dimensions. This multi-dimensional matching strategy can more comprehensively reflect the characteristics of the product and reduce the limitations of single-dimensional matching.

[0099] The outline of electronic components (including the body and pins) is its unique shape feature. Contour matching is more accurate. The number, position and shape of the pins of electronic components are the main contour features. Contour matching can accurately identify the details of the pins, thereby ensuring the consistency of the matching results in physical structure.

[0100] S350: Outputting standard data of the final matched standard electronic component according to the first matching result, the second matching result and the third matching result.

[0101] In a specific implementation, the present invention provides the following three result matching strategies: When the first matching result, the second matching result and the third matching result are all the same, outputting the standard data of the uniquely corresponding standard electronic component; When the first matching result, the second matching result and the third matching result contain standard data of at least two different standard electronic components, a push message for manual screening and confirmation is generated; When there are invalid matching results among the first matching results, the second matching results and the third matching results, push information for manual screening and confirmation is generated.

[0102] Through the matching strategies of the above three situations, step S350 can flexibly process the matching results according to different situations: Unique match: When the three matching results are consistent, the standard data is directly output. Multiple result conflict: When the matching results are inconsistent, manual intervention is prompted. Invalid match: When there is an invalid matching result, manual confirmation is prompted.

[0103] It can be seen that this strategy ensures the reliability and flexibility of the matching process, while providing clear guidance for manual intervention and improving the overall efficiency and accuracy of the system. It is understandable that the push information should contain detailed information (such as model, parameters, pictures, etc.) for each candidate result to facilitate manual selection.

[0104] S400: replacing product data of the electronic component product with standard data of the standard electronic component.

[0105] In a specific implementation, the standard data outputted in step S300 replaces the original product data. A backup of the original product data is retained for subsequent recovery or comparison when needed. The replaced standard data is stored in the enterprise's database or ERP system for subsequent production, sales and inventory management. In a specific implementation, a user notification step is also included. If the replacement operation is successful, the user is notified that the data has been standardized. If no matching standard data is found, the user is prompted to manually process or update the data dictionary.

[0106] In a preferred implementation, the present invention outputs the standard data of the final matched standard electronic components according to the first matching result, the second matching result and the third matching result, and specifically includes the following strategies: S10: When the first matching result, the second matching result and the third matching result are all invalid matching results.

[0107] In a specific implementation, when the three matching results are all invalid (ie, no matching standard electronic component data is found), the system enters the network search phase.

[0108] S20: Using the product data of the electronic component product to conduct an online search to obtain a number of candidate search results.

[0109] In a specific implementation, online search is to search for candidate results related to product data through Internet resources (such as the website of the original manufacturer of electronic components). By downloading the technical documents of the original manufacturer, including data sheets, specifications, technical guides, etc.

[0110] In a specific implementation, a search engine may be used or a specific electronic component database may be accessed for searching.

[0111] S30: Generate push information for manual screening and confirmation, where the push information is used to manually select and confirm several candidate search results.

[0112] When the online search obtains several candidate results, the system generates push information to prompt manual screening and confirmation. The candidate search results are packaged as push information, including detailed document information for each candidate result. A manual selection interface is provided to allow users to confirm or reject each candidate result.

[0113] S40: taking the manually selected and confirmed candidate search results as standard data of standard electronic components and storing them in an electronic component data dictionary tool.

[0114] In a specific implementation, according to the manually selected candidate results, they are stored as standard data in the data dictionary for subsequent use. The manually confirmed candidate results are formatted into a standard data format. They are stored in the electronic component data dictionary tool and the data dictionary is updated.

[0115] The present invention also provides an electronic component product information processing system based on a data dictionary tool, which is used to execute the above-mentioned electronic component product information processing method based on a data dictionary tool. The electronic component product information processing system based on a data dictionary tool includes: An acquisition module, which is used to acquire product data of the electronic component product to be processed, wherein the product data includes basic product information, parameter information and product pictures; A data processing module, which is used to input the product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components; A matching module, which uses an electronic component data dictionary tool to match product data and outputs standard data of matching standard electronic components; A replacement module replaces the product data of the electronic component product with the standard data of the standard electronic component.

[0116] In the specific implementation, the data processing module uses the electronic component data dictionary tool to match the product data, including: Identify the product model and product category in the basic product information; Input the product model and product category into the electronic component data dictionary tool for matching, and output a first matching result that matches the product model and product category; Inputting the parameter information into an electronic component data dictionary tool for matching, and outputting a second matching result in which the parameter information matches; Inputting the product image into an electronic component data dictionary tool for matching, and outputting a third matching result that matches the product image; According to the first matching result, the second matching result and the third matching result, standard data of the standard electronic component that is finally matched is output.

[0117] For other structures of the electronic component product information processing method and system based on a data dictionary tool described in this embodiment, refer to the prior art.

[0118] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Therefore, any modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for processing electronic component product information based on a data dictionary tool, characterized in that: The steps include: Acquire product data of the electronic component product to be processed, wherein the product data includes basic product information, parameter information and product pictures; Inputting product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components; Use electronic component data dictionary tools to match product data and output standard data of matching standard electronic components; The product data of the electronic component product is replaced with the standard data of the standard electronic component.

2. The electronic component product information processing method based on a data dictionary tool according to claim 1, characterized in that: Use electronic component data dictionary tools to match product data, including: Identify the product model and product category in the basic product information; Input the product model and product category into the electronic component data dictionary tool for matching, and output a first matching result that matches the product model and product category; Inputting the parameter information into an electronic component data dictionary tool for matching, and outputting a second matching result in which the parameter information matches; Inputting the product image into an electronic component data dictionary tool for matching, and outputting a third matching result that matches the product image; According to the first matching result, the second matching result and the third matching result, standard data of the standard electronic component that is finally matched is output.

3. The electronic component product information processing method based on a data dictionary tool according to claim 2, characterized in that: The parameter information is input into the electronic component data dictionary tool for matching, specifically including: Identify the number of parameter items in the parameter information, input the number of parameter items into an electronic component data dictionary tool for matching, and output a number of candidate matching results; Identify a parameter field of each parameter item of the parameter information; Match the parameter field of each parameter item with several candidate matching results one by one, and sort the candidate matching results according to the matching degree; The candidate matching result with the highest matching degree is output as the second matching result.

4. The electronic component product information processing method based on a data dictionary tool according to claim 2, characterized in that: Input the product image into the electronic component data dictionary tool for matching, including: Perform image matching between the product image and electrical component images of multiple standard electronic component data to obtain a first candidate matching result with the highest matching degree; When the product image is identified to contain text information, the text information is input into the electronic component data dictionary tool for matching, and a second candidate matching result matching the text information is output; Apply the Canny algorithm to detect product edges in the product image, the first candidate matching result, and the second candidate matching result, respectively, and output a product edge image, a first candidate edge image, and a second candidate edge image, respectively; Performing contour tracking on the product edge image, the first candidate edge image, and the second candidate edge image, and outputting the final product edge image, the first candidate edge image, and the second candidate edge image including the pin edge contour; The product edge image is compared with the first candidate edge image and the second candidate edge image respectively to obtain a third matching result.

5. The electronic component product information processing method based on a data dictionary tool according to claim 4, characterized in that: Before applying the Canny algorithm to detect product edges in the product image, the first candidate matching result, and the second candidate matching result, the following steps are specifically included: Convert the image data of the product image, the first candidate matching result, and the second candidate matching result into grayscale images and output them respectively; A guided filter is used to enhance the edge information in each grayscale image.

6. The electronic component product information processing method based on a data dictionary tool according to claim 2, characterized in that: Outputting the standard data of the final matched standard electronic components according to the first matching result, the second matching result and the third matching result specifically includes: When the first matching result, the second matching result and the third matching result are all the same, outputting the standard data of the uniquely corresponding standard electronic component; When the first matching result, the second matching result and the third matching result contain standard data of at least two different standard electronic components, a push message for manual screening and confirmation is generated; When there are invalid matching results among the first matching results, the second matching results and the third matching results, push information for manual screening and confirmation is generated.

7. The electronic component product information processing method based on a data dictionary tool according to claim 6, characterized in that: Outputting the standard data of the final matched standard electronic components according to the first matching result, the second matching result and the third matching result specifically includes: When the first matching result, the second matching result and the third matching result are all invalid matching results; Using product data of electronic components to conduct online search, a number of candidate search results are obtained; Generate push information for manual screening and confirmation, wherein the push information is used to manually select and confirm a number of candidate search results; The candidate search results confirmed by manual selection are used as standard data of standard electronic components and stored in an electronic component data dictionary tool.

8. An electronic component product information processing system based on a data dictionary tool, characterized in that: include: An acquisition module, which is used to acquire product data of the electronic component product to be processed, wherein the product data includes basic product information, parameter information and product pictures; A data processing module, which is used to input the product data of the electronic component product into a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components; A matching module, which uses an electronic component data dictionary tool to match product data and outputs standard data of matching standard electronic components; A replacement module replaces the product data of the electronic component product with the standard data of the standard electronic component.

9. The electronic component product information processing system based on a data dictionary tool according to claim 8, characterized in that: The data processing module uses the electronic component data dictionary tool to match product data, including: Identify the product model and product category in the basic product information; Input the product model and product category into the electronic component data dictionary tool for matching, and output a first matching result that matches the product model and product category; Inputting the parameter information into an electronic component data dictionary tool for matching, and outputting a second matching result in which the parameter information matches; Inputting the product image into an electronic component data dictionary tool for matching, and outputting a third matching result that matches the product image; According to the first matching result, the second matching result and the third matching result, standard data of the standard electronic component that is finally matched is output.

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