Data processing method and system based on electronic component product data dictionary

Abstract

The invention discloses a data processing method and system based on an electronic component product data dictionary. The data processing method comprises the following steps: obtaining product data of an electronic component product to be processed; and calling a preset electronic component data dictionary tool. And inputting the basic information of the product into the electronic component data dictionary tool for matching, and obtaining a plurality of first matching results which are output by the electronic component data dictionary tool and are matched with the basic information of the product. And matching the parameter information with the standard data of the plurality of first matching results, and outputting a plurality of second matching results matched with the parameter information. And matching the product picture with the standard data of the plurality of second matching results, and outputting a final matching result matched with the product picture. And replacing the product data of the electronic component product with the standard data of the standard electronic component corresponding to the final matching result.

Description

Technical Field

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

[0002] Electronic components are the basic building blocks of electronic devices and systems, such as Figure 2 as shown, covering components such as complex integrated circuits (ICs), semiconductor devices, etc. They play a key role in electronic devices, such as signal processing, power management, data storage and transmission, and are widely used in fields such as consumer electronics, communications, computers, automotive electronics, industrial control, and medical devices.

[0003] Due to the particularity (diversity, rapid iteration) of the electronic component industry, 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 brings many challenges to enterprises. Currently, enterprises rely on self-established product information management systems for product information management, and the product information management between enterprises is different. Due to the lack of a unified product information standard and the inability to constrain or difficult to require other enterprises in the supply chain; this results in different product information for the same electronic component product in different enterprises in the supply chain.

[0004] Different enterprises in the upstream and downstream of 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, it will lead to inconsistent product information in links such as orders, outbound, transportation, inbound, and sales. This requires manual verification and checking in each link, increasing a large amount of labor costs, delaying the collaborative efficiency of the supply chain, and affecting the business efficiency of enterprises. On the other hand, due to different product information for the same product, it may be registered repeatedly many times, resulting in chaotic inventory management and the occurrence of duplicate inventory. This not only increases inventory costs but also may affect the accuracy of production plans, thereby affecting the business efficiency of enterprises. Summary of the Invention

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

[0006] To solve the above problems, the present invention is implemented according to the following technical solutions: In the first aspect, the present invention provides a data processing method based on a data dictionary of electronic component products, including the following steps: Obtain the product data of the electronic component product to be processed, where the product data includes product basic information, parameter information, and product pictures; Call a preset electronic component data dictionary tool, which stores standard data of multiple standard electronic components; Input the basic product information into the electronic component data dictionary tool for matching, and obtain several first matching results output by the electronic component data dictionary tool that match the basic product information; Match the parameter information with the standard data of several first matching results, and output several second matching results that match the parameter information; Match the product picture with the standard data of several second matching results, and output the final matching result that matches the product picture; Replace the product data of the electronic component product with the standard data of the standard electronic component corresponding to the final matching result.

[0007] Combined with the first aspect, the present invention also provides the first preferred implementation manner of the first aspect. Specifically, when inputting the basic product information into the electronic component data dictionary tool for matching, the exact matching algorithm, fuzzy matching algorithm, regular expression matching algorithm, and multi-level matching algorithm are specifically adopted.

[0008] Combined with the first aspect, the present invention also provides the second preferred implementation manner of the first aspect. Specifically, when matching the parameter information with the standard data of several first matching results, it specifically includes: Identify the number of parameter items of the parameter information, match the number of parameter items with the standard data of several first matching results, and output several target matching results; Identify the parameter fields of each parameter item of the parameter information; Match the parameter fields of each parameter item with the standard data of several target matching results one by one, and sort the candidate target matching results according to the matching degree; Output the target matching results with the top five matching degrees as the second matching results.

[0009] Combined with the first aspect, the present invention also provides the third preferred implementation manner of the first aspect. Specifically, when matching the parameter fields of each parameter item with the standard data of several target matching results one by one, the exact matching algorithm is specifically adopted.

[0010] Combined with the first aspect, the present invention also provides the fourth preferred implementation manner of the first aspect. Specifically, when matching the product picture with the standard data of several second matching results, it specifically includes: Perform image matching on the product picture and the pictures of electrical components in the standard data of several second matching results to obtain the first candidate matching result with the highest matching degree; When it is recognized that the product picture contains text information, match the text information with the standard data of several second matching results, and output the second candidate matching results that match the text information; Apply the Canny algorithm to detect the product edges in the product picture, 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; 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; Compare the product edge image with the first candidate edge image and the second candidate edge image respectively to obtain the final matching result.

[0011] Combined with the first aspect, the present invention also provides a fifth preferred implementation manner of the first aspect. Specifically, before applying the Canny algorithm to detect the product edges in the product picture, the first candidate matching result and the second candidate matching result respectively, it further specifically includes: Convert the image data of the product picture, the first candidate matching result and the second candidate matching result into grayscale images and output them respectively; Use a guided filter to enhance the edge information in each grayscale image.

[0012] Combined with the first aspect, the present invention also provides a sixth preferred implementation manner of the first aspect. Specifically, when performing contour tracing on the product edge image, the first candidate edge image and the second candidate edge image, a contour tracking algorithm based on the eight-neighborhood method is specifically adopted.

[0013] Combined with the first aspect, the present invention also provides a seventh preferred implementation manner of the first aspect. Specifically, input the product basic information into the electronic component data dictionary tool for matching. When the electronic component data dictionary tool outputs an invalid matching result; Input the parameter information into the electronic component data dictionary tool for matching, and output several second matching results that match the parameter information.

[0014] Combined with the first aspect, the present invention also provides an eighth preferred implementation manner of the first aspect. Specifically, input the product basic information into the electronic component data dictionary tool for matching. When the electronic component data dictionary tool outputs an invalid matching result; Input the parameter information into the electronic component data dictionary tool for matching. When the electronic component data dictionary tool outputs an invalid matching result; Use the product data of the electronic component product to perform an online search to obtain several candidate search results; Generate push information confirmed by manual screening, where the push information is used to manually select and confirm a number of candidate search results; According to the candidate search results confirmed by manual selection, use them as the standard data of standard electronic components and store them in the electronic component data dictionary tool.

[0015] In a second aspect, the present invention also provides a data processing system based on an electronic component product data dictionary, including: An acquisition module, which is used to acquire the product data of the electronic component product to be processed, and the product data includes product basic information, parameter information, and product pictures; A call module, which is used to call a preset electronic component data dictionary tool, and the electronic component data dictionary tool stores the standard data of multiple standard electronic components; A first matching module, which is used to input the product basic information into the electronic component data dictionary tool for matching, and obtain a number of first matching results output by the electronic component data dictionary tool that match the product basic information; A second matching module, which is used to match the parameter information with the standard data of a number of first matching results, and output a number of second matching results that match the parameter information; A third matching module, which is used to match the product picture with the standard data of a number of second matching results, and output a final matching result that matches the product picture; A replacement module, which is used to replace the product data of the electronic component product with the standard data of the standard electronic component corresponding to the final matching result.

[0016] Compared with the prior art, the beneficial effects of the present invention are: The present invention provides a data processing method based on an electronic component product data dictionary, including the following steps: Acquire the product data of the electronic component product to be processed, and the product data includes product basic information, parameter information, and product pictures. Call a preset electronic component data dictionary tool, and the electronic component data dictionary tool stores the standard data of multiple standard electronic components. Input the product basic information into the electronic component data dictionary tool for matching, and obtain a number of first matching results output by the electronic component data dictionary tool that match the product basic information. Match the parameter information with the standard data of a number of first matching results, and output a number of second matching results that match the parameter information. Match the product picture with the standard data of a number of second matching results, and output a final matching result that matches the product picture. Replace the product data of the electronic component product with the standard data of the standard electronic component corresponding to the final matching result.

[0017] Through a data processing method based on the data dictionary of electronic component products, the present invention effectively solves the problem that it is difficult to achieve a unified standard in traditional product information management systems, resulting in frequent information inconsistencies in links such as orders, inventory, and transportation among upstream and downstream enterprises in the supply chain. The present invention compares non-standardized product data with a preset standard data dictionary through multi-level matching (basic information, parameter information, product pictures), and finally outputs standardized product information, thereby realizing the unified management of product information.

[0018] This technical solution not only improves the collaborative efficiency of the supply chain, reduces the manual verification costs and time delays caused by information inconsistencies, but also optimizes inventory management, avoiding duplicate inventory and inventory backlog problems. In addition, through standardized data dictionary tools, enterprises can better integrate the information flow of the supply chain, achieve seamless sharing and interoperability of data, and further enhance the overall transparency and management efficiency of the supply chain. Brief Description of the Drawings

[0019] The following further elaborates on the specific implementation manners of the present invention with reference to the accompanying drawings, where: Figure 1 is a schematic flow chart of a data processing method based on the data dictionary of electronic component products of the present invention; Figure 2 is a product schematic diagram of an electronic component. Specific Embodiment

[0020] The following describes the preferred embodiments of the present invention with reference to 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.

[0021] Due to the particularity (diversity, rapid iteration) of the electronic component industry, 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 brings many challenges to enterprises. Currently, enterprises rely on independently established product information management systems for product information management, and the product information management among enterprises is different. Due to the lack of a unified product information standard and the inability to restrict or difficult to require other enterprises in the supply chain; this results in different product information for the same electronic component product in different enterprises in the supply chain.

[0022] Different enterprises in the upstream and downstream of the supply chain often use different product information management, resulting in different product information for the same product. During the production and operation process of enterprises, it will lead to inconsistent product information in links such as orders, outbound, transportation, inbound, and sales. This requires manual verification and checking in each link, increasing a large amount of labor costs, delaying the collaborative efficiency of the supply chain, and affecting the business efficiency of enterprises. On the other hand, due to different product information, the same product may be registered repeatedly, resulting in chaotic inventory management and duplicate inventory situations. This not only increases inventory costs but may also affect the accuracy of production plans, thereby affecting the business efficiency of enterprises.

[0023] It can be seen that it is difficult to carry out standardized management of product information in the current field of electronic components, resulting in the following problems: 1. Enterprise internal problems: It leads to the situation of repeated registration and duplicate inventory of products of the same model; redundant inventory is generated, affecting production plans and business efficiency.

[0024] 2. Enterprise cooperation problems: Errors are prone to occur during the cooperation process among supply chain enterprises. From the chains of orders, outbound, transportation, inbound, and sales, the product information of enterprises in different roles is different. Manual verification and checking are required in each link, inevitably generating a large amount of labor costs, delaying the collaborative efficiency of the supply chain, and affecting business efficiency.

[0025] 3. Enterprise external problems: It affects the analysis effect of the products operated by enterprises in society or in the field market. When there is also the same non-standard situation in the market, it is not easy to obtain the market acceptance situation of competing products from third-party platforms, which will affect the decision-making judgment of enterprises on product operation.

[0026] Therefore, the present invention provides a data processing method based on an electronic component product data dictionary, mainly solving the problem of non-standardization of product information among enterprises in the current field of electronic components. Through the data dictionary tool of the present invention, automatic standardized management of electronic component information is realized, providing product information standardized management for individual enterprises and enterprise supply chain systems in the field of electronic components.

[0027] Therefore, with reference to Figure 1 , the present invention provides a flow schematic diagram of a data processing method based on an electronic component product data dictionary. A data processing method based on an electronic component product data dictionary of the present invention, this method can be executed by an electronic component product information processing system, which can be implemented in the form of hardware and / or software, and this system can be configured in a computer. As Figure 1 shown, this method includes: S100: Obtain the product data of the electronic component products to be processed, where the product data includes product basic information, parameter information, and product pictures.

[0028] S200: Invoke a preset electronic component data dictionary tool, which stores the standard data of multiple standard electronic components.

[0029] S300: Input the product basic information into the electronic component data dictionary tool for matching, and obtain several first matching results output by the electronic component data dictionary tool that match the product basic information.

[0030] S400: Match the parameter information with the standard data of several first matching results, and output several second matching results that match the parameter information.

[0031] S500: Match the product pictures with the standard data of several second matching results, and output the final matching results that match the product pictures.

[0032] S600: Replace the product data of the electronic component products with the standard data of the standard electronic components corresponding to the final matching results.

[0033] Through the data processing method based on the electronic component product data dictionary, the present invention effectively solves the problem that it is difficult for traditional product information management systems to achieve a unified standard, resulting in frequent information inconsistencies in the order, inventory, transportation, and other links between upstream and downstream enterprises in the supply chain. By means of multi-level matching (basic information, parameter information, product pictures), the present invention compares non-standard product data with a preset standard data dictionary, and finally outputs standardized product information, thereby realizing the unified management of product information.

[0034] This technical solution not only improves the collaborative efficiency of the supply chain, reduces the manual verification cost and time delay caused by information inconsistencies, but also optimizes inventory management, avoiding duplicate inventory and inventory backlog problems. In addition, through the standardized data dictionary tool, enterprises can better integrate the supply chain information flow, achieve seamless sharing and interoperability of data, and further improve the overall transparency and management efficiency of the supply chain.

[0035] On the other hand, based on the product basic information, parameter information, and product pictures of electronic component products, and using an electronic data dictionary tool, the present invention adopts a multi-level matching strategy to improve the matching accuracy and reliability between electronic component products and standard electronic components. The multi-dimensional matching method is more accurate than the single-dimensional matching, and can effectively avoid incorrect matching caused by incomplete or ambiguous information. Specifically, each step of this embodiment is described in detail.

[0036] S100: Obtain the product data of the electronic component products to be processed, where the product data includes product basic information, parameter information, and product pictures.

[0037] In a specific implementation, the product basic information includes product model, product type, brand, manufacturer, and production date. The parameter information can involve the electrical characteristics, physical characteristics, etc. of the product. For example: the resistance value, accuracy, and power of a resistor; the capacitance, withstand voltage value, and dielectric type of a capacitor; the number of pins, operating voltage, and operating frequency of a chip, etc.

[0038] It should be noted that the product picture is the front view of the product and is an externally photographed picture. Side views, three-dimensional views, etc. can be referred to. The picture should clearly show the shape, size, markings, and other features of the product for subsequent comparison and identification.

[0039] In a specific implementation, product basic information such as product basic information, parameter information, and product pictures is usually obtained from the product documents provided by the supplier.

[0040] S200: Invoke a preset electronic component data dictionary tool, where the electronic component data dictionary tool stores the standard data of multiple standard electronic components.

[0041] 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 (front views of products), etc., and is used for comparison with the input product data. The electronic component data dictionary tool also serves as a database of standard data.

[0042] In a specific implementation, the source of the standard data can be that the manufacturer (original factory) of the electronic component provides detailed technical documents for its products, including data sheets, specification sheets, technical guides, etc. These documents are the most authoritative source of standard data because they directly come from the product design and production side. The data sheet can be downloaded through the official website of the manufacturer.

[0043] Among them, the data dictionary tool exists in the form of a database or data table and can be accessed through a software interface or a data interface. On the other hand, the data dictionary tool can be a software system running on a server, supporting multi-user access and data interaction.

[0044] In a specific implementation, the product data obtained in step S100 (preferably in JSON format) 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.

[0045] S300: Input the basic product information into the electronic component data dictionary tool for matching, and obtain a number of first matching results output by the electronic component data dictionary tool that match the basic product information.

[0046] The core principle of the present invention is to find the matching standard electronic components by comparing the input product data with the standard data in the data dictionary and output their standard data. On the other hand, it can be understood that S300 is a pre - placed basic screening step to conduct a preliminary screening among numerous standard electronic components and then perform an accurate screening. Such a matching strategy can greatly improve the matching efficiency and speed.

[0047] In a specific implementation, using the electronic component data dictionary tool to match product data specifically includes: S310: Identify the product model and product type in the basic product information.

[0048] In a specific implementation, extract the product model and product type from the basic product information. These information usually exist in text form. For example, the product model is STM32F407VET6, and the product type is ARM microcontroller - MCU.

[0049] S320: Input the product model and product type into the electronic component data dictionary tool for matching, and output the first matching results that match the product model and product type.

[0050] In a specific implementation, the matching strategy: Search for the standard data in the data dictionary that exactly matches the input product model and type. If the data dictionary supports fuzzy matching, a certain error range can be allowed, such as being insensitive to the case of letters in the model.

[0051] In a specific implementation, if a matching item is found, output the identifier (such as ID) and related information of the matching standard data as the first matching result. If no matching item is found, output the first matching result of "not matched".

[0052] In a specific implementation, the present invention provides the following specific implementation manners: (1) Input data preparation Before performing the matching, it is necessary to ensure that the input product model and product type data are accurate and formatted. This includes the following steps: Data extraction: Extract the product model and product type from the basic product information. These information usually exist in text form, such as "Model: 103J" and "Type: Resistor".

[0053] Data cleaning: Remove extra spaces, unify the case of letters (such as convert "103J" to "103j"), correct spelling mistakes, etc.

[0054] 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").

[0055] (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.

[0056] (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.

[0057] 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.

[0058] 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]$.

[0059] 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.

[0060] (4) Matching result output: After the matching is completed, the corresponding information needs to be output based on the matching results.

[0061] S400: Match the parameter information with standard data of a plurality of first matching results, and output a plurality of second matching results matching the parameter information.

[0062] In specific implementation, the present invention provides the following implementation manners: (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 structured (such as JSON, XML) or unstructured (such as text description) forms. If the parameter information is unstructured text, use natural language processing (NLP) techniques 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%.

[0063] Unit unification: Convert all parameter values to a unified unit. For example: Resistance value: Convert 10kΩ to 10000Ω. Capacitance value: Convert 1μF to 0.000001F. Power: Convert 0.25W to 0.25 (unit is watt). Use a predefined unit conversion table to complete this operation.

[0064] Formatting process: Separate the parameter value and the unit to ensure that the numerical value is in floating-point or integer format for subsequent numerical comparison. Example: Convert 10kΩ to {"value": 10000, "unit": "Ω"}.

[0065] Error handling: If some parameters are missing or in incorrect format, record the error information and prompt the user to supplement or correct. For missing parameters, you can choose to skip or mark them as "unmatched".

[0066] (2) Parameter matching strategy Objective: Match the extracted and formatted parameter information with the standard parameters in the data dictionary.

[0067] Exact match: For parameter information (such as model number, standard grade, etc.), it is required to be exactly the same to succeed in matching.

[0068] Optimization of matching algorithm: For complex parameter matching, machine learning algorithms (such as decision trees, support vector machines) can be introduced to improve the accuracy and efficiency of matching.

[0069] (3) Output of matching result: Output the matching result of the parameter information according to the matching strategy.

[0070] In specific implementation, the present invention provides the following implementation manners. Input the parameter information into an electronic component data dictionary tool for matching, which specifically includes: S410: Identify the number of parameter items of the parameter information, match the number of parameter items with the standard data of several first matching results, and output several target matching results.

[0071] Specifically, detailed implementation description 1. Parameter item number identification: Determine the number of parameter items included in the input parameter information. Analyze 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, power, etc. Example: If the parameter information is { "resistance": "10kΩ", "tolerance": "±5%", "power_rating": "0.25W"}, then the number of parameter items is 3.

[0072] 2. Preliminary matching: Initially screen out the potentially matching standard data based on the number of parameter items. Directly match the number of parameter items with the first matching result to find the standard data that contains 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 the target matching results.

[0073] 3. Output the target matching results: Generate a preliminary target matching list for subsequent detailed matching. Operation: Output several target matching results, and each result contains the identifier (such as ID) of the standard data and partial matching information.

[0074] S420: Identify the parameter fields of each parameter item of the parameter information.

[0075] In specific implementation, parameter field identification: Analyze the specific fields of each parameter item, including the parameter name and the parameter value. Analyze the input parameter information item by item to extract the parameter name and the parameter value. Example: Parameter name: resistance, parameter value: 10kΩ. Parameter name: tolerance, parameter value: ±5%. Parameter name: power_rating, parameter value: 0.25W It is necessary to perform formatting processing on the parameter items: Convert the parameter values to a unified format for subsequent 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.

[0076] S430: Match the parameter fields of each parameter item one by one with the standard data of several target matching results, and sort the candidate target matching results according to the matching degree.

[0077] Detailed implementation description, item-by-item matching: Perform detailed matching on each parameter item. For each candidate matching result, compare the standard parameter with the input parameter item by item to determine the matching degree. Use an exact matching algorithm to quantify the matching degree of each candidate matching result. For each candidate matching result, calculate its comprehensive matching degree according to the matching degree scores of all parameter fields.

[0078] The exact match requires that the input parameters be exactly the same as the standard data, which can minimize errors caused by data deviation. In the field of electronic components, even a tiny parameter difference (such as resistance value, voltage range, or package size) may cause the product to malfunction or its performance to decline. Through exact matching, the accuracy and consistency of product data can be ensured, and other problems caused by inaccurate data can be avoided.

[0079] S440: Output the top five target matching results in terms of matching degree as the second matching result.

[0080] Specifically, select the top five target matching results in terms of matching degree from the sorted candidate matching results and output them as the final second matching result.

[0081] By subdividing the matching process of parameter information into S410 to S440, the present invention can complete the matching task of parameter information more precisely. This step-by-step refinement method not only improves the accuracy of matching but also ensures the reliability and efficiency of the matching process by sorting and selecting the optimal matching results.

[0082] S500: Match the product picture with the standard data of several second matching results and output the final matching result that matches the product picture.

[0083] In the specific implementation of the present invention, S500 is the last step. Based on S400, exact matching is performed to achieve the situation where product information, parameter information, and product pictures are all matched. Matching the product picture with the standard data of several second matching results specifically includes: S510: Perform image matching between the product picture and the pictures of electrical components in the standard data of several second matching results to obtain the first candidate matching result with the highest matching degree.

[0084] In the specific implementation of the present invention, both the product picture and the pictures of electrical components in multiple standard electronic component data are the front views of the product, that is, they are taken from directly above the electrical components after the electrical components are placed horizontally, including the main body of the electrical components and multiple pins distributed on the side walls of the main body of the electrical components.

[0085] In the specific implementation, the structural similarity index (SSIM) or feature matching algorithms (such as SIFT, SURF) are used to calculate the similarity between the product picture and each standard picture. Select the standard picture with the highest matching degree as the first candidate matching result.

[0086] S520: When it is recognized that the product picture contains text information, match the text information with the standard data of several second matching results and output the second candidate matching result that matches the text information.

[0087] In a specific implementation, optical character recognition (OCR) technology is used to extract the text information in the product picture, and it is matched with the text information in the data dictionary to find the matching text information as the second candidate matching result. An OCR tool (such as Tesseract) is used to extract the text information from the product picture. The extracted text is formatted to remove extra spaces or special characters. Standard data that exactly matches or is fuzzy-matched with the extracted text information is searched for in the data dictionary, and the text information with the highest matching degree and its corresponding standard picture are output.

[0088] S530: Apply the Canny algorithm to detect the product edges in the product picture, 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.

[0089] In a specific implementation, the Canny edge detection algorithm performs preliminary edge localization on the product images in the product picture to be detected, the first candidate matching result, and the second candidate matching result respectively, and obtains their edge point sets. Specifically, the specific steps of the Canny operator of the Canny edge detection algorithm are as follows: 1. Smooth the image with a Gaussian filter to obtain the smoothed image ; 2. Use the first-order partial derivative to obtain the gray-scale gradients gx and gy in the horizontal and vertical directions of the image, where the gradient amplitude and the gradient direction can be expressed as:

[0090] 3. Perform non-maximum suppression on the obtained gradient amplitude; 4. Apply the hysteresis thresholding method to screen all possible edge points. Define two thresholds Max and Min for the gradient amplitude. If it is less than Min, this edge point is eliminated; if it is greater than Max, this point is a definite edge and is retained; if it is between max and min, if this point is connected to a definite edge, it is retained, otherwise it is discarded.

[0091] Therefore, the Canny operator will obtain the edge contour of the electronic component product with continuous edges, exclude discontinuous edge points, and at the same time discard false edge points.

[0092] Before applying the Canny algorithm to detect the product edges in the product picture, the first candidate matching result, and the second candidate matching result respectively, it also specifically includes: S531: Convert the image data of the product picture, the first candidate matching result, and the second candidate matching result into grayscale images respectively and output them.

[0093] In the present invention, the converted grayscale images are output for use in subsequent steps. The grayscale images reduce the data volume, thereby reducing the computational complexity.

[0094] S532: Use a guided filter to enhance the edge information in each grayscale image.

[0095] In a specific implementation, a guided filter is applied to process the grayscale image. This filter can adjust the filtering intensity according to the local statistical characteristics of the image to 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 edges while reducing the noise in the non-edge regions, which is very important for subsequent edge detection and contour segmentation.

[0096] S540: Perform contour tracking 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 contours.

[0097] In the present invention, the edges obtained using pixel-level operators such as Canny are pixel-level, which are continuous edges composed of several individual pixels connected together. Subsequent processing needs to convert the discrete edges into ordered edges for storage, so contour tracking of the edge image is required.

[0098] In a specific implementation, a contour tracking algorithm for electronic component products is designed based on the eight-neighborhood method. The specific steps of contour tracking are as follows: 1) Based on the pixel-level operator, obtain discrete edge points.

[0099] 2) According to the image characteristics of the pins of the electronic component, set the edge points at the lower boundary of the image as the contour starting point.

[0100] 3) Search for the next edge point clockwise in the eight neighborhoods around the starting point.

[0101] 4) The termination condition is that there are no edge points in all eight neighborhoods.

[0102] S345: Compare the product edge image with the first candidate edge image and the second candidate edge image respectively to obtain the third matching result.

[0103] In a specific implementation, using the standard contour of the standard electronic component product in the standard data as a reference, the product edge image is compared with the first candidate edge image and the second candidate edge image respectively.

[0104] 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. Analyze the differences between the product edge image and the first candidate edge image, and the second candidate edge image, including deviations in shape, number of pins, pin positions, etc.

[0105] In another specific implementation, a contour matching algorithm: uses dynamic time warping (DTW) or Hausdorff distance to measure the similarity between two contours. This method can judge the degree of contour matching by calculating the minimum distance between the two.

[0106] Hausdorff distance: Calculate the maximum minimum distance between the product edge image and the first candidate edge image, and the second candidate edge image respectively. If the distance is less than the preset threshold, it is considered a match. Output the matching candidate edge image, and use the standard data of the corresponding standard electronic component as the third matching result.

[0107] It can be understood that by combining image matching (based on visual similarity), text matching (based on the text information extracted by OCR), and contour matching (based on shape and pin details), the product 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.

[0108] The contour of an electronic component (including the body and pins) is its unique shape feature. Through more accurate contour matching, the number, position, and shape of the pins of the electronic component are the main contour features. Contour matching can accurately identify the details of the pins, thus ensuring the consistency of the matching result in the physical structure.

[0109] In another specific implementation, contour tracing is performed on the product edge image, the first candidate edge image, and the second candidate edge image, which specifically includes the following steps: Step (1): Convert the input image to a grayscale image and perform binary processing to extract the contour.

[0110] Step (2): Use the cv2.findContours function to extract the contours in the binary image. In a specific implementation: contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cv2.RETR_EXTERNAL: Only extract the outermost contour, which is suitable for the product edge.

[0111] cv2.CHAIN_APPROX_SIMPLE: Compresses contour points and only retains key points.

[0112] Step (3): cv2.approxPolyDP can be used to perform polygon approximation on the contour to simplify the contour representation.

[0113] # Polygon approximation epsilon = 0.01 * cv2.arcLength(contours[0], True) # Approximation accuracy approx = cv2.approxPolyDP(contours[0], epsilon, True) Step (4), draw the extracted contour on a blank template and save it as an image. Output the final product edge image, first candidate edge image, and second candidate edge image including the pin edge contour.

[0114] The above steps can also achieve contour tracing of the product edge image, first candidate edge image, and second candidate edge image, effectively extract continuous contours, and support subsequent contour analysis and processing.

[0115] S600: Replace the product data of the electronic component product with the standard data of the standard electronic component corresponding to the final matching result.

[0116] In a specific implementation, replace the original product data with the standard data output in step S500. Keep a backup of the original product data for subsequent recovery or comparison if needed. Store the replaced standard data in the enterprise's database or ERP system for subsequent production, sales, and inventory management. In a specific implementation, it also includes a user notification step. If the replacement operation is successful, notify the user that the data has been standardized. If no matching standard data is found, prompt the user to handle it manually or update the data dictionary.

[0117] In a preferred implementation, the present invention further includes the following steps: S1000: Input the product basic information into the electronic component data dictionary tool for matching. When the electronic component data dictionary tool outputs an invalid matching result; S20000: Input the parameter information into the electronic component data dictionary tool for matching and output several second matching results that match the parameter information.

[0118] In a specific implementation, the present invention provides the following implementation method. Input the parameter information into the electronic component data dictionary tool for matching, specifically including: S2100: Identify the number of parameter items in the parameter information, input the number of parameter items into the electronic component data dictionary tool for matching, and output several candidate matching results.

[0119] Specifically, detailed implementation description 1. Parameter item number identification: Determine the number of parameter items included in the input parameter information. Analyze 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, power, etc. Example: If the parameter information is { "resistance": "10kΩ", "tolerance": "±5%", "power_rating": "0.25W"}, then the number of parameter items is 3.

[0120] 2. Preliminary matching: Initially screen out the standard data that may match according to the number of parameter items. Input the number of parameter items into the data dictionary tool to find the 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.

[0121] 3. Output candidate matching results: Generate a preliminary candidate matching list for subsequent detailed matching. Operation: Output several candidate matching results, and each result includes the identifier (such as ID) of the standard data and partial matching information.

[0122] S2200: Identify the parameter fields of each parameter item in the parameter information.

[0123] In specific implementation, parameter field identification: Analyze the specific fields of each parameter item, including the parameter name and parameter value. Analyze 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 It is necessary to format the parameter items: Convert the parameter values to a unified format for subsequent matching. For example, convert the resistance value from "10kΩ" to "10000Ω". Convert the tolerance from "±5%" to decimal form "0.05". Ensure that the units and formats of all parameter values are consistent.

[0124] S2300: Match the parameter fields of each parameter item with several candidate matching results one by one, and sort the candidate matching results according to the matching degree.

[0125] Detailed implementation description, item-by-item matching: Conduct detailed matching for each parameter item. For each candidate matching result, compare the matching degree between its standard parameter and the input parameter one by one. Use an exact matching algorithm to quantify the matching degree of each candidate matching result.

[0126] S2400: Output the candidate matching result with the highest matching degree as the second matching result.

[0127] Specifically, select the result with the highest matching degree from the sorted candidate matching results. Select the candidate matching result with the highest matching degree as the final second matching result.

[0128] In another preferred implementation, the data processing method further includes: S10: Input the basic product information into the electronic component data dictionary tool for matching. When the electronic component data dictionary tool outputs an invalid matching result.

[0129] S20: Input the parameter information into the electronic component data dictionary tool for matching. When the electronic component data dictionary tool outputs an invalid matching result.

[0130] S30: Use the product data of electronic components to conduct an online search to obtain several candidate search results.

[0131] In a specific implementation, the online search is through Internet resources (such as searching for candidate results related to product data on the websites of original manufacturers of electronic components. Download technical documents of original manufacturers, including data sheets, specification sheets, technical guides, etc.

[0132] In a specific implementation, a search engine or a specific electronic component database can be used for searching.

[0133] S40: Generate a push message for manual screening and confirmation. The push message is used to manually select and confirm several candidate search results.

[0134] After several candidate results are obtained through the online search, the system generates a push message to prompt manual screening and confirmation. Package the candidate search results into a push message, including the detailed document information of each candidate result. Provide a manual selection interface to allow users to confirm or reject each candidate result.

[0135] S50: According to the candidate search results confirmed by manual selection, use them as the standard data of standard electronic components and store them in the electronic component data dictionary tool.

[0136] In a specific implementation, according to the candidate results selected manually, they are stored in the data dictionary as standard data for subsequent use. Format the candidate results confirmed manually into the standard data format. Store them in the electronic component data dictionary tool and update the data dictionary.

[0137] The present invention also provides a data processing system based on the electronic component product data dictionary for performing the above-mentioned data processing method based on the electronic component product data dictionary. The data processing system based on the electronic component product data dictionary includes: An acquisition module, which is used to acquire the product data of the electronic component product to be processed, and the product data includes product basic information, parameter information, and product pictures; An invocation module, which is used to invoke a preset electronic component data dictionary tool, and the electronic component data dictionary tool stores the standard data of multiple standard electronic components; A first matching module, which is used to input the product basic information into the electronic component data dictionary tool for matching, and obtain several first matching results output by the electronic component data dictionary tool that match the product basic information; A second matching module, which is used to match the parameter information with the standard data of several first matching results, and output several second matching results that match the parameter information; A third matching module, which is used to match the product picture with the standard data of several second matching results, and output the final matching result that matches the product picture; A replacement module, which is used to replace the product data of the electronic component product with the standard data of the standard electronic component corresponding to the final matching result.

[0138] For other structures of the data processing method and system based on the electronic component product data dictionary in this embodiment, refer to the prior art.

[0139] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Therefore, any modifications, equivalent changes, and decorations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A data processing method based on an electronic component product data dictionary, 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; Calling a preset electronic component data dictionary tool, wherein the electronic component data dictionary tool stores standard data of a plurality of standard electronic components; Inputting the basic product information into the electronic component data dictionary tool for matching, and obtaining a plurality of first matching results output by the electronic component data dictionary tool that match the basic product information; Matching the parameter information with standard data of a plurality of first matching results, and outputting a plurality of second matching results matching the parameter information; Matching the product image with the standard data of the plurality of second matching results, and outputting a final matching result that matches the product image; The product data of the electronic component product is replaced with the standard data of the standard electronic component corresponding to the final matching result.

2. A data processing method based on an electronic component product data dictionary according to claim 1, characterized in that: The basic product information is input into the electronic component data dictionary tool for matching, specifically using an exact matching algorithm, a fuzzy matching algorithm, a regular expression matching algorithm and a multi-level matching algorithm.

3. The data processing method based on the electronic component product data dictionary according to claim 1, characterized in that: Matching the parameter information with the standard data of the first matching results specifically includes: Identifying the number of parameter items of the parameter information, matching the number of parameter items with standard data of a plurality of first matching results, and outputting a plurality of target matching results; Identify a parameter field of each parameter item of the parameter information; Match the parameter fields of each parameter item with the standard data of several target matching results one by one, and sort the candidate target matching results according to the matching degree; The target matching results with the top five matching degrees are output as the second matching results.

4. The data processing method based on the electronic component product data dictionary according to claim 3 is characterized in that: The parameter fields of each parameter item are matched one by one with the standard data of several target matching results, specifically using an exact matching algorithm.

5. The data processing method based on the electronic component product data dictionary according to claim 1, characterized in that: Matching the product image with the standard data of the second matching results specifically includes: Perform image matching between the product image and the electrical component images in the standard data of the second matching results 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 matched with the standard data of a plurality of second matching results, 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 final matching result.

6. The data processing method based on the electronic component product data dictionary according to claim 5, 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.

7. The data processing method based on the electronic component product data dictionary according to claim 5, characterized in that: Contour tracing is performed on the product edge image, the first candidate edge image and the second candidate edge image, specifically using a contour tracing algorithm based on the eight-neighborhood method.

8. A data processing method based on an electronic component product data dictionary according to any one of claims 1 to 7, characterized in that: Inputting the basic product information into the electronic component data dictionary tool for matching, when the electronic component data dictionary tool outputs an invalid matching result; The parameter information is input into an electronic component data dictionary tool for matching, and a plurality of second matching results matching the parameter information are output.

9. The data processing method based on the electronic component product data dictionary according to claim 8, characterized in that: Inputting the basic product information into the electronic component data dictionary tool for matching, when the electronic component data dictionary tool outputs an invalid matching result; Inputting the parameter information into an electronic component data dictionary tool for matching, when the electronic component data dictionary tool outputs an invalid matching result; 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.

10. A data processing system based on an electronic component product data dictionary, 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 calling module, which is used to call 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 first matching module, which is used to input the basic product information into the electronic component data dictionary tool for matching, and obtain a plurality of first matching results output by the electronic component data dictionary tool that match the basic product information; A second matching module, which is used to match the parameter information with the standard data of the first matching results, and output a plurality of second matching results that match the parameter information; A third matching module, which is used to match the product image with the standard data of the second matching results, and output a final matching result that matches the product image; A replacement module is used to replace the product data of the electronic component product with the standard data of the standard electronic component corresponding to the final matching result.

Images