Commodity information generation method and device, computer equipment and storage medium

Abstract

The invention discloses a commodity information generation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining corresponding multi-source heterogeneous data for a target commodity, and carrying out the preprocessing of the multi-source heterogeneous data; calling a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data; wherein the rule base comprises a commodity template base, a product naming rule base, a code generation rule base and a luminosity generation rule base; performing information synthesis on the standardized data to obtain a standard data packet; and verifying the standard data packet, and outputting the standard data packet passing the verification as commodity information of the target commodity. According to the method, the problems of low generation speed, lack of one-key import capability, high labor cost, high error cost and the like in a commodity information generation mode can be effectively solved, the commodity information generation efficiency is remarkably improved, and quick generation and one-key batch import of the commodity information are realized.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, and in particular to a method, apparatus, computer equipment, and storage medium for generating product information. Background Technology

[0002] In the digital operation process of the eyewear industry, product information serves as the core foundation for marketing promotion, sales conversion, and inventory management. Its completeness, accuracy, and standardization directly impact the efficiency of business operations. However, the product information structure for individual eyewear items (covering single models of frames, lenses, contact lenses, etc.) is extremely complex, requiring a large number of professional parameters. For example, frames involve key attributes such as size, material, and nose pad type; lenses require specific parameters such as refractive index, Abbe number, and coating; and contact lenses also have their own unique specifications. For eyewear companies with a massive number of SKUs, building a detailed and standardized product information system for each new product has become a demanding and highly repetitive core task. Currently, the inefficient information creation process has become a major bottleneck restricting the speed of new product launches and driving up operational labor costs.

[0003] Currently, the industry generally uses a primitive model of "dispersed documents and manual entry" to construct product information for individual eyeglasses. The specific process exhibits obvious discrete characteristics: First, there is the data preparation stage, where the raw data of product information is usually stored in multiple non-standardized files such as PDF product manuals, Excel price parameter tables, and Word description documents provided by suppliers, lacking a unified data storage medium; second, there is the manual search and copy stage, where operations staff need to search through these scattered documents one by one and manually identify the various parameter information of the target product, which is time-consuming and labor-intensive; finally, there is the manual filling and system entry stage, where staff need to use "copy-paste" or retype to fill the found information into the corresponding fields in the management software backend to complete the entry of information such as product brand, variety, model, and diopter.

[0004] This model suffers from several core flaws that directly hinder the core needs of "rapid generation, one-click import, and reduced manpower" for product information: First, the generation speed is extremely slow, failing to meet the demands of rapid new product launches. Information generation speed relies entirely on manual operation efficiency. From cross-checking information across multiple documents to completing full field entry, creating complete information for a single product often takes several minutes or even longer. When faced with hundreds or thousands of new product launch tasks, the process response is sluggish, severely slowing down the new product launch cycle. Second, it completely lacks "one-click import" capabilities, with zero automation. The existing system merely acts as a passive data receiver, lacking the ability to intelligently identify and process external structured data. It cannot directly receive complete product information data packets in standardized Excel or CSV formats, nor can it automatically complete data parsing, cleaning, and accurate mapping of system fields. All data "transfer" work must be done manually, becoming a direct cause of low efficiency and high labor costs. Third, labor costs remain high, accompanied by exorbitant error costs. The entire process is a typical labor-intensive model, requiring companies to invest a lot of human resources in repetitive, low-value, mechanical data entry work, which directly increases operating costs. More seriously, high-intensity repetitive work is prone to human errors such as incorrect parameter filling, incorrect price entry, and mismatch between descriptions and images, which in turn leads to customer complaints, sales losses, and damage to brand reputation, resulting in huge indirect error correction costs.

[0005] The fundamental problem with existing technologies is that their data entry design and processing logic remain at the "manual workshop" level, making it difficult to adapt to the core requirements of modern business for efficiency and scale. Therefore, the industry urgently needs a transformative data entry solution to upgrade from "manual, line-by-line input" to "one-click batch import and automatic generation," fundamentally solving industry pain points and achieving the core goals of cost reduction and efficiency improvement. Summary of the Invention

[0006] This invention provides a method, apparatus, computer device, and storage medium for generating product information, aiming to improve the efficiency of product information generation.

[0007] In a first aspect, embodiments of the present invention provide a method for generating product information, including: For the target product, acquire the corresponding multi-source heterogeneous data and preprocess the multi-source heterogeneous data; The pre-processed multi-source heterogeneous data is intelligently generated by calling a preset rule base to obtain standardized data; wherein, the rule base includes a product template library, a product naming rule base, an encoding generation rule base, and a photometric generation rule base; The standardized data is synthesized to obtain a standard data packet; The standard data packet is verified, and the standard data packet that passes the verification is output as the product information of the target product.

[0008] Secondly, embodiments of the present invention provide a product information generation device, comprising: The data acquisition unit is used to acquire corresponding multi-source heterogeneous data for the target product and to preprocess the multi-source heterogeneous data. The information generation unit is used to call a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data; wherein, the rule base includes a product naming rule base, an encoding generation rule base, a photometric generation rule base, and a product template library; An information synthesis unit is used to synthesize information from the standardized data to obtain a standard data packet; The verification output unit is used to verify the standard data packet and, after the verification is successful, output the standard data packet as the product information of the target product.

[0009] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the product information generation method as described in the first aspect.

[0010] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the product information generation method as described in the first aspect.

[0011] This invention provides a method, apparatus, computer device, and storage medium for generating product information. The method includes: acquiring corresponding multi-source heterogeneous data for a target product and preprocessing the multi-source heterogeneous data; calling a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data; wherein the rule base includes a product template library, a product naming rule library, an encoding generation rule library, and a photometric generation rule library; synthesizing information from the standardized data to obtain a standard data packet; verifying the standard data packet, and outputting the verified standard data packet as the product information of the target product. This invention, by acquiring and preprocessing multi-source heterogeneous data, generating standardized data using a rule base, and then synthesizing and verifying the standard data packet, effectively solves the problems of slow generation speed, lack of one-click import capability, high labor costs, and high error costs in the existing product information generation mode in the eyewear industry. This significantly improves the efficiency of product information generation, enabling rapid generation and one-click batch import of product information, and reducing labor input. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a product information generation method provided in an embodiment of the present invention; Figure 2 This is an overall architecture diagram of a product information generation method provided in an embodiment of the present invention; Figure 3 This is a schematic block diagram of a product information generation device provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0016] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0017] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0018] Please see below. Figure 1 The present invention provides a product information generation method, which specifically includes steps S101 to S104.

[0019] Step S101: For the target product, obtain the corresponding multi-source heterogeneous data and preprocess the multi-source heterogeneous data; Step S102: Call the preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data; wherein, the rule base includes a product template library, a product naming rule base, an encoding generation rule base, and a photometric generation rule base; Step S103: Perform information synthesis on the standardized data to obtain a standard data packet; Step S104: Verify the standard data packet and output the verified standard data packet as the product information of the target product.

[0020] In this embodiment, combined with Figure 2 First, multi-source heterogeneous data of the target product is acquired and preprocessed. Then, by calling a preset rule library containing a product template library, a product naming rule library, an encoding generation rule library, and a photometric generation rule library, intelligent information generation processing is performed on the preprocessed multi-source heterogeneous data to obtain standardized data. The standardized data is then synthesized into a standard data package, which is validated. Finally, the validated standard data package is output as the product information of the target product.

[0021] This embodiment acquires and preprocesses multi-source heterogeneous data, generates standardized data using a rule base, and then synthesizes and verifies standard data packets. This effectively solves the problems of slow generation speed, lack of one-click import capability, high labor costs, and high error costs in the existing product information generation model in the eyewear industry. This significantly improves the efficiency of product information generation, enabling rapid generation and one-click batch import of product information, while reducing labor input.

[0022] It should also be noted that this embodiment is not a simple optimization of existing technology, but a technological paradigm innovation. Its technological effects are reflected in multi-dimensional breakthroughs: First, the technical means realize the transformation from "manual operation" to "intelligent decision-making." Existing technologies rely on manual labor to complete repetitive tasks, while this embodiment, by introducing rule engine technology, endows the system with knowledge-based automatic decision-making and content generation capabilities, upgrading it from a simple tool to a highly efficient intelligent assistant. Furthermore, the production mode is upgraded from a discrete, skill-dependent "manual workshop" to a highly automated, standardized "information production line," realizing the large-scale, batch processing of commodity information, resulting in a qualitative leap in production capacity. Based on this, the output quality transforms from "uneven" to "standardized and professional." Under existing technologies, information quality is greatly affected by human factors. However, this embodiment enforces unified output standards through a rule base, ensuring that all product information maintains a high degree of consistency in terminology, style, and professionalism. Ultimately, economic benefits are transformed from a "cost center" to an "efficiency engine." In existing technologies, product information entry is a purely labor-intensive cost process. This embodiment, by significantly improving efficiency and accuracy, not only reduces labor costs and error costs but also makes product information management a core driver of business growth, helping enterprises respond quickly to market changes and freeing up human resources to develop higher-value businesses.

[0023] In one embodiment, step S101 includes: Raw data from different sources is collected to obtain the multi-source heterogeneous data; wherein, the raw data includes a product supplier information table, a CSV file in the database, and product data obtained through an API interface; Data cleaning is performed on the multi-source heterogeneous data; wherein, the data cleaning includes null value processing, outlier detection, duplicate item identification, and non-standard character processing; Data normalization is performed on the cleaned, multi-source heterogeneous data according to preset standard rules.

[0024] In this embodiment, the multi-source heterogeneous data is collected from different sources, such as information tables provided by product suppliers, CSV files stored in databases, and product data obtained from relevant systems via API interfaces. Since the data formats and quality vary significantly from source to source, data cleaning is necessary. Null value handling prevents errors caused by missing data in subsequent processing by filling in default values ​​and deleting null records to ensure data integrity. Outlier detection identifies data that deviates from the normal range, which may be due to entry errors or special circumstances; these outliers are corrected or removed to ensure data accuracy. Duplicate item identification removes duplicate data records, reducing data redundancy and improving processing efficiency. Non-standard character processing converts special characters and garbled text in the data into standardized character forms, facilitating subsequent unified processing.

[0025] After data cleaning, data normalization processing is required according to preset standard rules to convert data with different formats and ranges into a unified standard form, thereby ensuring data comparability and consistency. For example, length data presented in different units can be uniformly converted to meters; product categories following different coding rules are recoded according to preset classification standards. Through data normalization processing, a high-quality, standardized data foundation can be provided for subsequent intelligent information generation, thereby helping to improve the accuracy and efficiency of the entire product information generation process. The standard rules mentioned here can also refer to automatically concatenating input terms (such as product brand, variety, model, etc.) into a standard combination name. For example, the input brand "××" and variety "1.56 aspherical lens" can be uniformly converted into the standard term "××1.56 aspherical lens".

[0026] In a specific embodiment, the core technology for handling null values ​​is rule-based filling, which involves filling based on business logic or statistical values ​​(such as mean and mode). For example, if the "+ / -" field for the spherical / cylindrical power of a lens is missing, the system will automatically fill it with "-1.00" after inputting a value (such as "1"). The core technology for outlier detection is the statistical boundary method, which involves defining reasonable upper and lower limits for the data (such as controlling photometric values ​​in increments of 0.25, 0.50, 1.00, etc.) to achieve detection. For example, it can identify values ​​such as 0.30 and 1.26 in the spherical / cylindrical lens "photometric" value that exceed the normal range. The core technology for duplicate item identification is precise deduplication, which involves comparing and deduplicating unique identifiers (such as product codes). For example, based on the unique SKU code generated by the system, completely duplicate records are deleted. When dealing with non-standard characters, the core technology is the regular expression method, that is, by defining specific patterns, character searching, replacement, and formatting are completed. For example, the price "￥1280 yuan" is uniformly cleaned into the number "1280", or the date format is uniformly standardized.

[0027] In one embodiment, the step S102 includes: Obtain the product type of the target product; According to the product type, select a pre-configured import template through the product template library; Based on the import template, determine the first fields that can be directly imported and the second fields that need to be generated secondarily by calling the rule library; Combine the first fields and the second fields to generate the standardized data.

[0028] In this embodiment, when performing intelligent information generation processing, first, an appropriate import template is selected according to the product type of the target product (such as "lens", "frame", "contact lens", etc.). Through the pre-configured import templates in the product template library, it can be ensured that the import and processing of data meet the specific requirements of this product type. For example, for glasses lens products, its template can focus on information such as lens power, material, refractive index, etc.; while for frame products, the template can focus more on aspects such as style, material, color, etc.

[0029] After determining the import template, further distinguish the first fields that can be directly imported and the second fields that need to be generated secondarily by calling the rule library. The first fields that can be directly imported are usually those data that already have standard formats and contents in the original data, such as the brand name and model of the product. These data can be directly extracted from the preprocessed multi-source heterogeneous data and imported into the subsequent processing process. The second fields that need to be generated secondarily by calling the rule library are often data that need to be processed and generated according to specific rules. For example, the product code needs to be generated according to the code generation rule library, and the product naming may need to combine the product naming rule library to combine the input related terms into a standard name according to the rules.

[0030] In addition, in the process of combining the first fields and the second fields to generate standardized data, the accuracy and integrity of the data should be ensured. For the first fields, it is necessary to ensure that there is no data loss or error during the import process; for the second fields, they should be generated strictly according to the rules in the rule library. The generated standardized data will provide a unified and standardized data basis for the subsequent information synthesis step, making the finally generated product information meet the standard requirements in terms of format and content, thereby improving the quality and efficiency of product information generation and better meeting the needs of modern commerce for product information management.

[0031] In practical applications, template instructions can be used to record each product and call the corresponding rule base to generate standardized, professional information, which is then combined with the basic fields. For example, a "single-vision lens" template might contain the following configuration: column_A (brand) is directly mapped to the product_brand field; column_B (refractive index) is directly mapped to the lens_index field; column_C (spherical range) triggers the photometric generation rule base (D3) to generate standard expressions such as joint photometric values; if there is no corresponding column, the product naming rule base (D1) is triggered to automatically generate a standard product name; if there is no corresponding column, the encoding generation rule base (D2) is triggered to automatically generate a unique SKU code.

[0032] In one embodiment, step S102 further includes: Attribute identification and extraction are performed on the multi-source heterogeneous data to obtain the key attributes of the multi-source heterogeneous data; The product naming rule library is used to call a naming template that matches the product type; Fill the key attributes into the corresponding positions in the naming template; A standardized product name for the target product is generated based on a naming template filled with key attributes, and the standardized product name is set as the second field.

[0033] In this embodiment, when calling the product naming rule library, the attributes of multi-source heterogeneous data are first identified and extracted to accurately find key attributes related to the product, such as Brand: "××"; Variety: "board material"; Model: "1001"; Color coding: "-11"; Color name: "-11", etc. These key attributes are the basic information for generating standardized product names, and the accuracy of their extraction directly affects the quality of the final product name.

[0034] Next, the product naming rule library is used to retrieve naming templates that match the product type (such as frames, lenses, sunglasses). Different types of products may have different naming rules and templates. For example, the naming template for frames is: [Brand]-[Product Type]-[Model Specification]-[Color Code]; the naming template for lenses is: [Brand]-[Product Type]-[Power]-[Under-light Prescription]. By selecting an appropriate naming template, it can be ensured that the generated product names conform to industry standards and user habits.

[0035] Next, fill the extracted key attributes into the corresponding positions in the naming template. When filling, it is necessary to strictly follow the requirements of the naming template to ensure that each attribute is accurately placed in the appropriate position. For example, if the template content is: [Brand]-[Variety]-[Model Specification]-[Color], the filled content would be: "××-Sheet Material-1001-11".

[0036] Then, standardized product names are generated based on the naming template filled with key attributes. These standardized product names are unique, accurate, and standardized, clearly reflecting the characteristics and attributes of the product. In subsequent information synthesis and processing, these standardized product names can serve as important identifiers and information carriers, facilitating the management and application of product information.

[0037] In one embodiment, step S102 further includes: The key attributes are converted into short codes using a predefined code table; The code is obtained by using a code generation rule base to call a preset code structure template and concatenating the short code according to the code structure template. Determine whether the product code exists in a pre-set database; If it is determined that the product code exists in a pre-set database, a duplicate code prompt will be generated; If it is determined that the product code does not exist in the pre-set database, then the product code is set as the unique product code of the target product, and the unique product code is set as the second field.

[0038] In this embodiment, when calling the code generation rule base, the previously extracted key attributes are first converted into short codes using a predefined code table to facilitate subsequent code concatenation and management, making the codes more concise and clear. For example, if the product category is "eyeglass frames", the converted short code is "JJ"; if the material is "β titanium", the converted short code is "BT"; if the model number is "1001", the converted short code is "1001"; and if the color is "black", the converted short code is "BK".

[0039] Next, the code generation rule base is used to call a preset code structure template. Different types of products may have different code structure templates. The content of the code structure template can be: [Brand]-[Variety]-[Model Specification]-[Color Code]. Then, according to the called code structure template, the converted short codes are concatenated to obtain the product code. For example, the short code segment content is: FJ, BT52, BK, which is concatenated according to the template as: "FJBT1001BK".

[0040] After obtaining the product code, to ensure its uniqueness and prevent confusion and errors during product information management and application, it is necessary to determine whether the product code already exists in a pre-set database. By comparing the product code with existing codes in the database, if it is determined that the product code already exists, it indicates that the product code cannot be added, and a duplicate code warning is generated, reminding operators to re-examine and adjust the code generation process to generate a unique product code.

[0041] If the product code does not exist in the pre-set database, it will be set as the unique product code for the target product. This unique product code serves as an important identifier for the product, facilitating operations such as querying, statistics, and tracking, thereby improving the efficiency and accuracy of product information management.

[0042] In practical applications, this coding generation method can significantly improve the standardization and uniqueness of product codes, reducing errors and duplication that may occur with manual coding. Simultaneously, the unified management and invocation of the rule base makes the coding generation process more automated and standardized, meeting the needs of modern commerce for efficient management of product information. Moreover, as the variety of products continues to increase and business develops, only corresponding adjustments and updates to the coding structure templates and code tables in the rule base are needed to easily cope with new product coding requirements, demonstrating strong scalability and flexibility.

[0043] In one embodiment, step S102 further includes: Obtain the input photometric range for the target product; The input photometric range is subjected to parameter parsing and standardization to obtain a standardized photometric range; wherein, the standardization process includes symbol processing, precision and step size determination, range validity check, business rule verification, and standard list generation; The optical rules in the photometric generation rule base are invoked to perform logical judgment and calculation on the standardized photometric range to obtain the joint photometric of the target product, and the joint photometric is set as the second field.

[0044] In this embodiment, when calling the photometric generation rule base, the input photometric range for the target product is first obtained. This input photometric range can come from information provided by the product supplier, user input, or other relevant data sources. Considering that the input photometric range may have inconsistent formats and precision, parameter parsing and standardization processing are performed on it. Specifically: The sign handling aims to ensure that the use of plus and minus signs in the photometric range conforms to standards and remains consistent. For example, "+8.00" should be recognized as the positive number 8.00, and "-8.00" should be recognized as the negative number -8.00. In the field of optics, "+8.00" and "8.00" have the same meaning, and should be stored as the same value 8.0 in the database; Determining the accuracy and step size involves clarifying the accuracy and step size of the photometric data. Typically, photometric values ​​are measured in steps of 0.25 or 0.50. Therefore, it is necessary to determine whether to generate intermediate values ​​using 0.25 or 0.50 based on business rules (configured in the photometric generation rule base). The range validity check is used to determine whether the input photometric range is within a reasonable range. If it exceeds the normal photometric range, it may be due to an input error or a special case, requiring correction or further verification. For example, it checks whether the starting value is indeed less than or equal to the ending value. In the example "[+8.00]-[-8.00]", +8.0 is greater than -8.0, which is a decreasing range. The program should be able to correctly handle both increasing (-8.00 to +8.00) and decreasing (+8.00 to -8.00) logic. Business rule validation involves invoking business rules to check whether the range falls within a reasonable range allowed by the system. For example, whether the spherical mirror is between -25.00 and +25.00; The standard list generation is based on the above processing to generate a structured photometric array. For example, when the step size is 0.25, it generates: [-8.00, -7.75, -7.50, ..., +7.75, +8.00].

[0045] The ultimate goal is to standardize the output, ensuring that each output value is formatted as a string with two decimal places for display, storage, or subsequent calculations.

[0046] After standardization, optical rules from the photometric generation rule base are invoked to perform logical judgments and calculations within the standardized photometric range. These optical rules can encompass photometric combination rules, conversion rules, and more. By performing logical judgments and calculations on the standardized photometric range, the joint photometric value of the target product is obtained. This joint photometric value, a more practical and business-relevant representation, is designated as the second field to provide accurate photometric data for subsequent product information generation.

[0047] In a specific embodiment, the combined photometric calculation rule is as follows: Based on the spherical and cylindrical lenses, the combined photometric power is automatically calculated, i.e., combined photometric power = spherical lens power + 1 / 2 × astigmatism power. For example, if the prescription is -3.00DS / -1.00DC, the calculated combined photometric power is: -3.00 + (-1.00 / 2) = -3.50D; and if the prescription is +1.50DS / -0.75DC, the calculated combined photometric power is: +1.50 + (-0.75 / 2) = +1.125D.

[0048] In one embodiment, step S104 includes: The standard data packet is validated using multi-dimensional validation rules; wherein, the multi-dimensional validation rules include non-empty validation of key fields, completeness validation of required parameter combinations, format validation, and value range validation. The validated standard data packets are written to the pre-configured target management system in batches via API or database interface.

[0049] This embodiment performs multi-dimensional verification on the standard data package after obtaining it, using multi-dimensional verification rules to ensure the accuracy and completeness of the data. Specifically, the key field non-empty verification checks for missing data in important fields of the standard data package. For example, in product information, fields such as product SKU, product name (brand), variety, and model must exist and not be empty. If empty values ​​appear, the data does not meet the requirements and can be marked for further processing. The mandatory parameter combination completeness verification requires that certain specific parameter combinations be complete. For example, if the product type is "lens," it must include photometric parameters such as spherical range and cylindrical range. If some information in these mandatory parameter combinations is missing, the standard data package may not accurately describe the product and needs to be supplemented. Format verification checks the data format; for example, prices must be greater than 0. If the data format does not meet the requirements, it needs to be converted or corrected to ensure compliance with the standard. Value range verification checks whether the data values ​​are within a reasonable range. For example, the upper and lower limits of the spherical lens range must be within optically reasonable values ​​(e.g., [-25.00, +25.00]), the cylindrical lens range is usually 0 to negative values ​​(e.g., [0.00, -6.00]), and ADD (additional diopter, which is the extra refractive power added during optometry to correct presbyopia or hyperopia and help patients see near objects clearly) must be positive.

[0050] Once the standard data packets pass the aforementioned multi-dimensional verification rules, these verified data can be written in batches to the pre-configured target management system via API or database interface. This batch writing method significantly improves data transmission and storage efficiency, reducing the system's processing burden. In this way, the target management system obtains accurate and complete product standard data packets, providing reliable data support for subsequent product management, sales, and other business operations, ensuring the smooth operation of the entire product information management process.

[0051] Figure 3 This is a schematic block diagram of a product information generation device 300 provided in an embodiment of the present invention. The device 300 includes: The data acquisition unit 301 is used to acquire corresponding multi-source heterogeneous data for the target product and to preprocess the multi-source heterogeneous data. The information generation unit 302 is used to call a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data; wherein, the rule base includes a product naming rule base, an encoding generation rule base, a photometric generation rule base, and a product template library; Information synthesis unit 303 is used to synthesize information from the standardized data to obtain a standard data packet; The verification output unit 304 is used to verify the standard data packet, and after the verification is passed, output the standard data packet as the product information of the target product.

[0052] In one embodiment, the data acquisition unit 301 includes: The data acquisition unit is used to collect raw data from different sources to obtain the multi-source heterogeneous data; wherein, the raw data includes a product supplier information table, a CSV file in the database, and product data obtained through an API interface; A data cleaning unit is used to clean the multi-source heterogeneous data; wherein, the data cleaning includes null value processing, outlier detection, duplicate item identification, and non-standard character processing; The data normalization unit is used to perform data normalization processing on multi-source heterogeneous data after data cleaning according to preset standard rules.

[0053] In one embodiment, the information generation unit 302 includes: A type acquisition unit is used to acquire the product type of the target product; The template selection unit is used to select a pre-configured import template from the product template library according to the product type; The field determination unit is used to determine, based on the import template, a first field that can be directly imported, and a second field that needs to be generated by calling the rule base. The field combining unit is used to combine the first field and the second field to generate the standardized data.

[0054] In one embodiment, the information generation unit 302 further includes: An attribute extraction unit is used to identify and extract attributes from the multi-source heterogeneous data to obtain the key attributes of the multi-source heterogeneous data. The template calling unit is used to call a naming template that matches the product type using the product naming rule library; An attribute filling unit is used to fill the key attributes into the corresponding positions of the naming template; The name generation unit is used to generate a standardized product name for the target product based on a naming template filled with key attributes, and set the standardized product name as the second field.

[0055] In one embodiment, the information generation unit 302 further includes: An attribute conversion unit is used to convert the key attributes into short codes using a predefined code table; The encoding splicing unit is used to call a preset encoding structure template using the encoding generation rule base, and splice the short code according to the encoding structure template to obtain the product code; The coding determination unit is used to determine whether the product code exists in a pre-set database; The duplicate alert unit is used to generate a duplicate code alert if it is determined that the product code exists in a pre-set database. The encoding generation unit is configured to, if it is determined that the product code does not exist in a pre-set database, set the product code as a unique product code for the target product and set the unique product code as the second field.

[0056] In one embodiment, the information generation unit 302 further includes: A photometric acquisition unit is used to acquire the input photometric range for the target product; A photometric standardization unit is used to perform parameter parsing and standardization processing on the input photometric range to obtain a standardized photometric range; wherein, the standardization processing includes symbol processing, precision and step size determination, range validity check, business rule verification, and standard list generation; The photometric calculation unit is used to call the optical rules in the photometric generation rule base, perform logical judgment and calculation on the standardized photometric range, obtain the joint photometric of the target product, and set the joint photometric as the second field.

[0057] In one embodiment, the verification output unit 304 includes: The data verification unit is used to verify the standard data packet using multi-dimensional verification rules; wherein, the multi-dimensional verification rules include non-empty verification of key fields, completeness verification of required parameter combinations, format verification, and value range verification. The batch write unit is used to write verified standard data packets to a pre-set target management system in one batch via API or database interface.

[0058] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0059] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed, can perform the steps provided in the above embodiments. The storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0060] This invention also provides a computer device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the computer device may also include various network interfaces, power supplies, and other components.

[0061] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0062] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for generating product information, characterized in that, include: For the target product, acquire the corresponding multi-source heterogeneous data and preprocess the multi-source heterogeneous data; The pre-processed multi-source heterogeneous data is intelligently generated by calling a preset rule base to obtain standardized data; wherein, the rule base includes a product template library, a product naming rule base, an encoding generation rule base, and a photometric generation rule base; The standardized data is synthesized to obtain a standard data packet; The standard data packet is verified, and the standard data packet that passes the verification is output as the product information of the target product.

2. The product information generation method according to claim 1, characterized in that, The step of acquiring corresponding multi-source heterogeneous data for the target product and preprocessing the multi-source heterogeneous data includes: Raw data from different sources is collected to obtain the multi-source heterogeneous data; wherein, the raw data includes a product supplier information table, a CSV file in the database, and product data obtained through an API interface; Data cleaning is performed on the multi-source heterogeneous data; wherein, the data cleaning includes null value processing, outlier detection, duplicate item identification, and non-standard character processing; Data normalization is performed on the cleaned, multi-source heterogeneous data according to preset standard rules.

3. The product information generation method according to claim 1, characterized in that, The process involves calling a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data, including: Obtain the product type of the target product; Based on the product type, select a pre-configured import template from the product template library; Based on the import template, the first field that can be directly imported and the second field that needs to be generated by calling the rule base are determined. The standardized data is generated by combining the first field and the second field.

4. The product information generation method according to claim 3, characterized in that, The step of calling a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data also includes: Attribute identification and extraction are performed on the multi-source heterogeneous data to obtain the key attributes of the multi-source heterogeneous data; The product naming rule library is used to call a naming template that matches the product type; Fill the key attributes into the corresponding positions in the naming template; A standardized product name for the target product is generated based on a naming template filled with key attributes, and the standardized product name is set as the second field.

5. The product information generation method according to claim 4, characterized in that, The step of calling a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data also includes: The key attributes are converted into short codes using a predefined code table; The code is obtained by using a code generation rule base to call a preset code structure template and concatenating the short code according to the code structure template. Determine whether the product code exists in a pre-set database; If it is determined that the product code exists in a pre-set database, a duplicate code prompt will be generated; If it is determined that the product code does not exist in the pre-set database, then the product code is set as the unique product code of the target product, and the unique product code is set as the second field.

6. The product information generation method according to claim 5, characterized in that, The step of calling a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data also includes: Obtain the input photometric range for the target product; The input photometric range is subjected to parameter parsing and standardization to obtain a standardized photometric range; wherein, the standardization process includes symbol processing, precision and step size determination, range validity check, business rule verification, and standard list generation; The optical rules in the photometric generation rule base are invoked to perform logical judgment and calculation on the standardized photometric range to obtain the joint photometric of the target product, and the joint photometric is set as the second field.

7. The product information generation method according to claim 1, characterized in that, The step of verifying the standard data packet and outputting the verified standard data packet as the product information of the target product includes: The standard data packet is validated using multi-dimensional validation rules; wherein, the multi-dimensional validation rules include non-empty validation of key fields, completeness validation of required parameter combinations, format validation, and value range validation. The validated standard data packets are written to the pre-configured target management system in batches via API or database interface.

8. A product information generation device, characterized in that, include: The data acquisition unit is used to acquire corresponding multi-source heterogeneous data for the target product and to preprocess the multi-source heterogeneous data. The information generation unit is used to call a preset rule base to perform intelligent information generation processing on the preprocessed multi-source heterogeneous data to obtain standardized data; wherein, the rule base includes a product naming rule base, an encoding generation rule base, a photometric generation rule base, and a product template library; An information synthesis unit is used to synthesize information from the standardized data to obtain a standard data packet; The verification output unit is used to verify the standard data packet and, after the verification is successful, output the standard data packet as the product information of the target product.

9. A computer device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the product information generation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the product information generation method as described in any one of claims 1 to 7.

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