A multi-source brand information consistency checking method in a generative search scenario

Abstract

The application discloses a kind of under the generation formula search scene multi-source brand information consistency checking method, belong to artificial intelligence and information retrieval cross technical field.This kind of under the generation formula search scene multi-source brand information consistency checking method, including the following steps:S1.multisource brand information acquisition: obtain the multi-source data source for generating response content dependent generation formula search system, extract the brand information related to target brand from each data source, form original brand information set;The multi-source data source includes but is not limited to web index, knowledge graph, training data set, brand official database and third-party information platform, and the brand information includes brand basic information, brand identification information, brand association information and brand compliance information.The application realizes the comprehensive accurate checking of multi-source, multi-type brand information, improves search response accuracy and compliance, protects brand image, adapts to the development needs of generation formula search technology.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of artificial intelligence and information retrieval, specifically to a method for verifying the consistency of multi-source brand information in a generative search scenario. Background Technology

[0002] With the rapid development of generative artificial intelligence technology, generative search systems have been widely applied in various information retrieval scenarios. Their core advantage lies in their ability to generate accurate, coherent, and tailored search responses for users based on multiple data sources. In brand-related generative search scenarios, the data sources relied upon by the search system exhibit diverse characteristics, including web indexes, knowledge graphs, training datasets, official brand databases, and third-party information platforms. The brand information in these data sources is often maintained and updated by different entities, with varying update frequencies and information standards.

[0003] In existing technologies, generative search systems typically perform only simple information aggregation when extracting brand information from multiple sources, lacking a consistency verification process for brand information from different data sources. Due to issues such as asynchronous information updates, inconsistent wording, data errors, or violations among multiple data sources, the brand-related responses output by generative search systems may contain inconsistencies, numerical deviations, and insufficient compliance—for example, inconsistent statements about brand establishment dates across different data sources, differences in brand logo parameters, and violations of compliance regulations in promotional information. Such problems not only mislead users and negatively impact their search experience but may also damage the brand's image and even trigger brand compliance risks.

[0004] Currently, existing information verification methods mostly target single data sources or specific types of information, failing to meet the verification needs of multi-source, multi-type brand information in generative search scenarios. Furthermore, they lack a linkage mechanism with generative search systems, hindering the effective use of verification results for optimizing search response content. Therefore, there is an urgent need for a consistency verification method that can cover multiple data sources and various types of brand information, and can link with generative search systems, addressing the technical problems of inconsistent brand information, low verification efficiency, and difficulty in ensuring compliance in existing technologies. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for verifying the consistency of multi-source brand information in a generative search scenario. This method enables comprehensive and accurate verification of brand information from multiple data sources, ensuring that the brand-related response content output by the generative search system is consistent with the benchmark brand information, thereby improving the accuracy and compliance of the search response. It also provides brands with a basis for information correction and protects their brand image.

[0006] In view of the above problems, the technical solution proposed by the present invention is:

[0007] A method for verifying the consistency of multi-source brand information in a generative search scenario includes the following steps:

[0008] S1. Multi-source brand information collection: Acquire multi-source data sources on which the generative search system relies to generate response content, extract brand information related to the target brand from each data source, and form an original brand information set; the multi-source data sources include but are not limited to web page indexes, knowledge graphs, training datasets, official brand databases and third-party information platforms, and the brand information includes basic brand information, brand identification information, brand association information and brand compliance information;

[0009] S2. Information Preprocessing: Cleaning, standardizing and feature extraction are performed on each piece of brand information in the original brand information set to obtain standardized brand information and corresponding brand feature vectors; the preprocessing includes removing invalid information, unifying the information expression format and eliminating ambiguous expressions; the brand feature vectors include text features, numerical features and semantic features.

[0010] S3. Construction of benchmark brand information: Based on brand information in the official brand database, combined with industry standards and brand owner preset rules, a benchmark brand information database is constructed. The benchmark brand information database includes standard values, allowable fluctuation ranges and verification rules for various types of brand information.

[0011] S4. Multi-dimensional Consistency Verification: Based on the benchmark brand information database, a combination of semantic similarity comparison, feature matching, and rule verification is used to perform multi-dimensional consistency verification between standardized brand information and benchmark brand information, obtaining the verification result for each piece of brand information; the verification dimensions include information completeness, expression consistency, numerical accuracy, and compliance;

[0012] S5. Verification Result Processing and Feedback: Summarize and analyze the verification results of all brand information, mark inconsistencies and clarify the type and degree of inconsistency, and generate a verification report; feed the verification report back to the generative search system to guide the optimization and adjustment of search response content, and push the inconsistency information to the brand for the brand to correct the information.

[0013] Furthermore, in step S1, the basic brand information includes the brand name, brand abbreviation, brand establishment time, brand entity information, and brand business scope; the brand identification information includes brand logo parameters, brand standard colors, brand slogans, and brand visual specifications; the brand association information includes information on the brand's products, brand partners, and brand honors and qualifications; and the brand compliance information includes brand trademark registration information, patent information, and compliance requirements for promotion.

[0014] Furthermore, in step S2, the feature extraction process specifically includes: using natural language processing technology to segment and remove stop words from textual brand information to extract textual features; normalizing numerical brand information to extract numerical features; encoding brand information using a pre-trained semantic model to extract semantic features; and fusing textual features, numerical features, and semantic features to form a brand feature vector.

[0015] Furthermore, in step S3, the verification rules include: semantic similarity threshold rules for text-based brand information, allowable deviation range rules for numerical brand information, standard matching rules for brand identity information, and compliance judgment rules for brand compliance information; the benchmark brand information database supports dynamic updates, and automatically updates the benchmark information and verification rules when the brand owner updates brand information or industry standards change.

[0016] Furthermore, in step S4, the multi-dimensional consistency verification specifically includes:

[0017] S41. Information Integrity Verification: Check whether the standardized brand information contains the required information items specified in the benchmark brand information database. If any required information items are missing, it is determined to be an information inconsistency and marked as an integrity missing type.

[0018] S42. Expression consistency verification: Calculate the semantic similarity between standardized brand information and benchmark brand information. If the semantic similarity is lower than a preset threshold, it is determined to be an expression inconsistency and marked as an expression deviation type. The semantic similarity is calculated using the edit distance algorithm or the cosine similarity algorithm.

[0019] S43. Numerical accuracy verification: Compare the numerical information in the standardized brand information with the corresponding standard value in the benchmark brand information. If the numerical deviation exceeds the allowable fluctuation range, it is determined that the numerical values ​​are inconsistent and marked as numerical deviation type.

[0020] S44. Compliance Verification: Check whether the standardized brand information meets the compliance requirements in the benchmark brand information database. If there are any violations of trademark usage specifications, patent usage specifications, or publicity compliance requirements, it will be judged as a non-compliance and marked as a compliance violation type.

[0021] Furthermore, in step S42, the semantic similarity threshold can be dynamically adjusted according to the brand information type, wherein the semantic similarity threshold of core identifier information such as brand name and brand LOGO parameters is not less than 95%, and the semantic similarity threshold of brand association information is not less than 85%.

[0022] Furthermore, in step S5, the verification report includes a list of data sources covered by the verification, the total number of brand information items, the number of inconsistent information items, the distribution of each type of inconsistent information item, the specific content of each inconsistent information item, the degree of deviation, and correction suggestions; the generative search system, based on the verification report, removes, corrects, or supplements the search response content involving inconsistent information to ensure that the generated response content is consistent with the benchmark brand information.

[0023] Furthermore, step S6, verification iteration optimization, is included: periodically collecting search response content from the generative search system and update information from multiple data sources, repeating steps S1-S5, and iteratively optimizing the verification model and benchmark brand information database to improve the accuracy and efficiency of consistency verification.

[0024] Furthermore, in step S1, the multi-source brand information collection adopts a combination of timed capture and real-time monitoring. Timed capture is used to obtain regular brand information from each data source, while real-time monitoring is used to capture sudden updates and abnormal information of brand information, ensuring the timeliness of the collected brand information.

[0025] Furthermore, in step S2, the elimination of ambiguous expressions specifically includes: identifying ambiguous expressions such as homophones, abbreviations, and misspellings in the brand name, and correcting the ambiguous expressions to standard expressions by combining the benchmark brand information database and contextual information; and performing semantic disambiguation on brand-related information with polysemous words in combination with brand scenarios to ensure the uniqueness of information expression.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] 1. It covers multiple data sources and various types of brand information, solving the technical problem of difficulty in uniformly verifying multi-source brand information in generative search scenarios. It achieves comprehensive verification of basic brand information, identification information, related information and compliance information, ensuring the consistency of brand information.

[0028] 2. Employing a multi-dimensional verification method, combining semantic similarity comparison, feature matching, and rule verification, the accuracy of verification is improved. It can accurately identify various inconsistencies such as missing information, expression deviation, numerical deviation, and compliance violations. Furthermore, the verification standards can be dynamically adjusted according to the type of brand information to adapt to different scenario requirements.

[0029] 3. A dynamically updated benchmark brand information database was built, which combined timed crawling with real-time monitoring to ensure the timeliness of brand information and the applicability of verification rules, and to respond promptly to updates to brand information and industry standards.

[0030] 4. Enables the linkage between verification results and the generative search system and brands, which can guide the optimization and adjustment of search response content, improve the user search experience, and provide brands with information correction basis, protect brand image, and reduce brand compliance risks;

[0031] 5. Add a verification and iteration optimization process. Through regular iterations, continuously improve the accuracy and efficiency of verification to adapt to the development needs of generative search technology and brand information management. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the overall process of a multi-source brand information consistency verification method in a generative search scenario according to the present invention.

[0033] Figure 2 This is a schematic diagram illustrating the specific process of multi-dimensional consistency verification in an embodiment of the present invention. Detailed Implementation

[0034] 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 embodiments of the present invention, and not all embodiments. 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.

[0035] Please see Figures 1-2 This invention provides a technical solution: a method for verifying the consistency of multi-source brand information in a generative search scenario, the specific implementation steps of which are as follows:

[0036] Step S1. Multi-source brand information collection: Taking a well-known home appliance brand (hereinafter referred to as "target brand") as the verification object, obtain the multi-source data sources relied upon by the generative search system, including: web page index (web page content from search engines such as Baidu and Sogou), knowledge graph (target brand-related entries in Baidu Knowledge Graph and Alibaba Knowledge Graph), training dataset of the generative search system, target brand official database (information from the brand's official website and official mini-program), and third-party information platforms (Tianyan Check, Qichacha, industry information platforms).

[0037] Brand information is collected using a combination of scheduled crawling and real-time monitoring: Scheduled crawling is set to 2 AM daily to capture regular brand information from various data sources; real-time monitoring is conducted via API calls to monitor brand information in official brand databases and third-party information platforms. When information updates or anomalies (such as false brand information or illegal advertising) are detected, the collection process is immediately triggered. Brand information relevant to the target brand is extracted from various data sources to form an original brand information set, including:

[0038] Brand basic information: Brand name "XX Home Appliances", brand abbreviation "XX", brand establishment date "May 10, 2000", brand entity information "XX Home Appliances Co., Ltd.", brand business scope "R&D, production, sales and after-sales service of household electrical appliances";

[0039] Brand identification information: Brand LOGO parameters (size 100*100px, color mode RGB, main color #0066CC), brand standard colors (#0066CC, #FFFFFF), brand slogan "XX Home Appliances, Enjoy Smart Life", brand visual specifications (LOGO usage ratio, font requirements);

[0040] Brand association information: Products under the brand (refrigerators, washing machines, air conditioners, etc.), partners (a chip supplier, a logistics enterprise), honors and qualifications (National High-Tech Enterprise, China Top Brand Product);

[0041] Brand compliance information: Trademark registration information (registration number XXXXXXX, designated goods in Class 11), patent information (invention patent XXXXXXX, titled "An energy-saving air conditioner control method"), compliance promotion requirements (absolute terms such as "most advanced" and "top-level" are prohibited from being used).

[0042] Step S2. Information preprocessing: Perform cleaning, standardization and feature extraction processing on each piece of brand information in the original brand information set:

[0043] 1. Cleaning processing: Remove invalid information (such as blank information, duplicate information, information irrelevant to the target brand), for example, delete advertising content irrelevant to the target brand and duplicate product information in web page indexes;

[0044] 2. Standardization processing: Unify the information expression format, for example, unify the brand establishment date to the "YYYY-MM-DD" format, unify numerical information to the Arabic numeral format, unify the brand LOGO parameters to the "size + color mode + main color" format;

[0045] 3. Ambiguous expression elimination: Identify homophones (for example, "XX Home Appliances" is miswritten as "XX Home Store"), abbreviations (for example, "XX Home Appliances" is abbreviated as "XX Appliance"), and typos of the brand name, and correct them to the standard expression "XX Home Appliances" in combination with the information of the target brand official database; for polysemous brand association information (for example, "partner" may be expressed as "cooperation partner" or "collaborator"), unify and correct it to "partner" in combination with the brand scenario to ensure the uniqueness of information expression;

[0046] 4. Feature Extraction: Natural language processing techniques (such as jieba segmentation and TF-IDF algorithm) are used to segment and remove stop words from textual brand information (such as brand slogans and business scope) to extract textual features; numerical brand information (such as brand establishment time and logo size) is normalized to extract numerical features; brand information is encoded using a pre-trained semantic model (such as BERT model) to extract semantic features; textual features, numerical features, and semantic features are fused to form a brand feature vector for subsequent consistency verification.

[0047] Step S3. Benchmark Brand Information Construction: Using the brand information in the target brand's official database as the core, and combining it with home appliance industry standards (such as the "Home Appliance Brand Information Standard") and the brand's preset rules, a benchmark brand information database is constructed. The benchmark brand information database includes standard values, allowable fluctuation ranges, and verification rules for various types of brand information, as detailed below:

[0048] 1. Standard values: Consistent with the standard expressions of brand basic information, brand identity information, brand association information, and brand compliance information extracted in step S1;

[0049] 2. Allowable fluctuation range: Allowable fluctuation range for numerical information, such as the brand establishment time must not have any deviation, and the logo size is allowed to have a deviation of ±5px;

[0050] 3. Verification rules:

[0051] (1) Semantic similarity threshold rules for text-based brand information: The semantic similarity threshold of core identification information (brand name, LOGO parameters) is ≥95%, and the semantic similarity threshold of brand-related information is ≥85%;

[0052] (2) Rules for allowable deviation range of numerical brand information: The logo size is allowed to have a deviation of ±5px, and other numerical information is not allowed to have a deviation;

[0053] (3) Standard matching rules for brand identity information: Brand logo parameters, standard colors, and advertising slogans must be completely matched with the baseline information (except for the logo size, which is allowed to have a deviation of ±5px).

[0054] (4) Rules for determining the compliance of brand compliance information: Absolute promotional terms are prohibited, trademark and patent information must be consistent with the benchmark information, and false trademark and patent information is not allowed.

[0055] The benchmark brand information database supports dynamic updates. When a brand updates its brand information (such as changing its brand slogan) or when there are changes in the home appliance industry standards, the benchmark information and verification rules are automatically updated through interface linkage.

[0056] Step S4. Multi-dimensional Consistency Verification: Based on the benchmark brand information database, a combination of semantic similarity comparison, feature matching, and rule verification is used to perform multi-dimensional consistency verification between standardized brand information and benchmark brand information.

[0057] S41. Information Integrity Verification: Check whether the standardized brand information contains the required information items (such as brand name, brand entity information, trademark registration information) specified in the benchmark brand information database. If a third-party information platform does not contain the trademark registration information of the target brand, it is determined to be inconsistent information and marked as an incomplete information type.

[0058] S42. Expression Consistency Verification: The cosine similarity algorithm is used to calculate the semantic similarity between standardized brand information and benchmark brand information. For example, if a brand slogan in a webpage index is expressed as "XX Home Appliances, Enjoy a Smart and Quality Life," its semantic similarity with the benchmark slogan "XX Home Appliances, Enjoy a Smart Life" is 92%, which is higher than the threshold of 85% for brand association information, and is therefore judged as expression consistent. However, if a brand name in a knowledge graph is expressed as "XX Stores," its semantic similarity with the benchmark brand name is 88%, which is lower than the threshold of 95% for core identifier information, and is therefore judged as expression inconsistent and marked as an expression deviation type.

[0059] S43. Numerical Accuracy Verification: Compare the numerical information in the standardized brand information with the corresponding standard values ​​in the benchmark brand information. For example, if a brand logo size is described as 108px*108px on a third-party platform, which exceeds the allowable fluctuation range (±5px), it is determined to be a numerical inconsistency and marked as a numerical deviation type; if the logo size is 103px*103px, which is within the allowable fluctuation range, it is determined to be a numerical consistency.

[0060] S44. Compliance Verification: Check whether standardized brand information complies with the compliance requirements in the benchmark brand information database. For example, if a webpage index contains the promotional phrase "XX home appliance, top-of-the-line energy-saving air conditioner," which violates the compliance requirement of "prohibiting the use of absolute terms," ​​it is judged as inconsistent in compliance and marked as a compliance violation type; if all promotional phrases comply with the requirements, it is judged as consistent in compliance.

[0061] Step S5. Verification Result Processing and Feedback: Summarize and analyze the verification results of all brand information, mark inconsistencies, clarify the type of inconsistency (completeness missing, description deviation, numerical deviation, compliance violation) and the degree of deviation (minor, moderate, severe), and generate a verification report. The verification report includes: a list of data sources covered by the verification (6 data sources), the total number of brand information entries (120), the number of inconsistencies (8), the distribution of each type of inconsistency (2 instances of completeness missing, 3 instances of description deviation, 1 instance of numerical deviation, and 2 instances of compliance violation), the specific content of each inconsistency (e.g., a third-party platform lacks trademark registration information, a webpage contains absolute promotional language), the degree of deviation, and correction suggestions (e.g., supplementing trademark registration information, deleting absolute promotional language).

[0062] The verification report is fed back to the generative search system via an interface. Based on the verification report, the generative search system processes the search response content involving inconsistent information: it removes response content containing compliance violations, corrects information with expression or numerical deviations, and supplements information with missing completeness to ensure that the generated response content is consistent with the benchmark brand information. At the same time, the inconsistent information is pushed to the target brand's information management platform for the brand to communicate with the relevant data source maintainers to correct the information.

[0063] Step S6. Verification and Iterative Optimization: Set a weekly iteration cycle, regularly collect search response content from the generative search system and update information from multiple data sources, and repeat steps S1-S5 to iteratively optimize the verification model (such as the semantic similarity calculation model) and the benchmark brand information database. For example, by analyzing historical verification data, adjust the semantic similarity threshold to improve the accuracy of expression consistency verification; if the brand updates its brand logo parameters, promptly update the corresponding content in the benchmark brand information database to ensure the accuracy and timeliness of the verification.

Claims

1. A method for verifying the consistency of multi-source brand information in a generative search scenario, characterized in that, Includes the following steps: S1. Multi-source brand information collection: Acquire multi-source data sources on which the generative search system relies to generate response content, extract brand information related to the target brand from each data source, and form an original brand information set; the multi-source data sources include but are not limited to web page indexes, knowledge graphs, training datasets, official brand databases and third-party information platforms, and the brand information includes basic brand information, brand identification information, brand association information and brand compliance information; S2. Information Preprocessing: Cleaning, standardizing and feature extraction are performed on each piece of brand information in the original brand information set to obtain standardized brand information and corresponding brand feature vectors; the preprocessing includes removing invalid information, unifying the information expression format and eliminating ambiguous expressions; the brand feature vectors include text features, numerical features and semantic features. S3. Construction of benchmark brand information: Based on brand information in the official brand database, combined with industry standards and brand owner preset rules, a benchmark brand information database is constructed. The benchmark brand information database includes standard values, allowable fluctuation ranges and verification rules for various types of brand information. S4. Multi-dimensional Consistency Verification: Based on the benchmark brand information database, a combination of semantic similarity comparison, feature matching, and rule verification is used to perform multi-dimensional consistency verification between standardized brand information and benchmark brand information, obtaining the verification result for each piece of brand information; the verification dimensions include information completeness, expression consistency, numerical accuracy, and compliance; S5. Verification Result Processing and Feedback: Summarize and analyze the verification results of all brand information, mark inconsistent information and clarify the type and degree of inconsistency, and generate a verification report; The verification report is fed back to the generative search system to guide the optimization and adjustment of search response content. At the same time, inconsistencies are pushed to the brand for correction.

2. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, In step S1, the basic brand information includes the brand name, brand abbreviation, brand establishment time, brand entity information, and brand business scope; the brand identity information includes brand logo parameters, brand standard colors, brand slogan, and brand visual specifications; the brand association information includes information on the brand's products, brand partners, and brand honors and qualifications; and the brand compliance information includes brand trademark registration information, patent information, and compliance requirements for promotion.

3. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, In step S2, the feature extraction process specifically includes: using natural language processing technology to segment and remove stop words from textual brand information to extract textual features; normalizing numerical brand information to extract numerical features; encoding brand information using a pre-trained semantic model to extract semantic features; and fusing textual features, numerical features, and semantic features to form a brand feature vector.

4. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, In step S3, the verification rules include: semantic similarity threshold rules for text-based brand information, allowable deviation range rules for numerical brand information, standard matching rules for brand identity information, and compliance judgment rules for brand compliance information; the benchmark brand information database supports dynamic updates, and automatically updates the benchmark information and verification rules when the brand owner updates the brand information or industry standards change.

5. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, In step S4, the multi-dimensional consistency verification specifically includes: S41. Information Integrity Verification: Check whether the standardized brand information contains the required information items specified in the benchmark brand information database. If any required information items are missing, it is determined to be an information inconsistency and marked as an integrity missing type. S42. Expression consistency verification: Calculate the semantic similarity between standardized brand information and benchmark brand information. If the semantic similarity is lower than a preset threshold, it is determined to be an expression inconsistency and marked as an expression deviation type. The semantic similarity is calculated using the edit distance algorithm or the cosine similarity algorithm. S43. Numerical accuracy verification: Compare the numerical information in the standardized brand information with the corresponding standard value in the benchmark brand information. If the numerical deviation exceeds the allowable fluctuation range, it is determined that the numerical values ​​are inconsistent and marked as numerical deviation type. S44. Compliance Verification: Check whether the standardized brand information meets the compliance requirements in the benchmark brand information database. If there are any violations of trademark usage specifications, patent usage specifications, or publicity compliance requirements, it will be judged as a non-compliance and marked as a compliance violation type.

6. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 5, characterized in that, In step S42, the semantic similarity threshold can be dynamically adjusted according to the brand information type. The semantic similarity threshold of core identifier information such as brand name and brand logo parameters is not less than 95%, and the semantic similarity threshold of brand association information is not less than 85%.

7. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, In step S5, the verification report includes a list of data sources covered by the verification, the total number of brand information, the number of inconsistent information, the distribution of each type of inconsistent information, the specific content of each inconsistent information, the degree of deviation, and correction suggestions. Based on the verification report, the generative search system removes, corrects, or supplements the search response content involving inconsistent information to ensure that the generated response content is consistent with the benchmark brand information.

8. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, It also includes step S6. Verification Iteration Optimization: Periodically collect the search response content of the generative search system and the update information of multiple data sources, repeat steps S1-S5, and iteratively optimize the verification model and the benchmark brand information database to improve the accuracy and efficiency of consistency verification.

9. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, In step S1, the multi-source brand information collection adopts a combination of timed capture and real-time monitoring. Timed capture is used to obtain regular brand information from various data sources, while real-time monitoring is used to capture sudden updates and abnormal information of brand information, ensuring the timeliness of the collected brand information.

10. The method for verifying the consistency of multi-source brand information in a generative search scenario according to claim 1, characterized in that, In step S2, the elimination of ambiguous expressions specifically includes: identifying ambiguous expressions such as homophones, abbreviations, and misspellings in the brand name, and correcting the ambiguous expressions to standard expressions by combining the benchmark brand information database and contextual information; and performing semantic disambiguation on brand-related information with polysemous words in combination with brand scenarios to ensure the uniqueness of information expression.

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