Ai intelligent monitoring and brand optimization system

Abstract

The application discloses an AI intelligent monitoring and brand optimization system, which comprises a multi-platform large model interaction and data collection module, a brand mention identification and ranking calculation module, an public opinion sentiment analysis and risk assessment module, a content source adoption rate statistics module, a competitive product benchmarking and optimization strategy generation module, and a multi-format report generation and data export module. The application realizes the automatic parallel monitoring of multiple AI large models, replaces the manual sampling method, greatly improves the monitoring coverage and data acquisition efficiency, forms a standardized brand influence data collection system, and solves the problems of insufficient sample size and low efficiency in traditional manual monitoring.

Description

Technical Field

[0001] This invention relates to the field of brand digital marketing and generative engine optimization technology, specifically to an AI intelligent monitoring and brand optimization system. Background Technology

[0002] With the rapid popularization of generative AI big data models, user information acquisition paths are gradually shifting from traditional search engines to AI-powered dialogue interactions. The order in which brands are mentioned and their expression tendencies in the big data model's responses have become core factors influencing user perception. Generative Engine Optimization (GEO) has become an emerging core direction for brand digital marketing. Current brand monitoring systems are mostly designed for traditional search channels and cannot adapt to the conversational output format of big data models. Most brands rely on manual sampling to verify their performance in big data model responses, resulting in limited monitoring coverage, insufficient data sample size, and large ranking statistical errors. Existing technologies cannot achieve parallel automated monitoring of big data models across multiple platforms, making it difficult to quantify differences in brand mention rates, ranking positions, and sentiment tendencies across different models. There is a lack of real-time identification and risk warning mechanisms for negative brand expressions, leading to delayed discovery of potential reputational issues. Furthermore, there is a lack of quantitative evaluation methods for whether brand-generated content resources are adopted by the big data model and their contribution; optimization decisions rely heavily on experience-based judgments, lacking data support and failing to form a closed-loop management system encompassing monitoring, analysis, optimization, and verification. Summary of the Invention

[0003] The purpose of this invention is to provide an AI-powered intelligent monitoring and brand optimization system to address the problems existing in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an AI intelligent monitoring and brand optimization system, including a multi-platform large model interaction and data collection module, a brand mention recognition and ranking calculation module, a public opinion sentiment analysis and risk assessment module, a content source adoption rate statistics module, a competitor benchmarking and optimization strategy generation module, and a multi-format report generation and data export module; The multi-platform large model interaction and data acquisition module is used to connect to multiple AI large model platforms, generate standardized query requests in batches based on the configured brand keyword set, collect the answer text and page snapshot data returned by each platform for the same query, and complete the structured identification and standardized storage of the original monitoring data. The brand mention recognition and ranking calculation module is used to perform semantic entity recognition on the collected full answer text, locate the mention positions of the target brand and competitor brands in the answer text, and calculate the ranking position of each brand in a single answer and the ranking distribution characteristics in the full sample. The sentiment analysis and risk assessment module is used to determine the sentiment polarity of statements involving the target brand in the response text, statistically analyze the sentiment distribution characteristics of different platforms, identify negative expressions, and assess their spread risk level. The content source adoption rate statistics module is used to import the set of content links to be monitored, match the overlap between the citation source identifiers and content fragments in the large model's answers, and count the frequency of each content link being adopted by the AI ​​model and the number of platforms it covers. The competitor benchmarking and optimization strategy generation module is used to compare the differences between the target brand and the competitor brand in terms of mention rate, average ranking, and sentiment score, and to generate corresponding generative engine optimization directions and content adjustment suggestions based on the differences. The multi-format report generation and data export module is used to integrate full-dimensional monitoring data, generate structured analysis reports, and support multi-format file export and authorized online data sharing.

[0005] Furthermore, the multi-platform large model interaction and data acquisition module includes a request configuration unit, a parallel scheduling unit, and a data standardization unit; The request configuration unit is used to manage the monitoring keyword library, query template set, and monitoring cycle configuration, and supports keyword grouping by brand business line and product category; the parallel scheduling unit is used to send query requests to multiple large model platforms in parallel based on the interface adaptation rules of each platform, and dynamically adjust the request interval and the number of concurrent tasks; the data standardization unit is used to uniformly convert the different formats of answer text and response metadata returned by different platforms into a fixed structure storage format, and associate each piece of data with a corresponding keyword identifier and platform identifier.

[0006] Furthermore, the brand mention recognition and ranking calculation module includes an entity recognition unit, a position calibration unit, and a comprehensive ranking calculation unit; The entity recognition unit, based on a pre-trained semantic entity recognition model, extracts all brand entities from the response text and marks their order of appearance in the text; The ranking unit determines the ranking of each brand within a single answer according to the order in which the brands first appear in the answer text. The comprehensive ranking calculation unit uses a brand ranking weighted score formula to calculate the comprehensive ranking index of the target brand. The formula is as follows:

[0007] in, The overall ranking index of the target brand. Let represent the ranking of the target brand in the i-th query sample. Let be the weight coefficient corresponding to the i-th query sample. The weight coefficient is positively correlated with the user search popularity of the query keywords. This represents the total number of query samples used in the calculation.

[0008] Furthermore, the public opinion sentiment analysis and risk assessment module includes a sentence segmentation unit, a sentiment calculation unit, and a risk level determination unit; The statement segmentation unit segments a set of independent statements containing the target brand name from the full response text, and removes contextual content that is irrelevant to the brand. The sentiment computing unit calculates the sentiment score for each brand-related statement based on the domain sentiment dictionary and semantic dependency analysis results, and obtains the average sentiment score for a single platform through a sentiment tendency quantification formula:

[0009] in, The average sentiment score for a single platform. Let j be the sentiment score for the brand-related statement. This refers to the total number of brand-related statements on the platform. The risk level determination unit classifies the corresponding risk level of negative content dissemination based on the proportion of negative statements and the number of platforms covered.

[0010] Furthermore, the content source adoption rate statistics module includes a link management unit, a fragment matching unit, and an adoption rate calculation unit; The link management unit is used to input and maintain a list of content links to be monitored, and to store the publishing entity, publishing time and content summary information of the corresponding links; The segment matching unit compares the text fingerprints of the large model's response text with the corresponding link's main text content to identify content segments with overlapping features. The adoption rate calculation unit calculates the overall adoption rate of a single piece of content using a content adoption rate weighted formula:

[0011] in, The overall adoption rate of a single piece of content. This represents the total number of answers that were accepted for this content. The number of large model platforms covered by this content. This represents the total number of queries within the monitoring period. To answer the adoption weight, To cover the platform's weight, and meet the requirements .

[0012] Furthermore, the competitor benchmarking and optimization strategy generation module includes a competitor data aggregation unit, a difference dimension analysis unit, and an optimization strategy generation unit; The competitor data aggregation unit is used to collect all the mention rate, ranking distribution, and sentiment score data of each competitor brand to form a benchmark dataset corresponding to the target brand. The difference dimension analysis unit calculates the difference in indicators between the target brand and competitors from the dimensions of keywords, platforms, and time, respectively, to locate the shortcomings of the brand's performance. The optimization strategy generation unit matches the identified shortcomings with a preset optimization strategy library and outputs three types of adjustment suggestions: keyword expansion, content optimization, and source distribution.

[0013] Furthermore, the multi-format report generation and data export module includes a data visualization unit, a report template unit, and an export and sharing unit; The data visualization unit transforms ranking changes, sentiment distribution, and adoption rate trend data into trend charts, comparison charts, and percentage charts; the report template unit has four built-in report templates: current situation insight, sentiment analysis, monthly summary, and public opinion early warning, which automatically fill in the monitoring data and analysis conclusions of the corresponding dimensions; the export and sharing unit supports the generation of editable document format, portable document format, and table format files, and generates online sharing links with permission control.

[0014] Furthermore, when extracting brand entities, the entity recognition unit simultaneously matches synonyms and abbreviation variations of the brand name, mapping different expressions of the same brand to a unified brand identifier, thus avoiding duplicate and omission statistics.

[0015] Furthermore, when identifying negative statements, the risk level determination unit simultaneously extracts the core topics and related events corresponding to the negative statements, and categorizes, stores, and statistically analyzes the negative content according to topic categories.

[0016] Furthermore, when performing text fingerprint comparison, the segment matching unit excludes common-sense statements and public data content, and only determines the overlap of content segments with original attributes.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves automated parallel monitoring of multiple AI models, replacing manual sampling, significantly improving monitoring coverage and data acquisition efficiency, and forming a standardized brand influence data collection system. It solves the problems of insufficient sample size and low efficiency in traditional manual monitoring. Through a multi-dimensional quantitative calculation model, it achieves precise quantification of brand ranking, sentiment, and content adoption rate, transforming the brand's performance in the large model ecosystem from qualitative descriptions into comparable and traceable quantitative indicators, providing stable data support for brand optimization decisions. A full-process risk warning mechanism is established to promptly identify negative statements on various platforms and assess the risk of their spread, helping brands proactively avoid reputational risks and enhancing their initiative in managing public opinion on generative channels. A competitor benchmarking and optimization strategy generation system is constructed, using multi-dimensional difference analysis to pinpoint brand weaknesses and match corresponding optimization directions, forming a complete closed loop from data monitoring to strategy output, improving the targeting and effectiveness of generative engine optimization. Attached Figure Description

[0018] Figure 1 This is a system module diagram of the present invention; Figure 2 This is a flowchart of the control method of the present invention; Figure 3 This is a schematic diagram of the multi-platform large model interaction and data acquisition module of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1-3 This invention provides an AI-powered intelligent monitoring and brand optimization system, including a multi-platform large-scale model interaction and data collection module, a brand mention recognition and ranking calculation module, a public opinion sentiment analysis and risk assessment module, a content source adoption rate statistics module, a competitor benchmarking and optimization strategy generation module, and a multi-format report generation and data export module.

[0021] The multi-platform large-scale model interaction and data acquisition module serves as the system's data input layer, responsible for connecting with multiple mainstream AI large-scale model platforms to complete the configuration of monitoring tasks, request sending, and raw data collection. This module shields the interface differences between different platforms through a unified adaptation layer, providing standardized raw response data to the upper-layer analysis module. In actual operation, operations personnel first enter the target brand name, competitor brand list, core keyword set, and query template in the request configuration unit. The keyword set can be grouped and managed according to categories such as brand terms, product terms, industry terms, and scenario terms. The query template generates complete query statements based on keywords, conforming to user questioning habits, ensuring that the collected response data closely reflects real user query scenarios. The request configuration unit also supports setting monitoring cycles, allowing for the configuration of regular monitoring tasks at fixed time intervals, as well as the creation of temporary, one-off monitoring tasks, meeting the different monitoring needs of daily brand operations and special events.

[0022] After configuration, the parallel scheduling unit receives monitoring tasks and generates a batch query request queue based on the keywords and templates in the tasks. The parallel scheduling unit incorporates adaptation rules for various platforms, handling access frequency limits and response format differences across platforms. It employs a multi-threaded concurrency mechanism to simultaneously send query requests to multiple large model platforms, dynamically adjusting the request sending interval to avoid triggering platform access restrictions due to excessively frequent requests. During request sending, the parallel scheduling unit synchronously records metadata such as the sending time, corresponding keywords, and target platform for each request, providing a unique identifier for subsequent data association. Once the platform returns a response, the parallel scheduling unit transmits the raw response data to the data standardization unit, simultaneously recording the request's response status and latency for monitoring the interface availability of each platform.

[0023] The data standardization unit receives raw response data from different platforms. First, it extracts the main body of the responses, removing irrelevant fields, formatting tags, and interactive controls to retain only the clean text. Simultaneously, the unit attaches a unified structured identifier to each response, including fields such as keyword ID, platform ID, query time, and response version number, converting the raw data of different formats into standardized data entries with a unified structure. After standardization, the data is stored in the system's raw database, awaiting processing by subsequent analysis modules. For platforms where data cannot be obtained via API, the data standardization unit also supports uploading snapshot data of the page. Text recognition technology is used to extract the response text from the snapshot, completing the standardization process and ensuring the system can cover more large-scale model platforms and application scenarios.

[0024] The brand mention recognition and ranking calculation module receives standardized response text data, completes brand entity recognition, ranking position calibration, and comprehensive ranking index calculation, and outputs the brand's ranking statistics across various dimensions. The module's processing flow sequentially goes through three stages: entity recognition, position calibration, and comprehensive calculation, gradually transforming the raw text into quantifiable ranking indicators, enabling measurable and comparable brand performance.

[0025] The entity recognition unit loads a pre-trained semantic entity recognition model, trained on a large-scale brand corpus, capable of accurately identifying different expressions of various brand entities. When processing a single answer text, the entity recognition unit first performs word segmentation and part-of-speech tagging, then extracts all brand entities from the text using the entity recognition model, recording the position and frequency of each brand entity. For identified brand entities, the entity recognition unit matches them with the brand knowledge base in the system, mapping different expressions of the same brand, such as full name, abbreviation, alias, and foreign language translation, to the same brand ID, avoiding duplicate or missed statistics due to differences in expression. For example, when both the full name and common abbreviation of a brand appear in the answer, the system classifies them as the same brand and merges their ranking and mention counts. After entity recognition is complete, the unit outputs a list of all brand entities contained in the answer and their first occurrence positions, which is then passed to the ranking unit for further processing.

[0026] The ranking unit receives a list of brand entities and sorts them according to the order in which each brand first appears in the answer text. A higher ranking indicates a higher exposure priority for the brand in that answer, making it more likely to attract user attention. The ranking unit assigns a corresponding ranking position to each brand, with the first appearance of a brand assigned position 1, the second appearance 2, and so on. For the same brand appearing multiple times in the same answer, only the first appearance is used for ranking; it is not included in the ranking repeatedly. When the answer uses a structured format such as a list or bullet points to list brands, the ranking unit ranks them according to the order of the list, maintaining consistency with the ranking rules for natural text and ensuring uniformity in ranking statistics across different answer formats. After ranking a single answer is completed, the data is aggregated into the full sample ranking database for subsequent comprehensive ranking calculations and distribution statistics.

[0027] The comprehensive ranking calculation unit extracts all ranking data within a specified monitoring period from the full sample ranking database and combines it with the weight coefficients of each query keyword to calculate the comprehensive ranking index of the target brand. The calculation uses a brand ranking weighted score formula:

[0028] in, This is the overall ranking index for the target brand. The smaller the value, the higher the overall ranking and the better the brand performance. This represents the ranking of the target brand in the i-th query sample. If the target brand is not mentioned in the sample, the ranking is the sum of the total number of brands mentioned in the sample plus one. Let be the weight coefficient corresponding to the i-th query sample. The weight coefficient is positively correlated with the user search popularity of the query keywords. The higher the popularity of the keywords, the greater the weight and the greater the impact on the overall ranking. This represents the total number of query samples used in the calculation. Through weighted calculation, the comprehensive ranking index more accurately reflects the brand's overall performance in high-attention user queries, avoiding the dilution of core keyword performance by a large number of low-popularity long-tail keywords. In addition to the comprehensive ranking index, the comprehensive ranking calculation unit also calculates distribution indicators such as the target brand's mention rate, Top 3 share, and Top 10 share, outputting ranking data by keyword and platform, presenting the brand's ranking performance from multiple dimensions.

[0029] The sentiment analysis and risk assessment module analyzes the sentiment of content related to the target brand in response texts, identifies negative expressions, and assesses the risk of their spread, providing data support for brand reputation management. Starting at the sentence level, this module accurately assesses the sentiment of each expression involving the brand, then aggregates these sentiment characteristics at the platform level and overall, enabling quantitative monitoring of brand reputation health.

[0030] The statement segmentation unit first filters statements containing the target brand name from the full response text, segmenting the complete response into independent statement fragments using punctuation marks as boundaries. Then, it matches each statement to check for the presence of entity descriptions related to the target brand. For statements containing the target brand, the segmentation unit extracts the statement and its immediate context statements to form complete semantic fragments, avoiding semantic gaps caused by sentence breaks that could affect sentiment assessment. For statements containing multiple brands, the segmentation unit labels each brand's corresponding statement fragment to ensure that sentiment analysis accurately corresponds to a specific brand and avoids interference between sentiment expressions from different brands. The segmented set of brand-related statements is then passed to the sentiment computing unit for further processing.

[0031] The sentiment computing unit calculates the sentiment score for each statement based on a domain sentiment lexicon and semantic dependency analysis technology. The sentiment lexicon includes four categories of entries: positive sentiment words, negative sentiment words, degree adverbs, and negation adverbs. During calculation, the system first identifies sentiment words and modifiers in the statement, and then determines the object and polarity of the sentiment words by combining semantic dependency relationships. For statements containing negation words, the system reverses the sentiment polarity; for statements containing degree adverbs, the system scales the sentiment score accordingly based on the adverb's strength. Ultimately, each statement receives a sentiment score within a given range, with positive statements receiving positive scores, negative statements receiving negative scores, and neutral statements approaching zero.

[0032] After the sentiment score of a single statement is calculated, the sentiment calculation unit calculates the average sentiment score for a single platform using the sentiment tendency quantification formula:

[0033] in, The average sentiment score is for a single platform; a higher score indicates a more positive overall reputation for the brand on that platform. The sentiment score for the j-th brand-related statement; This represents the total number of brand-related statements on the platform. In addition to the average sentiment score for the single platform, the sentiment calculation unit also tracks distribution indicators such as the percentage of positive statements, neutral statements, and negative statements, as well as sentiment scores for different topic dimensions, presenting the brand's reputation characteristics from multiple dimensions and helping the brand accurately identify its reputation strengths and areas for improvement.

[0034] The risk level assessment unit evaluates negative content based on sentiment analysis results. First, it statistically analyzes the number and percentage of negative statements within the monitoring period, as well as the number of major platforms covered by the negative content. When the percentage of negative statements exceeds a set threshold and covers multiple platforms, it is classified as high-risk; when the percentage is in the middle range or appears only on a single platform, it is classified as medium-risk; and when the percentage is low and scattered, it is classified as low-risk. In addition to identifying negative statements, the risk level assessment unit also extracts the core topics and related events corresponding to these statements, categorizing and storing them according to categories such as product quality, service experience, and brand rumors. This helps brands quickly pinpoint the core direction of negative issues and develop targeted response plans.

[0035] The content source adoption rate statistics module is used to evaluate the adoption rate of brand-owned or advertised content by the large model, quantifying the contribution value of content assets to the brand's GEO (Generative Adversarial Team). This module uses text matching technology to identify brand content referenced in the large model's responses, and calculates the adoption frequency and coverage of that content, addressing the pain point of the inability to quantify the value of brand content.

[0036] The Link Management unit provides content link entry and management functions. Operations personnel can import content links to be monitored in batches, including brand website articles, official social media content, press releases, and industry collaboration content. During entry, metadata such as the publishing entity, publication time, content type, and core keywords for each piece of content is simultaneously filled in. It supports categorization, filtering, and management by content type, publishing channel, and publication time. The Link Management unit also supports grouping content links to correspond to different content delivery projects or operational activities, facilitating the statistical analysis of content effectiveness by project and the evaluation of the actual contribution of different content strategies.

[0037] The fragment matching unit is responsible for comparing the overlap between the large model's answer text and the content to be monitored. First, the fragment matching unit captures the main text of all links to be monitored, removing irrelevant content such as navigation, advertisements, and copyright information, extracting the clean main text. Then, the system performs text fingerprint calculation on the main text, segmenting the text into fixed-length fragments and generating a unique fingerprint identifier for each fragment. For each large model's answer text, the system also calculates its text fingerprint and compares it with the fingerprint database of all content to be monitored. When the number and length of overlapping fragments reach a set threshold, the answer is determined to have adopted the corresponding content information. During the comparison process, the system excludes content lacking originality, such as common sense, publicly available data, and industry-standard expressions, only judging the overlap of content fragments with original attributes to avoid misjudgments. After matching is complete, the fragment matching unit records the link to the adopted content for each answer, as well as the position and length of the overlapping fragments, providing basic data for subsequent adoption rate calculations.

[0038] The adoption rate calculation unit calculates the overall adoption rate for each piece of content based on the matching results, using a content adoption rate weighted formula:

[0039] in, This represents the overall adoption rate of a single piece of content. A higher value indicates a greater degree of adoption of the content by the larger model and a greater contribution to the brand's GEO. This represents the total number of accepted answers to this question, reflecting the frequency of the content's acceptance. The number of major model platforms covered by this content reflects its cross-platform influence. The total number of queries within the monitoring period is used to normalize the adoption rate metric. To answer the adoption weight, To cover the platform's weight, and meet the requirements The weight can be adjusted according to operational needs; it can be increased when more emphasis is placed on adoption frequency. The value can be increased when platform coverage is a greater concern. Values. This weighted formula allows the overall adoption rate to simultaneously consider the depth and breadth of content adoption, providing a more comprehensive assessment of the value of individual pieces of content. In addition to the adoption rate of individual content, the adoption rate calculation unit also tracks aggregated metrics such as overall content adoption rate, platform-specific adoption rate, and content type-specific adoption rate, helping brands understand the contribution of their overall content assets and optimize content production and distribution strategies.

[0040] The competitor benchmarking and optimization strategy generation module compares multi-dimensional data of the target brand and its competitors to identify the brand's strengths and weaknesses, and then generates targeted optimization suggestions, realizing a closed loop from data monitoring to optimization actions, and improving the implementation and effectiveness of GEO optimization.

[0041] The competitor data aggregation unit first collects full monitoring data from all competitor brands, including core indicators such as mention rate, overall ranking index, average sentiment score, and content adoption rate, forming a complete benchmarking dataset. The system supports adding multiple competitor brands simultaneously, covering both direct and indirect competitors, enabling comprehensive industry benchmarking analysis. During data aggregation, the system standardizes the statistical criteria and time ranges for each brand to ensure the comparability of benchmarking data and avoid distortions caused by differences in statistical methods.

[0042] The Difference Analysis Unit compares and analyzes data between the target brand and its competitors from multiple dimensions. In terms of keywords, it compares the ranking and mention rates of each brand under different core keywords, identifying which keywords are the brand's strengths and which are weaknesses compared to competitors. In terms of platforms, it compares the performance of each brand across different major platforms, identifying platforms where the brand performs well and those that need improvement. In terms of time, it compares the trends of each brand's metrics over time, analyzing the effectiveness of optimization efforts and the brand's development trajectory. Through this multi-dimensional difference analysis, the system can accurately pinpoint the target brand's position in the industry, as well as specific weaknesses and areas for improvement, providing a clear direction for subsequent strategy generation.

[0043] The optimization strategy generation unit, based on identified weaknesses, matches them with the system's built-in optimization strategy library to generate actionable optimization suggestions. The strategy library is divided into three main categories according to optimization direction: keyword expansion, content optimization, and source distribution. For weaknesses in the keyword dimension, the system outputs corresponding long-tail keyword expansion suggestions, recommending high-popularity, low-competition keyword directions. For weaknesses in the content quality dimension, the system outputs content optimization suggestions, including content structure adjustments, improved information completeness, and supplementation with authoritative data. For weaknesses in the source coverage dimension, the system outputs content distribution channel suggestions, recommending high-authority content publishing platforms that are easily indexed by large data models. The generated optimization suggestions are linked to specific weaknesses, ensuring that each suggestion is supported by data, avoiding vague, experience-based advice, and improving the executability of the optimization strategies.

[0044] The multi-format report generation and data export module is responsible for organizing the monitoring and analysis results from all dimensions into standardized reports, supporting export in multiple formats and online sharing, meeting the delivery needs of different scenarios, and reducing the data processing costs for operations personnel.

[0045] The data visualization unit first processes various statistical data for visualization, transforming data such as ranking trends, brand mention rate comparisons, sentiment distribution ratios, and content adoption rate rankings into visual charts such as line charts, bar charts, and pie charts. It also supports customizing the dimensions and time range of the charts. These visual charts intuitively present data changes and comparative relationships, improving the readability and professionalism of reports and allowing non-technical personnel to quickly understand the data conclusions.

[0046] The report template unit includes four standard report templates: AI Status Insight Report, Sentiment Analysis Report, Monthly Operations Report, and Public Opinion Warning Report. The AI ​​Status Insight Report focuses on the brand's ranking and mention rate across various platforms, used for assessing the brand's current status and keyword expansion. The Sentiment Analysis Report focuses on the brand's emotional leanings across platforms and comparisons with competitors, used for evaluating brand reputation. The Monthly Operations Report summarizes the month's data changes, compares with the previous month's data, summarizes content shortcomings and action directions, used for monthly operational review. The Public Opinion Warning Report focuses on new negative content and risk assessment, used for public opinion response. After operations personnel select the corresponding template, the system automatically extracts the relevant data and charts from the database, populates them into the report template, and generates a complete analysis report, significantly improving report creation efficiency.

[0047] The export and sharing unit supports exporting generated reports to various file formats, including portable document formats, editable document formats, and table formats, to meet different use cases. Simultaneously, the system supports generating online sharing links, allowing users to set access permissions, validity periods, and watermark protection for convenient internal and external data sharing. In addition to complete reports, the export and sharing unit also supports batch export of raw data, exporting structured table data containing fields such as keywords, platforms, rankings, and sentiment scores, meeting the needs of customized analysis and secondary processing.

[0048] This embodiment also provides a method for intelligent monitoring and optimization of brand influence for large-scale AI models, including the following steps: Step 1: System Initialization and Monitoring Task Configuration After the system is started, the operators configure the monitoring task in the multi-platform large model interaction and data collection module, enter the target brand and competitor brand information, import the monitoring keyword set and query template, set the monitoring period and coverage platform, and the system enters the pending execution state after the task is created.

[0049] Step 2: Parallel Data Acquisition from Multiple Platforms The parallel scheduling unit automatically starts the data collection task according to the configured monitoring cycle, sends query requests to all target large model platforms in parallel, receives the response data returned by each platform, processes it through the data standardization unit, and stores it in the raw database to complete one round of data collection.

[0050] Step 3: Quantitative Calculation of Brand Ranking The brand mention recognition and ranking calculation module reads the newly collected answer data, performs brand entity recognition, ranking position calibration and comprehensive ranking index calculation in sequence, and outputs the ranking statistics results of the target brand and competitors.

[0051] Step 4: Sentiment Analysis and Risk Assessment The public opinion sentiment analysis and risk assessment module extracts brand-related statements, calculates the sentiment score of individual statements and the platform's average sentiment score, identifies negative statements and assesses the risk level of their spread, and generates public opinion analysis results.

[0052] Step 5: Content Adoption Rate Statistics The content source adoption rate statistics module matches the answer text with the content library to be monitored, identifies the links of adopted content, calculates the comprehensive adoption rate and overall adoption index of each piece of content, and outputs the content value statistics results.

[0053] Step 6: Competitor Benchmarking and Strategy Generation The competitor benchmarking and optimization strategy generation module aggregates full competitor data, compares differences from multiple dimensions, identifies brand weaknesses, and generates corresponding optimization suggestions by matching the optimization strategy library.

[0054] Step 7: Report Generation and Data Export The multi-format report generation and data export module integrates comprehensive data analysis to generate corresponding analysis reports, supports exporting files in multiple formats and sharing them online, and completes the entire process for this monitoring cycle.

[0055] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An AI-powered intelligent monitoring and brand optimization system, characterized by: It includes modules for multi-platform large model interaction and data collection, brand mention recognition and ranking calculation, public opinion sentiment analysis and risk assessment, content source adoption rate statistics, competitor benchmarking and optimization strategy generation, and multi-format report generation and data export. The multi-platform large model interaction and data acquisition module is used to connect to multiple AI large model platforms, generate standardized query requests in batches based on the configured brand keyword set, collect the answer text and page snapshot data returned by each platform for the same query, and complete the structured identification and standardized storage of the original monitoring data. The brand mention recognition and ranking calculation module is used to perform semantic entity recognition on the collected full answer text, locate the mention positions of the target brand and competitor brands in the answer text, and calculate the ranking position of each brand in a single answer and the ranking distribution characteristics in the full sample. The sentiment analysis and risk assessment module is used to determine the sentiment polarity of statements involving the target brand in the response text, statistically analyze the sentiment distribution characteristics of different platforms, identify negative expressions, and assess their spread risk level. The content source adoption rate statistics module is used to import the set of content links to be monitored, match the overlap between the citation source identifiers and content fragments in the large model's answers, and count the frequency of each content link being adopted by the AI ​​model and the number of platforms it covers. The competitor benchmarking and optimization strategy generation module is used to compare the differences between the target brand and the competitor brand in terms of mention rate, average ranking, and sentiment score, and to generate corresponding generative engine optimization directions and content adjustment suggestions based on the differences. The multi-format report generation and data export module is used to integrate full-dimensional monitoring data, generate structured analysis reports, and support multi-format file export and authorized online data sharing.

2. The AI ​​intelligent monitoring and brand optimization system according to claim 1, characterized in that: The multi-platform large model interaction and data acquisition module includes a request configuration unit, a parallel scheduling unit, and a data standardization unit. The request configuration unit is used to manage the monitoring keyword library, query template set, and monitoring cycle configuration, and supports keyword grouping by brand business line and product category; the parallel scheduling unit is used to send query requests to multiple large model platforms in parallel based on the interface adaptation rules of each platform, and dynamically adjust the request interval and the number of concurrent tasks; the data standardization unit is used to uniformly convert the different formats of answer text and response metadata returned by different platforms into a fixed structure storage format, and associate each piece of data with a corresponding keyword identifier and platform identifier.

3. The AI ​​intelligent monitoring and brand optimization system according to claim 1, characterized in that: The brand mention recognition and ranking calculation module includes an entity recognition unit, a position calibration unit, and a comprehensive ranking calculation unit; The entity recognition unit, based on a pre-trained semantic entity recognition model, extracts all brand entities from the response text and marks their order of appearance in the text; The ranking unit determines the ranking of each brand within a single answer according to the order in which the brand first appears in the answer text; The comprehensive ranking calculation unit uses a brand ranking weighted score formula to calculate the comprehensive ranking index of the target brand. The formula is as follows: ； in, The overall ranking index of the target brand. Let represent the ranking of the target brand in the i-th query sample. Let be the weight coefficient corresponding to the i-th query sample. The weight coefficient is positively correlated with the user search popularity of the query keywords. This represents the total number of query samples used in the calculation.

4. The AI ​​intelligent monitoring and brand optimization system according to claim 1, characterized in that: The public opinion sentiment analysis and risk assessment module includes a sentence segmentation unit, a sentiment calculation unit, and a risk level determination unit; The statement segmentation unit segments a set of independent statements containing the target brand name from the full response text, and removes contextual content that is irrelevant to the brand. The sentiment computing unit calculates the sentiment score for each brand-related statement based on the domain sentiment dictionary and semantic dependency analysis results, and obtains the average sentiment score for a single platform through a sentiment tendency quantification formula: ； in, The average sentiment score for a single platform. Let j be the sentiment score for the brand-related statement. This refers to the total number of brand-related statements on this platform. The risk level determination unit classifies the corresponding risk level of negative content dissemination based on the proportion of negative statements and the number of platforms covered.

5. The AI ​​intelligent monitoring and brand optimization system according to claim 1, characterized in that: The content source adoption rate statistics module includes a link management unit, a fragment matching unit, and an adoption rate calculation unit; The link management unit is used to input and maintain a list of content links to be monitored, and to store the publishing entity, publishing time and content summary information of the corresponding links; The segment matching unit compares the text fingerprints of the large model's response text with the corresponding link's main text content to identify content segments with overlapping features. The adoption rate calculation unit calculates the overall adoption rate of a single piece of content using a content adoption rate weighted formula: ； in, The overall adoption rate of a single piece of content. This represents the total number of answers that were accepted for this content. The number of large model platforms covered by this content. This represents the total number of queries within the monitoring period. To answer the adoption weight, To cover the platform's weight, and meet the requirements .

6. The AI ​​intelligent monitoring and brand optimization system according to claim 1, characterized in that: The competitor benchmarking and optimization strategy generation module includes a competitor data aggregation unit, a difference dimension analysis unit, and an optimization strategy generation unit. The competitor data aggregation unit is used to collect all the mention rate, ranking distribution, and sentiment score data of each competitor brand to form a benchmark dataset corresponding to the target brand. The difference dimension analysis unit calculates the difference in indicators between the target brand and competitors from the dimensions of keywords, platforms, and time, respectively, to locate the shortcomings of the brand's performance. The optimization strategy generation unit matches the identified shortcomings with a preset optimization strategy library and outputs three types of adjustment suggestions: keyword expansion, content optimization, and source distribution.

7. The AI ​​intelligent monitoring and brand optimization system according to claim 1, characterized in that: The multi-format report generation and data export module includes a data visualization unit, a report template unit, and an export and sharing unit; The data visualization unit transforms ranking changes, sentiment distribution, and adoption rate trend data into trend charts, comparison charts, and percentage charts; the report template unit has four built-in report templates: current situation insight, sentiment analysis, monthly summary, and public opinion early warning, which automatically fill in the monitoring data and analysis conclusions of the corresponding dimensions. The export and sharing unit supports generating editable document formats, portable document formats, and table format files, and also generates online sharing links with access control.

8. The AI ​​intelligent monitoring and brand optimization system according to claim 3, characterized in that: When extracting brand entities, the entity recognition unit simultaneously matches synonyms and abbreviation variations of the brand name, mapping different expressions of the same brand to a unified brand identifier, thus avoiding duplicate and omission statistics.

9. The AI ​​intelligent monitoring and brand optimization system according to claim 4, characterized in that: When identifying negative statements, the risk level determination unit simultaneously extracts the core topics and related events corresponding to the negative statements, and categorizes, stores, and statistically analyzes the negative content according to topic categories.

10. The AI ​​intelligent monitoring and brand optimization system according to claim 5, characterized in that: When performing text fingerprint comparison, the segment matching unit excludes common-sense statements and public data content, and only determines the overlap of content segments with original attributes.

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