Wine industry AI intelligent service system supporting multi-modal generation and cross-model reasoning

Abstract

The invention relates to the technical field of application of artificial intelligence in the vertical industry, in particular to a wine industry AI intelligent service system supporting multi-modal generation and cross-model reasoning. According to the invention, three core modules are constructed: a multi-modal fused intelligent question and answer module for realizing accurate generation of professional tasting copywriting and marketing materials and identification and generation of wine contract files; the knowledge-enhanced cross-model reasoning intelligent customer service module is fused with a wine industry professional knowledge base, and initiative interaction is triggered through user behavior perception and cross-model scheduling, so that question and answer professionality is improved; and the industry data-oriented intelligent analysis and decision support module is used for carrying out dynamic integration and visual analysis on multi-source data and providing an intelligent product selection strategy and dynamic inventory early warning for wine enterprises. According to the invention, the full-link intelligent service of the wine industry from content creation, intelligent customer service to data decision is realized.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence application technology in vertical industries, specifically to an AI intelligent service system for the wine industry that supports multimodal generation and cross-model reasoning. Background Technology

[0002] In recent years, artificial intelligence technology has developed rapidly, especially large language models (LLMs) and generative AI, which have demonstrated powerful capabilities in multiple fields. Existing technologies can already achieve tasks such as multimodal content generation, user intent understanding, and basic data analysis. However, in vertical industries like the alcoholic beverage industry, which have deep traditional foundations and professional barriers, general-purpose AI models still have significant limitations in terms of domain knowledge depth, business process understanding, and multimodal professional applications.

[0003] In the field of multimodal intelligent content creation, current research focuses on enabling large language models to collaboratively generate content across different modalities. Timo Schick et al. proposed Toolformer to enhance the model's external connectivity, allowing it to automatically learn and use external tools, thereby improving creation efficiency and functional scalability. However, while these general-purpose multimodal generation models can utilize external tools, they lack inherent adherence to the wine industry's proprietary conceptual framework and quality standards. This makes them highly susceptible to professional errors in tasks such as recommendation, copywriting generation, or tasting report generation, or to outputting content that is inconsistent with the brand's tone.

[0004] In terms of multi-model collaboration and knowledge enhancement, existing single large models have significant limitations in handling the complex cross-domain business of the wine industry. Due to significant differences in knowledge coverage, logical reasoning depth, and factual consistency control among different models, a single model struggles to independently complete the entire intelligent service task, from wine-related Q&A and brand story generation to sales forecasting and marketing strategies. This leads to issues such as one-sided intelligent customer service responses and broken reasoning chains. To address these limitations, Tran et al. proposed Multi-Agent Collaboration Mechanisms, which systematically summarize cross-model collaboration methods in terms of role division, collaboration strategies, and communication methods, and verified that complementary model capabilities can improve the stability and scalability of handling complex tasks. However, this research mainly focuses on the theoretical framework at the architectural level and lacks data constraints and professional knowledge alignment mechanisms for real-world industry scenarios.

[0005] In terms of data analysis and intelligent decision-making, the information systems of enterprises in the current wine industry generally suffer from the problem of "data silos." Because many wineries still use traditional business systems deployed in a decentralized manner, data related to production, sales, and user operations are scattered across different platforms, lacking unified data standards and real-time synchronization mechanisms. This results in unstable data quality and poor accessibility. Consequently, downstream AI applications targeting end-user businesses cannot obtain real-time information support from upstream supply chain links, limiting the basis for model inference and making it difficult to achieve accurate predictions and actionable decision recommendations based on insights across the entire business chain. Especially in scenarios with significant market volatility and strict management of wine product shelf-life, this data fragmentation problem further amplifies business risks, becoming one of the core technological bottlenecks restricting the industry's intelligent upgrade.

[0006] In summary, while existing technologies have made some progress in large-scale language models, multimodal generation, and collaborative interaction, significant limitations remain: First, the lack of specialized knowledge modeling and semantic alignment mechanisms for the wine industry leads to generalized content responses and superficial business understanding. Second, single models have limitations in cross-task processing and professional reasoning, making it difficult to support the complete decision-making process for wine supply, operation, and marketing. Third, insufficient controllability in industry-level applications makes it difficult to verify the professionalism of output content and ensure its practical implementation. Therefore, there is an urgent need for an AI service system for the wine industry that can achieve complementary capabilities through multi-model collaborative reasoning and knowledge enhancement mechanisms, combined with multimodal generation and business decision support, to meet the professional, refined, and practical needs of the wine industry's intelligent development. Summary of the Invention

[0007] To address the aforementioned issues, this invention discloses an AI-powered intelligent service system for the wine industry that supports multimodal generation and cross-model inference. This system constructs an intelligent platform that supports dynamic scheduling and collaboration of multiple AI sources, enabling it to autonomously select models and achieve complementary capabilities. Simultaneously, it creates a full-chain intelligent solution covering content creation, compliance review, intelligent customer service, user insights, supply chain management, and B2B decision-making, thereby improving the overall operational efficiency and user experience of the wine industry.

[0008] A wine industry AI-powered intelligent service system supporting multimodal generation and cross-model inference mainly includes:

[0009] The multimodal fusion intelligent question answering module is used to perform multimodal parsing of user-input text, images and files. It distributes tasks through intent recognition and keyword routing mechanisms, and uses multi-model fault-tolerant queues and finite state machine mechanisms to generate marketing copy, visual content or structured contracts that conform to the wine industry standards. It mainly includes wine promotion intelligent agents, visual generation intelligent agents, image recognition OCR intelligent agents, and contract generation and review intelligent agents.

[0010] The knowledge-enhanced cross-model reasoning intelligent customer service module is used to build an intelligent agent with proactive interaction capabilities. By calculating the user's scrolling depth and dwell time perception behavior characteristics, combined with retrieval augmented generation (RAG) technology and dual-path reordering mechanism, it calls a large language model cluster to perform cross-model collaborative reasoning and multi-turn dialogue in professional fields.

[0011] The intelligent analysis and decision support module for industry data integrates multi-source heterogeneous business data, generates visual dashboards through dynamic multi-dimensional analysis models, and provides intelligent product selection recommendations and inventory replenishment early warning decisions for wine companies and terminal stores based on collaborative filtering algorithms and time series prediction models.

[0012] Furthermore, the multimodal fusion intelligent question-answering module specifically includes:

[0013] The multimodal input parsing unit is used to perform structured processing on user-uploaded data: word segmentation and vectorization encoding of text; extraction of visual features from images; extraction of metadata and unstructured text from wine contract documents;

[0014] The semantic understanding and intent recognition unit is used to parse user commands based on a large language model, use a preset keyword routing mechanism (such as "WeChat Moments", "Xiaohongshu", etc.) to divert tasks to the corresponding scene templates, and transform unstructured requirements into routing commands that the system can execute.

[0015] The intelligent question-answering generation unit is used to schedule the output results of the corresponding intelligent agents. In the image recognition OCR intelligent agent, a static model priority configuration queue is built, and a finite state machine (FSM) is used for task scheduling. When a high-priority model returns a service unavailable or authentication failure status code, it automatically switches to a backup model node until a response containing non-empty text content is obtained, ensuring the stability of the recognition service. In the alcohol promotion intelligent agent, the generated content is matched and detected against alcohol industry sensitive words and compliance rules.

[0016] The knowledge-enhanced cross-model reasoning intelligent customer service module specifically includes:

[0017] The user behavior perception and proactive interaction unit is used to build a built-in page behavior monitor, construct user behavior vectors in real time, determine user intent by calculating scroll depth ratio and silent time, and trigger proactive interaction signaling when a preset threshold is met, generating proactive greeting content that integrates user interest tags.

[0018] The cross-model collaborative reasoning scheduling unit is used to execute hybrid strategy routing. When a recommendation keyword is hit, the business logic is executed directly. When the keyword is not hit, the request is encapsulated and distributed to the user-specified or system default base model through the API adaptation layer, realizing the complementarity and fallback of cross-model capabilities.

[0019] The knowledge-enhanced multi-turn dialogue management unit is used to build a knowledge base for the vertical field of the wine industry. It adopts a parent-child segmentation strategy to segment and vectorize knowledge. During the dialogue, it performs hybrid retrieval, combining the BM25 algorithm and the Cross-Encoder reordering model to filter the Top-K relevant knowledge fragments as context input to the large language model, so as to achieve accurate question answering based on facts.

[0020] Furthermore, the user behavior perception and proactive interaction unit calculates the scroll depth ratio through the following logic to trigger proactive interaction:

[0021] ,

[0022] In the formula, Indicates the scroll depth ratio. This indicates the height of the scrolled page. Indicates the total height of the page content. Indicates the current viewport height; the system sets a depth threshold. When detected And the time spent in the bottom area The system determines that the user has completed the information scan and triggers an active service.

[0023] The intelligent analysis and decision support module for industry data includes a dynamic multidimensional analysis unit, specifically comprising:

[0024] The multi-source data integration and preprocessing unit is used to automatically identify and connect to business databases such as SQLite or MySQL through a unified access interface; at the same time, it automatically identifies changes in table structure and mapping of key business fields, and uses built-in multi-dimensional dynamic filters to support data filtering by region and category.

[0025] The dynamic multidimensional analysis and visualization unit is used to build regional and category sales matrices and price band analysis models, map the analysis results into heat maps and stacked bar charts, display regional consumer preferences and channel price sensitivity in real time, and calculate the return on investment (ROI) of advertising to quantify marketing effectiveness.

[0026] The intelligent recommendation and decision support unit is used to calculate the similarity between alcoholic beverages based on business scenario parameters and collaborative filtering algorithms, predict the preference scores of users or stores for candidate products, and generate recommendation schemes. At the same time, it combines a time-series prediction model that incorporates holiday factors to calculate future sales, and generates a replenishment warning list when the predicted value exceeds the current inventory.

[0027] Furthermore, the intelligent recommendation and decision support unit uses an item-based collaborative filtering algorithm to calculate product similarity, as shown in the following formula:

[0028] ,

[0029] In the formula, This represents the similarity between alcoholic beverage product i and product j. This represents user u's purchase or rating behavior for alcoholic beverage product i, where U is the set of users who have rated both product i and product j. The system selects the set of products that best matches the target scenario based on the similarity matrix and generates recommendation cards by sorting them according to the predicted scores.

[0030] Significant effects of the present invention:

[0031] 1. The multimodal fusion intelligent question answering module, the knowledge-enhanced cross-model reasoning intelligent customer service module, and the intelligent analysis and decision support module for industry data constructed in this invention work together to form an AI intelligent service system for the wine industry that supports multimodal generation and cross-model reasoning, significantly improving the digital operation level of the wine industry.

[0032] 2. To address the instability of multimodal model invocation, this invention innovatively introduces a multimodal fault-tolerance mechanism based on a static priority queue and a finite state machine (FSM), improving the robustness and low-latency response capability of the system service. Compared to traditional technologies that rely solely on a specific API, this system can automatically identify abnormal states such as service overload or authentication failure and switch to a backup model within milliseconds. This not only ensures business continuity in high-concurrency scenarios such as beverage order recognition but also effectively balances model invocation cost and recognition accuracy through a priority strategy.

[0033] 3. Addressing the stringent advertising regulations and distribution contract risks inherent in the liquor industry, this invention incorporates an intelligent agent for liquor promotion and a smart agent for contract review, achieving end-to-end compliance and risk control within the vertical industry. Through a pre-built sensitive word database and a mechanism to block inappropriate expressions, it ensures that generated marketing copy complies with industry regulations. Simultaneously, by combining a vectorized clause matrix with semantic analysis of contracts, it can automatically identify legal risks such as unequal liability and unclear authorization. This mechanism effectively reduces compliance risks and legal hazards for liquor companies in their digital operations.

[0034] 4. This invention breaks away from the traditional passive customer service model of waiting for user inquiries, achieving a service model upgrade from "passive Q&A" to "proactive care." It utilizes micro-behavior perception technology based on DOM event flow and leverages scroll depth ratio... By quantifying dwell time, this mechanism accurately captures users' potential interests or churn tendencies. This proactive interaction mechanism provides timely, personalized guidance when users hesitate, significantly improving user retention and sales conversion rates in the alcohol e-commerce sector, creating an intelligent service with a "digital consultant" experience.

[0035] 5. Addressing the pain points of high knowledge barriers and the susceptibility of general-purpose models to factual errors in the wine industry, this invention employs a Retrieval Enhancement Generation (RAG) strategy based on parent-child segmented indexing. This strategy resolves the issues of fragmented and "illusion" knowledge within vertical domains, ensuring the rigor of professional Q&A. By performing coarse-grained and fine-grained two-layer segmentation of knowledge units, it utilizes sub-block vectors to improve retrieval accuracy while preserving the complete technical context through parent blocks. This mechanism effectively avoids model inference breaks caused by missing context, ensuring that the generated tasting reports and cultural interpretations are highly aligned with industry standards in terms of professionalism.

[0036] 6. This invention integrates multi-source business data to construct a precise decision-making and inventory optimization system for the wine industry. In terms of intelligent recommendation, it employs an item-based weighted collaborative filtering algorithm, calculating user-product interaction behavior through a specific similarity formula to achieve accurate product selection recommendations. Regarding inventory management, it introduces a time-series forecasting model incorporating holiday correction factors, effectively solving the forecasting challenges caused by the strong seasonal fluctuations in wine sales. It automatically generates scientific replenishment warnings and shelf-ready strategies, helping companies optimize inventory costs and improve turnover efficiency. Attached Figure Description

[0037] Figure 1 This is a functional module diagram of an AI-powered intelligent service system for the wine industry that supports multimodal generation and cross-model reasoning, as proposed in this invention.

[0038] Figure 2 This is a system framework diagram of an AI-powered intelligent service system for the wine industry that supports multimodal generation and cross-model reasoning, as proposed in this invention.

[0039] Figure 3 This is a schematic diagram illustrating the specific operation steps of the multimodal fusion intelligent question-answering module in an embodiment of the present invention.

[0040] Figure 4 This is a schematic diagram illustrating the specific operation steps of the intelligent customer service module with knowledge-enhanced cross-model reasoning in an embodiment of the present invention.

[0041] Figure 5 This is a schematic diagram illustrating the specific operation steps of the intelligent analysis and decision support module for industry data in an embodiment of the present invention. Detailed Implementation

[0042] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, and the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0043] The AI-powered intelligent service system for the wine industry described in this invention employs an all-in-one container technology, fully encapsulating multimodal algorithm models, business logic services, and data processing engines within a single system image. This constructs a self-contained, portable intelligent service closed-loop environment and generates a unified Docker image based on layered construction technology. The image integrates a unified computing architecture runtime library adapted to different GPU architectures, shielding it from heterogeneous underlying hardware and ensuring the system can directly access hardware computing power on a single server. A Python interpreter and deep learning framework are pre-installed within the image. All system dependencies are statically compiled and version-locked, eliminating "environment consistency" issues. Within the single container, a lightweight process manager is introduced as the root process, responsible for simultaneously starting and monitoring multiple core sub-services.

[0044] With the support of the aforementioned underlying architecture, see Figure 1 The document showcases the functional module architecture of the system of this invention. The system is mainly divided into a multimodal fusion intelligent question-answering module, a knowledge-enhanced cross-model reasoning intelligent customer service module, and a data-driven intelligent analysis and decision support module, with specific implementation pages provided.

[0045] See Figure 2 The diagram illustrates the system framework of this invention, which mainly includes the specific units and brief functions of the multimodal fusion intelligent question answering module, the knowledge-enhanced cross-model reasoning intelligent customer service module, and the industry data-oriented intelligent analysis and decision support module.

[0046] See Figure 3 The specific implementation steps of the multimodal fusion intelligent question-answering module are as follows, which is used for marketing content generation, business document processing, and contract compliance review in the vertical field of alcoholic beverages:

[0047] (1) Multimodal input parsing: Users upload text, images, or files through the front end. After receiving the data, the system performs input parsing, which mainly includes:

[0048] Text: Receives natural language commands from users, cleans the text, removes irrelevant characters, segments the text, performs vectorization encoding, and marks key entities;

[0049] Image: Preliminary feature extraction is performed on user-uploaded wine receipts;

[0050] Documents: For alcohol contracts, the system uses a document parsing engine to extract the document's metadata and unstructured text data.

[0051] (2) Semantic understanding and intent recognition: This unit adopts a generative intent recognition architecture based on the Large Language Model (LLM), which utilizes the powerful semantic reasoning capabilities of LLM to achieve in-depth analysis and accurate routing of complex user commands.

[0052] Text semantic understanding: The system uses a pre-trained language model to parse the user's input natural language and extract deep semantic features and contextual information. Simultaneously, it combines structured information from the wine industry to map text entities to specific visual generation parameters.

[0053] Intent Classification and Routing: The system has a built-in keyword routing mechanism that guides the LLM (Local Management Module) to perform logical reasoning and matching by constructing structured classification descriptions and advanced instructions. The system pre-defines specific business category tags and their natural language definitions in the Prompt, such as "WeChat Moments" and "Xiaohongshu" (Little Red Book). Based on these descriptions and rules, the LLM compares the user input and calculates the best-matching category through logical reasoning, solving the classification problem in semantically overlapping areas. The reasoning results are then transformed into executable routing instructions, enabling automated navigation of the business process to the corresponding scenario template and prompt word chain.

[0054] (3) Intelligent Question Answer Generation: Based on the identified task type, the system intelligently schedules the corresponding workflow or large language model, selecting technologies such as Dify, SiliconFlow, Tongyi Qianwen, Xunfei Xinghuo, and Huoshan Ark. Finally, it generates and outputs results in various formats, including text answers, images, videos, or files. The specific implementation process is described below.

[0055] The intelligent agent for alcohol promotion: The system uses pre-set Prompt templates to generate differentiated copy for the alcohol industry with a single click. For example, for Xiaohongshu (Little Red Book) copy, it automatically extracts the sensory highlights of the wine, combines them with emojis to generate "planting the seed" notes focusing on lifestyle and emotional resonance, and generates trending hashtags; live-stream copy includes structured scripts that introduce pain points, explain product advantages, and guide sales. After generation, the system automatically triggers a compliance review mechanism for alcohol promotional content. By building a database of sensitive words and prohibited expressions specific to the alcohol industry (such as those involving exaggerated efficacy or misleading language), the system performs string and rule matching checks on the generated text; if non-compliant content is detected, the output is interrupted and the user is prompted to make corrections, thereby reducing compliance risks in alcohol marketing.

[0056] Visual Generation Agent: For the image generation part, the system sends a request to the image generation model service interface. The high-resolution image link returned by the model is rendered by the system to the front end for display, thus achieving low-latency generation of visual content related to alcohol. For the video generation part, given the time-consuming nature of video rendering, this system calls the Volcano Engine API, employing a two-stage processing mechanism of task submission and asynchronous query. After the user submits the video script, the backend service sends a task creation request to the video generation model platform, which includes a storyboard description optimized for alcohol display. Subsequently, the system polls the task status according to a preset time strategy based on the unique task identifier returned by the model platform. When a task completion status is detected, the generated video resource address is extracted and returned to the front end for display.

[0057] Image recognition OCR agent:

[0058] For the scenario of beverage receipt recognition, this embodiment designs the following specific execution structure. The system pre-builds a static model priority configuration queue in memory space, denoted as... The model nodes are arranged sequentially according to a comprehensive performance-cost ratio. Each node's data structure includes a model identifier, interface service endpoint, maximum token count, and random sampling parameters. When the system receives the binary stream file corresponding to the beverage receipt image uploaded from the front end, it initiates a finite state machine (FSM) for task scheduling. First, the MIME type of the binary file is validated. After successful validation, the image data is converted to Base64 encoding using a buffer stream processing mechanism, and a standardized JSON request body is constructed accordingly. This body embeds system-level prompts for text extraction from the beverage receipt, ensuring the integrity of the model's output recognition results and consistency with the original layout. Subsequently, the system initializes the model pointer i=0 and reads the current model node M from the priority queue. i The system sends an identification request to the corresponding model server endpoint via HTTP POST. Simultaneously, a built-in error feature matching module performs rule-based parsing and regular expression matching on the HTTP status codes and error messages returned by the model. When a "403 Forbidden" status code is detected, and the error message contains a model inactive feature field; or a "429 Too Many Requests" status code indicates concurrency limiting; or a "503 Service Unavailable" status code indicates service overload, the finite state machine determines that the current model node is unavailable and automatically executes a pointer increment operation i=i+1, seamlessly switching to the next backup model M. i+1Retry. If a "400 Bad Request" status code is returned, indicating an abnormal image data format, or a "401 Unauthorized" status code is returned, indicating global authentication failure, the system determines the retry is invalid, immediately interrupts the queue, and returns an error message to the front end. Once any model returns a "200 OK" status code and the response body contains a non-empty text content field, the state machine immediately terminates its iteration, records the currently active model ID to metadata, and returns the cleaned structured text to the front end.

[0059] If pointer i has traversed all elements in the queue without receiving a valid response, the system triggers a global circuit breaker exception, returns a specific status code, and outputs the message "The current account has not activated any available OCR visual models." At the same time, the complete fault chain is recorded in the log so that the administrator can perform service governance.

[0060] Contract Generation and Review Agent: Users input the contract title and requirement description for a wine-related business scenario. Based on the user-inputted information about the parties involved and the transaction terms, the system automatically generates a complete and standardized contract text file using a large language model, and outputs it as a file for user download and archiving. After a user uploads an existing wine contract file, the system parses the contract text and uses a contract review model to analyze issues such as legal risks, unequal liability, and unclear authorization in the contract terms, ultimately generating a structured contract review report.

[0061] See Table 1 for specific test results.

[0062] Table 1 Test Table for Multimodal Fusion Intelligent Question Answering Module

[0063] Test function points Test case description Expected results Actual results Test status Keyword routing Enter "Little Red Book (Xiaohongshu) captions for red wine recommendations" Trigger specific business logic processing Match keywords to return copy that matches the Xiaohongshu style. pass Image recognition (OCR) Upload invoice image for recognition Recognize document text and return field information The document text was successfully recognized, field information was returned, and the document was successfully copied. pass Contract tools Upload a contract that lacks a breach of contract clause. The system identifies missing clauses and provides risk warnings and improvement suggestions. Accurately identify missing "liability for breach of contract" clauses, provide three specific modification suggestions, and offer document downloads. pass

[0064] See Figure 4 The specific implementation steps of the knowledge-enhanced cross-model reasoning intelligent customer service module are as follows, applicable to application scenarios such as wine consultation, wine selection recommendation, tasting explanation and industry knowledge Q&A:

[0065] (1) User Behavior Awareness and Proactive Interaction: This unit has a built-in page behavior monitor and sliding window counter. It constructs user behavior vectors in real time by listening to the DOM event stream and triggers proactive interaction signals based on the following quantification logic. First, the system calculates the user's scroll depth ratio in real time. ,in, This indicates the height of the scrolled page. Indicates the total height of the page content. Indicates the current viewport height. Sets the depth threshold. When the user scroll depth is detected And the time spent in the bottom area At this point, the system determines that the user has completed a full scan of the current information. Secondly, the system maintains a global silent timer. The timer is reset whenever a scrolling event, click event, or mouse movement event is detected. If the timer accumulates... (No action for 30 seconds), and the current session's interaction depth (If the user has previously scrolled or clicked,) the system determines that the user is in a potential churn state. When any of the above proactive interaction trigger conditions are met, the system calls the proactive interaction session instance, performs weighted fusion processing on the current user behavior characteristics and the interest tags pre-labeled in the user profile, and generates proactive interaction content.

[0066] (2) Cross-model collaborative reasoning scheduling: Before sending user input to the large model, the system executes hybrid strategy routing through the front-end preprocessing layer. When the user inputs recommendation keywords, the system intercepts the large model request, directly executes the pre-set business logic, and jumps to the recommended wine product page, achieving millisecond-level response and reducing model call costs. When the rule keywords are not matched, the system encapsulates the user input into a standardized request object and enters the model call process. If it is detected that the user has explicitly selected a specific model on the front-end interface, the system sends the encapsulated Prompt to the interface of the specified model through the API adaptation layer; if it is detected that the user has selected "automatic mode" or is in an unconfigured state, the system triggers the default routing strategy and automatically points the request to the pre-set baseline model (configured as Doubao model in this embodiment). This strategy uses a pre-set highly stable model as a fallback to ensure that a standardized intelligent response can still be obtained when the user has no clear preference, avoiding service interruption caused by difficulty in model selection.

[0067] (3) Knowledge-Enhanced Multi-Turn Dialogue Management: This embodiment proposes a knowledge-enhanced multi-turn dialogue management method for the vertical field of the wine industry, with the corresponding execution subject being an intelligent customer service agent. This method relies on the Dify platform to construct a dialogue flow orchestration and knowledge retrieval mechanism, employing a Retrieval Enhancement Generation (RAG) technical architecture to achieve knowledge enhancement and inference constraints on the Large Language Model (LLM). Unlike traditional methods that rely on full parameter fine-tuning, this embodiment significantly improves the professionalism, accuracy, and interpretability of the model in the wine industry scenario without changing the basic model parameters through an external structured wine industry knowledge base. Its core process includes the following two stages.

[0068] Construction and processing of vertical domain knowledge bases

[0069] To address the specialized characteristics of the liquor industry, this embodiment constructs a domain knowledge base covering the entire liquor industry chain, including but not limited to: product dimension (product information, brand lineage, vintage characteristics, and aroma classification of various liquors such as baijiu, wine, beer, huangjiu, and spirits); process dimension (brewing techniques such as raw material selection, fermentation methods, distillation processes, and aging cycles); flavor dimension (sensory evaluation indicators, tasting terminology, and flavor wheel descriptions); cultural dimension (historical evolution of liquors, regional cultural labels, geographical indications of famous liquor-producing areas, and knowledge of liquor etiquette and customs); and market dimension (price ranges, consumption scenario classifications, and pairing recommendations). The above knowledge can be derived from internal company documents, industry standard documents, or authoritative publicly available data, and the knowledge base content can be updated as needed.

[0070] To avoid the semantic fragmentation problem caused by traditional character-length segmentation, this embodiment adopts a parent-child segmentation strategy. The system uses "complete knowledge units" as the granularity, employing custom regular expressions (such as chapter titles) for coarse-grained segmentation to construct parent blocks, ensuring the preservation of complete technical context. Within the parent block, fine-grained segmentation is further performed using paragraphs or punctuation marks to generate child blocks, ensuring that cross-segment information is not lost at slice boundaries. All child blocks are associated with their parent blocks through metadata fields. Then, a pre-built embedding model is invoked to vectorize only the child blocks, mapping them to high-dimensional semantic vectors. .

[0071] Knowledge retrieval and dialogue generation

[0072] When a user initiates a conversation, the system executes a high-precision recall process combining hybrid retrieval and two-stage re-ranking. The specific steps are as follows: The system receives the user's input q, performs word segmentation and noise reduction on it, and extracts key constraint information, such as type of wine, aroma type, and usage scenario. Then, a dual-path retrieval is performed, using an embedding model to transform the user's query q into a high-dimensional query vector. Calculate its relationship with the sub-block vector in the knowledge base. Cosine similarity: Then, the BM25 (Best Matching 25) algorithm is used to perform precise matching between the user query and the document database, focusing on capturing proper nouns and key entities. The system calculates the relevance score Score(D,Q) between query Q and document D, and the calculation formula is as follows:

[0073] ,

[0074] in, For the i-th word in the user's query; Indicator The word frequency in document D; |D| is the length of the current document D; avgdl is the average length of the document set; b are adjustment parameters, used to control the effects of word frequency saturation and document length normalization, respectively. Inverse document frequency (IVF) is used to measure word frequency. Discrimination level.

[0075] The system is based on preset weight coefficients. and By combining the Embedding model's ability to generalize and understand synonyms and paraphrased sentences with the BM25 algorithm's ability to precisely match specific entities, the two retrieval results are weighted and fused to calculate the final recall score:

[0076] ,

[0077] After obtaining a preliminary set of candidate sub-blocks based on the Final_Score, the system automatically backtracks and loads the corresponding complete parent block text based on the preliminary candidate sub-block ID. The Cross-Encoder reordering model is invoked to perform deep semantic interaction scoring on the "user question-parent block text" pair, rearranging the order of candidate documents and selecting the Top-K parent block fragments with the highest relevance. Finally, the system uses the selected Top-K parent blocks as external knowledge context, combined with the system's preset Prompt and the user's original question, to construct a structured prompt word template input to the large language model. The system schedules different LLMs to execute generation tasks according to the user's selection, and the user can provide feedback on the answers to achieve multi-round question answering.

[0078] See Table 2 for specific test results.

[0079] Table 2 Test Table for the Knowledge-Enhanced Cross-Model Reasoning Intelligent Customer Service Module

[0080] Test function points Test case description Expected results Actual results Test status User Behavior Awareness The page remains idle for 10 seconds. Trigger proactive greeting message Send "It's a pleasure to serve you again. Do you have any new alcoholic beverage requests?" after 10 seconds. pass Multi-model scheduling Switching between different AI models for dialogue Different models return differentiated responses The response styles of the Doubao and DeepSeek models differ significantly. pass Alcohol Knowledge Base Question: "What is the aroma profile of Moutai liquor?" Accurately answer "soy sauce aroma type", and may supplement with its processing characteristics. The answer was accurately "soy sauce flavor type," and a brief introduction to the raw materials and processing methods was provided. pass Mall Database Asking "Can you recommend some wine for me?" The returned recommendations should be directly linked to products currently available in the store and include clickable product links to guide users to view them. Clickable links have been successfully displayed; clicking them will take you to the recommended products. pass Contextual logical reasoning First, tell customer service, "I like full-bodied baijiu, but I don't like the sauce-flavored type." Then ask, "Would Moutai suit me?" The system can perform logical reasoning based on context to determine that Moutai is a sauce-flavored liquor, which conflicts with user preferences, and thus provide a negative recommendation. Answer: "Moutai is a typical soy sauce-flavored liquor, which might not be suitable for you based on your preferences. I recommend a strong-flavored liquor..." pass Persistence of conversations Refresh the page and re-enter. Maintain historical dialogue records Successfully loaded the history of the last 5 rounds of conversation pass

[0081] See Figure 5 The specific implementation steps of the intelligent analysis and decision support module for industry data are as follows: it is used to systematically analyze data on alcoholic beverages, channels, and users, and to provide interpretable business decision support for alcohol companies, distributors, and terminal stores:

[0082] (1) Multi-source data integration and preprocessing: The system automatically monitors and connects to multiple data sources, including product information data, product sales records, inventory data, etc., and completes the initialization and connection configuration of the underlying database. By automatically identifying the corresponding fields of the data tables, the system dynamically builds and updates the data pool for analysis.

[0083] (2) Dynamic Multidimensional Analysis and Visualization: The system continuously monitors changes in the data pool and performs calculations using specific analytical models for the wine industry. The system builds an interactive analysis dashboard based on the Streamlit framework, synchronizing the analysis results to the front end in a graphical manner in real time, achieving dynamic visualization of key operating indicators for the wine industry, specifically including:

[0084] Regional and Category Heat Mapping: The system constructs a regional-category sales matrix using administrative regions (provinces, cities) and alcoholic beverage categories (such as baijiu, red wine, beer, huangjiu, etc.) as two dimensions. .in, This represents the cumulative sales volume of alcoholic beverage category k within region r. The system maps this matrix to a heatmap to identify regional best-selling alcoholic beverages and regional consumer preferences.

[0085] Price range and channel sensitivity analysis: Dividing alcoholic beverage products into different price ranges The data also includes statistics on the sales volume and revenue contribution of each price range across different sales channels. .in, This represents the sales revenue of price band l within channel c. The system generates a stacked bar chart to help wineries identify the main price ranges and price-sensitive characteristics of each channel.

[0086] Advertising performance attribution: The system correlates different ad types with sales conversion results for alcoholic beverages to calculate the return on investment (ROI). .in, Revenue generated after advertising campaign. Based on pre-launch sales revenue. The cost of advertising is used to quantify the impact of different marketing actions on the sales of various alcoholic beverage categories.

[0087] (3) Intelligent recommendation and decision support: mainly for B-end wine merchants, distributors and terminal stores, providing intelligent decision-making services based on algorithm models.

[0088] In the intelligent recommendation section, the system receives user-input parameters related to the business scenario, including geographical location, target audience age group, consumption scenario, and store type. Based on its alcohol product database, the system automatically matches the most suitable SKU combination for that business scenario and outputs a comprehensive decision-making solution that includes "alcohol information recommendations, store operation recommendations, and product listing suggestions."

[0089] The system uses an item-based collaborative filtering algorithm to perform similarity matching on alcoholic beverages. The similarity calculation formula is as follows:

[0090] ,

[0091] in, Let U represent user u’s purchase or rating behavior for alcoholic beverage product i, where U is the set of users who have rated both product i and product j.

[0092] Based on the calculated similarity matrix, the system selects the K sets S(i) of alcoholic beverages that best match the target scenario and predicts the potential preference score of users or stores for candidate product i:

[0093] ,

[0094] The system is based on the predicted score The cards are sorted from highest to lowest quality, and the final recommendation cards displayed on the front end are generated.

[0095] The decision support section primarily focuses on time-series forecasting and inventory alerts. Given the strong correlation between alcohol sales and holidays and seasonality, the system incorporates a built-in time-series forecasting model with holiday factor corrections to predict future sales. Based on this, the system calculates replenishment demand using real-time inventory data. When predicted sales exceed current inventory, an inventory alert is automatically triggered, and a sorted replenishment suggestion list is generated according to the replenishment demand.

[0096] See Table 3 for specific test results.

[0097] Table 3 Test Table for Intelligent Analysis and Decision Support Module for Industry Data

[0098] Test function points Test case description Expected results Actual results Test status Sales trend forecast Select products to forecast 30-day sales. Return the predicted data and confidence intervals Generate a 30-day forecast curve and provide the confidence interval. pass Inventory warning View the list of low-inventory items Displays items that need restocking and the recommended quantity. Provide suggestions on restocking the product and its urgency. pass User segmentation analysis Upload user transaction data file Complete user value segmentation Successfully clustered and exported analysis results pass Intelligent wine recommendation system Fill in the store and customer information, then click "Recommend". Based on the store's location, type, target customer group, and consumption scenario, intelligent wine recommendations are generated. Output wine information, business data, and shelf placement suggestions. pass Data visualization analysis Access data visualization platform Page loaded successfully; core KPI indicator cards displayed correctly; main chart containers rendered successfully. Page load time < 5 seconds; chart rendering success rate 100%; core indicator data accuracy 100%. pass

[0099] The technical means disclosed in this invention are not limited to those disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features.

Claims

1. A wine industry AI intelligent service system supporting multimodal generation and cross-model reasoning, characterized in that, The system includes the following modules: The multimodal fusion intelligent question answering module is used to receive and parse user input such as text, images, and documents related to the wine industry. Specifically, for wine invoice images, cross-model calls and fault-tolerant switching are performed through a pre-built static priority queue in memory and a finite state machine (FSM) scheduling mechanism to extract structured features containing wine product SKUs and vintages. For natural language input, a sensitive word and illegal expression feature library based on the wine industry advertising law is configured. After the content is generated, string and rule matching detection is performed to intercept illegal content involving exaggerated efficacy claims. The knowledge-enhanced cross-model reasoning intelligent customer service module is used to monitor the DOM event stream of the front-end page in real time. It triggers proactive interaction based on the quantitative formula of scroll depth ratio and page dwell time. It also adopts the retrieval enhancement generation RAG technology based on parent-child segmented index to divide wine documents into coarse-grained parent blocks and fine-grained child blocks. It only performs vectorized retrieval on child blocks and recalls the corresponding parent blocks to generate professional answers containing complete brewing process and flavor context. The intelligent analysis and decision support module for industry data is used to integrate multi-source heterogeneous data from wine companies to build a dynamic multi-dimensional analysis system that includes regional and category heatmap analysis, price band and channel sensitivity analysis, and advertising effectiveness attribution. It also combines a time-series forecasting model that incorporates holiday correction factors to adapt to seasonal fluctuations in wine sales, as well as an item-based collaborative filtering algorithm, to generate intelligent product selection recommendations and dynamic inventory replenishment strategies.

2. The AI-powered intelligent service system for the wine industry supporting multimodal generation and cross-model reasoning as described in claim 1, characterized in that: The multimodal fusion intelligent question-answering module includes: The multimodal input parsing unit is used to perform structured processing on user-uploaded data: word segmentation and vectorization encoding of text; extraction of visual features from images; and extraction of terms metadata and unstructured text data from wine contract documents using a document parsing engine. The semantic understanding and intent recognition unit has a built-in keyword routing mechanism. By pre-setting specific alcohol business category labels and their natural language definitions in the Prompt, it guides the pre-trained language model to compare user input for logical reasoning, calculate the best matching category, and transform complex instructions into system-executable routing instructions to be routed to the corresponding business scenarios. The intelligent question-and-answer generation unit is used to schedule intelligent agents for alcohol promotion, visual generation, image recognition OCR, or contract generation and review based on the identified task type. Among them, for the visual generation agent, a two-stage processing mechanism of task submission and asynchronous query is adopted. The task status is polled according to a preset strategy based on a unique task identifier until the generated visual content of alcohol is obtained.

3. The AI-powered intelligent service system for the wine industry supporting multimodal generation and cross-model reasoning as described in claim 2, characterized in that: The specific execution logic of the image recognition OCR agent is as follows: a static priority queue containing multiple models is pre-built in memory. Each node contains a model identifier and a cost indicator; when a beverage order image stream is received, a finite state machine is started for task scheduling, the model pointer i is initialized to 0, and the model nodes M in the queue are read sequentially. i Send an identification request; perform regular expression matching on the HTTP status codes and error messages returned by the model: if a status code indicates service overload or concurrency throttling, determine that the current model node is unavailable and automatically increment the pointer i=i+1, switching to the next backup model M. i+1 If a status code indicating authentication failure or abnormal data format is detected, the queue is interrupted until any model returns a response containing valid text content, at which point structured text containing VAT invoice code, alcohol product SKU, and logistics tracking number is extracted. If there is still no valid response after traversing the queue, a global circuit breaker will be triggered.

4. The AI-powered intelligent service system for the wine industry supporting multimodal generation and cross-model inference as described in claim 2, characterized in that: The intelligent question-and-answer generation unit also includes an intelligent agent for alcohol promotion and an intelligent agent for contract review: The intelligent agent for alcohol promotion is configured with a database of sensitive words and illegal expressions based on the advertising law for the alcohol industry. After generating the copy, it automatically performs string and rule matching detection to identify and block illegal content involving exaggerated efficacy or inducement to drink; The intelligent agent for contract review is used to parse alcohol distribution contract documents, extract clause metadata, perform semantic analysis based on a vectorized clause matrix, identify clauses that lack equivalence of responsibility or unclear authorization, and generate a structured review report containing risk point prompts.

5. The AI-powered intelligent service system for the wine industry supporting multimodal generation and cross-model reasoning according to claim 1, characterized in that: The knowledge-enhanced cross-model reasoning intelligent customer service module includes: The user behavior perception and proactive interaction unit has a built-in page behavior monitor and silent timer. It listens to the DOM event stream of the front-end page in real time, constructs a user behavior vector by calculating the scroll depth ratio and page dwell time, determines whether the user is in a deep reading state or a potential churn state, and triggers proactive interaction signals when the preset threshold conditions are met, generating greeting content that integrates user interest tags. The cross-model collaborative reasoning scheduling unit is used to perform hybrid strategy routing before sending user input to the large model: when the user input is found to match recommendation keywords, the request is directly intercepted and the pre-set business logic is executed; when it does not match, the request is encapsulated and distributed to the user-specified or system default benchmark large language model through the API adaptation layer, so as to achieve the complementarity and fallback of cross-model capabilities. The knowledge-enhanced multi-turn dialogue management unit is used to maintain a vertical domain knowledge base covering information on wine products, brewing processes, flavor descriptions, and wine culture. It adopts parent-child segmented indexing to enhance the generation of RAG technology, uses the retrieved industry knowledge fragments as constraint context input to the large language model, and supports persistent storage and backtracking of multi-turn dialogue history.

6. The AI-powered intelligent service system for the wine industry supporting multimodal generation and cross-model inference as described in claim 5, characterized in that: The user behavior perception and proactive interaction unit calculates the scroll depth ratio using the following formula. To determine user intent and trigger proactive interaction: ， In the formula, This indicates the height of the scrolled page. Indicates the total height of the page content. Indicates the current viewport height. When detected... Exceeding the preset threshold And the length of stay in the area meets the requirements. When, or when the accumulated time of the silent timer is detected. And the depth of interaction When triggered, proactive interaction is achieved, and personalized content is pushed based on the user's alcohol preference tags in the user profile.

7. The AI-powered intelligent service system for the wine industry supporting multimodal generation and cross-model inference as described in claim 5, characterized in that: The knowledge-enhanced multi-turn dialogue management unit specifically adopts the following strategy, combining parent-child segmented indexing with dual-path hybrid retrieval: During the knowledge base construction phase, using complete knowledge units as the granularity, regular expressions are used to perform coarse-grained segmentation of documents such as wine product information, brewing process, flavor description, and wine culture, constructing a parent block that retains the complete technical context, and using punctuation marks to perform fine-grained segmentation within the parent block to generate sub-blocks, and establishing a unique index mapping relationship between the sub-blocks and their parent blocks through metadata fields; During the vectorization process, the Embedding model is invoked to vectorize only the sub-blocks, mapping them into high-dimensional semantic vectors and storing them. During the retrieval and recall phase, a dual-path hybrid retrieval is performed: the first path uses the Embedding model to transform the user query q into a query vector. Calculate its relationship with the sub-block vector cosine similarity The second approach uses the BM25 algorithm to calculate the keyword matching between the user query and the sub-block, and calculates the relevance score Score(D,Q): ， In the formula, For the i-th word in the user's query; Indicator The word frequency in document D; |D| is the length of the current document D; avgdl is the average length of the document set; b is an adjustment parameter; Inverse document frequency; The system is based on preset weight coefficients. and The two search results are weighted and fused to calculate the final recall score: 。 During the reordering and generation phase, a preliminary set of candidate sub-blocks is obtained based on the Final_Score. The corresponding complete parent block text is automatically backtracked and loaded using the index mapping relationship. The Cross-Encoder reordering model is called to perform deep semantic interaction scoring on the user query and the parent block text. The Top-K parent blocks with the highest relevance are selected as context input to the large language model.

8. The AI-powered intelligent service system for the wine industry supporting multimodal generation and cross-model reasoning according to claim 1, characterized in that: The intelligent analysis and decision support module for industry data includes: The multi-source data integration and preprocessing unit connects multiple heterogeneous data sources, including product information, sales records, and inventory data. It automatically monitors field changes and builds an analysis data pool. It also supports filtering of wine data by region, category, and year using built-in multi-dimensional dynamic filters. The dynamic multidimensional analysis and visualization unit is used to combine specific analysis models for the wine industry: constructing a sales matrix based on administrative regions and wine categories, mapping individual sales volume to a heat distribution to identify regional preferences; statistically analyzing the sales distribution characteristics of each price range under different sales channels to construct a channel sensitivity model; and calculating the return on advertising investment to attribute advertising effectiveness. The intelligent recommendation and decision support unit is used to calculate the difference between the corrected predicted sales and real-time inventory based on a time-series forecasting model that incorporates a holiday correction factor, and to provide dynamic early warnings to adapt to the seasonal fluctuations in alcohol sales. At the same time, it uses an item-based collaborative filtering algorithm to calculate the similarity of interaction behaviors between alcohol products and generate intelligent product selection and recommendation schemes.

9. The intelligent analysis and decision support module for industry data according to claim 8, characterized in that, The specific analysis logic of the dynamic multidimensional analysis unit is as follows: Regional and Category Heat Mapping: Constructing a Sales Matrix Based on Administrative Region r and Alcohol Category k ,in For each sales transaction, the matrix values ​​are mapped to a heat map to identify the consumption preferences for specific types of alcohol in different regions. Price Band and Channel Sensitivity Analysis: Alcoholic beverages are divided into different price bands (l), and sales revenue for each price band across different sales channels (c) is calculated. ,in Using unit price, construct the distribution characteristics of price bands across various channels such as catering and retail; Advertising performance attribution: Correlating different advertising types with sales conversion results of alcoholic beverage products to calculate the return on investment (ROI) of advertising. ,in For revenue after deployment, Based on the benchmark income, For advertising costs.

10. The intelligent analysis and decision support module for industry data according to claim 8, characterized in that, The intelligent recommendation and decision support unit uses an item-based collaborative filtering algorithm for product recommendation. Specific steps include: calculating the similarity of user interaction behaviors for different alcoholic beverage products i and j in historical sales data. The specific formula is as follows: ， In the formula, Let U represent the rating or interaction behavior of user u with alcoholic beverage product i, where U is the set of users who have rated the same product. Based on the similarity matrix, select K sets of similar products S(i) and calculate the latent preference prediction value. : ； The system sorts and recommends wines for stocking based on predicted values. This unit also incorporates a time-series forecasting model, incorporating holiday factors to correct the forecast results and adapt to seasonal fluctuations in wine sales. It calculates the difference between the corrected predicted sales volume and real-time inventory, generating an alert list that includes replenishment priorities.

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