Multi-language semantic aggregation and seller behavior tracking system

Abstract

The invention relates to the field of cross-language e-commerce information analysis and intelligent behavior tracking, and provides a multi-language semantic aggregation and seller behavior tracking system. The system comprises a multi-language keyword analysis module, a data acquisition module, a semantic aggregation and clustering module, a dynamic track recording module, a collection and marking module, a visual analysis and display module, a multi-input consistency control module, an external platform docking and multi-source data fusion module and an output and touch module. The system has the advantages of cross-language understanding, multi-source input fusion, multi-dimensional behavior recording, structured behavior comparison, modular output and the like, is widely applicable to cross-border e-commerce industry scenes such as mothers and infants, pets, clothing, consumer electronics and the like, and is also adaptive to various foreign trade task environments such as investment attraction, purchasing, exhibition screening and the like; the method is especially suitable for enterprise customers who need to screen high-potential target sellers in multi-platform, multi-language and multi-behavior signals.

Description

Technical Field

[0001] This application relates to the field of computer information processing technology, and in particular to a multilingual semantic aggregation and seller behavior tracking system. Background Technology

[0002] In traditional cross-border trade, businesses struggle to efficiently acquire information on potential customers from different countries and language environments, particularly in systematically identifying their behavioral patterns and sales trends. Existing solutions, such as SEO analytics tools, manual searches, and multi-platform web crawlers, often focus only on extracting static data at the product or sales level, resulting in low accuracy, poor visualization, weak dynamism, and an inability to process data across languages.

[0003] Furthermore, mainstream platforms (such as Google Shopping, Alibaba, Amazon, and Xiaoman) still have limitations in seller behavior tracking and language aggregation: First, they typically only support keyword retrieval or page scraping, lacking structured semantic understanding and cross-language alignment capabilities. Second, most systems focus on product display and sales trends, lacking a multi-dimensional fusion mechanism based on "behavioral trajectory + semantic clustering." Third, most current tools lack a modular behavioral scoring mechanism, making it impossible to dynamically assess the potential of target sellers or provide refined labeling, thus limiting foreign trade enterprises' ability to identify and continuously follow up with high-quality customers.

[0004] Therefore, there is an urgent need for an intelligent system with multilingual semantic recognition capabilities and support for structural aggregation and dynamic behavior perception, so as to open up the complete path from keywords to behavior analysis, from data aggregation to proactive outreach, and improve the customer discovery and intelligent management capabilities of foreign trade enterprises in a multilingual environment.

[0005] It should be noted that the above content is not necessarily prior art, nor is it intended to limit the scope of patent protection of this application. Summary of the Invention

[0006] This application provides a multilingual semantic aggregation and seller behavior tracking system to solve or alleviate one or more of the technical problems mentioned above.

[0007] The system includes: a multilingual keyword parsing module, a data acquisition module, a semantic aggregation and clustering module, a dynamic trajectory recording module, a collection and tagging module, a visualization analysis and display module, a multi-input consistency control module, an external platform integration and multi-source data fusion module, and an output and outreach module; wherein: The multilingual keyword parsing module is used to receive multilingual keywords input by the user; perform semantic standardization processing on the multilingual keywords; and generate a multilingual keyword vector based on the semantically standardized multilingual keywords. The data acquisition module is used to extract target information of target sellers that match the multilingual keyword vectors from target websites and / or target platforms. The semantic aggregation and clustering module is used to vectorize the target information based on a semantic embedding model to obtain a target vector; and to perform semantic clustering on the target vector to obtain a semantic clustering graph. The dynamic trajectory recording module is used to record the behavioral events of the target seller; the behavioral events include page update events, product release events, and / or activity change events; The collection and tagging module is configured to support users in collecting, categorizing, tagging, and recording the status of the target sellers. The visualization analysis and display module is used to generate system processing results based on the semantic clustering graph; and to display the system processing results through a graphical interface; the graphical interface includes a tag heat map and a target seller behavior trend map; The multi-input consistency control module is used to determine the semantic consistency between information when a user submits information through multiple input paths; and to provide fusion suggestions or correction guidance when conflicts exist; the input paths include keywords, images, web page links and documents; The external platform integration and multi-source data fusion module is used to connect to external platforms or systems to obtain the target seller information and keyword popularity; and to fuse data from multiple input paths to achieve unified field structure and semantic consistency. The output and delivery module is used to export the system processing results into a document in a target format; and to send the document in the target format to the target terminal through a preset method.

[0008] Optionally, the multilingual keyword parsing module includes: a language recognition and preprocessing submodule and multiple translation submodules; wherein, The language recognition and preprocessing submodule is used to identify the language category of the multilingual keywords; and to perform the semantic standardization processing on the multilingual keywords; the semantic standardization processing includes cleaning processing, stop word removal processing, and case normalization processing. The plurality of translation sub-modules are used to determine the target translation sub-module among the plurality of translation sub-modules based on preset performance indicators; the preset performance indicators include response time and translation quality score.

[0009] Optionally, the data acquisition module includes a web crawler submodule, a template parsing submodule, an OCR recognition submodule, and a task retry and exception recovery submodule; wherein, The web crawler submodule is used to extract the content of a specified page based on Playwright or Puppeteer in order to obtain target page data. The template parsing submodule is used to extract target templates from the template library according to preset fields; and to extract structured data information from the target webpage data based on the target templates. The OCR recognition submodule is used to perform optical character recognition on images or documents in the target page data in order to extract unstructured data information. Based on the structured data information and the unstructured data information, the target information of the target seller is obtained; The task retry and exception recovery submodule is used to: detect whether any exceptions occur during the page content extraction process; and execute the corresponding processing strategy if an exception occurs.

[0010] Optionally, the semantic aggregation and clustering module is further configured to: generate text semantic vectors based on the multilingual semantic embedding model and the target information to obtain the target vector; the multilingual semantic embedding model supports unified vector representation of multilingual texts to achieve cross-language semantic association calculation; and perform clustering analysis operations based on the constructed semantic index structure and similarity retrieval mechanism to generate the semantic clustering map; the clustering analysis includes density clustering, hierarchical clustering, or vector distance partitioning.

[0011] Optionally, the dynamic trajectory recording module includes: A behavior log recording submodule is used to record the behavioral events of the target seller; A time stamp submodule for automatically adding timestamps when the target seller's behavioral events occur; An event recognition submodule is used to identify whether the target seller's behavior events have changed based on a preset strategy; The behavioral events include new product launches, page structure changes, promotional activity updates, and changes in shipping frequency.

[0012] Optionally, the collection and tagging module further includes: A collection management submodule used to receive user collection instructions and store the target seller's identifier; A tagging and categorizing submodule for tagging and classifying multi-dimensional information of target sellers; The multi-dimensional information includes the target seller's language origin, semantic cluster number, user-defined grouping, activity level, and key behavioral prompt tags.

[0013] Optionally, the visualization analysis and display module includes a graphical interface construction submodule and an interactive function submodule; The graphical interface construction submodule is used to build a graphical interface based on the front-end framework to support access from multiple terminals and visualization adaptation. The interactive function submodule is configured to implement interactive functions such as keyword highlighting, filtering, switching of semantic clustering graph view, and display of behavior trend heatmap.

[0014] Optionally, the output and outreach module further includes a document export submodule and a notification sending submodule; wherein, The document export submodule is used to export the system processing results into files including Excel, PDF, and HTML formats. The notification sending submodule is used to send the file and related notification information to the target terminal via email service protocol, webhook or network interface.

[0015] Optionally, the system also includes an access control and auditing module to support multi-user authentication, role and permission configuration, and behavior auditing; The access control and auditing module is based on a token mechanism and a role-based access control policy to achieve data isolation and collaborative permission configuration between the main account and sub-accounts.

[0016] Optionally, the system further includes a non-matching item analysis module for detecting anomalous data that fails to match effectively during semantic clustering or behavior tracking. The unmatched item analysis module generates a difference report based on the abnormal data and provides user tips or repair suggestions to help optimize the subsequent analysis process.

[0017] Optionally, the system also includes a behavior log module, used to record the user's operation path, data access trajectory, exported behavior records, and tag configuration change history in the system; The behavior log module supports timestamp marking and categorized retrieval by user, time, or operation type.

[0018] Optionally, the output and reach module includes a link generation and behavior tracking submodule; wherein, the link generation and behavior tracking submodule is used to generate short links and QR codes accessible on mobile devices; collect the access behavior, click paths and conversion status of receiving users, and feed them back to the system for subsequent statistical analysis and recommendation optimization.

[0019] Optionally, the system further includes a multilingual template management module, used to generate corresponding output templates based on the target language, industry, and usage scenario; The output template includes a field arrangement structure, content display logic, and style rendering rules, and supports user-defined field selection and template adjustment.

[0020] Optionally, the system further includes a collection synchronization module and a sending record module; wherein, The collection synchronization module is used to synchronize collection data and tag information between the main account and sub-accounts, so as to realize cross-account data sharing and permission inheritance; The sending record module is used to record historical sending behavior information, including sending time, recipient, export format, and sending channel, to support behavior auditing and subsequent tracking. Optionally, the system is configured with offline access support and a local caching mechanism to retain system analysis results and user interaction records within a certain time range when the terminal device has no network connection; after the network is restored, the cached data is automatically synchronized to the remote server to ensure task continuity and data consistency.

[0021] Optionally, the multi-input consistency control module includes the following sub-modules: The semantic classification and comparison submodule is used to perform semantic label recognition and classification judgment on the content of each input channel, and generate consistency prompt information when there is semantic deviation or conflict. The dominant input priority strategy submodule is used to support users in setting the dominant input path. The system uses the core semantics it represents as a benchmark to perform the fusion and correction of the content of other paths. The similarity scoring and threshold judgment submodule is used to map each input content to a unified semantic space, calculate the semantic similarity between them, and judge the consistency of the input content according to the preset threshold. The AutoCorrection Suggestion submodule is used to automatically provide keyword replacement suggestions, image correction prompts, or dominant path switching suggestions when there are semantic inconsistencies in the input content.

[0022] Optionally, the external platform interface and multi-source data fusion module includes: The platform integration submodule is used to connect with mainstream cross-border platforms, including Alibaba, Amazon, 1688, Shopee, Lazada, Google Shopping, and eBay, based on API protocols or WebDriver technology, and extract structured data, keyword popularity information, and seller behavior records from the target page. The enterprise system access submodule is used to connect with internal enterprise systems such as ERP, CRM, and CDP, receive user-defined lead data, parse the field structure, and achieve data synchronization. The multi-source fusion processing submodule is used to integrate product field content from keywords, image recognition, web page structure parsing and document OCR, and perform semantic vector normalization, tag standardization and field format unification operations; The field conflict identification submodule is used to detect semantic duplication or information source deviation between fields and prompt the user to set priority rules or complete field fusion according to the system's default logic.

[0023] The embodiments of this application employing the above-described technical solution may have the following advantages: High language compatibility and semantic consistency: Supports multilingual keyword input and combines multilingual semantic vector models (such as BERT, SentencePiece, LaBSE) to achieve semantically normalized expression. Automatically identifies and matches similar seller entries in more than 15 languages, significantly improving cross-language semantic processing capabilities and matching accuracy.

[0024] Multi-input fusion and consistency verification capabilities: The system supports multi-path input combinations such as keywords, images, web links, and documents, and introduces a multi-input consistency control module to identify semantic conflicts, label biases, and field redundancy between inputs, automatically recommend the dominant channel and fusion path, and effectively ensure the semantic accuracy of data aggregation and subsequent analysis.

[0025] Dynamic behavior tracking and multi-dimensional timeline analysis: Real-time recording of seller behavior changes, including metrics such as new product launches, page structure adjustments, and shipping frequency. Supports comparisons across multiple time dimensions (day / week / month), outputting trend analysis reports to identify activity levels, stability, and growth potential.

[0026] External platform integration and multi-source data fusion capabilities: The system can achieve data interoperability with mainstream external platforms (such as Alibaba, Amazon, Shopee, 1688, Google Shopping, etc.) and integrate internal ERP / CRM / CDP data resources. With the help of field standardization and semantic fusion algorithms, a unified data structure is built, which significantly improves information coverage and analysis accuracy.

[0027] Flexible collection and tagging management mechanism: Users can freely configure the collection structure, tag types, and rating system to achieve seller profile classification management. Tags support automatic recommendation, batch tagging, and import / export, adapting to the workflows of different sales or purchasing teams.

[0028] Visualized results display and intelligent behavior scoring: Provides various interactive graphical interfaces such as behavior trajectory heatmaps, tag clustering maps, and seller rating radar charts. The built-in scoring engine comprehensively calculates the seller potential index and supports custom weighting of scoring factors according to industry rules.

[0029] Multi-format export and email system integration capabilities: Supports exporting to Excel (.xlsx), PDF, CSV, or API (JSON / RESTful) structures for system integration or email attachments. Configurable multi-language email templates, combined with system-generated content, enable one-click sending, forming a closed-loop tracking system for sales communication.

[0030] Modular architecture design and strong platform compatibility: All functions are encapsulated as independent modules, supporting on-demand combination and flexible deployment. It can seamlessly integrate with mainstream ERP, CRM, and EDM systems, providing standard Webhook and API interfaces.

[0031] Intelligent operation and maintenance and access control mechanism: Based on Kubernetes containerized deployment, combined with Prometheus + Grafana to achieve health monitoring and dynamic scaling. It adopts JWT authentication and RBAC access control mechanisms, supporting master-sub-account collaboration, multi-tenant data isolation, and behavior auditing.

[0032] Wide applicability to various industries and scenarios: Suitable for popular cross-border industries such as maternal and infant products, pet supplies, apparel, and consumer electronics. It also supports various foreign trade business scenarios, including lead generation, follow-up on overseas exhibitions, potential analysis of platform sellers, and target customer behavior modeling.

[0033] This system boasts high practicality, scalability, and universal deployment capabilities, providing foreign trade enterprises, cross-border e-commerce platforms, and international buyers with integrated data analysis and intelligent customer tracking solutions. It possesses significant commercial conversion value and promising prospects for industry promotion. Attached Figure Description

[0034] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0035] Figure 1 This illustration schematically shows the overall system architecture of the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 2 This illustration schematically shows a flowchart of seller semantic clustering processing in a multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 3 This illustration schematically shows a flowchart of the dynamic trajectory recording system for a multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 4This illustration schematically shows a flowchart of the multilingual quotation generation process of the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 5 This illustration shows a flowchart of the Webhook integration and external platform linkage of the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 6 This illustration schematically shows the structure of the visualization analysis and feedback evaluation module of the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 7 This schematic diagram illustrates the optional submodule structure of the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 8 This diagram schematically illustrates the overall functional module structure of the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application. Figure 9 This illustration schematically shows a flowchart of user behavior tracking and module invocation in a multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application; Figure 10 The flowchart illustrating the processing of the multi-input consistency control module in the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application is shown in the illustration. Figure 11 The diagram illustrates the processing structure of the external platform interface and multi-source data fusion module in the multilingual semantic aggregation and seller behavior tracking system according to Embodiment 1 of this application. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0037] It should be noted that the descriptions involving "first," "second," etc., in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0038] It should be noted that, in any stage of this application involving the collection, storage, use, transmission, and processing of data, each stage strictly adheres to the laws, regulations, industry standards, and regulatory requirements of the data source, usage location, and relevant countries and regions to ensure the legality and compliance of data activities. In the collection stage, the purpose, method, and scope of collection are clearly communicated to the data subject in a prominent manner. Collection is conducted only after obtaining the data subject's legal authorization, ensuring that the collection process follows the "minimum necessary" principle and does not exceed the scope of data collection. In the storage stage, storage periods are limited, and data is promptly deleted or anonymized / encrypted after the storage purpose is achieved. In the usage stage, a strict data security protection mechanism is implemented, using field-level desensitization technology and processing the original data according to preset desensitization rules. For different types of data, multiple desensitization strategies, such as data generalization, data anonymization, and data encryption, are employed to effectively mitigate the risk of sensitive information leakage and ensure that all data used is securely processed and desensitized, comprehensively protecting the rights and interests of data subjects and data security. In the transmission and processing stages, the confidentiality and security of data are ensured during transmission and processing.

[0039] In the description of this application, it should be understood that the numerical labels before the steps do not indicate the order of the steps, but are only used to facilitate the description of this application and to distinguish each step, and therefore should not be construed as a limitation of this application.

[0040] First, a definition of the terminology used in this application is provided: Keyword vectors: These are numerical vectors that convert user-input keyword text into fixed-dimensional vectors for semantic computation and cluster analysis. They are often implemented using multilingual semantic embedding models such as mBERT and LaBSE.

[0041] Structured information refers to data formats with fixed fields and explicit labels, such as company name, country, website, contact information, product name, etc.

[0042] Unstructured information: refers to data whose fields cannot be directly extracted, such as natural language text, text in images, PDF manuals, etc., which need to be converted into structured content through parsing or OCR recognition. Semantic clustering: refers to the process of grouping multilingual but semantically similar entries into the same category based on semantic similarity algorithms (such as Top-K nearest neighbors + DBSCAN).

[0043] Multilingual semantic embedding models: These refer to pre-trained language models that support the semantic vectorization of multilingual texts, such as Sentence-Transformers and XLM-RoBERTa.

[0044] Dynamic trajectory: refers to the continuous recording and visual representation of seller behavior (such as product updates, email responses, etc.) over time.

[0045] Behavioral events: refer to the monitorable behavioral nodes generated by sellers in the system or platform, such as registration, quoting, modifying product information, responding to emails, etc.

[0046] Behavioral activity level: This refers to an indicator used by the system to evaluate the seller's recent activity level, which is based on a comprehensive score considering factors such as frequency of behavior, type of behavior, and time.

[0047] Clustering graph: refers to a semantic aggregation structure diagram displayed in a two-dimensional or three-dimensional graphical manner, often used to show cluster categories, centroids, and distribution density.

[0048] Tag collection: refers to the management operations performed by users on target sellers, such as tagging, grouping, and collecting, to facilitate subsequent searching, filtering, and batch operations.

[0049] Tracking module: This refers to the sub-module in the system used to automatically record, update, and display the seller's behavioral history, supporting timeline display and behavioral trend analysis.

[0050] Visualization module: This refers to the functional module in the system used to present the analysis results graphically, supporting chart interaction, layer switching, and status linkage.

[0051] Output module: This refers to the functional module that exports structured or analytical results to formats such as Excel, PDF, and HTML, and supports template configuration and style customization.

[0052] Unmatched item report: refers to the set of items that failed to be successfully aligned or identified during semantic matching or aggregation, used to generate a difference comparison report.

[0053] Multi-input consistency control module: This module determines the semantic consistency between different inputs when users input information through various means such as keywords, images, web links, and documents. It supports the classification of semantic tags, determination of the dominant channel, calculation of similarity scores, and conflict alerts and corrections to ensure the accuracy and stability of the system's input data fusion.

[0054] External Platform Integration and Multi-Source Data Fusion Module: This module enables interconnection with external data platforms (such as e-commerce platforms or enterprise information systems) and integrates data from different sources. It supports integration with e-commerce platforms (such as 1688 and Amazon) and internal enterprise systems (such as ERP and CRM), extracting field information and unifying semantic structure and label standards. It also features field conflict identification, priority judgment of trusted sources, and execution of format fusion rules, ensuring semantic uniformity and structural consistency of multi-source input data and improving the accuracy of system aggregation and comparison.

[0055] Multimodal embedding refers to the technique of converting different types of data, such as images and text, into a unified vector space and fusing them together to facilitate joint semantic analysis.

[0056] Example 1 like Figure 1 As shown, this embodiment provides a multilingual semantic aggregation and seller behavior tracking system, including: a multilingual keyword parsing module, a data acquisition module, a semantic aggregation and clustering module, a dynamic trajectory recording module, a collection and tagging module, a visualization analysis and display module, a multi-input consistency control module, an external platform integration and multi-source data fusion module, and an output and outreach module, among other core modules. Specifically, as... Figure 8 As shown, the system consists of multiple core functional modules, forming an end-to-end integrated processing architecture. These mainly include: a multilingual keyword parsing module: used to receive user-input keywords and complete language recognition, semantic standardization, and vector generation; a data acquisition module: integrating web crawling, template parsing, OCR recognition, and anomaly retry sub-modules to crawl information from multilingual websites; a semantic aggregation and clustering module: supporting multilingual embedding and image-text fusion to complete unified semantic modeling and seller clustering; a dynamic trajectory recording module: used to monitor changes in seller behavior and establish time-series behavior models; a collection and tagging module: used for users to collect, tag, annotate, and track seller records; a visualization analysis and display module: used to generate graphical outputs such as clustering graphs, behavior trend charts, and satisfaction analysis; an output and outreach module: used to export results in PDF / Excel formats and integrates email systems and Webhook push notifications to third-party platforms; and a multi-input consistency control module: used to perform semantic consistency checks, dominant path determination, and correction suggestions in scenarios involving multiple input paths. External platform integration and multi-source data fusion module: used to integrate with external e-commerce platforms and internal ERP / CRM systems to achieve field fusion, conflict identification and unified processing.

[0057] This system can adapt to the needs of complex cross-border business environments and is suitable for diverse scenarios such as international e-commerce, global procurement, and export manufacturing. It has good practicality, scalability, and industrial promotion value.

[0058] It should be noted that, in the specific implementation of this application, each module undertakes a specific data processing and analysis task, and multiple modules are loosely integrated through microservice mechanisms or API interfaces, supporting horizontal scaling and elastic deployment. The following sections describe the composition and technical implementation of each module in conjunction with its functions.

[0059] like Figure 1 and Figure 8 As shown, the data acquisition module is used to extract target information of target sellers that matches the multilingual keyword vector from the target website and / or target platform.

[0060] In this embodiment, the module supports obtaining structured or unstructured seller information from multiple channels such as global multilingual websites, e-commerce platforms, industry directories, government filing databases, or social media.

[0061] This module includes the following sub-modules: Web crawler module: Used to simulate browser behavior and crawl dynamically rendered web pages, including product detail pages, store homepages, contact information pages, etc., based on tools such as Puppeteer or Playwright; supports automatic recognition of page language encoding and page element positioning strategies to adapt to different platform page structures.

[0062] Template parsing submodule: Used to identify different target website structures, extract and semantically annotate web page content at the field level based on the template library, and supports XPath, JSONPath and other methods to extract target fields.

[0063] The OCR recognition submodule is used to process image or document data containing seller information (such as screenshots of business cards, PDF pages, etc.). It is based on frameworks such as Tesseract, Google Vision, or PaddleOCR to realize multilingual optical character recognition and extract unstructured text content.

[0064] The Task Retry and Exception Recovery submodule is used to automatically execute multi-round retry and error recovery strategies and generate log records when page loading failures, structural matching anomalies, or other situations occur during data crawling, ensuring the continuity and stability of data crawling tasks.

[0065] Optionally, the data acquisition module also supports a proxy pool scheduling mechanism and a User-Agent (UA) spoofing strategy to effectively reduce the risk of being blocked by the target site.

[0066] In this embodiment, the data acquisition module can be integrated with social media APIs (such as Twitter and LinkedIn) to extract sellers' public opinion dynamics and tag trends. It can also be configured with anti-scraping identification bypass strategies and asynchronous task queues to adapt to large-scale data crawling scenarios. Through the coordinated operation of the above sub-modules, this module can achieve integrated extraction and processing of structured information (such as company name, product fields, and contact information) and unstructured information (such as page screenshots and PDF descriptions), significantly improving the accuracy, completeness, and robustness of target seller data acquisition, and providing a reliable data foundation for subsequent semantic aggregation and behavioral analysis modules.

[0067] like Figure 1 and Figure 8As shown, the multilingual keyword parsing module provided in this embodiment is used to receive multilingual keywords input by the user and perform language recognition, semantic standardization processing, and multilingual semantic vector generation. This module provides a unified entry point and semantic foundation for subsequent data crawling, semantic clustering, and behavior analysis of the system.

[0068] This module mainly includes the following sub-modules: Language recognition and preprocessing submodule: This module is used to automatically identify the language of user-input keywords and perform standardized operations such as cleaning, stop word removal, and case normalization to ensure the consistency and stability of semantic processing.

[0069] Multiple translation sub-modules: These are used to translate user-input keywords into multilingual content supported by the system, including translation engines such as DeepL, Google Translate, and Azure Translator. The system dynamically selects the optimal translation module based on response time and quality scores, and supports displaying candidate translation items and consistency scoring.

[0070] Keyword decomposition and part-of-speech tagging submodule: Based on natural language processing tools such as spaCy, HanLP or Stanza, it performs word segmentation, part-of-speech tagging and core word extraction on the input phrase to construct structured keyword fragments.

[0071] Semantic Vector Generation Submodule: Calls multilingual embedding models (such as mBERT, LaBSE, Sentence-BERT, XLM-RoBERTa, etc.) to convert keywords and their phrases into semantic vectors of a unified dimension for cross-lingual semantic alignment and clustering analysis.

[0072] The terminology dictionary matching and standardization submodule is used to compare user-input keywords with the system's built-in multilingual category thesaurus, unify terminology naming and field style, and reduce semantic errors caused by language differences.

[0073] User intent recognition and keyword expansion submodule (optional): Combines users' historical search records and behavior patterns, uses generative models (such as GPT-like models or sentence vector expansion models) to infer the search intent behind keywords, automatically completes and recommends expanded terms, and improves matching coverage.

[0074] In some embodiments, when the system detects that the user's input language is inconsistent with the target market's main language, it will automatically provide a prompt and call the translation submodule to perform proofreading and vector consistency judgment to ensure the correctness of the semantic input direction.

[0075] Preferably, the module supports the following enhanced features: It supports caching and reusing keyword semantic embedding vectors, sharing semantic encoding results with the semantic aggregation module, and avoiding redundant calculations; All user-input keywords and translations are recorded in a keyword log database for building user language preference models and long-term analysis. It supports cross-industry and cross-language root word restoration and synonym normalization strategies to adapt to the diversity of terminology in international scenarios.

[0076] In this embodiment, the module can further combine contextual prompts to perform semantic error correction and analogy recommendations on user input, thereby improving the coverage of cross-contextual keywords. It also supports batch keyword import, making it suitable for foreign trade scenarios involving parallel processing of multiple products.

[0077] Through the collaborative processing of the above sub-modules, this module can transform multilingual inputs into standardized, structured, and semantically consistent vector representations, providing a high-quality input foundation for subsequent data matching, seller clustering, and content recommendation.

[0078] The semantic aggregation and clustering module is used to vectorize the target information based on a semantic embedding model to obtain target vectors; and to perform semantic clustering on the target vectors to obtain a semantic clustering graph. This module is the core semantic processing unit of this system. It can use deep language models (such as mBERT, LaBSE, XLM-R, etc.) to convert multilingual text into a unified semantic vector space, and perform synonym or near-synonymous information aggregation based on clustering algorithms (such as KMeans, DBSCAN).

[0079] In some embodiments, the semantic aggregation and clustering module can support multimodal information fusion by introducing image feature vectors (using models such as ResNet / VisionTransformer). Semantic similarity calculation supports parallel computation of cosine similarity and Mahalanobis distance. The module output, after dimensionality reduction, is used for visualization and matching comparison.

[0080] In an optional embodiment, the semantic aggregation and clustering module may further include: a multilingual embedding model submodule: supporting semantic representation models such as mBERT, LaBSE, and GPT-Embedding; and a clustering analysis submodule: using K-Means, DBSCAN, or hierarchical clustering algorithms to group similar sellers or products.

[0081] Multimodal Embedding and Fusion Submodule: Encodes image information (such as product images) and text information together to improve clustering accuracy. Multimodal Fusion Submodule: Extracts features from product images using models (such as ResNet, CLIP, VisionTransformer) and concatenates them with text vectors to generate a comprehensive representation, enhancing the clustering's adaptability to multi-source data. Clustering Analysis Submodule: Groups semantic vectors based on DBSCAN, K-Means, or hierarchical clustering algorithms, automatically identifying synonymous sellers or similar products; supports dynamic cluster number estimation and boundary adjustment mechanisms. Top-K Matching (Semantic Comparison) Submodule: Performs nearest neighbor search in the vector space for each data point before clustering; if the similarity is high, it is merged into a known cluster; if it is below a set threshold, a separate "unmatched" category is created. Semantic Space Dimensionality Reduction and Visualization Interface: Integrates dimensionality reduction algorithms such as UMAP or t-SNE, converting clustering results into a two-dimensional graph and transmitting it to the visualization module, facilitating user understanding of the clustering basis and semantic logic.

[0082] In an optional embodiment, the semantic aggregation and clustering module is further configured to: generate a text semantic vector based on a multilingual embedding model and the target information to obtain the target vector; the multilingual embedding model includes BERT, Sentence-BERT, or M-BERT; construct an index based on HNSW; and obtain the semantic clustering graph based on the target vector, the index, Top-K retrieval, and the DBSCAN clustering algorithm.

[0083] In this embodiment, by converting target information into a unified semantic vector representation and combining it with HNSW index construction and Top-K retrieval, semantic similarity calculation of texts in different languages ​​is achieved. Furthermore, through the DBSCAN clustering algorithm, highly semantically related information sets can be identified, thereby generating a semantic clustering graph and improving the efficiency of information integration.

[0084] In practical applications, such as Figure 2As shown, the multilingual semantic aggregation and seller behavior tracking system provided in this embodiment first receives keywords, phrases, or product description text input by the user through a multilingual keyword parsing module. It then performs steps such as language recognition, semantic standardization, keyword translation, and vector generation to obtain multilingual keyword vectors, which serve as the basic input for subsequent data acquisition and clustering calculations. After obtaining the multilingual keyword vectors, the system calls the data acquisition module to crawl webpage content from the multilingual platform and extracts target fields through structured templates and OCR recognition. Subsequently, the semantic aggregation and clustering module converts the text and image information into unified semantic vectors and performs semantic comparison and Top-K matching. Finally, it uses DBSCAN or K-Means algorithms to achieve seller semantic classification, outputting a semantic clustering graph and constructing a clear, cross-language unified multilingual seller profile.

[0085] In another optional embodiment, the module can dynamically adjust cluster boundaries based on user feedback and supports manual review and rollback mechanisms for clustering results. The system can also provide clustering stability indicators to assist the operations team in evaluating the credibility of semantic classification.

[0086] like Figure 3 and Figure 8 As shown, the dynamic trajectory recording module is used to track changes in the behavior of target sellers, including behavioral events such as page updates, new product launches, price adjustments, and changes in reviews, to build time series trajectories and support behavior monitoring, trend modeling, and anomaly warnings.

[0087] This module includes the following main sub-modules: Page Change Detection Submodule: Regularly crawls key pages such as seller homepages and product pages, and identifies page changes such as additions, deletions, and moves by comparing DOM structure and image snapshot differences, and captures behaviors such as new product launches, price adjustments, and promotions.

[0088] Behavioral event recognition submodule: Combines structural changes and semantic tags to identify key behavior types (new product launches, price reductions, promotional updates, comment additions / reductions, etc.); supports user-defined behavior types and keyword configurations.

[0089] The Behavior Log Recording and Timestamp Annotation Submodule records the time of occurrence of the behavior, the page changes, and the behavior type, forming a behavior log that can serve as the basis for subsequent analysis and backtracking.

[0090] Behavioral Activity Modeling Submodule: Based on the frequency, magnitude of change, and types of behavior within a unit of time, it generates an activity score index to measure the seller's recent dynamic level.

[0091] Anomaly detection and early warning submodule (optional): Based on historical behavior paths, identify behavioral mutations or high-risk patterns (such as large-scale removal of products or social media updates) through models (such as LSTM, Isolation Forest), trigger webhooks or email alerts, and write them to the log system.

[0092] Behavioral Trend Prediction Submodule (Optional): Uses time series algorithms (such as ARIMA, LSTM) to predict behavioral change trends, providing support for sales strategy formulation.

[0093] Trajectory Visualization Submodule (optional): Generates graphical results such as behavior curves, heatmaps, and timelines to intuitively display the behavior evolution process.

[0094] This module can be linked with the favorites and tag modules to perform more frequent monitoring and priority alerts for key sellers; it also supports customizable alert levels and behavioral intervention strategy configurations.

[0095] The example processing flow is as follows ( Figure 3 (as shown) S1: The system crawls the target seller's page at a set period to obtain the DOM structure and visual snapshot; S2: Compare historical page content, identify areas of structural change, and extract potential behavioral signals; S3: Combine semantic tags to identify behavior types, such as "new arrivals", "price reduction", "promotion", etc. S4: Calculate behavioral activity levels to assess market dynamics and operational activity; S5: Compare the current behavior with historical behavior sequences to identify abnormal patterns; S6: If an anomaly is detected, an alert will be triggered and the behavior log will be recorded.

[0096] In this embodiment, the module can interface with the target platform's Webhook mechanism to achieve near real-time seller behavior monitoring. The system also provides a behavioral trend comparison and analysis tool, facilitating users to horizontally evaluate the dynamic intensity and responsiveness of multiple sellers.

[0097] This module establishes a complete seller behavior recording and trend assessment system, supporting continuous content monitoring, behavior type classification, anomaly identification, and behavior graph output. By deploying this module, the system can provide foreign trade enterprises with dynamic assessments of target sellers, risk predictions, and priority suggestions for cooperation, improving the accuracy and timeliness of transaction decisions.

[0098] like Figure 8 As shown, the favorites and tagging module supports users in favorite, tagging, grouping and classifying, and managing the behavior status of target sellers. It is an important channel for user interaction and interest feedback.

[0099] This module includes the following main sub-modules: The Favorites Management submodule allows users to save seller entries, clustering results, or product information to multi-level favorites folders. It supports folder creation, renaming, and moving for easy structured management.

[0100] The tag and color label submodule allows users to add multi-dimensional tags and color classifications to their favorites for filtering, grouping, and visualization; the system supports tag popularity statistics and recent use sorting.

[0101] Collaborative Sharing Submodule: Supports sharing of favorites and tags, and permission allocation, in scenarios involving main accounts and sub-accounts. The main account can configure access permissions for sub-accounts, enabling multi-role collaborative management within the enterprise.

[0102] User feedback collection submodule: The system can automatically record user interactions with sellers, such as sending emails, exporting quotes, and clicking to view, as "positive feedback"; while removing items from favorites, reporting, or long-term inactivity are recorded as "negative feedback" or "no response".

[0103] Feedback Fusion and Interest Modeling Submodule: Integrates user tag preferences, operation history and feedback behavior to generate personalized interest vectors for the optimization of recommendation systems or ranking logic.

[0104] Key monitoring linkage sub-module (optional): Sellers who collect items will be given priority in the behavior tracking scope. The system supports setting the monitoring intensity and reminder mechanism. Once a key change occurs (such as price reduction, new product launch, surge in reviews, etc.), the system will automatically notify the user via email or system notification.

[0105] Behavioral Notes and Implicit Signal Acquisition Submodule (Optional): Users can manually add feedback descriptions, and the system supports recording implicit behaviors such as clicks, dwell time, and swipes, and performing behavior clustering.

[0106] In another optional embodiment, users can set reminder cycles and change thresholds for favorited records. When the behavior of the seller whose records are favorited fluctuates beyond the set values, the system will automatically send an alert. Furthermore, dynamic filtering rules can be formed through tag combinations to achieve automatic tagging and grouping.

[0107] In addition, this module supports multi-dimensional annotation management of seller information, including language source, semantic cluster number, activity level, custom grouping and key behavior prompts, which facilitates quick identification and subsequent filtering and analysis.

[0108] This module enables users to efficiently manage collections and categories in a multilingual seller environment, build personalized user profiles by combining behavioral feedback, and work with the recommendation system to optimize foreign trade target identification and interaction strategies.

[0109] like Figure 6 As shown, the visualization analysis and display module is used to graphically display the system processing results, intuitively presenting key indicators such as semantic clustering, user behavior, recommendation accuracy and satisfaction trends, to assist users in understanding and decision-making.

[0110] This module supports various graphical display formats, including tag heatmaps, seller behavior trend charts, semantic clustering distribution maps, geographic heatmaps, and satisfaction analysis charts. The front-end can be built using the React + TailwindCSS framework, combined with graphics libraries such as ECharts, D3.js, or Plotly, to achieve high-performance rendering on the browser side.

[0111] The module can be further divided into the following sub-modules: The chart visualization submodule supports diverse data visualization methods such as radar charts, heatmaps, time trend charts, and tag clouds, which are used to display clustering structures, behavioral trends, and semantic relevance.

[0112] The table and card display submodule is used to display recommendation results, behavior records, and rating data in a structured manner, and supports pagination, search, and sorting operations.

[0113] Geographic Information Display Submodule: Integrates with Leaflet or Mapbox to display seller distribution maps and regional clustering popularity, supporting filtering and zooming interactions.

[0114] Matching accuracy assessment submodule: Displays metrics such as recommendation accuracy, recall, and F1 score by language, category, or source, and generates trend analysis charts.

[0115] The user feedback collection submodule records user actions, such as "confirm match", "mark as error", and "re-recommend", providing feedback samples for subsequent model optimization.

[0116] Satisfaction Analysis Submodule: Based on data such as click-through rate, page dwell time, and ratings, it generates satisfaction trend charts and hot behavior charts to evaluate system performance.

[0117] Recommendation optimization engine linkage sub-module (optional): Combines user feedback behavior and historical interest vectors to dynamically adjust the recommendation ranking logic and achieve adaptive optimization of the model.

[0118] In addition, the module supports configurable dashboard and view switching functions, allowing users to freely choose display dimensions (such as country, language, and popularity range) and indicator views to build a personalized visual interface and improve data insight and responsiveness in multilingual scenarios.

[0119] In this embodiment, the module can embed a configurable "data story mode" to automatically play out the evolution of seller behavior and the recommendation matching process in a timeline format, suitable for sales review and customer presentation. The chart results can also be exported as interactive HTML reports for external sharing.

[0120] Through a closed-loop mechanism of graphical result presentation and real-time feedback collection, this module not only enhances user comprehension and interactive experience, but also provides key support for subsequent system self-learning and recommendation strategy optimization.

[0121] The multi-input consistency control module is used to perform semantic content consistency judgment and fusion processing when users provide multiple input sources (keywords, images, web links, documents, etc.), preventing result confusion or misjudgment due to semantic deviation. This module ensures the stability of the system processing path and the accuracy of the aggregation results through mechanisms such as semantic classification comparison, dominant channel determination, and similarity threshold judgment.

[0122] This module includes the following sub-modules: Semantic classification and comparison submodule: Calculates similarity (such as cosine similarity and Mahalanobis distance) for semantic vectors generated from multiple input sources to determine whether they have a consistent semantic direction.

[0123] The dominant input priority strategy submodule selects the dominant input source as the basis for task control based on the completeness, confidence score and priority strategy of the input, while other inputs are used as supplementary or corrective sources.

[0124] Similarity scoring and threshold judgment submodule: If semantic contradictions or significant category differences are detected between inputs, the system will prompt the user and suggest adjusting the input combination to avoid the system entering an ambiguous path.

[0125] Automatic correction suggestion submodule (optional): When semantic conflicts are minor, perform vector weighted fusion on secondary input content to improve system robustness and flexibility.

[0126] Consistency scoring mechanism: Output consistency scores for multi-input tasks, which can be used by subsequent modules as processing path parameters and displayed to prompt users on the front end.

[0127] In this embodiment, the system supports a semantic classification and comparison mechanism for multiple input paths. Users can upload keywords and images, and the system automatically evaluates whether their semantic directions are consistent. If the matching degree is high, they are jointly entered into the analysis process; if the matching degree is lower than the threshold, the main path is determined and automatically eliminated.

[0128] like Figure 4 As shown, the module processing flow may include: S1: Users input various types of information (keywords, web pages, images, etc.); S2: The system extracts semantic vectors and auxiliary labels respectively; S3: Perform vector comparison and classification, and calculate similarity; S4: If a conflict occurs, prompt the user and preserve the primary input source; S5: If they match, merge the processes and write them to the processing log.

[0129] This module is suitable for complex cross-language and cross-media input scenarios, and can effectively improve the system's input error tolerance and semantic stability, providing a more reliable foundation for subsequent semantic aggregation and recommendation analysis.

[0130] The external platform interface and multi-source data fusion module is used to connect the system with external data platforms to achieve the collection, fusion, and structured integration of multi-source information (such as e-commerce platforms, search engines, social media, third-party databases, etc.), thereby improving the breadth and accuracy of semantic analysis and behavior recognition.

[0131] This module includes the following sub-modules: Platform adaptation submodule: Connects to public pages, APIs, or search results pages of mainstream platforms such as Amazon, Alibaba, 1688, Shopee, and independent websites to perform data collection and format adaptation.

[0132] The data crawling and structure extraction submodule supports multiple data parsing methods, including template crawling, HTML parsing, OCR recognition, and image content analysis, to extract target fields (such as title, price, image, description, etc.).

[0133] Cross-platform field standardization submodule: performs unified field mapping and terminology standardization processing for structural differences across different platforms, solving the problems of semantic fragmentation and inconsistent data formats.

[0134] Multi-source information aggregation submodule: merges data items from the same or similar semantic sources into a unified seller or product node to achieve cross-platform comparison and information completion.

[0135] Data quality assessment and weighted fusion mechanism: Multi-source data is scored and weighted based on factors such as source credibility, crawling success rate, and field completeness, to avoid interference with analysis due to redundant information or low-quality content.

[0136] In this embodiment, the system supports importing product information from multiple platforms through standardized crawling interfaces and template rules, and can automatically classify them into the same cluster according to the rules, thereby improving the coverage of multi-source comparison.

[0137] like Figure 9 As shown, the typical processing flow of this module includes: S1: Access data from multiple external platforms (web pages / APIs); S2: Perform field extraction and semantic classification; S3: Unify field structure and match to semantic nodes; S4: Integrate to form a complete seller profile or product description; S5: Pass to the semantic clustering and visualization module.

[0138] This module serves as the system's external information bus, enabling the integration of information from multiple platforms, languages, and dimensions, providing a comprehensive and rich data foundation for subsequent cluster analysis, activity assessment, and intelligent recommendations.

[0139] The output and outreach module is used to export the system processing results into a document in a user-specified format and outreach to target users or external systems through preset methods. This module supports outputting quotation analysis content as Excel, PDF, HTML pages, or JSON data streams, forming a complete sales execution loop.

[0140] This module includes the following sub-modules: Quotation content generation submodule: Based on the user-selected template and comparison results, automatically generates structured content including product parameters, price information, and image display. Supports bilingual or multilingual versions.

[0141] Export format configuration submodule: Supports selection of export format (Excel, PDF, CSV, HTML), and allows customization of field order, visual style and naming rules.

[0142] Email sending submodule: Connects to user-authorized email addresses (such as Microsoft 365 and Gmail), supporting attachment embedding, body text editing, and one-click SMTP sending.

[0143] The Webhook push submodule allows users to configure the target URL, event triggering conditions, and encryption keys. The system will automatically push the data to an external platform after the specified operation.

[0144] Document Export Submodule: Supports exporting clustering results, behavior tags, recommended content, etc. into structured documents, and records export logs.

[0145] Sending Records and Status Tracking Submodule: Records information such as sending time, recipient address, and response status code for users to track results.

[0146] In this embodiment, the module also supports generating short links and QR codes for mobile devices to improve access convenience; at the same time, it can be configured with filtering rules such as "only push to specific countries, cluster tags or behavioral activity" to achieve precise distribution.

[0147] like Figure 5 As shown, the output process includes: S1: The user selects the content to be output and the target format; S2: The system generates a structured document and performs visual rendering; S3: Select the sending method (e.g., email, Webhook); S4: Record export and reach logs, and complete status feedback.

[0148] This module serves as an external connection bridge for the system, enabling real-time visualization of analysis results, which helps shorten sales response cycles and improve customer conversion efficiency.

[0149] Part Two: System Extension Modules (Optional Deployment) To enhance the system's flexibility, security, and interoperability during deployment, in some embodiments, the system can also be configured with the following optional modules as supplements and support to the core functional modules. These modules have good hot-swappable characteristics and can be enabled or disabled through a unified configuration center to adapt to different enterprise scenarios and deployment requirements.

[0150] [1] Webhook and API integration module: This module is used to connect system analysis results to third-party platforms, including CRM, ERP, email systems, BI tools, and automated workflow platforms (such as Zapier and n8n). It supports RESTful API calls, Webhook event triggering, token authentication, and a failure retry mechanism.

[0151] [2] Access Control and Auditing Module: User authentication and access control are implemented based on JWT and RBAC models, supporting field-level access control, role-based configuration, and sensitive operation auditing to ensure system security.

[0152] 【3】Unmatched Items Analysis Module: This module is used to identify data items that failed to be successfully categorized in semantic clustering, field comparison, or behavioral analysis. It supports difference suggestions, field misalignment correction recommendations, and abnormal data export, improving system fault tolerance and output accuracy.

[0153] [4] Multilingual Template Management Module: This tool is used for unified management of multilingual content templates such as emails, quotes, and notifications. It supports placeholder replacement, semantic mapping, and automatic language switching. Field structure, styles, and industry templates are configurable.

[0154] [5] User behavior log module: It records key user operation paths (such as export, annotation, feedback, etc.), supporting log backtracking, behavior preference analysis, and abnormal behavior alerts. This module can also provide data support for recommendation optimization and permission auditing modules.

[0155] 【6】Quotation Version Management Module: It is used to track different versions of quotation content, record the trajectory of field changes, support field-level difference comparison, snapshot rollback and approval annotation, and enhance the transparency and management efficiency of enterprises in project quotation.

[0156] 【7】Collection synchronization and sending history module: It is used for sharing favorites, synchronizing sending records, and compiling collaborative record statistics between the main account and sub-accounts, and supports unified data archiving and permission isolation configuration.

[0157] 【8】Offline access and local caching module: It supports a local caching mechanism in network interruption scenarios, retaining recent analysis results during network outages and automatically synchronizing them after the connection is restored, ensuring data continuity and user experience.

[0158] [9] Security and Operations Module (Optional): This module supports containerized deployment based on Kubernetes, monitoring mechanisms using Prometheus+Grafana, and integrates anomaly reporting tools such as Sentry. It also supports database backup strategies and service health checks.

[0159]

[10] Task retry and exception recovery module (optional): This mechanism provides automatic retry and exception compensation for failed scenarios in web scraping, OCR recognition, and translation tasks, thereby improving system stability.

[0160]

[11] Multi-tenant configuration management module (optional): It supports multi-tenant configuration capabilities in SaaS scenarios, allowing each tenant to independently configure field templates, tag systems, and language styles, ensuring data isolation and policy flexibility.

[0161]

[12] API Gateway and Service Discovery Module (Optional): It is used to manage communication and invocation relationships between microservices, and supports dynamic service discovery, routing configuration, canary release, and permission filtering mechanisms.

[0162] The aforementioned optional modules form a loosely coupled and highly scalable architecture with the main system functional modules, supporting flexible combination and deployment in different business scenarios. All modules can be uniformly managed by the configuration center, possessing capabilities such as rapid integration, version control, and adjustable permissions, providing strong support for the cross-industry and cross-platform deployment of the system of this invention.

[0163] System extension modules (optional components and enhancements) To enhance the system's security, interoperability, and intelligence, in some embodiments, the system may also be configured with the following optional modules to support enterprise-level deployment requirements in complex scenarios: 1. Webhook and API Integration Module This module is used to interface system analysis results with external business systems, including CRM, ERP, email platforms, or automated workflow tools (such as Zapier and n8n), enabling information linkage and automatic triggering between systems. The module supports RESTful API calls, real-time Webhook event pushes, failure retry mechanisms, and key authentication.

[0164] Specifically, the module may include: Webhook push submodule: Used to push data to a specified address in real time after an event is triggered, and supports retrying on failure and status logging.

[0165] The RESTful interface submodule provides standard GET / POST interfaces and supports third-party query calls.

[0166] Platform integration sub-module: Supports data synchronization and linkage with external CRM, ERP, email systems, etc.

[0167] This module significantly enhances the system's automation capabilities and flexibility in external integration.

[0168] 2. Access Control and Auditing Module This module implements multi-user access control and management based on JWT and RBAC to ensure data security and compliant system use.

[0169] Main functions include: JWT Authentication Submodule: Used for authentication and session management.

[0170] RBAC role model: Supports field-level permission configuration and role-based hierarchical control.

[0171] Audit Log Submodule: Records users' sensitive operations, configuration changes, and export behaviors for easy review and tracking.

[0172] This module helps build a multi-role permission system that meets enterprise management requirements.

[0173] 3. Multilingual Template Management Module This module is used to generate localized output templates for different countries / regions, including email templates, quotation structures, and semantic tag styles, ensuring that the content output is standardized.

[0174] It may include the following sub-modules: Template editing submodule: Edit template content using a WYSIWYG (What You See Is What You Get) method.

[0175] Template matching submodule: Automatically switches templates based on the target country or customer language.

[0176] Version control submodule: records historical versions and has rollback capabilities.

[0177] This module ensures the consistency and professionalism of content presentation in multilingual foreign trade scenarios.

[0178] 4. Unmatched Items Analysis Module Used to identify anomalous data that has not been successfully categorized during semantic clustering or behavioral analysis, assisting users in fixing matching problems.

[0179] It may include: The Unmatched Items Identification submodule lists items that failed to align or process.

[0180] Difference Hints Submodule: Highlights differences in field structure or semantics.

[0181] Supplementary suggestions submodule: Provides field correction suggestions and re-comparison strategies.

[0182] This module improves the system's robustness and matching success rate.

[0183] 5. User Behavior Log Module Records user operation paths, query behaviors, tag changes, and export records in the system, supporting audit analysis and abnormal behavior alerts.

[0184] 6. Collection synchronization and sending record module It supports sharing favorites and sending history between the main account and sub-accounts, improving the efficiency of enterprise team collaboration.

[0185] 7. Multi-input consistency control module This module is used to automatically identify semantic differences and perform fusion judgment when the user inputs multiple sources (such as keywords, images, web links, and PDF files) to ensure that the final output results are accurate and consistent.

[0186] It may include: Semantic classification and comparison submodule: compares the core semantic content of multiple input sources with the context for consistency.

[0187] Dominant Input Priority Strategy Submodule: Selects the dominant input path based on confidence level or user preference.

[0188] Similarity scoring and threshold judgment submodule: Sets semantic consistency judgment threshold to avoid misfusion.

[0189] The autocorrect suggestion submodule provides suggestions or automatic corrections when semantic conflicts exist.

[0190] This module significantly enhances the accuracy and stability of the system under multimodal input.

[0191] 8. External platform integration and multi-source data fusion module This module is used to integrate data from multiple third-party platforms (such as e-commerce platforms, product catalog sites, CRM systems, etc.) and standardize it for further analysis by this system.

[0192] Core capabilities include: Data crawling submodule: Supports obtaining external structured and unstructured data through API, RSS, web crawling and other methods.

[0193] Field Mapping Submodule: Maps external field structures to the system's standard model.

[0194] Platform adaptation submodule: Supports data access protocols and formats for different platforms (such as Amazon, Alibaba International Station, Shopify).

[0195] This module builds a "data middle platform" capability, providing rich corpus and contextual information for subsequent clustering, comparison and recommendation.

[0196] In addition, in some embodiments, the system may further include the following auxiliary enhancement modules: Operation trajectory playback module: used to record and replay user operation paths, supporting behavior auditing and backtracking; Quotation Version Comparison Module: Used for comparing and tracing the differences in fields between different versions of quotation documents; Multilingual auxiliary editing module: provides functions such as terminology matching, grammar checking, and translation suggestions; Data import and cleaning module: Supports importing Excel, CSV, or PDF files and standardizes field structures; Intelligent recommendation keyword expansion module: Expands and recommends matching tags based on keyword semantics to enhance clustering matching accuracy; Local caching and offline module: Supports content access and delayed synchronization in offline scenarios.

[0197] These modules, as extension components of the system, can be deployed as needed and combined with the aforementioned main system modules to build a complete architecture. In enterprise-level, cross-platform, multilingual, and multi-role scenarios, the extension modules can significantly enhance the platform's stability, security, and intelligence.

[0198] To more clearly illustrate the functional structure and collaborative relationships between the modules of the system of this invention, such as Figure 1As shown, this system includes the following main modules: multilingual keyword parsing module, data acquisition module, semantic aggregation and clustering module, dynamic trajectory recording module, collection and tagging module, visualization analysis and display module, multi-input consistency control module, external platform integration and multi-source data fusion module, and output and outreach module (see details of the system structure). Figure 1 (See the block diagram of the modules shown). The modules described above work sequentially or in parallel, forming a closed-loop processing flow from data input, semantic analysis, behavior recognition, user interaction to result output.

[0199] In the following exemplary applications, the system's comprehensive capabilities in foreign trade contexts, such as cross-language semantic recognition, behavior tracking, and intelligent recommendation, will be demonstrated by combining the actual application of each module in typical scenarios.

[0200] Example application 1: such as Figure 9 As shown, the specific process is as follows: S1: Access data from multiple external platforms (web pages / APIs).

[0201] S2: Perform field extraction and semantic classification.

[0202] S3: Unify field structure and match to semantic nodes.

[0203] S4: Integrate to form a complete seller profile or product description.

[0204] S5: Pass to the semantic clustering and visualization module.

[0205] Example application 2: such as Figure 3 As shown, the seller behavior analysis process is as follows: S1, Initialize Monitoring: The system starts a periodic behavior monitoring task, sets the target seller list and tracking parameters (such as number of reviews, sales volume, page changes, etc.).

[0206] S2, Dynamically capture behavioral data: The crawler logic of the data acquisition module periodically accesses the seller's page, captures behavioral trajectory information, and records timestamps.

[0207] S3, Trend Modeling and Graph Generation: The system uses the time series modeling and visualization analysis module to draw a trend graph of seller behavior evolution and mark key nodes.

[0208] S4, generate early warning prompts: When high-risk behaviors such as a sudden drop in sales or the deletion of key pages are detected, the system will automatically trigger an early warning prompt and synchronize it with the user.

[0209] like Figure 2As shown, the clustering and recommendation results of the system can be displayed through radar charts, tag clouds, and cluster number cards. Users can click, mark, and provide feedback on the recommended content, which facilitates subsequent behavior tracking and personalized model optimization.

[0210] Example application 3: such as Figure 5 As shown, the output and reach process is as follows: S1, Template Customization: Users can select or edit the quotation template format and set the language type and output fields.

[0211] S2, Data Population and Export: The system automatically populates the field content in the template based on the matching results and exports it as a PDF or Excel file.

[0212] S3, Email Sending and Log Recording: Through the email integration function, the system sends quotation documents to the designated contact person's email address and records the sending time and status.

[0213] S4, Customer Behavior Tracking: The system analyzes customer behaviors such as opening, downloading, and replying through email feedback or data returned from the CRM system, providing a basis for subsequent optimization.

[0214] Example Application 4: The user feedback and recommendation optimization process is as follows: like Figure 2 As shown, the clustering and recommendation results of the system can be displayed through radar charts, tag clouds, and cluster number cards. Users can click, mark, and provide feedback on the recommended content, which facilitates subsequent behavior tracking and personalized model optimization.

[0215] S1, User Browsing Recommendations: Users can view a list of products or seller cards automatically recommended by the system on the system interface.

[0216] S2, Operations and Ratings: Users can provide feedback on recommended content by "confirming the match", "marking errors", or rating the content.

[0217] S3, System Feedback Recording: Feedback information is recorded in real time to the user feedback submodule and saved together with the current recommendation vector.

[0218] S4, Recommendation Model Fine-tuning: Based on batch user feedback, the system uses feedback data to fine-tune the recommendation engine, improving the accuracy of the next round of recommendations.

[0219] Example application 5: Figure 7 As shown, the system deployment and data integration scenarios are as follows: S1, Initial Deployment: Developers deploy system microservices using Docker and configure front-end display, back-end services, database and storage resources.

[0220] S2, External System Access: The enterprise configures the CRM system interface to synchronize customer files and historical inquiry information via RESTful API.

[0221] S3, Task Distribution and Scheduling: Tasks such as quote generation, data scraping, and matching calculation are pushed into an asynchronous task queue and processed concurrently by the background server.

[0222] S4, Data Storage and Backup: All structured data and vector representations are uniformly stored in the database and embedded storage module, and a scheduled backup mechanism is enabled.

[0223] Example application 6: Figure 4 As shown, the multilingual quotation scenario is as follows: S1, Users select target market language and pricing requirements: Users select their target country or region (such as Germany, France, or Spain) on the front-end page and specify the desired language combination for the generated quote (such as Chinese-German bilingual or Chinese-French bilingual).

[0224] S2, Semantic parsing and language adaptation: The system activates the multilingual keyword parsing module to identify the language, standardize the semantics, and map the vectors of the keywords or product information entered by the user. At the same time, it calls the translation submodule to translate the core fields (such as product name and parameter description) into the target language.

[0225] S3, intelligent filling and formatting of quotation templates: Based on semantic matching results and preset templates, the system automatically fills in bilingual field content and renders bilingual quotation templates with a unified structure and standardized layout (including combinations of Chinese and German, Chinese and French, etc.) to ensure consistent layout and accurate language correspondence.

[0226] S4, Output and Reach: Users can export bilingual quotation documents with one click (supporting PDF, Excel and other formats) and send them to target customers or business platforms through the system's integrated email module, external sharing links or Webhook interface, achieving efficient communication across contexts and systems.

[0227] In this embodiment, Figure 4 The three stages of semantic comparison, clustering mapping, and language template rendering form a closed-loop collaboration, enabling the system to accurately generate structured bilingual quotation content in complex language contexts, significantly improving the response efficiency of multilingual business communication.

[0228] Example application 7: Figure 10 As shown, the multi-input consistency control process is as follows: S1, the user selects multiple input options: Users can provide multiple input channels (such as keywords, product webpages, product images, and Excel documents) in the front-end system to obtain more complete product information and compare it with competitors.

[0229] S2, the system identifies the semantics of each channel: The multi-input consistency control module calls sub-modules such as semantic parsing, OCR recognition, and structure extraction to perform language recognition and semantic tag generation on the input content of each channel, and form a standardized semantic vector.

[0230] S3, Perform semantic consistency checks: The system compares the main product features of each input channel (such as product name, purpose, specifications, and image matching degree), calculates the similarity score, and determines whether there are semantic conflicts or data mismatches. If a significant difference is found, the "dominant channel" will be marked and the user will be notified.

[0231] S4, triggering conflict warning and suggested fixes: When there are semantic discrepancies in the input content, the system will display the discrepancies through pop-up prompts or field highlighting, and recommend the dominant channel or provide correction suggestions (such as replacing images, re-uploading files, etc.) based on historical usage preferences.

[0232] S5, after successful consistency verification, proceeds to the main process: After completing semantic comparison and consistency correction, the system integrates other auxiliary information based on the dominant channel and passes the unified semantic result to subsequent modules (such as comparison analysis, clustering engine, quotation generation, etc.) to perform tasks.

[0233] In this embodiment, as Figure 10 As shown, the system solves the problems of semantic mismatch, redundancy conflict and data disorder that may occur between multimodal inputs through the multi-input consistency control module, which significantly improves the stability and intelligence level of the system when processing multi-input tasks and ensures the accuracy and usability of the final results.

[0234] Example application 8: Figure 11 As shown, the process for external platform integration and multi-source data fusion is as follows: S1, Configure external platform interfaces: System administrators configure the required integration platforms in the configuration center, including CRM (such as HubSpot), ERP (such as Kingdee Cloud), email platforms (such as SendGrid), image recognition APIs (such as Baidu Image Search), or translation service platforms (such as DeepL). Users can bind API keys to data field mapping tables through the system interface.

[0235] S2, Multi-channel input task trigger: Users can initiate tasks by uploading Excel files, entering keywords, pasting product webpage links, or dragging and dropping product images in any combination. The system automatically determines the dominant input channel and activates the corresponding sub-modules (such as OCR recognition, webpage parsing, semantic standardization, etc.).

[0236] S3, Multi-source Data Synchronization and Structure Fusion: The system synchronously retrieves structured data such as customer fields, historical inquiries, and product catalogs from external platforms, and performs field alignment, terminology comparison, and language standardization processing with the user's current task data to form a unified intermediate semantic representation.

[0237] S4, Module Orchestration and Task Deployment: The system calls the required sub-modules based on the task type, such as the "semantic comparison module," "clustering engine," and "quotation template rendering module," and automatically orchestrates the task flow according to priority and dependencies. The scheduling module supports asynchronous concurrent processing and failure retry strategies to ensure stable task execution.

[0238] S5, Output and Return: The analysis results generate standardized documents (such as bilingual quotations and comparison tables) in the output and outreach module, and are automatically pushed back to the CRM or ERP system via Webhook, API, or email interface. A receipt log is generated and archived after the push is completed.

[0239] In this embodiment, Figure 11 The system demonstrates the entire process of interfacing with multiple external platforms, integrating heterogeneous data sources, and efficiently completing semantic task orchestration. This showcases the system's integration and adaptation capabilities and automation level in multi-source heterogeneous environments, providing enterprise customers with efficient and scalable intelligent solutions.

[0240] In addition to the exemplary applications described above, this system can also support the following extended functions and optional implementation methods to further enhance system adaptability and user experience: Real-time collaborative editing: Supports multiple users to edit the same quotation online simultaneously. All changes are synchronized and automatically merged in real time, ensuring version consistency. Suitable for cross-departmental collaboration or remote team office scenarios.

[0241] Batch template management: Allows users to upload and categorize multiple email templates and quotation templates in batches, supports template tag filtering, historical version tracking and one-click application, improving the template reuse efficiency of large enterprises.

[0242] Drag-and-drop field configuration interface: Users can freely drag and drop the field order, set the display logic and field grouping through the graphical interface to quickly generate customized quotation templates that meet their own industry standards.

[0243] Full-text translation and terminology database integration: Supports one-click translation of entire quotations or email content into the target language. The system can automatically reference a professional terminology database to ensure the accuracy and industry standardization of multilingual translation.

[0244] Third-party plugin extension mechanism: The plugin architecture supports the introduction of third-party functional modules such as OCR enhancement packages, private database connectors, and regional currency converters to meet the personalized needs of specific industries or regions.

[0245] Security and compliance module support: built-in GDPR compliance mechanism, encrypted storage option and operation audit log, to ensure the legality and compliance of the system in the process of cross-border data processing.

[0246] Data feedback and recommendation training mechanism: The final quote content and feedback data adopted by users can be used as a training set and fed back to the recommendation engine to continuously optimize the matching accuracy and build a closed-loop learning system.

[0247] Deep CRM / ERP Integration: The system supports deep integration with the enterprise's existing Customer Relationship Management System (CRM) or Enterprise Resource Planning System (ERP), connecting the entire data chain from customer lead identification to sales conversion.

[0248] Cross-platform application support: In addition to the web client, it also supports mobile application development based on Flutter or ReactNative, and has offline mode, breakpoint resume and local storage functions to ensure data stability and access at any time.

[0249] Security and compliance module expansion: Built-in GDPR compliance mechanism, operation audit logs and encrypted storage options ensure the legality and compliance of cross-border data processing, suitable for regions with strict data requirements such as the EU and the US.

[0250] Advanced analytics dashboards: Provide advanced users with visual BI dashboards that display key metrics (such as match success rate, response rate, customer behavior heatmaps, etc.) to help optimize decision-making and conduct operational reviews.

[0251] Through the expansion of the above optional implementation methods, this application provides a seller analysis system with high composability and intelligent evolution capabilities, which is suitable for the lightweight deployment needs of small and medium-sized foreign trade enterprises, and can also support the deep integration and intelligent decision-making scenarios of multinational groups in large-scale data operations.

[0252] As can be seen from the above typical application scenarios, this application has good industry adaptability and promotional value, and can be widely used in various business scenarios such as cross-border e-commerce product selection, foreign trade company customer development, exhibitor discovery, and government investment promotion matching.

[0253] In summary, this application constructs an intelligent foreign trade system integrating "cross-language understanding, multi-platform semantic aggregation, behavioral trajectory tracking, customer screening, and email outreach." This system realizes a closed-loop process from data acquisition, semantic processing, and behavioral analysis to intelligent recommendation and multi-channel outreach. It not only improves customer response efficiency in cross-language environments but also makes innovative technical contributions in system architecture, semantic fusion algorithms, and foreign trade business docking mechanisms, possessing good stability, scalability, and promising prospects for widespread application. The embodiments of this application have the following technical advantages and beneficial effects: Strong multilingual semantic aggregation capability: The multilingual semantic aggregation scheme proposed in this application integrates keyword normalization, terminology alignment, semantic vector mapping and translation consistency assessment, and has significant advantages such as wide cross-language semantic coverage, strong adaptability to upstream and downstream systems and high platform independence, enabling efficient and consistent semantic fusion in a multilingual environment.

[0254] High accuracy in seller behavior trajectory recognition: The seller behavior trajectory recognition mechanism proposed in this application constructs a complete time series trajectory based on the target platform's browsing history, page changes, operation frequency and behavior chain structure, and supports abnormal behavior monitoring and trend modeling. It has the characteristics of clear structure, strong time sensitivity and high data traceability.

[0255] Strong Multilingual Alignment and Behavioral Synchronization Recommendation Capabilities: This application's embodiments construct a "multilingual text comparison and behavioral synchronization recommendation mechanism," supporting the aggregation of product information from different languages ​​into a unified seller profile, improving the accuracy of cross-language matching and recommendation consistency. This solution boasts advantages such as high semantic comparison accuracy, stable clustering effects, and applicability to various recommendation scenarios (e.g., customer screening, potential buyer discovery, and market popularity assessment).

[0256] The scope of protection of this application is not limited to the above embodiments. For each module, step and functional unit in this application, those skilled in the art can make the following equivalent or alternative solutions based on the actual application environment or technological evolution without creative effort. All the following variations and combinations should be considered as part of the scope of protection of this application: 1. Multilingual embedding models can be replaced by transformer series models such as GPT Embedding, mBERT, and LaBSE.

[0257] 2. The vector retrieval module can be replaced by FAISS with databases that support real-time vector retrieval, such as Milvus or Elasticsearch.

[0258] 3. Seller behavior data can be replaced by the platform's open API behavior log access method instead of the browser crawling mechanism.

[0259] 4. The multilingual translation engine can be replaced with services such as DeepL, ChatGPT API, and Google Translate.

[0260] 5. Semantic clustering methods can be replaced by other clustering algorithms such as DBSCAN and hierarchical clustering instead of k-means.

[0261] 6. The text similarity comparison module can be replaced by Siamese network or other sentence pair modeling algorithms.

[0262] 7. The visualization component can be replaced by graphics libraries such as ECharts and Highcharts instead of D3.js.

[0263] 8. In addition to PDF and Excel, the visual export formats can also include Markdown, PPT plugins, HTML5 Canvas images, etc.

[0264] 9. The access control mechanism can be replaced by RBAC+JWT with OAuth 2.1 combined with SAML single sign-on.

[0265] 10. The user feedback module can be expanded into a workflow engine based on annotation task flow.

[0266] 11. The behavior path modeling method can be replaced by LSTM with Transformer + temporal embedding structure.

[0267] 12. The table export function can be implemented by replacing ExcelJS with Apache POI or SheetJS.

[0268] 13. In addition to supporting the web interface, the user interface can also be extended to a hybrid mode of mini-programs, desktop, and mobile.

[0269] 14. The seller rating logic can be replaced by a supervised learning model score instead of heuristic rules.

[0270] 15. Vector similarity can be measured using methods such as cosine similarity, Euclidean distance, and Mahalanobis distance.

[0271] 16. The tag system supports hierarchical classification structures or user-defined tag templates.

[0272] 17. The system deployment architecture can evolve from a monolithic architecture to a microservice architecture based on Docker and Kubernetes.

[0273] 18. Model training strategies can be accelerated and optimized based on transfer learning or AutoML automatic modeling systems.

[0274] 19. Page parsing techniques can be replaced by regular expressions, XPath, or structured DOM tree analysis modules.

[0275] 20. Star rating supports anonymous ratings, batch uploads, and tag-filtered display.

[0276] 21. The multilingual interface can automatically switch based on browser language, user login region, or IP address.

[0277] 22. Behavior logs can be pushed to message queues such as Kafka and RabbitMQ in real time for further processing.

[0278] 23. Aggregation rules can be converted from system presets to custom script plugins that can be configured by enterprise users.

[0279] 24. The report export function can include automatic generation capabilities such as page numbers, table of contents, and version difference highlighting.

[0280] 25. Field completion and recommendation functions can be implemented based on vector nearest neighbor matching or hot word comparison strategies.

[0281] 26. The system deployment platform supports multiple environments such as AWS, Alibaba Cloud, Tencent Cloud, and local private cloud.

[0282] 27. The log system supports sharded storage and time-series backtracking by country, language, and tenant.

[0283] 27. The translation service interface can be configured with a hot standby mechanism and load balancing strategy to ensure service continuity.

[0284] 28. The quotation chart can be integrated with BI tools to generate a multi-dimensional dynamic analysis visualization panel.

[0285] 29. The customer behavior intent prediction module can be expanded into a customer scoring system based on lifecycle modeling.

[0286] The above equivalent implementation methods and extended solutions do not depart from the technical concept and protection scope of this invention.

[0287] Example 2 Another embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is used to implement the various functional steps of the "multilingual semantic aggregation and seller behavior tracking system" described in the embodiments of this application.

[0288] In some specific implementations, the computer program may include, but is not limited to, the following functional logic: input parsing, cross-language keyword matching, semantic clustering analysis, behavior trajectory recording, visualization, data export and outreach, and other sub-module operation flows. During runtime, the program can automatically complete the analysis of input information, target seller behavior recognition and semantic clustering, graphical output of comparison results, and subsequent operations such as email push, webhook synchronization, and behavior tracking.

[0289] In this embodiment, the computer-readable storage medium may include, but is not limited to, the following types: flash memory, solid-state drive (SSD), multimedia card (MMC), secure digital card (SD), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic disk, magnetic tape, optical disk (such as CD-ROM, DVD), etc.

[0290] In some embodiments, the computer-readable storage medium may be an internal storage unit of the device itself, such as an integrated hard disk or memory chip; or it may be an external pluggable expansion storage unit, such as a USB flash drive, portable hard drive, TF card, or network attached storage (NAS). Furthermore, in certain usage scenarios, a hybrid internal and external structure may be adopted to meet the program loading and data processing needs under multiple environments and loads.

[0291] In this embodiment, the computer-readable storage medium can be used not only to store the system's core program code, natural language model parameters, structured rules and configuration files, but also to cache user behavior logs, exported documents, task snapshots and operation records, providing necessary support for the stable operation and continuous upgrade of the system.

[0292] In some embodiments, see Figure 3 The computer program can be deployed in the modular architecture of the multilingual semantic aggregation and seller behavior tracking system described in this application. By coordinating sub-modules such as keyword parsing, semantic clustering, behavior recording, visualization display and output outreach, it can realize the automated execution of system tasks and data closed-loop processing.

[0293] The system architecture proposed in this application is highly modular and scalable, and can flexibly adapt to different business processes and industry needs. It supports personalized configuration, visual management and intelligent feedback optimization, and is suitable for multiple scenarios such as intelligent sales, international business communication, global product recommendation and customer relationship maintenance. It has broad application prospects and promotional value.

[0294] Obviously, those skilled in the art should understand that the modules or steps of the embodiments of this application described above can be implemented using general-purpose computer devices. They can be centralized on a single computer device or distributed across a network of multiple computer devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computer device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of this application are not limited to any particular combination of hardware and software.

[0295] It should be noted that the above are merely preferred embodiments of this application and do not limit the scope of patent protection of this application. Any equivalent structural or procedural changes made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A multilingual semantic aggregation and seller behavior tracking system, characterized in that, include: The module includes a multilingual keyword parsing module, a data acquisition module, a semantic aggregation and clustering module, a dynamic trajectory recording module, a collection and tagging module, a visualization analysis and display module, a multi-input consistency control module, an external platform integration and multi-source data fusion module, and an output and outreach module; among which: The multilingual keyword parsing module is used to receive multilingual keywords input by the user; perform semantic standardization processing on the multilingual keywords; and generate a multilingual keyword vector based on the semantically standardized multilingual keywords. The data acquisition module is used to extract target information of target sellers that match the multilingual keyword vectors from target websites and / or target platforms. The semantic aggregation and clustering module is used to vectorize the target information based on a semantic embedding model to obtain a target vector; and to perform semantic clustering on the target vector to obtain a semantic clustering graph. The dynamic trajectory recording module is used to record the behavioral events of the target seller; the behavioral events include page update events, product release events, and / or activity change events; The collection and tagging module is configured to support users in collecting, categorizing, tagging, and recording the status of the target sellers. The visualization analysis and display module is used to generate system processing results based on the semantic clustering graph; and to display the system processing results through a graphical interface; the graphical interface includes a tag heat map and a target seller behavior trend map; The multi-input consistency control module is used to determine the semantic consistency between information when a user submits information through multiple input paths; and to provide fusion suggestions or correction guidance when conflicts exist; the input paths include keywords, images, web page links and documents; The external platform integration and multi-source data fusion module is used to connect to external platforms or systems to obtain the target seller information and keyword popularity; and to fuse data from multiple input paths to achieve unified field structure and semantic consistency. The output and delivery module is used to export the system processing results into a document in a target format; and to send the document in the target format to the target terminal through a preset method.

2. The system according to claim 1, characterized in that, The multilingual keyword parsing module includes: a language recognition and preprocessing submodule and multiple translation submodules; wherein... The language recognition and preprocessing submodule is used to identify the language category of the multilingual keywords; and to perform the semantic standardization processing on the multilingual keywords; the semantic standardization processing includes cleaning processing, stop word removal processing, and case normalization processing. The plurality of translation sub-modules are used to determine the target translation sub-module among the plurality of translation sub-modules based on preset performance indicators; the preset performance indicators include response time and translation quality score.

3. The system according to claim 1, characterized in that, The data acquisition module includes a web crawler submodule, a template parsing submodule, an OCR recognition submodule, and a task retry and exception recovery submodule; wherein... The web crawler submodule is used to extract the content of a specified page based on Playwright or Puppeteer in order to obtain target page data. The template parsing submodule is used to extract target templates from the template library according to preset fields; and to extract structured data information from the target webpage data based on the target templates. The OCR recognition submodule is used to perform optical character recognition on images or documents in the target page data in order to extract unstructured data information. Based on the structured data information and the unstructured data information, the target information of the target seller is obtained; The task retry and exception recovery submodule is used to: detect whether any exceptions occur during the page content extraction process; and execute the corresponding processing strategy if an exception occurs.

4. The system according to claim 1, characterized in that, The semantic aggregation and clustering module is further used to: generate text semantic vectors based on the multilingual semantic embedding model and the target information to obtain the target vector; the multilingual semantic embedding model supports unified vector representation of multilingual texts to achieve cross-language semantic association calculation; and perform clustering analysis operations based on the constructed semantic index structure and similarity retrieval mechanism to generate the semantic clustering map; the clustering analysis includes density clustering, hierarchical clustering, or vector distance partitioning.

5. The system according to claim 1, characterized in that, The dynamic trajectory recording module includes: A behavior log recording submodule is used to record the behavioral events of the target seller; A time stamp submodule for automatically adding timestamps when the target seller's behavioral events occur; An event recognition submodule is used to identify whether the target seller's behavior events have changed based on a preset strategy; The behavioral events include new product launches, page structure changes, promotional activity updates, and changes in shipping frequency.

6. The system according to claim 1, characterized in that, The collection and tagging module also includes: A collection management submodule used to receive user collection instructions and store the target seller's identifier; A tagging and categorizing submodule for tagging and classifying multi-dimensional information of target sellers; The multi-dimensional information includes the target seller's language origin, semantic cluster number, user-defined grouping, activity level, and key behavioral prompt tags.

7. The system according to claim 1, characterized in that, The visualization analysis and display module includes a graphical interface construction submodule and an interactive function submodule; The graphical interface construction submodule is used to build a graphical interface based on the front-end framework to support access from multiple terminals and visualization adaptation. The interactive function submodule is configured to implement interactive functions such as keyword highlighting, filtering, switching of semantic clustering graph view, and display of behavior trend heatmap.

8. The system according to claim 1, characterized in that, The output and delivery module further includes a document export submodule and a notification sending submodule; wherein... The document export submodule is used to export the system processing results into files including Excel, PDF, and HTML formats. The notification sending submodule is used to send the file and related notification information to the target terminal via email service protocol, webhook or network interface.

9. The system according to claim 1, characterized in that, The system also includes an access control and auditing module to support multi-user authentication, role and permission configuration, and behavior auditing. The access control and auditing module is based on a token mechanism and a role-based access control policy to achieve data isolation and collaborative permission configuration between the main account and sub-accounts.

10. The system according to claim 1, characterized in that, The system also includes a non-matching item analysis module, used to detect abnormal data that fails to match effectively during semantic clustering or behavior tracking. The unmatched item analysis module generates a difference report based on the abnormal data and provides user tips or repair suggestions to help optimize the subsequent analysis process.

11. The system according to claim 1, characterized in that, The system also includes a behavior log module, which is used to record the user's operation path, data access trajectory, exported behavior records and tag configuration change history in the system; The behavior log module supports timestamp marking and categorized retrieval by user, time, or operation type.

12. The system according to claim 1, characterized in that, The output and reach module includes a link generation and behavior tracking submodule; wherein, the link generation and behavior tracking submodule is used to generate short links and QR codes accessible on mobile devices; collect the access behavior, click path and conversion status of receiving users, and feed them back to the system for subsequent statistical analysis and recommendation optimization.

13. The system according to claim 1, characterized in that, The system also includes a multilingual template management module, which is used to generate corresponding output templates based on the target language, industry, and usage scenario. The output template includes a field arrangement structure, content display logic, and style rendering rules, and supports user-defined field selection and template adjustment.

14. The system according to claim 1, characterized in that, The system also includes a collection synchronization module and a sending record module; wherein... The collection synchronization module is used to synchronize collection data and tag information between the main account and sub-accounts, so as to realize cross-account data sharing and permission inheritance; The sending record module is used to record historical sending behavior information, which includes sending time, recipient, export format and sending channel, to support behavior auditing and subsequent tracking.

15. The system according to claim 1, characterized in that, The system is configured with offline access support and a local caching mechanism to retain system analysis results and user interaction records within a certain time range when the terminal device has no network connection; after the network is restored, the cached data is automatically synchronized to the remote server to ensure task continuity and data consistency.

16. The system according to claim 1, characterized in that, The multi-input consistency control module includes the following sub-modules: The semantic classification and comparison submodule is used to perform semantic label recognition and classification judgment on the content of each input channel, and generate consistency prompt information when there is semantic deviation or conflict. The dominant input priority strategy submodule is used to support users in setting the dominant input path. The system uses the core semantics it represents as a benchmark to perform the fusion and correction of the content of other paths. The similarity scoring and threshold judgment submodule is used to map each input content to a unified semantic space, calculate the semantic similarity between them, and judge the consistency of the input content according to the preset threshold. The AutoCorrection Suggestion submodule is used to automatically provide keyword replacement suggestions, image correction prompts, or dominant path switching suggestions when there are semantic inconsistencies in the input content.

17. The system according to claim 1, characterized in that, The external platform integration and multi-source data fusion module includes: The platform integration submodule is used to connect with mainstream cross-border platforms, including Alibaba, Amazon, 1688, Shopee, Lazada, Google Shopping, and eBay, based on API protocols or WebDriver technology, and extract structured data, keyword popularity information, and seller behavior records from the target page. The enterprise system access submodule is used to connect with internal enterprise systems such as ERP, CRM, and CDP, receive user-defined lead data, parse the field structure, and achieve data synchronization. The multi-source fusion processing submodule is used to integrate product field content from keywords, image recognition, web page structure parsing, and document OCR, and perform semantic vector normalization, tag standardization, and field format unification operations. The field conflict identification submodule is used to detect semantic duplication or information source deviation between fields and prompt the user to set priority rules or complete field fusion according to the system's default logic.

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