Cross-border e-commerce operation method and device based on multi-collaboration and incremental learning

Abstract

The embodiment of the invention discloses a cross-border e-commerce operation method and device based on multi-collaboration and incremental learning, and the method comprises the steps: carrying out the data collection, generating a strategy, carrying out the reflection evaluation of the execution feedback of the strategy, carrying out the incremental optimization and loop iteration, forming a complete technical closed loop, avoiding the traditional dependence on an API interface, and improving the operation efficiency. A multi-platform data acquisition barrier is broken through, more original and more comprehensive data can be captured, meanwhile, freshness of a data source is ensured, self-diagnosis is performed based on three-dimensional feedback, and a strategy is intelligently and accurately optimized instead of being pushed over, so that spiral rising of the strategy quality is realized, the problem of strategy collapse is thoroughly solved, and the strategy quality is improved. The multi-Agent collaboration mechanism realizes linkage generation and optimization of strategies and multi-modal contents, the platform rule adaptation passing rate is improved to 95% from 78% in the prior art, the incremental updating mechanism reduces the computing resource consumption by 83%, the operation cost is remarkably reduced, and the method is suitable for large-scale popularization and application. The core pain points of data fragmentation, rule dynamics, decision complexity and high manpower dependence in the cross-border e-commerce industry are systematically solved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and cross-border e-commerce technology, specifically to a cross-border e-commerce operation method and apparatus based on multi-collaboration and incremental learning. Background Technology

[0002] Currently, the cross-border e-commerce industry is characterized by multi-platform, multi-scenario, and highly dynamic operations. Practitioners generally face three core pain points, and existing technologies have not yet formed effective solutions.

[0003] First, there are structural barriers to data collection across multiple platforms. Existing cross-border e-commerce tools largely rely on official platform APIs for data acquisition. On the one hand, for emerging platforms like Temu or small and medium-sized sellers lacking API access, data acquisition channels are completely blocked. On the other hand, APIs can only provide basic structured data such as order volume and sales revenue, failing to cover unstructured web-based data such as competitor reviews, influencer interactions, and platform rules, resulting in incomplete data collection and biased decision-making. For example, a cross-border furniture seller, unable to obtain sentiment data from TikTok competitors' reviews, caused their product messaging optimization to deviate from target market preferences, resulting in a conversion rate 20% lower than the industry average.

[0004] Secondly, operational strategy iteration faces the risks of "collapse" and "efficiency bottlenecks." Traditional strategy updates employ a full rewrite model, requiring the reconstruction of a complete strategy document with each iteration. This not only consumes significant computing and human resources but also easily leads to "strategy collapse," resulting in the loss of core experience. Real-world testing revealed that a seller operating on both TikTok and Amazon experienced a 18% drop in conversion rate after 10 rounds of strategy iterations. The core operational messaging was compressed from 12,000 characters to 800 characters, with key pricing logic and influencer collaboration rules lost. Furthermore, the full rewrite model extends the strategy update cycle to 3-5 business days, making it unsuitable for the 3-5 daily rule changes common on cross-border platforms.

[0005] Meanwhile, the adaptation of multimodal content and strategies lacks an automated collaborative mechanism. Multimodal content such as product images and text, and short videos are the core carriers of cross-border operations, but in current technology, content generation and operational strategies are disconnected: strategy formulation does not fully consider the feasibility of content production, and content generation relies solely on generic templates without dynamic adjustments based on platform rules. For example, a 60-second feature explanation video generated for TikTok by a 3C product seller only received 1 / 5 of the views of similar compliant content because it did not adapt to the platform's "#ForYouPage" traffic recommendation rules; and Amazon A+ page images and text, because they did not synchronize the core selling point of "environmental certification" in the strategy, saw a click-through rate increase of less than 5%.

[0006] Patent CN120765354A discloses a method and system for one-click product uploading across multiple cross-border e-commerce platforms, which only achieves cross-platform synchronization of product information through API interfaces. It fails to address the data collection problem in scenarios without APIs and does not involve a strategy iteration mechanism. Patent CN120891952A discloses an AI browser-based intelligent assistant system, which only integrates the browser with basic AI capabilities, enabling only simple data capture and question answering. It lacks a multi-agent collaborative architecture and cannot support the generation and evolution of complex operational strategies. Existing technologies cannot solve the collaborative pain points in cross-border e-commerce operations, namely "incomplete data, easily collapsed strategies, and poor content adaptation." Summary of the Invention

[0007] To address the aforementioned shortcomings, embodiments of the present invention disclose a cross-border e-commerce operation method and apparatus based on multi-collaboration and incremental learning, which can solve the core technical problems of data fragmentation across multiple platforms, low efficiency and easy collapse of strategy iteration, and insufficient adaptability of multimodal content and strategies, while reducing the technical threshold and operating costs for small and medium-sized sellers.

[0008] The first aspect of this invention discloses a cross-border e-commerce operation method based on multi-collaboration and incremental learning, including: Based on interface-free crawling of e-commerce data from multiple platforms, the e-commerce data includes basic operational data, content interaction data, and environmental constraint data. The e-commerce data is cleaned and standardized to form a standardized dataset. The unstructured data in the e-commerce data is transformed into structured feature vectors through a preset cross-border vertical pre-trained model. By using a combination of serial startup and parallel collaboration, an initial operation strategy and adapted multimodal content are generated based on the standardized dataset, and real-time compliance verification is performed on the initial operation strategy and multimodal content. Execute the initial operation strategy and multimodal content that meet compliance verification, and when the preset triggering conditions are met, collect the three-dimensional execution feedback data corresponding to the initial operation strategy and multimodal content, perform reflective analysis based on the three-dimensional execution feedback data, and output a structured reflection report; The structured reflection report is transformed into incremental strategy entries through a preset incremental update mechanism. After incremental processing, it is updated to the current strategy library. The incremental processing includes semantic deduplication, conflict coordination, and priority sorting to update the initial strategy and multimodal content to obtain the updated strategy and content. The updated strategies and content are synchronized to all operational terminals, and after a preset period, e-commerce data is re-captured to trigger a new round of iteration.

[0009] As an optional implementation, in the first aspect of the present invention, the e-commerce data is cleaned and standardized to form a standardized dataset, including: The e-commerce data is deduplicated. Duplicate e-commerce data collected from different pages on the same platform is identified and merged. Identical e-commerce data collected from the same data source in adjacent time windows is removed. Determine the reasonable range of e-commerce data, remove e-commerce data outside the reasonable range, and identify and remove logically contradictory data and abnormal timestamp data; Complete the missing e-commerce data based on a pre-set machine learning model; E-commerce data is standardized according to a standard format, and the structured data in the e-commerce data is transformed into structured feature vectors through a pre-set cross-border vertical pre-trained model.

[0010] As an optional implementation, in a first aspect of the present invention, generating an initial operational strategy and adapted multimodal content based on the standardized dataset includes: The initial operation strategy is generated by calling a pre-built cross-border operation strategy template library and combining the environmental constraint data and historical effective strategy data in the standardized dataset. The historical effective strategy data is obtained by filtering historical strategies in the historical strategy execution record library that meet the improvement threshold in the execution effect within a preset historical time period. The improvement threshold includes improving the conversion rate by more than or equal to 15%.

[0011] As an optional implementation, in the first aspect of the present invention, combining environmental constraint data and historical effective strategy data in the standardized dataset includes: Extract expert collaboration script templates and event planning frameworks from historical strategies that meet the threshold for raising the threshold, and adapt the collaboration script template set and event planning frameworks to the current product category in the standard dataset. Extract competitor pricing strategy data, target market consumption trend reports, and platform traffic characteristic data from environmental constraint data to generate a pricing scheme that includes a dynamic pricing formula and a platform placement schedule scheme that specifies the placement ratio for different platform time periods.

[0012] As an optional implementation, in the first aspect of the present invention, the preset triggering condition includes an automatic triggering condition and a manual triggering condition, wherein the automatic triggering condition includes: The strategy execution meets a preset operational cycle duration; By monitoring real-time data, changes in the rules of the target e-commerce platform can be identified; The system detects fluctuations in at least one core performance indicator of the initial operational strategy or multimodal content, and the fluctuations exceed a preset abnormal threshold.

[0013] As an optional implementation, in the first aspect of the present invention, reflective analysis based on the three-dimensional execution feedback data includes: By comparing the actual data after the initial operational strategy was implemented with the preset target using attribution algorithms, inefficient nodes can be identified. Real-time capture and comparison of platform rule changes during the execution of the initial operation strategy, and real-time compliance verification based on the changed platform rules; The user preferences of the target market are obtained based on a pre-set user behavior sequence analysis model.

[0014] As an optional implementation, in the first aspect of the present invention, the structured reflection report is converted into incremental policy entries through a preset incremental update mechanism, and then updated to the current policy library after incremental processing, including: The suggestions for optimization in the structured reflection report will be transformed into incremental strategy items with a uniform format. The newly added incremental policy entries are semantically compared and merged with the policy entries in the current policy library to determine whether the content of the incremental policy entries exceeds the platform's publishing limit. When the content of the incremental policy entries exceeds the platform's publishing limit, the incremental policy entries are sorted and filtered.

[0015] A second aspect of this invention discloses a cross-border e-commerce operation device based on multi-collaboration and incremental learning, comprising: Data acquisition module: used to crawl e-commerce data from multiple platforms without interface, the e-commerce data includes basic operational data, content interaction data and environmental constraint data, and cleans and standardizes the e-commerce data to form a standardized dataset, wherein the unstructured data in the e-commerce data is transformed into structured feature vectors through a preset cross-border vertical pre-trained model; Strategy generation module: used to generate initial operation strategies and adapted multimodal content based on the standardized dataset through serial startup and parallel collaboration, and to perform real-time compliance verification on the initial operation strategies and multimodal content; Strategy Reflection Module: This module executes the initial operational strategy and multimodal content that meet compliance verification. When preset trigger conditions are met, it collects three-dimensional execution feedback data corresponding to the initial operational strategy and multimodal content, performs reflection analysis based on the three-dimensional execution feedback data, and outputs a structured reflection report. The strategy incremental module is used to convert the structured reflection report into incremental strategy entries through a preset incremental update mechanism. After incremental processing, the entries are updated to the existing strategy library. The incremental processing includes semantic deduplication, conflict coordination, and priority sorting to update the initial strategy and multimodal content to obtain the updated strategy and content. Collaborative Iteration Module: This module is used to synchronize updated strategies and content to various operational terminals and trigger a new round of iteration by re-fetching e-commerce data after a preset period.

[0016] As an optional implementation, in a second aspect of the present invention, the e-commerce data is cleaned and standardized to form a standardized dataset, including: The e-commerce data is deduplicated. Duplicate e-commerce data collected from different pages on the same platform is identified and merged. Identical e-commerce data collected from the same data source in adjacent time windows is removed. Determine the reasonable range of e-commerce data, remove e-commerce data outside the reasonable range, and identify and remove logically contradictory data and abnormal timestamp data; Complete the missing e-commerce data based on a pre-set machine learning model; E-commerce data is standardized according to a standard format, and the structured data in the e-commerce data is transformed into structured feature vectors through a pre-set cross-border vertical pre-trained model.

[0017] As an optional implementation, in a second aspect of the present invention, generating an initial operational strategy and adapted multimodal content based on the standardized dataset includes: The initial operation strategy is generated by calling a pre-built cross-border operation strategy template library and combining the environmental constraint data and historical effective strategy data in the standardized dataset. The historical effective strategy data is obtained by filtering historical strategies in the historical strategy execution record library that meet the improvement threshold in the execution effect within a preset historical time period. The improvement threshold includes improving the conversion rate by more than or equal to 15%.

[0018] As an optional implementation, in a second aspect of the present invention, the environmental constraint data and historical effective strategy data in the standardized dataset are combined, including: Extract expert collaboration script templates and event planning frameworks from historical strategies that meet the threshold for raising the threshold, and adapt the collaboration script template set and event planning frameworks to the current product category in the standard dataset. Extract competitor pricing strategy data, target market consumption trend reports, and platform traffic characteristic data from environmental constraint data to generate a pricing scheme that includes a dynamic pricing formula and a platform placement schedule scheme that specifies the placement ratio for different platform time periods.

[0019] As an optional implementation, in a second aspect of the present invention, the preset triggering condition includes automatic triggering conditions and manual triggering conditions, wherein the automatic triggering condition includes: The strategy execution meets a preset operational cycle duration; By monitoring real-time data, changes in the rules of the target e-commerce platform can be identified; The system detects fluctuations in at least one core performance indicator of the initial operational strategy or multimodal content, and the fluctuations exceed a preset abnormal threshold.

[0020] As an optional implementation, in a second aspect of the present invention, reflective analysis based on the three-dimensional execution feedback data includes: By comparing the actual data after the initial operational strategy was implemented with the preset target using attribution algorithms, inefficient nodes can be identified. Real-time capture and comparison of platform rule changes during the execution of the initial operation strategy, and real-time compliance verification based on the changed platform rules; The user preferences of the target market are obtained based on a pre-set user behavior sequence analysis model.

[0021] As an optional implementation, in a second aspect of the present invention, the structured reflection report is converted into incremental policy entries through a preset incremental update mechanism, and then updated to the current policy library after incremental processing, including: The suggestions for optimization in the structured reflection report will be transformed into incremental strategy items with a uniform format. The newly added incremental policy entries are semantically compared and merged with the policy entries in the current policy library to determine whether the content of the incremental policy entries exceeds the platform's publishing limit. When the content of the incremental policy entries exceeds the platform's publishing limit, the incremental policy entries are sorted and filtered.

[0022] A third aspect of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the cross-border e-commerce operation method based on multi-agent collaboration and incremental learning disclosed in the first aspect of the present invention.

[0023] A fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the cross-border e-commerce operation method based on multi-agent collaboration and incremental learning disclosed in the first aspect of the present invention.

[0024] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention collects data and generates strategies, then reflects on and evaluates the execution feedback of these strategies, leading to incremental optimization and iterative iteration, forming a complete technical closed loop. This avoids the traditional reliance on API interfaces, breaks through the barriers of multi-platform data collection, and can capture more original and comprehensive data while ensuring the freshness of the data source. It performs self-diagnosis based on three-dimensional feedback and intelligently optimizes strategies precisely, rather than starting from scratch, thereby achieving a spiral improvement in strategy quality and completely solving the problem of strategy collapse. The multi-agent collaboration mechanism enables the linkage generation and optimization of strategies and multimodal content, increasing the platform rule adaptation pass rate from 78% in existing technologies to 95%. The incremental update mechanism reduces computing resource consumption by 83%, significantly reducing operating costs. It systematically solves the core pain points of data fragmentation, dynamic rules, complex decision-making, and high reliance on human resources in the cross-border e-commerce industry. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a cross-border e-commerce operation method based on multi-collaboration and incremental learning disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a cross-border e-commerce operation device based on multi-collaboration and incremental learning provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," "third," "fourth," etc., in the specification and claims of this invention are used to distinguish different objects, not to describe a specific order. The terms "comprising" and "having," and any variations thereof, in the embodiments of this invention are intended to cover non-exclusive inclusion. Exemplarily, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0029] This invention discloses a cross-border e-commerce operation method, device, electronic device, and storage medium based on multi-collaboration and incremental learning. It collects data, generates strategies, and then reflects on and evaluates the execution feedback of these strategies, followed by incremental optimization and iterative iteration to form a complete technical closed loop. This avoids traditional reliance on API interfaces, breaks through multi-platform data collection barriers, and can capture more original and comprehensive data while ensuring the freshness of the data source. It performs self-diagnosis based on three-dimensional feedback and intelligently optimizes strategies precisely, rather than starting from scratch, thereby achieving a spiral improvement in strategy quality and completely solving the strategy collapse problem. The multi-agent collaboration mechanism enables the linked generation and optimization of strategies and multimodal content. The platform rule adaptation pass rate is increased from 78% in existing technologies to 95%. The incremental update mechanism reduces computing resource consumption by 83%, significantly reducing operating costs. It systematically solves the core pain points of the cross-border e-commerce industry: data fragmentation, dynamic rules, complex decision-making, and high reliance on human resources.

[0030] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating the cross-border e-commerce operation method based on multi-collaboration and incremental learning disclosed in this invention. The execution entity of the method described in this invention is an execution entity composed of software and / or hardware. This execution entity can receive relevant information and send certain instructions via wired and / or wireless means. It may also have certain processing and storage functions. This execution entity can control multiple devices, such as remote physical servers or cloud servers and related software, or local hosts or servers and related software that perform related operations on devices located in a certain location. In some scenarios, multiple storage devices can also be controlled; these storage devices may be placed in the same location as the devices or in different locations. Figure 1 As shown, this cross-border e-commerce operation method based on multi-agent collaboration and incremental learning includes the following steps: 101. Based on interface-free crawling of e-commerce data from multiple platforms, the e-commerce data includes basic operational data, content interaction data and environmental constraint data, and the e-commerce data is cleaned and standardized to form a standardized dataset, wherein the unstructured data in the e-commerce data is transformed into structured feature vectors through a preset cross-border vertical pre-trained model.

[0031] This step is executed by the data acquisition and processing module, which is used to crawl basic operational data, content interaction data, and environmental constraint data from multiple platforms without APIs, and to clean and standardize them. Among them, unstructured data is transformed into structured feature vectors through cross-border vertical pre-trained models. The AI ​​browser platform performs API-free data scraping to collect three-dimensional data from multiple cross-border e-commerce platforms, specifically including: 1. Basic operational data, covering structured data such as order volume, conversion rate, average order value, inventory turnover rate, and refund rate; 2. Content interaction data, covering semi-structured data such as click-through rate, dwell time, and collection rate of product images and text, completion rate, likes, sentiment values ​​of comments, and number of reposts of short videos, and interaction data of influencer collaboration content; 3. Environmental constraint data, covering unstructured data such as platform rule update texts, competitor pricing strategies, target market consumption trend reports, exchange rate fluctuation data, and logistics timeliness data. After collection, the data is cleaned by a module to remove duplicates, outliers, and missing values ​​to form a standardized dataset. The unstructured data is transformed into structured feature vectors through a cross-border vertical pre-trained model, with feature dimensions including rule compliance tags, sentiment values, and consumer preference keywords.

[0032] In this embodiment, the AI ​​browser base incorporates a multi-platform adaptation engine, specifically designed to adapt to the technical architecture differences of over 15 mainstream cross-border platforms, including Amazon, TikTok, Temu, and Shopee. This engine provides customized adaptation for different platform webpage structures, such as Amazon Listing pages and TikTok short video playback pages, data storage formats like structured forms, and dynamically rendered content. This solves the problem of a single tool being incompatible with heterogeneous multi-platform environments, ensuring cross-platform data crawling even in API-free scenarios. The AI ​​browser supports simulating human browsing behavior, such as page scrolling, click interactions, and cookie simulation, and can parse JavaScript to render pages. A large amount of critical data from cross-border e-commerce platforms, such as real-time competitor reviews, influencer interaction trajectories, and dynamically updated platform rules, is dynamically loaded via JavaScript, which traditional crawlers cannot capture. Simulating human browsing bypasses platform anti-crawling mechanisms and overcomes the limitation of "only being able to obtain structured data accessible through APIs," enabling the complete crawling of unstructured data, such as rule text and comment content. The AI ​​browser, in conjunction with a pre-trained cross-border vertical model (fine-tuned based on DeepSeek-32B, with training data covering 100,000+ cross-border platform rule texts, 500,000+ competitor reviews, and 300,000+ operational cases), possesses semantic understanding capabilities specific to cross-border scenarios. This model can identify the semantic rules of data from different platforms (such as Amazon's refund rate statistics and TikTok's sentiment expression habits in comments), ensuring that unstructured data (such as comments with "complex installation instructions" and platform rule update texts) can be accurately identified and transformed into features that can be processed subsequently, supporting the effectiveness of data scraping. The AI ​​browser, relying on the system's security module, meets the compliance requirements for cross-border data collection: it employs SSL data encryption transmission technology to ensure secure data transmission during the collection process, preventing platform interception or identification as illegal scraping; it has built-in GDPR, CCPA, and other cross-border data compliance checklists to ensure that data collection complies with the privacy regulations of the target market (such as the EU and the US), avoiding legal risks; and platform permission isolation technology allows data from different platforms to be stored independently, avoiding permission conflicts in cross-platform data collection and further improving scraping stability.

[0033] The e-commerce data is cleaned and standardized to form a standardized dataset, including: deduplicating the e-commerce data; identifying and merging duplicate e-commerce data collected from different pages on the same platform; removing identical e-commerce data collected from the same data source in adjacent time windows; determining a reasonable range for the e-commerce data; removing e-commerce data outside the reasonable range; identifying and removing logically contradictory data and abnormal timestamp data; completing missing e-commerce data based on a preset machine learning model; standardizing the e-commerce data according to a standard format; and converting the structured data in the e-commerce data into structured feature vectors through a preset cross-border vertical pre-trained model.

[0034] The collected 3D data is first preprocessed through the data cleaning module. The core actions include deduplication: removing duplicate data across platforms, such as duplicate pricing information of the same competitor on TikTok and Amazon. Outlier Removal: Based on the built-in "Cross-border Data Outlier Identification Rule Library," invalid data is filtered out, such as order volume exceeding the category average by 10 times or abnormal fluctuations in comment sentiment. Missing Value Completion: Using interpolation algorithms adapted to cross-border scenarios, missing data is completed based on the average inventory turnover rate of similar products to ensure data integrity. Unstructured Data Structure Transformation: Unified and integrated dimensions. The 3D data contains a large amount of unstructured data, such as platform rule text and competitor reviews, and semi-structured data, such as short video interaction data. These need to be transformed through the cross-border data feature extraction module: Unstructured data, such as comments about "complex installation instructions" and new California environmental certification regulations, are transformed into structured feature vectors through a cross-border vertical pre-trained model. The feature dimensions are unified as "rule compliance label, sentiment value, consumer preference keywords, timestamp, applicable platform," etc.; Semi-structured data, such as short video completion rate and image / text dwell time, are standardized according to preset fields, such as uniformly retaining two decimal places for completion rate and uniformly converting dwell time to seconds; Structured data, such as order volume and average order value, directly use the original field format and supplement with related tags such as "platform identifier, category identifier," etc.

[0035] Furthermore, it achieves precise correspondence of three-dimensional data through three core association dimensions. Core association key: Using platform + category + timestamp as a unified association key, such as TikTok + furniture + 2024-05-01, it binds three types of data under the same operational scenario. For example: TikTok furniture order volume → corresponding short video completion rate (content interaction data) → TikTok home furnishing traffic rules during the same period (environmental constraint data); Semantic association: It establishes indirect associations by calculating feature vector similarity through the BERT model. For example, user reviews of environmentally friendly materials (content interaction data) and the new California CARB certification regulations (environmental constraint data) have a semantic similarity ≥ 0.8, automatically associating them as "environmentally compliant related data"; Causal association: It reserves a strategy-effect association field to lay the groundwork for subsequent attribution analysis in the reflection and evaluation layer. For example, it binds a certain pricing scheme (basic operational data) with the corresponding conversion rate (content interaction data) and competitor pricing (environmental constraint data).

[0036] 102. By using a serial startup and parallel collaboration approach, an initial operation strategy and adapted multimodal content are generated based on the standardized dataset, and real-time compliance verification is performed on the initial operation strategy and multimodal content.

[0037] The above-mentioned process of generating an initial operational strategy and adapted multimodal content based on the standardized dataset includes: calling a pre-built cross-border operational strategy template library and combining the environmental constraint data and historical effective strategy data in the standardized dataset to generate the initial operational strategy; the historical effective strategy data is obtained by filtering historical strategies in the historical strategy execution record library that have met the improvement threshold in the execution effect within a preset historical time period, and the improvement threshold includes improving the conversion rate by more than or equal to 15%.

[0038] Combining environmental constraint data and historical effective strategy data in the standardized dataset, the process includes: extracting influencer collaboration script templates and event planning frameworks from historical strategies that meet the enhancement threshold, and adapting the collaboration script template set and event planning framework to the corresponding current product category in the standard dataset; extracting competitor pricing strategy data, target market consumption trend reports, and platform traffic characteristic data from the environmental constraint data to generate a pricing scheme containing a dynamic pricing formula and a platform placement rhythm scheme with different placement ratios for different platform time periods.

[0039] The multi-agent collaborative generation module is activated, with three functional agents working together: 1. The Operational Strategy Agent calls upon the cross-border operational strategy template library, containing category-specific templates for over 10 mainstream platforms such as Amazon and TikTok. Combining the environmental constraint data collected in step 101 with historically effective strategies, it selects strategies with a conversion rate increase of ≥15% over the past 3 months to generate an initial operational strategy. This strategy includes a pricing plan with dynamic pricing formulas, platform placement schedules with time slot allocation ratios, influencer collaboration scripts with category-specific selling point descriptions, and event planning schemes; 2. The Multimodal Content Agent receives the core requirements output by the Operational Strategy Agent and generates an adapted... Content materials with different platform characteristics include Amazon A+ page images and text, TikTok short video scripts and materials, Shopee product detail page layout schemes, etc., and automatically embed platform-specific traffic tags (such as TikTok's ForYouPage adaptation tag and Amazon's A9 algorithm optimized keywords); 3. Compliance verification Agent calls a real-time updated multi-platform rule knowledge base, synchronizes platform rule changes every 2 hours, and performs compliance verification on the initial operation strategy and multimodal content. Verification dimensions include keyword bans, content duration limits, tag usage specifications, qualification certificate requirements, etc., and outputs a compliance score (out of 100 points) and a detailed list of non-compliance items.

[0040] This step employs a hybrid collaborative model centered on goal orientation, progressive responsibility, and synchronous verification. The specific collaboration process is as follows: The Operations Strategy Agent initiates the process, outputting core decision-making benchmarks: As the "initiator" of collaborative generation, the Operations Strategy Agent prioritizes its work—calling the cross-border operations strategy template library (containing templates for 10+ mainstream platform sub-categories), combining environmental constraint data collected from multiple sources (such as platform rules and competitor pricing) with historically effective strategies, achieving a conversion rate increase of ≥15% in the past 3 months, and generating an initial operations strategy that includes core content such as pricing schemes, campaign pacing, influencer messaging, and event planning. This clearly defines the operational goals to be achieved and the core execution requirements, providing a basis for subsequent Agent decisions. This step is the foundation of the collaborative process and must be completed first; it is a sequential initiation step. Multimodal content agents follow up in parallel to achieve content adaptation: Instead of waiting for the operational strategy agent to complete all strategy documents, the multimodal content agent initiates content generation simultaneously upon receiving its core requirements, such as highlighting time-saving and labor-saving selling points on TikTok or providing installation size information on Amazon. This involves generating multimodal materials tailored to different platform characteristics, including Amazon A+ page images and text, TikTok short video scripts, and automatically embedding platform-specific traffic tags such as TikTok's #ForYouPage adaptation identifier. Its workflow is entirely dependent on the core requirements of the operational strategy agent, representing a parallel execution phase based on pre-decision, ensuring consistency between content and strategy direction. The compliance verification agent performs synchronous verification throughout the entire process, with dynamic feedback and correction: The compliance verification agent starts simultaneously with the multimodal content agent, without relying on the closed-loop progress of the former two. Instead, it adopts a "real-time intervention + phased verification" approach: On the one hand, during the process of generating the initial strategy by the operational strategy agent, it synchronously verifies whether the keyword usage, pricing descriptions, and activity rules in the strategy comply with the platform's compliance requirements. On the other hand, after the multimodal content agent generates materials, it immediately verifies dimensions such as content duration, tag specifications, and qualification certificates, such as whether TikTok videos have English subtitles or whether Amazon listings include environmental certifications, and outputs a compliance score and a list of non-compliance items. Its core role is dynamic oversight, belonging to the parallel verification stage, ensuring that the strategy and content do not deviate from the compliance bottom line during the generation process. The overall collaboration logic can be summarized as: the operational strategy agent sets the direction (serial first) → the multimodal content agent adapts, and the compliance verification agent oversees (both in parallel) → the compliance verification results are fed back to the former two, completing the first round of collaborative optimization and forming a closed-loop collaboration of direction-execution-verification.

[0041] The core principle of conflict resolution is compliance first, effectiveness second, and feasibility as a fallback. The final decision-making power is based on the compliance conclusion of the compliance verification agent, the effectiveness goal of the operational strategy agent is optimization-oriented, and the feasibility of the multimodal content agent is the guarantee of implementation. The specific priority order and decision-making power allocation are as follows: First priority: The decision-making power for compliance conflicts belongs to the compliance verification agent: The premise of cross-border e-commerce operation is to comply with platform rules and target market regulations. Therefore, when the strategy or content conflicts with compliance rules, the conclusion of the compliance verification agent must prevail. The compliance verification agent must provide clear evidence of violation, such as specific platform rules and regulations. The former two must be adjusted unconditionally, and the compliance bottom line must not be crossed in pursuit of effectiveness or content completeness. Second priority: Decision-making power for effect-feasibility conflicts rests with the Operation Strategy Agent: When the multimodal content agent proposes that the content cannot adapt to the strategy requirements, such as the selling points required by the strategy not being visualized, or platform characteristics limiting content expression, the Operation Strategy Agent takes the lead in coordination. The Operation Strategy Agent needs to balance core effect goals with content feasibility, prioritizing the retention of core requirements with a conversion rate impact weight ≥0.8, such as the significant impact of environmental certification on the conversion rate of furniture categories, and adjusting non-core requirements, such as simplifying the expression of secondary functions, to ensure that the strategy goals are not deviated from while providing feasible space for content generation.

[0042] The fallback principle: Tripartite collaboration and confirmation, with data support as the final basis: If a conflict involves compliance bottom lines but the effectiveness and feasibility are difficult to balance, such as the timing of the campaign required by the strategy not being consistent with the optimal time for content release, the operations strategy agent will retrieve historical case data, such as the correlation data between conversion rates of similar products at different times, content release times and completion rates, combined with platform characteristic analysis provided by the multimodal content agent and the non-violation confirmation from the compliance verification agent, the three parties will collaborate to determine the optimal solution, ensuring that the decision is both data-supported and implementable. When the three agents experience decision-making conflicts, the following process should be followed to resolve the conflict and ensure that the collaboratively generated strategies and content are both compliant and effective: Conflict Identification and Reporting: If any agent discovers a decision-making conflict during its work, such as the multimodal content agent failing to meet strategy requirements or the compliance verification agent identifying a violation, it should immediately suspend its current work and report the conflict information through the system data interface. The conflict type (compliance conflict / effectiveness-feasibility conflict) and the decision-making basis for both parties must be clearly defined, such as historical performance data of the strategy agent, rules and regulations of the compliance agent, platform characteristic analysis of the content agent, and the potential impact of the conflict on operational goals (e.g., violations may lead to account penalties, infeasibility of content may lead to a decrease in conversion rates). Conflict Priority Determination: After receiving a conflict report, the system first determines whether it involves compliance bottom lines. If it involves issues such as keyword bans or missing qualifications, it is directly classified as a Level 1 conflict, initiating an emergency adjustment guided by compliance. If it does not involve compliance issues but is merely a conflict between effectiveness and feasibility, it is classified as a Level 2 conflict, entering the collaborative coordination process. Conflict Coordination and Solution Adjustment: Level 1 conflict, also known as compliance conflict: The compliance verification agent provides specific violation clauses and rectification suggestions, such as deleting prohibited keywords and supplementing CARB certification marks. The operations strategy agent adjusts the core requirements of the initial strategy based on the suggestions, and the multimodal content agent simultaneously optimizes the corresponding content materials. After adjustment, the content is immediately submitted to the compliance verification agent for re-verification until the compliance score is ≥85. Level 2 conflict, also known as effectiveness-feasibility conflict: The operations strategy agent takes the lead, retrieving similar case data from the cross-border operations knowledge base, such as the selling point expression scheme for 3C products on TikTok. Combined with platform adaptation suggestions provided by the multimodal content agent, such as "short videos should prioritize showcasing practical scenarios rather than text descriptions," the core requirements that rank in the top 80% of the comprehensive score (effectiveness weight × platform adaptability) are selected. The strategy wording is retained and optimized. The multimodal content agent regenerates content based on the adjusted requirements, ensuring that the content is both feasible and does not deviate from the core effectiveness objectives.Solution Validation and Closed Loop: The adjusted strategy and content must undergo dual validation—the compliance verification agent confirms compliance, the operational strategy agent confirms that core performance objectives have not been lost (e.g., conversion rate and click-through rate targets have not significantly decreased), and the multimodal content agent confirms that the content can be generated and implemented. After successful validation, the process moves to the next stage: three-dimensional reflection and evaluation. If validation fails, the coordination-adjustment-validation process is repeated until all constraints are met. This conflict resolution process, with compliance as the bottom line, data as support, and collaboration as the core, avoids the one-sidedness of single-agent decision-making while ensuring the synergy between the strategy and multimodal content, ultimately achieving the goal of compliant and feasible generation with satisfactory results.

[0043] 103. Execute the initial operation strategy and multimodal content that meet the compliance verification, and when the preset triggering conditions are met, collect the three-dimensional execution feedback data corresponding to the initial operation strategy and multimodal content, perform reflection analysis based on the three-dimensional execution feedback data, and output a structured reflection report.

[0044] The aforementioned preset triggering conditions include automatic triggering conditions and manual triggering conditions. The automatic triggering conditions include: the strategy execution meets a preset operating cycle duration; changes in the rules of the target e-commerce platform are identified through real-time data monitoring; and at least one core performance indicator of the initial operating strategy or multimodal content is detected to fluctuate, and the fluctuation exceeds a preset abnormal threshold.

[0045] The reflection and analysis based on the three-dimensional execution feedback data includes: comparing the actual data after the execution of the initial operation strategy with the preset target through attribution algorithms to locate inefficient nodes; capturing and comparing platform rule changes during the execution of the initial operation strategy in real time, and correcting real-time compliance verification based on the changed platform rules; and obtaining target market user preferences based on a preset user behavior sequence analysis model.

[0046] Construct a three-dimensional reflection system of "effectiveness-compliance-preference" and simulate the reflector logic execution evaluation of the ACE framework: 1. Effectiveness Reflection: Through attribution algorithms, integrating random forest and causal inference models, compare the actual data after strategy execution with the preset goals, such as a conversion rate target of 12% and a completion rate target of 40%, identify inefficient links, and clarify the relationship between strategy and effectiveness. For example, if a pricing strategy causes the conversion rate to be 15% lower than the industry average, it will be marked as a high-priority optimization item; 2. Compliance Reflection: Use an AI browser to capture and compare platform rule changes during strategy execution in real time, and correct any omissions in the compliance verification Agent in step 102. 1. Dynamic compliance risks that may be overlooked, such as Amazon suddenly updating its list of prohibited keywords for listings, require identification and labeling of relevant violations; 2. Preference reflection: Mining user preferences in the target market through user behavior sequence analysis models (based on LSTM networks), specifically including click path analysis, comment keyword clustering, and extraction of purchase decision factors, for example, if the mention rate of safety certification statements in comments from European and American maternal and infant users is ≥30%, then this preference will be included in the optimization direction; After the three-dimensional reflection is completed, a structured reflection report will be output, which includes items to be optimized, priorities (levels 1-5, with level 1 being the highest), suggestions for optimization directions, and supporting data.

[0047] The specific triggering conditions and execution logic are as follows: Core triggering mode: Automatic triggering accounts for ≥90%. Automatic triggering is based on preset rules and real-time data feedback, requiring no manual intervention, ensuring the timeliness and objectivity of reflection and evaluation. It specifically includes three scenarios. The most basic automatic triggering scenario is triggering when the strategy execution cycle is met. For example, if an operational cycle is 7 days, after the initial strategy or the incrementally updated strategy completes one operational cycle, the system automatically triggers a reflection and evaluation. The data collection layer simultaneously captures basic operational data for that cycle, such as conversion rate, average order value, content interaction data like short video completion rate and comment sentiment, and environmental constraint data, such as whether platform rules have changed. The reflection and evaluation layer then initiates a three-dimensional analysis of "effect-compliance-preference," outputting a structured reflection report. If cross-platform multi-cycle iterations are involved, a comprehensive in-depth reflection is automatically triggered after the 3rd / 5th cycle, not only evaluating the single-cycle effect but also comparing multi-cycle data trends, such as whether the month-on-month growth of conversion rate has slowed, providing more long-term optimization suggestions. Real-time triggering of compliance risks: Given the highly dynamic nature of cross-border platform rules changing 3-5 times daily, the compliance reflection module adopts a real-time monitoring + immediate triggering logic. The compliance verification and update module is explicitly connected to the AI ​​browser base in real time. When the AI ​​browser captures rule changes on the target platform, such as Amazon adding listing material requirements or TikTok... When adjusting accessibility rules, regardless of whether the current operational cycle is in progress, the system will immediately and automatically trigger a compliance review process. This involves comparing the existing strategies and the adaptability of multimodal content to the new rules, marking potential violations, and updating the compliance score to ensure timely correction of risks and avoid operational losses due to rule lag. Key performance indicator (KPI) anomaly triggering: To avoid the continued loss of inefficient strategies due to waiting for the full operational cycle, the system has a built-in KPI anomaly threshold library. When core performance indicators fluctuate abnormally during strategy execution, the system automatically triggers a performance review: for example, when the completion rate of TikTok short videos falls below 70% of the target value, or when Amazon listings... If the click-through rate drops by more than 20% for three consecutive days and the conversion rate fails to reach the preset target of 80%, the attribution algorithm will immediately activate, identifying inefficient links such as excessively high pricing or deviation from the content's selling points. It will simultaneously link compliance and preference reflections, quickly outputting emergency optimization suggestions and shortening the strategy adjustment cycle. Manual triggering provides operations personnel with a flexible intervention channel to adapt to personalized evaluation needs in special scenarios. Specific triggering conditions are: operations personnel actively initiate the process through the visual operations console, such as: needing to quickly verify the initial effects of a new strategy without waiting for the full cycle; a sudden major promotional activity by competitors causing a change in the market environment; or a concentrated occurrence of certain types of user feedback (such as product quality complaints).When manually triggered, operators can choose to reflect across all dimensions or focus on a single dimension. For example, if only preference reflection is triggered, the system can delve into the changes in preferences behind user complaints. The system will call the corresponding modules according to the selected dimension (such as the effect attribution module and the user preference analysis module), and supports custom evaluation indicators such as the target of reducing the new complaint rate. The reflection report will be generated within 10 minutes.

[0048] Whether triggered automatically or manually, the reflection and evaluation process follows a fixed workflow after initiation: data synchronization, three-dimensional parallel analysis, report generation, and associated incremental updates. 1. The data acquisition layer prioritizes synchronizing all data corresponding to the trigger scenario, including single-cycle data, real-time rule data, and data from abnormal periods. After cleaning, this data is transmitted to the reflection and evaluation layer. 2. The effect attribution module, compliance verification and update module, and user preference analysis module work in parallel, respectively completing the comparison between actual data and targets, verification of new rule adaptation, and mining of user behavior. 3. The system integrates the three-dimensional analysis results, sorts the items to be optimized according to priority levels 1-5, and generates a structured reflection report. 4. The report is automatically pushed to the incremental update layer, providing direct evidence for delta incremental updates, forming a seamless connection between triggering, evaluation, and optimization.

[0049] The specific steps for successful strategies to achieve automatic cross-platform compatibility: Step 1: Extraction and standardization of core components of the successful strategy. When a strategy for a certain platform (such as TikTok) is verified as a "successful strategy" (according to the patent definition: conversion rate improvement ≥15%, compliance pass rate 100%, user preference matching degree ≥80%), the system first initiates "core component extraction": The operation strategy agent calls the cross-border vertical pre-trained model (based on DeepSeek-32B fine-tuning) to semantically decompose the successful strategy and extract immutable core components (such as the product's core selling point "environmental certification", pricing logic 5%-10% lower than competitors), user preference satisfaction points, quick installation, and variable adaptation components such as ad placement time, content format, and tag usage); the BERT model is used to standardize the core components and generate a component-effect weight association library, such as the environmental certification selling point corresponding to a conversion rate improvement weight of 0.9, and the free installation service corresponding to a repurchase rate weight of 0.85, ensuring that core experience is not lost, while removing non-core elements that are strongly bound to the original platform (such as TikTok's exclusive #ForYouPage). (Tags). Step 2: Target Platform Characteristics and Constraint Analysis. The system automatically retrieves multi-platform adaptation engines and multi-platform rule knowledge bases to comprehensively analyze the core characteristics of target platforms such as Amazon and Shopee, and outputs a platform adaptation constraint report. Core analysis dimensions include: Traffic Rules: such as Amazon A9 algorithm keyword weight rules, Shopee's flash sale traffic tilt rules, and TikTok's rules linking short video duration and interaction rate; Content Standards: such as Amazon A+ page character limits and image / text layout requirements, Shopee's product detail page qualification certificate display position, and TikTok's caption and tag usage standards; User Preferences: Based on historical content interaction data and user behavior analysis of the target platform, different preferences are explored, such as Amazon users in Europe and America focusing on security certification, and Shopee users in Southeast Asia focusing on cost-effectiveness and logistics timeliness; Environmental Constraints: such as the target platform's exchange rate calculation rules, logistics timeliness data, competitor pricing range, and local regulatory requirements (such as EU platforms needing to comply with GDPR, and US platforms needing to display CARB certification). Step 3: Cross-Platform Modular Conversion of Strategy Components. Operational Strategy Agent and Multimodal Content Agent Collaborative work, based on the principle of "unchanged core components and customized adaptable components," completes the modular transformation of strategies: Core strategy component adaptation: Extracted core components, such as environmental certification selling points, are transformed into recognizable and actionable expressions for the target platform. For example, TikTok's environmental certification is visualized through short videos, while Amazon's listing features the CARB certification logo plus text descriptions of environmentally friendly materials, and Shopee's product detail page includes an environmental label plus localized language (such as Indonesian) explanations. Pricing logic is then adjusted by combining competitor pricing, exchange rates, and logistics costs on the target platform with dynamic pricing formulas. For instance, the original TikTok price of $949 + free installation is transformed into 450,000 Indonesian Rupiah + free shipping on Shopee, maintaining a 5% price advantage over competitors. Adaptability Component Restructuring: The delivery schedule is adjusted according to the target platform's active user times. For example, TikTok's active time in the US (19:00-21:00) is converted to Amazon Europe's (14:00-16:00). Multimodal content agents generate platform-adapted materials. For instance, the original 30-second TikTok short video is converted into step-by-step images and text on Amazon's A+ page and a 60-second simplified short video on Shopee, automatically embedding platform-specific traffic tags such as Amazon's A9 algorithm-optimized keywords and Shopee's "#PromoHarian" trending hashtag. Historical Case Reuse and Empowerment: The system retrieves successful compatibility cases for the same product category and target platform from the cross-border operations knowledge base. For example, 3C products are adapted from TikTok to Amazon, extracting adaptation experience such as keyword conversion rules and content format adaptation ratios to assist in current strategy conversion and reduce trial-and-error costs. Step 4: Cross-Platform Compliance Verification and Risk Correction. The compliance verification agent is involved throughout the entire process, performing dual compliance verification on the converted strategy and multimodal content to ensure that the compatibility process does not violate the platform's bottom line: First round of verification: The real-time rule knowledge base of the target platform is called in, and the verification dimensions include keyword bans, content duration, qualification certificate requirements, tag specifications, etc., and the compliance score and details of non-compliance items are output, such as Amazon listings not indicating material descriptions, Shopee short videos not using localized language; Second correction: The operation strategy agent and multimodal content agent automatically adjust according to the non-compliance items - such as adding material descriptions for Amazon listings, replacing Shopee short video subtitles with localized language, and submitting for compliance verification again after correction until the score meets the standard; Dynamic risk warning: The compliance verification update module monitors the target platform's rule changes in real time, and sets a compliance monitoring period of 7 days for the converted strategy. If the rules change during the period, compliance reflection is immediately triggered and the strategy is corrected accordingly. Step 5: Small-scale trial and error and effect optimization iteration.To avoid the risks that may arise from full synchronization, the converted strategy first enters a small-scale trial and error phase: the system uses a cross-platform synchronization engine to synchronize the strategy only to a portion of the operational units on the target platform, such as a product listing on Amazon or a single store on Shopee, with a trial period of one operational cycle (7 days). After the trial and error ends, a multi-dimensional reflection and evaluation is automatically triggered: performance reflection compares the trial and error data with preset targets, such as a click-through rate ≥10% and a conversion rate ≥8%, identifying areas where adaptation is insufficient, such as the target platform users being insensitive to the free installation selling point; preference reflection uncovers the differentiated needs of the target platform users, such as Shopee users being more concerned about after-sales guarantees; based on the reflection report, the strategy is optimized through a delta incremental update mechanism, such as adding a 1-year warranty selling point or adjusting pricing discounts, repeating the small-scale trial and error-evaluation-optimization process until the strategy's performance achieves ≥90% of the target value in the core indicators. Step 6: Full Synchronization and Case Study Accumulation; Once the strategy's effectiveness during the trial-and-error phase is achieved, the system uses a cross-platform synchronization engine to achieve one-click full synchronization—synchronizing the optimized strategy and accompanying multimodal content to all relevant operational units on the target platform, such as all stores and all product category listings. It also links to the visual console of the collaborative application layer, pushing synchronization completion notifications and real-time monitoring links to operational personnel. Simultaneously, the system categorizes the complete information of the entire compatibility process—including successful strategies from the original platform, target platform adaptation constraint reports, strategy conversion details, compliance verification records, trial-and-error data, and optimization effects—into the cross-border operations knowledge base. This provides reusable templates for subsequent cross-platform compatibility of similar strategies, shortening the compatibility cycle and improving efficiency by 60% compared to the initial compatibility test.

[0050] 104. The structured reflection report is converted into incremental strategy entries through a preset incremental update mechanism, and then updated to the current strategy library after incremental processing. The incremental processing includes semantic deduplication, conflict coordination and priority sorting to update the initial strategy and multimodal content to obtain the updated strategy and content.

[0051] Specifically, the optimization suggestions in the structured reflection report are converted into incremental strategy entries with a unified format; the newly added incremental strategy entries are semantically compared and merged with the strategy entries in the current strategy library to determine whether the content of the incremental strategy entries exceeds the platform's publishing limit; when the content of the incremental strategy entries exceeds the platform's publishing limit, the incremental strategy entries are sorted and filtered.

[0052] This step uses a delta incremental update mechanism instead of a traditional full rewrite, specifically including: 1. The organizer module converts the reflection report into standardized incremental update entries. Each entry contains a unique identifier ID, associated strategy module, specific optimization content (e.g., reducing TikTok short video length from 60s to 30s), effect weight (assigned based on the success rate of similar historical optimizations, ranging from 0-1), and applicable platform range; 2. A semantic embedding deduplication algorithm, based on the BERT model, calculates the semantic similarity between incremental entries and the existing strategy library, with a threshold set at 0.85, merging duplicate or conflicting content, updating effect weight values, and taking the average weight for duplicate entries; 3. If the total length of the merged strategies exceeds the content publishing limits of the corresponding platform, such as Amazon A+ page character limits or TikTok speech duration limits, a comprehensive sorting based on effect weight × platform compatibility is used, retaining the top 80% of core entries to ensure concise strategies without losing core information. Pre-merging preparation: Standardized incremental entry annotation. The organizer module has transformed the reflection report into standardized incremental update entries. Each entry contains fixed core fields: a unique identifier (ID), associated strategy modules (e.g., TikTok short video content, Amazon Listing information), specific optimization content (e.g., adding a shopping cart link, supplementing environmental certification labels), performance weight (assigned based on the success rate of similar optimizations in the past, ranging from 0 to 1, with higher values ​​closer to 1), and applicable platform scope (single platform / multi-platform). This standardized format provides a unified comparison benchmark for merging, ensuring that optimization entries from different sources can be quantitatively analyzed. Core merging actions: semantic similarity determination and conflict handling. Step 1: Semantic similarity calculation. A semantic embedding deduplication algorithm based on the BERT model is used to compare the semantic vectors of each new incremental entry with all entries in the existing strategy library, calculating a similarity score ranging from 0 to 1, preferably 0.85. That is, when the similarity between a new entry and an existing entry is ≥0.85, it is considered a duplicate entry; when the similarity is between 0.7 and 0.85, it is considered a conflicting entry; when the similarity is <0.7, it is considered a completely new entry and is directly retained without merging. Step 2: Duplicate Entries Merging Rules. If multiple duplicate entries exist, such as "Adding English Subtitles to TikTok Videos" and "Supplementing English Accessibility Subtitles to TikTok Short Videos" having a semantic similarity of 0.92, they will be merged into one entry. The more precise description of the optimized content will be retained, prioritizing the version that better aligns with platform rules (e.g., "Supplementing English Accessibility Subtitles"). The effect weight will be the average of all duplicate entries. For example, if the original entries had weights of 0.8 and 0.85, the merged entry will have a weight of 0.825. The applicable platform scope will be the union of the two. If the original entries were adapted to TikTok's US and European sites respectively, the merged entry will be adapted to all TikTok sites. Step 3: Conflicting Entries Coordination Rules.If conflicting entries exist, such as one entry suggesting compressing TikTok short videos to 25 seconds and another suggesting keeping them at 30 seconds, with a semantic similarity of 0.78, the core coordination criterion will be effect weight + platform adaptability: 1. Extract the effect weights of the two entries, such as 0.9 and 0.85; 2. Combine with platform adaptability, provided by the multimodal content agent, calculated based on platform rules (e.g., TikTok #ForYouPage prefers 25-30 second short videos) and user preference data, ranging from 0 to 1); 3. Calculate the comprehensive score of "effect weight × platform adaptability", retaining the entry with the higher score (e.g., the comprehensive score of the 25 second entry is 0.9 × 0.95 = 0.855, and the comprehensive score of the 30 second entry is 0.85 × 0.9 = 0.765, retaining the 25 second optimization suggestion); 4. If the difference in comprehensive scores is ≤ 0.05 (considered difficult to distinguish between superior and inferior), then the two entries will be merged into "TikTok short video length controlled at 25-30 seconds", with the effect weight taken as the average. Post-merge verification: Completeness and feasibility confirmation. After the merge, the system automatically verifies two rules: ① No core information is lost: Ensure that the merged item does not omit any key optimization points from the original item (e.g., when merging "supplementing English subtitles" and "optimizing subtitle font size", the complete requirement of "supplementing English subtitles + font size adaptation to platform accessibility rules" must be retained); ② No hidden conflicts: Verify whether the merged item is consistent with the core logic of the existing strategy library (e.g., the merged pricing optimization item must not create new conflicts with the platform's pricing rules or competitors' pricing strategies). If there are hidden conflicts, return to the conflict handling stage for re-coordination.

[0053] The specific process of incremental strategy ranking is as follows: The core objective of ranking is to prioritize and retain high-value core items under the platform's content publishing restrictions. The ranking is based on a comprehensive scoring logic of performance weight multiplied by platform compatibility. 1. Sorting trigger condition: Strategy length exceeds limit check.

[0054] After the merged incremental entries are integrated with the existing strategies, the system automatically compares them with the content publishing restriction rules of the corresponding platforms. These rules are built into the cross-border operation strategy template library, such as character limits for Amazon A+ pages, duration limits for TikTok short video scripts, and module limits for Shopee detail pages. If the total length after integration does not exceed the limit, no sorting is required, and the system directly enters the cross-platform synchronization process. If the total length exceeds the limit, such as exceeding the character limit for Amazon Listings or exceeding the platform's preferred duration for TikTok short video scripts, the sorting process is immediately initiated.

[0055] 2. Core ranking metric: Comprehensive score calculation.

[0056] The core basis for ranking is the comprehensive score = effect weight × platform adaptability. The definition and value logic of the two indicators are as follows: effect weight: the value is assigned by the incremental item itself and calculated based on the success rate of similar optimizations in history. For example, if the historical optimization success rate of supplementing the environmental certification mark is 95%, then the weight is 0.95. The average weight is taken for repeated merged items, and the weight of the finally retained item is taken for the items after conflict coordination. Platform Compatibility: Calculated jointly by the compliance verification agent and user preference analysis module. Core reference dimensions include: compliance with the latest platform rules, such as TikTok's accessibility caption requirements; matching target platform user preferences, such as Amazon users' attention to material descriptions; and adaptability to the platform's content format, such as Shopee's requirements for image and text layout on product detail pages. The dimension weighting is: compliance 40% + user preference 35% + content format adaptability 25%. 3. Sorting and Filtering Rules. Step 1: Sorting by Platform. If an incremental item applies to multiple platforms, such as simultaneously adapting to TikTok and Shopee, it is split and sorted by single platform. For example, TikTok-specific items are sorted separately, and Shopee-specific items are sorted separately to avoid filtering bias caused by cross-platform rule differences. Step 2: Descending Order and Threshold Filtering. All incremental items from the same platform are sorted from high to low based on their comprehensive score, retaining the top 80% of items. The core basis for the filtering ratio is to balance conciseness and the completeness of core information—through extensive testing, the top 80% of high-scoring items can cover 90%. The above optimizations also keep the strategy length within platform limits. Step 3: Core Information Verification. After filtering, additional verification is required: Whether all high-value items with an effect weight ≥ 0.9 are retained (these are core optimization items; even if ranked outside the top 80%, they must be retained if they do not violate platform restrictions); whether low-compliance items with a platform compatibility < 0.5 are removed (these are items that are likely to trigger compliance risks or user backlash after implementation and should not be retained). 4. Output after sorting: Final Incremental Strategy Package. After filtering, the system will categorize and integrate the retained items according to "related strategy modules" (e.g., TikTok content optimization, Amazon compliance supplementation), generating the final incremental strategy package. Each item will be labeled with a unique ID, comprehensive score, applicable platform, and execution priority, linked to the 1-5 priority levels in the reflection report, providing clear guidance for subsequent cross-platform synchronization and implementation.

[0057] In a typical scenario, the delta item generation module outputs three items: ID1, a shopping cart link for TikTok (weight 0.9, adapted for TikTok); ID2, English subtitles for TikTok (weight 0.8, adapted for TikTok); and ID3, CARB certification and material descriptions for Amazon (weight 0.95, adapted for Amazon). The merging process involves comparing the three items with the existing strategy library. Since the semantic similarity is less than 0.85, they are considered new items with no duplication or conflict, and all three are retained. The sorting process is also straightforward: after integration, the total duration of the TikTok-related items and the number of characters in the Amazon listing do not exceed platform limits, so sorting is not required. The three items are directly included in the final incremental strategy package and synchronized to the corresponding platforms for execution.

[0058] 105. The updated strategies and content are synchronized to all operating terminals, and after the preset period is met, e-commerce data is re-captured to trigger a new round of iteration.

[0059] The incrementally updated operational strategies and supporting multimodal content are synchronized to each operational agent to achieve linkage between strategy adjustment, content adaptation, and execution. After every 3-5 operational cycles, return to step S1 to collect data again and repeat steps 101-104 to form a closed loop of continuous strategy evolution. At the same time, the initial strategy, optimized content, and execution effect cases of each optimization are accumulated in the cross-border operation knowledge base. The cases are categorized and indexed by product category, platform, and optimization type for future reuse.

[0060] Dynamically adapting to real-time market and platform changes avoids rigid strategies: Cross-border e-commerce platform rules change 3-5 times daily, and environmental factors such as target market consumer preferences, competitor strategies, exchange rates, and logistics are constantly fluctuating. The closed-loop system re-collects data every 3-5 operational cycles, ensuring each iteration is based on the latest three-dimensional data, such as new platform rules, changing user preferences, and competitor pricing optimization strategies. This avoids the problem of traditional static strategies being difficult to adjust once formulated. For example, if Amazon suddenly adds new rules regarding furniture material specifications or TikTok users show increased focus on environmentally friendly selling points, the closed-loop system can capture these changes through the next step (step 101) of data collection and quickly translate them into strategy optimization actions through processes 102-104, ensuring that the strategy always aligns with the actual operational scenario.

[0061] Accumulating core experience enhances the precision and reusability of strategies: The closed loop is not simply repetition, but a two-way empowerment of iteration and accumulation. After each cycle, the system accumulates complete cases of the initial strategy, optimized content, and execution results into the cross-border operation knowledge base, categorized and indexed by product category, platform, and optimization type. As the number of cycles increases, the successful experiences accumulated in the knowledge base, such as "the optimal duration for TikTok short videos for 3C products" and "compliant keyword combinations for Amazon listings," become increasingly rich. High-value cases can be directly reused when generating subsequent strategies, reducing trial-and-error costs. At the same time, the reflection and evaluation layer extracts stable correlation logic between strategy and results based on multiple rounds of data comparison, such as increasing the conversion rate weight of safety certification selling points for maternal and infant products in the European and American markets, so that the strategy gradually focuses on high-value actions, continuously improving precision.

[0062] Continuously correcting strategy deviations and avoiding the accumulation of inefficient actions: Initial strategies or single incremental updates may have the problem of "partial adaptation but global inadequacy," such as a round of optimization solving compliance issues but failing to take into account changes in user preferences. A closed-loop system achieves progressive correction of deviations through multiple rounds of reflection and evaluation: the first round may only identify core compliance risks; the second round can discover the adaptation gap between strategy and preferences based on newly collected user behavior data; and the third round can optimize pricing logic by combining new competitor actions. This model, which focuses on a core optimization direction in each round and fills in the gaps in multiple rounds, avoids the long-term existence of inefficient actions, allowing the strategy to continuously approach the optimal balance of compliance, effectiveness, and user preferences in the cycle.

[0063] Lowering operating costs and technical barriers, and adapting to the needs of small and medium-sized sellers: For small and medium-sized sellers, the closed-loop automated cycle significantly reduces manual intervention costs—no need for a professional team to continuously monitor platform rules, analyze data, and adjust strategies. The system can start the cycle with one click and automatically output optimization results through a visual console. At the same time, the computational resource consumption during the cycle is concentrated only on the incremental update part, rather than a full rewrite. Compared with the traditional strategy iteration mode, the computational resource consumption is reduced by 83%, and the time for operations staff to adjust strategies is reduced from 4 hours per day to 1 hour, improving efficiency by 75%. In addition, the strategy after multiple cycles is fully adapted to the characteristics of the target platform and user needs. New sellers can directly reuse mature cases in the knowledge base and quickly get started with cross-platform operations.

[0064] Completely avoids strategy collapse and ensures core logic is not lost: In traditional full-scale rewrite models, core experiences are easily lost after multiple strategy iterations, such as the simplification of key pricing logic and influencer collaboration rules. However, the closed-loop system, based on a delta incremental update mechanism, optimizes only local items on the original strategy in each iteration. Core experiences, such as the proven competitive pricing logic and high-conversion influencer sales pitch framework, are retained in the strategy system through knowledge base accumulation and association with incremental items. In actual testing, after 10 rounds of strategy iteration, the core information retention rate still reached 92%, far exceeding the traditional solution's below 40%, fundamentally solving the strategy collapse problem.

[0065] The closed loop in this embodiment is not merely about updating data, but rather a co-evolution based on data updates and centered on incremental optimization of the decision model. The specific logic is as follows: At the data level, real-time updates across all dimensions provide fresh data for iteration: Each time step 101 is entered, the AI ​​browser re-captures three-dimensional data from multiple platforms, covering fundamental operational data such as the latest conversion rates and refund rates; content interaction data, including short video completion rates and comment sentiment values ​​from the past 1-2 operational cycles; and environmental constraint data, including newly added platform rules, changed competitor strategies, and new reports on target market consumer trends. The collected data is then processed by a cleaning module to form a standardized dataset perfectly matched to the current scenario, replacing historical data from the previous round. This ensures that subsequent strategy generation (step 102) and reflection / evaluation (step 103) are based on the latest and most comprehensive data foundation, avoiding decision-making biases caused by data lag.

[0066] At the decision-making model level: incremental optimization based on new data and cases, rather than complete replacement: The decision-making models in the patent, including cross-border vertical pre-trained models, effect attribution models, LSTM user preference analysis models, and BERT semantic deduplication models, will not be completely replaced due to closed-loop cycles. Instead, they will achieve incremental evolution through "fine-tuning with new data + empowerment with cases." The core logic is as follows: Cross-border vertical pre-trained model, based on DeepSeek-32B fine-tuning: Each closed-loop case and newly collected unstructured data, such as 10,000+ competitor reviews and 5,000+ platform rule texts, will be added to the model's training data. Lightweight fine-tuning will be performed regularly to improve the model's ability to identify and transform new scenarios and rules, such as more accurately converting comments related to ease of installation into feature vectors. Effect attribution model, user preference analysis model, etc.: Based on multi-round cyclic strategy-effect correlation data, the algorithm parameters are optimized, such as adjusting the feature weights of the random forest model and the sequence analysis window of the LSTM network, to make the attribution more accurate, such as quickly locating the low conversion rate caused by the lack of shopping links in short videos, and making preference mining more realistic, such as identifying new user needs for after-sales guarantee. The core logic of the model remains stable: incremental optimizations are only for adapting to new scenarios and improving accuracy, without replacing the core architecture of the model, such as the attribution logic of random forest + causal inference and the deduplication rule with a semantic similarity threshold of 0.85. This avoids the break in the strategy logic caused by a full replacement of the model and ensures the continuity and stability of the strategy.

[0067] For example, consider a Chinese company selling a smart thermos water bottle to the US (Amazon, TikTok) and Southeast Asia (Shopee). The system automatically starts collecting data and finds that the Amazon store's conversion rate was 8% last week, and while a certain TikTok video had high views, the purchase link had few clicks. Analyzing user reviews reveals that keywords such as safety certification and materials frequently appear in US user reviews; Southeast Asian users are more concerned about insulation time and price. The system also finds that Amazon recently updated its rules requiring clear labeling of materials for food contact products; and that competitors have reduced prices by 10% on Shopee. Based on this information, an initial strategy is generated. The core requirement is to highlight BPA (Body Pressure Assessment) on all platforms. The strategy focused on BPA-free safety; for Amazon, the pricing was set 5% higher than competitors; for TikTok, short videos highlighting the convenience of one-click temperature control and easy switching between coffee and tea were created, with shopping cart links added; for Shopee, a limited-time free shipping promotion was planned to counter competitors' price reductions. Upon receiving the strategy, specific materials were generated: an A+ page for Amazon, detailing the material and safety certifications with images and text; a 30-second short video script for TikTok demonstrating the one-click water temperature switching function; and a limited-time free shipping banner at the top of the Shopee product page. The entire process was reviewed simultaneously. It immediately discovered that the strategy agent's requirement of "absolute safety" constituted prohibited exaggeration on Amazon and had to be modified; the content agent's script for TikTok lacked space for subtitles, violating the platform's new accessibility rules. Therefore, the strategy agent was instructed to change the wording to meet FDA food-grade safety standards, and the content agent was required to revise the script. This involved multi-dimensional feedback... Upon review, it was discovered that while TikTok videos had a high completion rate, the conversion rate from clicks on purchase links was lower than the target. Attribution algorithm analysis suggested this might be due to insufficient visibility of the shopping cart link. Real-time monitoring revealed that Amazon had not updated its material rules, ensuring policy compliance. Analysis of newly generated user behavior data showed a high rewatch rate for the "pouring boiling water" segment in the video, indicating strong interest in "high-temperature resistance" and "heat retention" characteristics, which the existing strategy did not adequately emphasize. Two optimization suggestions were generated: optimize the shopping cart link prompt style in TikTok videos; and reinforce the selling point of 100℃ boiling water direct pouring and long-lasting heat retention in Shopee content. The system received the reflection report, initiated an incremental update, and converted the two suggestions into standardized incremental items. Checking the existing strategy library, no duplicates or conflicts were found. Since the content length did not exceed the limit, both suggestions were retained, forming an update package. The update package was automatically synchronized to the corresponding platform and content management backend for rapid adjustment. The system continued running, entering the next cycle.Three to five weeks later, it will start again from step 101, collect the latest data, and start a new round of generation, reflection and fine-tuning based on the optimized foundation. At the same time, the complete case of discovering that Southeast Asian users pay attention to thermal insulation characteristics and successfully optimizing them will be stored in the knowledge base. In the future, when the company expands to other Southeast Asian platforms or sells other thermal insulation products, this experience can be directly called upon to achieve cross-platform automatic compatibility of successful strategies.

[0068] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a cross-border e-commerce operation device based on multi-collaboration and incremental learning disclosed in an embodiment of the present invention. Figure 2 As shown, the cross-border e-commerce operation device based on multi-collaboration and incremental learning may include: a data acquisition module 201, a strategy generation module 202, a strategy reflection module 203, a strategy increment module 204, and a collaborative iteration module 205. The data acquisition module 201 is used to crawl e-commerce data from multiple platforms without interfaces. The e-commerce data includes basic operational data, content interaction data, and environmental constraint data. The e-commerce data is cleaned and standardized to form a standardized dataset. Unstructured data in the e-commerce data is transformed into structured feature vectors through a pre-set cross-border vertical pre-training model. The strategy generation module 202 is used to generate an initial operation strategy and adapted multimodal content based on the standardized dataset through serial startup and parallel collaboration. The initial operation strategy and multimodal content are evaluated in real time. Compliance verification; Strategy reflection module 203: used to execute the initial operation strategy and multimodal content that meet compliance verification, and when the preset trigger conditions are met, collect the three-dimensional execution feedback data corresponding to the initial operation strategy and multimodal content, perform reflection analysis based on the three-dimensional execution feedback data, and output a structured reflection report; Strategy increment module 204: used to convert the structured reflection report into incremental strategy items through a preset incremental update mechanism, and update it to the existing strategy library after incremental processing. The incremental processing includes semantic deduplication, conflict coordination, and priority sorting to update the initial strategy and multimodal content to obtain the updated strategy and content; Collaborative iteration module 205: used to synchronize the updated strategy and content to each operation terminal, and re-fetch e-commerce data after a preset period to trigger a new round of iteration.

[0069] Furthermore, in the data acquisition module 201, the e-commerce data is cleaned and standardized to form a standardized dataset, including: deduplicating the e-commerce data; identifying and merging duplicate e-commerce data collected from different pages on the same platform; removing identical e-commerce data collected from the same data source in adjacent time windows; determining a reasonable range for the e-commerce data; removing e-commerce data outside the reasonable range; identifying and removing logically contradictory data and abnormal timestamp data; completing missing e-commerce data based on a preset machine learning model; standardizing the e-commerce data according to a standard format; and converting the structured data in the e-commerce data into structured feature vectors through a preset cross-border vertical pre-trained model.

[0070] In the strategy generation module 202, an initial operational strategy and adapted multimodal content are generated based on the standardized dataset. This includes: calling a pre-built cross-border operational strategy template library and combining environmental constraint data and historical effective strategy data from the standardized dataset to generate the initial operational strategy; the historical effective strategy data is obtained by filtering historical strategies from the historical strategy execution record library that meet the improvement threshold within a preset historical time period, where the improvement threshold includes increasing the conversion rate by more than or equal to 15%. Further, combining the environmental constraint data and historical effective strategy data from the standardized dataset includes: extracting influencer collaboration script templates and event planning frameworks from historical strategies that meet the improvement threshold, and adapting the collaboration script template set and event planning framework based on the corresponding current product category in the standard dataset; extracting competitor pricing strategy data, target market consumption trend reports, and platform traffic characteristic data from the environmental constraint data to generate a pricing scheme containing a dynamic pricing formula and a platform placement rhythm scheme with different placement ratios for different platform time periods.

[0071] In the strategy reflection module 203, the preset trigger conditions include automatic and manual trigger conditions. Automatic trigger conditions include: strategy execution meeting a preset operational cycle duration; identifying rule changes on the target e-commerce platform through real-time data monitoring; and detecting fluctuations in at least one core performance indicator of the initial operational strategy or multimodal content, with the fluctuation exceeding a preset anomaly threshold. Further, reflection analysis is performed based on the three-dimensional execution feedback data, including: comparing the actual data after the initial operational strategy execution with preset targets using attribution algorithms to locate inefficient nodes; capturing and comparing platform rule changes during the execution of the initial operational strategy in real time, and correcting real-time compliance verification based on the changed platform rules; and obtaining target market user preferences based on a preset user behavior sequence analysis model.

[0072] In the strategy incremental module 204, the structured reflection report is converted into incremental strategy entries through a preset incremental update mechanism, and then updated to the current strategy library after incremental processing. This includes: converting the suggestions to be optimized in the structured reflection report into incremental strategy entries with a uniform format; performing semantic comparison and merging processing on the newly added incremental strategy entries and the strategy entries in the current strategy library; determining whether the content of the incremental strategy entries exceeds the platform's publishing limit; and sorting and filtering the incremental strategy entries when the content of the incremental strategy entries exceeds the platform's publishing limit.

[0073] This implementation breaks through the data collection barriers of multiple platforms. The AI ​​browser's API-free crawling technology is compatible with 15+ mainstream cross-border platforms, covering over 90% of cross-border operation scenarios. Compared to traditional API-dependent solutions, the compatibility range is increased by 60%, especially solving the data acquisition problems of small and medium-sized sellers and emerging platforms. The three-dimensional data collection mode increases data dimensions by 3 times, providing more comprehensive decision-making basis. Actual testing shows that the strategy conversion rate based on this invention's data is 22% higher than that of a single structured data strategy. It completely solves the strategy collapse problem. The incremental delta update mechanism retains core strategy entries and only updates and optimizes parts, achieving a 92% retention rate of core information during strategy iteration. Compared to traditional full-scale rewriting solutions, accuracy is improved by 17%-23%. In AppWorld-type intelligent agent task tests, the accuracy rate remains above 85% after 10 rounds of strategy iteration, while traditional solutions are below 40%. Simultaneously, incremental updates shorten the strategy iteration cycle from 3-5 working days to 4-6 hours, improving efficiency by 80%. It also enhances content and strategy... Improved Efficiency: The multi-agent collaboration mechanism enables the coordinated generation and optimization of strategies and multimodal content, increasing the platform rule adaptation pass rate from 78% to 95% compared to existing technologies. Real-world case studies have shown an average 25% increase in TikTok short video conversion rates, an 18% increase in Amazon listing click-through rates, and a 20% increase in Shopee detail page conversion rates. Significantly Reduced Operating Costs: The incremental update mechanism reduces computing resource consumption by 83%, reducing strategy adjustment time for operations staff from 4 hours per day to 1 hour, resulting in a 75% increase in efficiency. The case reuse function of the cross-border operations knowledge base shortens new strategy generation time by 60%, allowing small and medium-sized sellers to quickly get started without a professional technical team. Building a Technological Barrier: Integrating AI browser's API-free crawling technology with the incremental evolution logic of the ACE framework, a customized design effect-compliance-preference three-dimensional reflection system forms a closed-loop technology of API-free data collection + multi-agent collaboration + incremental evolution. This differs from the single-function positioning of existing technologies and cannot be easily copied or replaced.

[0074] Example 3 Please see Figure 3 , Figure 3This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device can be a computer, a server, etc. Of course, in certain cases, it can also be a mobile phone, tablet computer, monitoring terminal, or other smart device, as well as an image acquisition device with processing capabilities. Figure 3 As shown, the electronic device may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the cross-border e-commerce operation method based on multi-agent collaboration and incremental learning in Embodiment 1.

[0075] This invention discloses a computer-readable storage medium storing a computer program that enables a computer to perform some or all of the steps in the cross-border e-commerce operation method based on multi-cooperation and incremental learning in Embodiment 1.

[0076] This invention also discloses a computer program product, wherein when the computer program product is run on a computer, the computer performs some or all of the steps in the cross-border e-commerce operation method based on multi-collaboration and incremental learning in Embodiment 1.

[0077] This invention also discloses an application publishing platform, which is used to publish computer program products. When the computer program products are run on a computer, the computer executes some or all of the steps in the cross-border e-commerce operation method based on multi-collaboration and incremental learning in Embodiment 1.

[0078] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0079] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they can be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0081] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several requests to cause a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of the present invention.

[0082] In the embodiments provided by this invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0083] Those skilled in the art will understand that some or all of the steps in the various methods of the embodiments described can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0084] The foregoing has provided a detailed description of the cross-border e-commerce operation method, apparatus, electronic device, and storage medium based on multi-collaboration and incremental learning disclosed in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A cross-border e-commerce operation method based on multi-collaboration and incremental learning, characterized in that, include: Based on interface-free crawling of e-commerce data from multiple platforms, the e-commerce data includes basic operational data, content interaction data, and environmental constraint data. The e-commerce data is cleaned and standardized to form a standardized dataset. The unstructured data in the e-commerce data is transformed into structured feature vectors through a preset cross-border vertical pre-trained model. By using a combination of serial startup and parallel collaboration, an initial operation strategy and adapted multimodal content are generated based on the standardized dataset, and real-time compliance verification is performed on the initial operation strategy and multimodal content. Execute the initial operation strategy and multimodal content that meet compliance verification, and when the preset triggering conditions are met, collect the three-dimensional execution feedback data corresponding to the initial operation strategy and multimodal content, perform reflective analysis based on the three-dimensional execution feedback data, and output a structured reflection report; The structured reflection report is transformed into incremental strategy entries through a preset incremental update mechanism. After incremental processing, it is updated to the current strategy library. The incremental processing includes semantic deduplication, conflict coordination, and priority sorting to update the initial strategy and multimodal content to obtain the updated strategy and content. The updated strategies and content are synchronized to all operational terminals, and after a preset period, e-commerce data is re-captured to trigger a new round of iteration.

2. The cross-border e-commerce operation method based on multi-collaboration and incremental learning according to claim 1, characterized in that, The e-commerce data is cleaned and standardized to form a standardized dataset, including: The e-commerce data is deduplicated. Duplicate e-commerce data collected from different pages on the same platform is identified and merged. Identical e-commerce data collected from the same data source in adjacent time windows is removed. Determine the reasonable range of e-commerce data, remove e-commerce data outside the reasonable range, and identify and remove logically contradictory data and abnormal timestamp data; Complete the missing e-commerce data based on a pre-set machine learning model; E-commerce data is standardized according to a standard format, and the structured data in the e-commerce data is transformed into structured feature vectors through a pre-set cross-border vertical pre-trained model.

3. The cross-border e-commerce operation method based on multi-collaboration and incremental learning according to claim 1, characterized in that, Based on the standardized dataset, an initial operational strategy and adapted multimodal content are generated, including: The initial operation strategy is generated by calling a pre-built cross-border operation strategy template library and combining the environmental constraint data and historical effective strategy data in the standardized dataset. The historical effective strategy data is obtained by filtering historical strategies in the historical strategy execution record library that meet the improvement threshold in the execution effect within a preset historical time period. The improvement threshold includes improving the conversion rate by more than or equal to 15%.

4. The cross-border e-commerce operation method based on multi-collaboration and incremental learning according to claim 3, characterized in that, Combining environmental constraint data and historical effective strategy data from the standardized dataset, including: Extract expert collaboration script templates and event planning frameworks from historical strategies that meet the threshold for raising the threshold, and adapt the collaboration script template set and event planning frameworks to the current product category in the standard dataset. Extract competitor pricing strategy data, target market consumption trend reports, and platform traffic characteristic data from environmental constraint data to generate a pricing scheme that includes a dynamic pricing formula and a platform placement schedule scheme that specifies the placement ratio for different platform time periods.

5. The cross-border e-commerce operation method based on multi-collaboration and incremental learning according to claim 1, characterized in that, The preset triggering conditions include automatic triggering conditions and manual triggering conditions, wherein the automatic triggering conditions include: The strategy execution meets a preset operational cycle duration; By monitoring real-time data, changes in the rules of the target e-commerce platform can be identified; The system detects fluctuations in at least one core performance indicator of the initial operational strategy or multimodal content, and the fluctuations exceed a preset abnormal threshold.

6. The cross-border e-commerce operation method based on multi-collaboration and incremental learning according to claim 1, characterized in that, Reflective analysis based on the aforementioned three-dimensional execution feedback data includes: By comparing the actual data after the initial operational strategy was implemented with the preset target using attribution algorithms, inefficient nodes can be identified. Real-time capture and comparison of platform rule changes during the execution of the initial operation strategy, and real-time compliance verification based on the changed platform rules; The user preferences of the target market are obtained based on a pre-set user behavior sequence analysis model.

7. The cross-border e-commerce operation method based on multi-collaboration and incremental learning according to claim 1, characterized in that, The structured reflection report is converted into incremental policy entries through a preset incremental update mechanism, and then updated to the current policy library after incremental processing, including: The suggestions for optimization in the structured reflection report will be transformed into incremental strategy items with a uniform format. The newly added incremental policy entries are semantically compared and merged with the policy entries in the current policy library to determine whether the content of the incremental policy entries exceeds the platform's publishing limit. When the content of the incremental policy entries exceeds the platform's publishing limit, the incremental policy entries are sorted and filtered.

8. A cross-border e-commerce operation device based on multi-collaboration and incremental learning, characterized in that, include: Data acquisition module: used to crawl e-commerce data from multiple platforms without interface, the e-commerce data includes basic operational data, content interaction data and environmental constraint data, and cleans and standardizes the e-commerce data to form a standardized dataset, wherein the unstructured data in the e-commerce data is transformed into structured feature vectors through a preset cross-border vertical pre-trained model; Strategy generation module: used to generate initial operation strategies and adapted multimodal content based on the standardized dataset through serial startup and parallel collaboration, and to perform real-time compliance verification on the initial operation strategies and multimodal content; Strategy Reflection Module: This module executes the initial operational strategy and multimodal content that meet compliance verification. When preset trigger conditions are met, it collects three-dimensional execution feedback data corresponding to the initial operational strategy and multimodal content, performs reflection analysis based on the three-dimensional execution feedback data, and outputs a structured reflection report. The strategy incremental module is used to convert the structured reflection report into incremental strategy entries through a preset incremental update mechanism. After incremental processing, the entries are updated to the existing strategy library. The incremental processing includes semantic deduplication, conflict coordination, and priority sorting to update the initial strategy and multimodal content to obtain the updated strategy and content. Collaborative Iteration Module: This module is used to synchronize updated strategies and content to various operational terminals and trigger a new round of iteration by re-fetching e-commerce data after a preset period.

9. An electronic device, characterized in that, include: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the cross-border e-commerce operation method based on multi-collaboration and incremental learning as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program causes a computer to execute the cross-border e-commerce operation method based on multi-collaboration and incremental learning as described in any one of claims 1 to 7.

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