Full-link-based cross-border e-commerce decision-making method and system

Abstract

The invention relates to a full-link-based cross-border e-commerce decision-making method and system, and the method comprises the steps: obtaining multi-source heterogeneous data related to a commodity, and generating structured input data; analyzing and processing the structured input data by a pre-constructed analysis model, and constructing and maintaining an operation state vector corresponding to the commodity; performing cross-border e-commerce business-oriented analysis on the structured input data, and updating an operation state vector; monitoring the change of the operation state vector, and generating a corresponding state event; based on the state event, respectively executing a content optimization operation, an inventory decision operation and / or a reanalysis operation corresponding to the commodity, and feeding back an execution result to the analysis model to update the operation state vector; and generating an operation monitoring result based on the updated operation state vector, and triggering the state event again when an abnormal change is detected. Through adoption of the scheme, comprehensive description and continuous monitoring of the commodity operation state can be realized, and the integrity and accuracy of cross-border e-commerce decision making are improved.

Description

Technical Field

[0001] This invention relates to the field of e-commerce data analysis technology, and in particular to a cross-border e-commerce decision-making method and system based on the entire supply chain. Background Technology

[0002] With the rapid development of cross-border e-commerce, it has become common for goods to operate simultaneously on multiple e-commerce platforms and in multiple countries and regions. The display format of goods, user feedback content, user behavior characteristics, and inventory and logistics status are characterized by diverse data sources, complex data structures, and high frequency of changes.

[0003] In existing solutions, operational decisions for cross-border e-commerce products typically involve decentralized monitoring of metrics such as product traffic, conversion rates, user reviews, and inventory levels. Adjustments to product page content, inventory replenishment, or marketing strategies are then made based on preset rules or human experience. However, these technical solutions largely rely on fixed rules or manually set thresholds, making it difficult to uniformly model the complex relationships between multi-dimensional data.

[0004] Therefore, how to uniformly model the operational status of goods in a multi-source heterogeneous data environment, and drive content optimization, inventory decision-making, and continuous monitoring of cross-border e-commerce goods based on the analysis results of complex data relationships, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] Based on this, the present invention provides a cross-border e-commerce decision-making method and system based on the entire chain, which can realize a comprehensive characterization and continuous monitoring of the business status of goods, and improve the overallity and accuracy of cross-border e-commerce decision-making.

[0006] A cross-border e-commerce decision-making method based on the entire supply chain includes: acquiring multi-source heterogeneous data related to products, and performing entity alignment and time-series synchronization of the multi-source heterogeneous data based on the unique identifier of the product to generate structured input data; analyzing and processing the structured input data using a pre-built analysis model to construct and maintain an operational status vector corresponding to the product, wherein the operational status vector is used to characterize the comprehensive operational status of the product in terms of traffic, conversion, reputation, inventory, and profit; performing cross-border e-commerce business-oriented analysis on the structured input data, generating operational analysis results from the analysis model, and updating the operational status vector based on the operational analysis results; monitoring changes in the operational status vector, and generating a corresponding status event through the analysis model when the operational status vector meets preset trigger conditions; performing content optimization operations, inventory decision operations, and / or re-analysis operations corresponding to the product based on the status event, and feeding back the execution results to the analysis model to update the operational status vector; generating operational monitoring results based on the updated operational status vector, and triggering the status event again when abnormal changes are detected.

[0007] Optionally, generating structured input data includes: acquiring product data from multiple different data platforms, the product data including product image data, product text description data, user review data, and user behavior data; performing field semantic parsing on the product data from the different data platforms to generate a platform semantic field set; establishing a cross-platform field mapping relationship based on the platform semantic field set, and reconstructing the fields of the product data; performing entity consistency verification on the reconstructed product data based on the product's unique identifier, and performing time-series consistency verification on the product data that passes the entity consistency verification based on the timestamp, and using the product data that simultaneously satisfies both entity consistency verification and time-series consistency verification as the structured input data.

[0008] Optionally, constructing and maintaining the operating status vector corresponding to the product includes: constructing a traffic status feature set, a conversion status feature set, a reputation status feature set, and an inventory status feature set based on the structured input data; performing feature compression processing on each status feature set to generate corresponding status sub-vectors; assigning initial weights to each status sub-vector based on the stability performance of the structured input data in different time periods; adaptively adjusting the initial weights based on the prediction error of the operating status vector in historical periods; and combining each status sub-vector with adjusted weights to generate the operating status vector.

[0009] Optionally, the step of performing cross-border e-commerce business-oriented analysis on the structured input data, generating operational analysis results from the analysis model, and updating the business status vector based on the operational analysis results includes: parsing the product image data in the structured input data to extract a set of product display features; performing semantic parsing on the user review data in the structured input data to extract a set of user demand features; constructing a set of feature pairs based on the set of product display features and the set of user demand features; performing consistency calculation on the set of feature pairs to generate an initial matching result; performing reverse verification on the initial matching result based on historical conversion data to obtain a corrected matching result, and generating the operational analysis result based on the corrected matching result.

[0010] Optionally, the decision-making method satisfies one or more of the following: the consistency calculation of the feature pair set to generate an initial matching result includes: mapping the product display features and the user demand features to a unified semantic space, and calculating the semantic consistency score between the product display features and the user demand features based on the unified semantic space; calculating the evidence consistency score between the product display features and the user demand features based on the detection results of functional structures in product image data and functional parameter information in product text descriptions; calculating the conflict penalty factor for the corresponding feature pair based on the sentiment polarity distribution and degree of divergence of user demand features in historical user reviews; calculating the scene consistency score based on the matching relationship between the usage scenario information corresponding to the user demand features and the scene elements in the product display features; calculating the competitor matching result based on the display features of competitor products and the user demand features, and comparing the competitor matching result with the consistency calculation result of the feature pair to generate a relative consistency score; and performing a fusion calculation on the feature pair set based on the semantic consistency score, evidence consistency score, conflict penalty factor, scene consistency score, and relative consistency score to generate the initial matching result.

[0011] The step of reverse-validating the initial matching result based on historical conversion data to obtain a corrected matching result includes: constructing a validation dataset containing the initial matching result, historical conversion data, and page change information based on the time dimension, and aligning the changes in the initial matching result with the changes in the historical conversion data in terms of time; constructing a conversion prediction model based on the validation dataset, and calculating the deviation between the predicted conversion result and the actual historical conversion data to generate a matching effectiveness evaluation parameter; updating the initial matching result with a weighted average based on the matching effectiveness evaluation parameter, and reducing the weight of the corresponding initial matching result when the matching effectiveness evaluation parameter indicates that the corresponding initial matching result has a negative impact on the conversion; and using the updated matching result as the corrected matching result.

[0012] Optionally, monitoring the changes in the operating status vector and generating a corresponding status event through the analysis model when the operating status vector meets a preset trigger condition includes: periodically sampling the operating status vector within a preset time window to generate a historical status sequence; performing trend modeling on the historical status sequence to obtain a status change trend parameter; calculating a trend deviation based on the status change trend parameter; comparing the trend deviation with a historical fluctuation threshold range; and generating the status event when the trend deviation exceeds the historical fluctuation threshold range.

[0013] Optionally, the decision-making method satisfies one or more of the following: the step of trend modeling the historical state sequence to obtain state change trend parameters includes: collecting business state vectors at multiple time points to form corresponding historical state sequences, and performing time alignment, missing value repair, and outlier cleaning on the historical state sequences; based on the processed historical state sequences, performing trend anomaly detection, structural change point detection, and multivariate joint anomaly detection respectively to characterize the change features of the business state vectors within a preset time window, and generating a candidate anomaly set to characterize the state change trend parameters;

[0014] The calculation of trend deviation based on the state change trend parameters includes: performing root cause inference on the candidate anomaly set based on the root cause map describing the causal relationships between the state dimensions in the business state vector, and determining the potential root cause and its confidence level for each candidate anomaly; generating gating parameters based on the inventory availability status, page content change status, and advertising placement status, and determining whether the potential root cause is executable based on the potential root cause and its confidence level; when the potential root cause meets the preset gating conditions, generating the corresponding state event, and classifying the state event based on the impact degree of the potential root cause and the confidence level, and determining the corresponding execution strategy type according to the level of the state event; in the After a state event is generated, it is arranged with other state events according to a preset dependency relationship to form an event execution sequence. Within a preset cooldown time window, state events of the same type are merged. When the correction result of the operating state vector indicates that the anomaly has been mitigated, the triggering of repeated state events is suppressed. After the execution strategy corresponding to the state event is executed, the execution result is mapped to the state correction amount of the corresponding state dimension in the operating state vector, and the operating state vector is updated based on the state correction amount. Based on the updated operating state vector, the candidate anomaly is re-evaluated to see if it still exists. When the evaluation result indicates that the anomaly has not met the expected improvement conditions, a state event is generated again to enter the next round of decision-making process.

[0015] Optionally, the step of executing content optimization, inventory decision, and / or reanalysis operations corresponding to the product based on the state event, and feeding back the execution results to the analysis model to update the operating state vector, includes: generating an execution constraint parameter set based on the current operating state vector, wherein the execution constraint parameter set is used to constrain the execution range and adjustment magnitude of the content optimization, inventory decision, and / or reanalysis operations; determining the adjustable range of the corresponding operation based on the execution constraint parameter set, wherein the adjustable range of the operation is used to limit the adjustment range of the content parameters, the adjustment magnitude of the inventory decision, and / or the triggering conditions of the reanalysis operation; executing the content optimization, inventory decision, and / or reanalysis operations corresponding to the product within the adjustable range of the operation to achieve targeted adjustment of the product's operating state; recording the execution results of the operation and the corresponding operating state change information, and inputting the execution results and state change information as feedback data into the analysis model to update the operating state vector, providing a basis for subsequent state event identification and operating decision-making.

[0016] Optionally, the step of generating operational monitoring results based on the updated operational status vector and triggering the status event again when an abnormal change is detected includes: generating operational monitoring results based on the current operational status vector, and detecting whether an abnormal change has occurred in the operational status according to the operational monitoring results; in response to detecting the abnormal change, generating a status event and entering the corresponding decision-making process to obtain an execution result for the status event; mapping the execution result to a status correction amount for the corresponding status dimension in the operational status vector; updating the operational status vector based on the status correction amount to obtain an updated operational status vector; recalculating the status change trend parameter reflecting the operational status change trend based on the updated operational status vector; and in response to the status change trend parameter satisfying the re-triggering condition, determining that the abnormal change has not been eliminated or has further evolved, and generating the status event again to enter the next round of decision-making process.

[0017] A cross-border e-commerce decision-making system based on the entire supply chain includes: an analysis model, which is adapted to perform operational decisions using the cross-border e-commerce decision-making method based on the entire supply chain as described in any of the foregoing examples.

[0018] In summary, this invention provides a cross-border e-commerce decision-making method based on the entire supply chain. By introducing an analysis mechanism centered on multi-source heterogeneous data fusion and operational status vectors, it achieves continuous perception and dynamic control of product operational status. Through entity alignment and time-series synchronization of product data from different sources and with different structures, information silos and time discrepancies are eliminated from the data source, ensuring that operational analysis is built on a unified and reliable data foundation. Based on this, an analytical model is used to comprehensively model the status of products across multiple dimensions such as traffic, conversion, reputation, inventory, and profit, forming an operational status vector that dynamically reflects the overall picture of product operation, avoiding the one-sidedness and lag of traditional single-indicator analysis. After the operational status vector is constructed, continuous monitoring of its change process and judgment of triggering conditions enable timely identification of abnormal fluctuations or trend changes in product operation and automatic generation of corresponding status events, thereby realizing a shift from "passive post-event analysis" to "proactive process perception." Furthermore, based on the linkage of status events, operations such as content optimization, inventory decision-making, and re-analysis are performed, and the execution results are fed back to the analysis model, forming a closed-loop optimization mechanism of analysis-decision-feedback, so that the adjustment of commodity operation can be highly consistent with the actual operation status. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a cross-border e-commerce decision-making method based on the entire supply chain, as provided in an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram illustrating the generation of structured input data according to an embodiment of the present invention;

[0021] Figure 3 A flowchart illustrating an update process for an operational state vector, provided as an embodiment of the present invention;

[0022] Figure 4 A schematic diagram illustrating the generation process of a state event according to an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of the structure of a cross-border e-commerce decision-making system based on the entire supply chain, as provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the specific details described below are only a part of the embodiments of the present invention, and the present invention can be implemented in many other embodiments different from those described herein. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0026] Currently, some AI tools have emerged in the market to assist sellers' operations, but they generally suffer from problems such as limited functionality, fragmented data, and unintelligent strategy generation. With the development of multimodal AI, Large Language Modeling (LLM), and predictive analytics technologies, the cross-border e-commerce industry urgently needs an end-to-end AI decision-making system that integrates data from the entire chain, including product selection, operations, inventory, and returns, and provides actionable strategies to improve sellers' decision-making efficiency and profitability.

[0027] However, these AI tools generally suffer from the following technical problems:

[0028] 1) Limited functionality: Existing solutions typically focus on a single scenario, requiring enterprises to use multiple independent tools and operate across platforms, resulting in low efficiency.

[0029] 2) The problem of data silos is serious. Data is scattered between ERP, e-commerce platforms and analysis systems. Most tools cannot effectively connect the data and rely on manual integration, which affects the comprehensiveness of decision-making.

[0030] 3) Insufficient real-time performance, delayed dashboard updates, and inability to provide timely alerts or strategy adjustments.

[0031] 4) Limitations of AI capabilities: Most existing AI assistants are rule-driven and lack the ability to deeply understand and structurally analyze complex problems.

[0032] Based on this, the present invention aims to provide a full-chain AI model-driven intelligent decision-making system for cross-border e-commerce, with specific objectives including:

[0033] 1) Data fusion and intelligent analysis: Integrate multi-source data such as sales, inventory, reviews, and advertising, and generate actionable strategies through multimodal AI.

[0034] 2) Automated decision optimization: Based on the differences between market supply and demand, recommend high-potential products and keyword layouts; automatically analyze traffic structure and buyer needs to generate optimization solutions.

[0035] 3) Enhance real-time response capabilities: Provide dynamic dashboards and early warning systems (such as early warning of slow-moving inventory), turning passive into proactive.

[0036] 4) Lower the barrier to entry: Design tools with zero barriers to entry (such as text assistant), support batch processing and fine-tuning, and improve efficiency.

[0037] Specifically, this invention provides a cross-border e-commerce decision-making method based on the entire supply chain. By introducing an analysis mechanism centered on multi-source heterogeneous data fusion and operational status vectors, it achieves continuous perception and dynamic control of the operational status of goods. By aligning and synchronizing the entity data and time series of goods from different sources and with different structures, information silos and time discrepancies are eliminated from the data source, enabling operational analysis to be built on a unified and reliable data foundation. On this basis, the analysis model comprehensively models the status of goods in multiple dimensions such as traffic, conversion, reputation, inventory, and profit, forming an operational status vector that dynamically reflects the overall picture of goods operation, avoiding the one-sidedness and lag of traditional single-indicator analysis.

[0038] After the operational status vector is constructed, continuous monitoring of its change process and judgment of triggering conditions enable timely identification of abnormal fluctuations or trend changes in commodity operations, and automatic generation of corresponding status events. This achieves a shift from "passive post-event analysis" to "proactive process perception." Furthermore, based on the status events, operations such as content optimization, inventory decision-making, and re-analysis are executed in conjunction with the analysis model, and the execution results are fed back to the analysis model, forming a closed-loop optimization mechanism of analysis-decision-feedback. This ensures that adjustments to commodity operations remain highly consistent with the actual operational status.

[0039] In other words, this embodiment of the disclosure achieves full-link coverage from raw operational data to operational decision execution through phased collaborative control of "data alignment and fusion, state vector modeling, event triggering and closed-loop feedback." This method elevates the traditional commodity operation model, which relies on human experience or static rules, to an intelligent operation management process based on multi-dimensional state perception and dynamic event-driven processes. It can adapt to changes in the cross-border e-commerce business environment and commodity operation status, achieving a comprehensive characterization and continuous monitoring of commodity operation status, significantly improving the accuracy, timeliness, and overall operational stability of commodity operation decisions.

[0040] To enable those skilled in the art to better understand and implement this solution, the following detailed description of the specific solution, principles, advantages, and effects of the present invention is provided with reference to the accompanying drawings and specific embodiments.

[0041] See Figure 1 , Figure 1 The flowchart of a cross-border e-commerce decision-making method based on the entire supply chain provided in this embodiment of the invention is applicable to cross-border e-commerce business scenarios and is used to achieve dynamic perception, event triggering, and feedback optimization of product operation status. It may include:

[0042] S101: Acquire multi-source heterogeneous data related to the product, and perform entity alignment and time-series synchronization of the multi-source heterogeneous data based on the product's unique identifier to generate structured input data.

[0043] In some embodiments, during the operation of cross-border e-commerce businesses, product-related data typically originates from multiple different data sources, exhibiting differences in data structure, granularity, and time dimension. Multi-source heterogeneous data may include, but is not limited to: platform traffic data, user behavior data, order transaction data, review and reputation data, inventory and logistics data, and cost and profit data.

[0044] After acquiring multi-source heterogeneous data, entity alignment is performed on the data from different data sources based on the unique identifier of the product (as a non-limiting example, product SKU, product code, or globally unique product ID) to ensure that data pointing to the same product in different data sources can be correctly associated. At the same time, time-series synchronization is performed on the multi-source heterogeneous data to eliminate time deviations caused by different collection frequencies or time delays.

[0045] By entity alignment and temporal synchronization, structured input data in a unified format is generated, providing a standardized data foundation for subsequent analysis model processing.

[0046] In some embodiments, see Figure 2 The illustrated embodiment of the present invention provides a schematic diagram of generating structured input data. Step S101 may include:

[0047] S1011, acquire product data from multiple different data platforms, including product image data, product text description data, user review data, and user behavior data.

[0048] In some embodiments, when processing product data, product data can be obtained from multiple heterogeneous data platforms, which may include, but are not limited to, e-commerce platforms, content platforms, social platforms, and offline systems.

[0049] In this embodiment, the product data includes at least one or more of the following: product image data, product text description data, user review data, and user behavior data (such as click, browse, favorite, purchase, and other behavioral data).

[0050] It should be noted that different data platforms may differ in the organization structure of product data, field naming rules, data granularity, and update frequency, but the objects they describe all point to the product entity, so they can be used as the basic data source for subsequent unified processing.

[0051] S1012 performs semantic parsing on product data from different data platforms to generate a set of platform semantic fields.

[0052] In some embodiments, due to differences in the naming conventions and semantic expressions of product data fields across different data platforms—for example, the "price" field for the same product may correspond to "price," "amount," or "sale_price" on different platforms—it is necessary to perform field-level semantic parsing of product data for each platform.

[0053] In this embodiment, by parsing the field names, field contents, and their contextual information in the product data, a corresponding semantic tag can be assigned to each field, thereby forming a platform semantic field set for each data platform. The platform semantic field set is used to characterize the correspondence between product data fields and their actual business semantics within the platform.

[0054] S1013 establishes cross-platform field mapping relationships based on the platform's semantic field set and reconstructs the fields of product data.

[0055] In some embodiments, after obtaining the set of platform semantic fields corresponding to each of the multiple data platforms, a cross-platform field mapping relationship can be established between different platforms based on the consistency of field semantics.

[0056] As a non-restrictive example, if the "price" field in the first platform and the "sale_price" field in the second platform are both identified as "product price" after semantic parsing, then the two can be mapped to a unified standard field.

[0057] After establishing cross-platform field mapping relationships, the original product data is reconstructed to ensure that product data from different data platforms maintains consistency in field structure and semantics, thereby generating product data with a unified field structure.

[0058] S1014 performs entity consistency verification on the reconstructed product data based on the unique product identifier, and performs time-series consistency verification on the product data that passes the entity consistency verification based on the timestamp. The product data that simultaneously satisfies both entity consistency verification and time-series consistency verification will be used as structured input data.

[0059] In some embodiments, after the fields are reconstructed, product data on different platforms may still have issues such as duplicate entities or inconsistent times.

[0060] Therefore, in this embodiment, the reconstructed product data is first checked for entity consistency based on the unique identifier of the product (e.g., product ID, SKU code or a combination thereof) to confirm whether data from different platforms point to the same product entity.

[0061] After passing the entity consistency check, the product data is then checked for temporal consistency based on the timestamp information corresponding to the product data to ensure that the data update order of the same product at different time points is reasonable and there are no conflicts.

[0062] Ultimately, product data that simultaneously satisfies entity consistency and temporal consistency checks will be identified as structured input data for use in subsequent product analysis, modeling, or recommendation processes.

[0063] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and do not constitute a limitation on the scope of protection of the present invention. Without departing from the core idea of ​​the present invention, those skilled in the art can make equivalent substitutions or adjustments to the above steps.

[0064] In this way, by performing semantic parsing, cross-platform field mapping and field reconstruction on product data from multiple different data platforms, and combining entity consistency verification based on unique product identifiers and time-series consistency verification based on timestamps, unified structured processing of cross-platform product data is achieved. This effectively eliminates the differences in data structure and semantics between different platforms, avoids the problem of duplicate product entities or data conflicts, and significantly improves the accuracy, timeliness and reliability of product data fusion. As a result, it provides high-quality, stable and highly scalable structured input data for subsequent data analysis, modeling and business applications.

[0065] In one embodiment, a structured multimodal data pool for e-commerce scenarios is constructed, and the acquisition and processing methods include:

[0066] Cross-platform heterogeneous data alignment: Through ASIN encoding, product main images and detail videos (visual modality) from platform A, titles, five-point descriptions, and QA (text modality) from platform B, and real user reviews and rating time series (text + numerical modality) from platform C are aligned and synchronized in time.

[0067] Task-oriented feature deconstruction: For visual modalities, instead of general feature extraction, a pre-trained object detection model is used to segment the main product, scene props, and functional identifiers in the main image, and extract their color, texture, and spatial layout features; for text modalities, sentiment analysis is not only performed, but also syntactic analysis is used to extract the "usage scenario-pain point-functional point" triple from the comments, and the association is verified with the functional keywords in the product title.

[0068] Noise filtering and standardization: Design a review filtering algorithm based on behavioral weights (e.g., filtering short reviews suspected of being fraudulent), and normalize the traffic data of competitor ASINs to eliminate baseline interference from platform fluctuations. Address the pain points of "unknown reasons for declining sales" and "unclear how to launch new products."

[0069] The innovation of this solution lies not in a single technology, but in building an end-to-end intelligent pipeline from "multi-source heterogeneous data" to "business decision instructions":

[0070] Deep integration rather than simple aggregation: Cross-platform entity alignment is achieved through ASIN, creating a truly comprehensive "product information view".

[0071] Task-oriented feature engineering: All analysis models (visual segmentation, triple extraction) are designed for specific tasks in e-commerce operations (optimizing listings, analyzing reputation), producing directly understandable business knowledge rather than obscure data features.

[0072] Closed-loop decision support: The system's endpoint is not an analysis report, but a decision intelligence that can directly map data insights to specific action recommendations, fundamentally solving the problem of "having data but not knowing how to use it".

[0073] In other words, technical details are integrated into the business process framework, clarifying the inputs, processing, and outputs of each step, enabling readers to easily understand the system's operating logic and value, thereby eliminating any doubts.

[0074] S102 uses a pre-built analytical model to analyze and process structured input data, construct and maintain an operational status vector corresponding to the product, and the operational status vector is used to characterize the comprehensive operational status of the product in terms of traffic, conversion, reputation, inventory and profit.

[0075] In some embodiments, the analytical model can be a model built based on rules, statistical analysis, machine learning, or deep learning, with structured input data as input.

[0076] The analytical model performs multi-dimensional analysis on the structured input data, extracting key features that characterize the operational status of the product, and mapping these key features into an operational status vector. The operational status vector can include multiple dimensions, each corresponding to an operational indicator, reflecting the product's performance at different operational levels.

[0077] As a non-restrictive example, the operating state vector may include:

[0078] Traffic dimensions representing product exposure and clicks; conversion dimensions representing conversion rate and sales volume; word-of-mouth dimensions representing user ratings and sentiment; inventory dimensions representing inventory turnover rate and stockout risk; and profit dimensions representing gross profit margin and profit contribution.

[0079] Among them, the operational status vector is continuously maintained during system operation and changes dynamically as data is updated.

[0080] In one embodiment, step S102 may include:

[0081] A1) Based on structured input data, construct the traffic status feature set, conversion status feature set, reputation status feature set, and inventory status feature set respectively.

[0082] In some embodiments, structured input data may include multi-dimensional data from operational systems, transaction systems, user behavior systems, and inventory management systems. After parsing the structured input data, it is classified according to preset business dimensions to construct multiple sets of state features.

[0083] Among them, the traffic status feature set is used to characterize data features reflecting user access behavior, such as visit volume, click volume, and exposure volume; the conversion status feature set is used to characterize data features reflecting transaction conversion effect, such as order rate, payment rate, and conversion frequency; the reputation status feature set is used to characterize data features reflecting user feedback, such as user rating score, positive review rate, and complaint rate; and the inventory status feature set is used to characterize data features reflecting inventory operation status, such as inventory quantity, turnover rate, and stockout rate.

[0084] It should be noted that the specific composition of the above-mentioned state feature sets can be adjusted according to the actual business scenario, and this embodiment does not impose any restrictions on this.

[0085] A2) Perform feature compression processing on each set of state features to generate the corresponding state sub-vectors.

[0086] In some embodiments, since each set of state features typically contains raw feature data of multiple dimensions, direct use would result in high computational complexity and redundant information. Therefore, feature compression processing can be performed on each set of state features.

[0087] In this embodiment, feature selection, feature aggregation, or dimensionality reduction algorithms can be used to map each set of state features into a low-dimensional state sub-vector, thereby reducing data dimensionality while retaining the main state information. Thus, traffic state sub-vectors, conversion state sub-vectors, reputation state sub-vectors, and inventory state sub-vectors are obtained respectively.

[0088] A3) Based on the stability performance of the structured input data over different time periods, assign initial weights to each state sub-vector.

[0089] In some embodiments, the fluctuation of the indicators corresponding to each set of state features is statistically analyzed over multiple historical time periods, and different initial weights are assigned to different state sub-vectors by analyzing their stability levels.

[0090] As a non-restrictive example, state subvectors that exhibit relatively stable behavior and small fluctuations over different time periods can be assigned higher initial weights; while state subvectors that exhibit large fluctuations and high uncertainty can be assigned relatively lower initial weights.

[0091] The above method enables the initial weights to reflect the fundamental importance of each state sub-vector in business analysis.

[0092] A4) Based on the prediction error of the business status vector in the historical period, the initial weights are adaptively adjusted.

[0093] In some embodiments, operating results are predicted using the generated operating state vectors within a historical period, and the predicted results are compared with the actual results to calculate the prediction error.

[0094] Based on the contribution of each state sub-vector to the prediction error, its corresponding initial weights are adaptively adjusted. As a non-restrictive example, the weights of state sub-vectors that have a significant impact on the prediction error can be reduced, while the weights of state sub-vectors that can effectively reduce the prediction error can be increased accordingly. Through continuous iterative adjustments, the weight allocation can gradually approach the optimal state.

[0095] A5) Combine each state subvector with adjusted weights to generate an operating state vector.

[0096] In some embodiments, the traffic status subvector, conversion status subvector, reputation status subvector, and inventory status subvector are multiplied by their respective adjusted weights and then fused according to a preset combination rule to obtain a unified business status vector.

[0097] The operational status vector can comprehensively reflect the overall status of current business activities across multiple dimensions, and can be used for subsequent business evaluation, trend prediction, or decision support.

[0098] It should be noted that different state subvectors can be combined using weighted summation, concatenation, or other vector fusion methods, and this embodiment of the invention does not limit this.

[0099] The technical solution of this invention constructs a multi-dimensional set of business status features from structured input data, and generates a business status vector after feature compression, weight allocation, and adaptive adjustment. This reduces data dimensionality and computational complexity while comprehensively and accurately representing the overall status of business activities across multiple dimensions such as traffic, conversion, reputation, and inventory. Furthermore, by dynamically optimizing weights based on historical prediction errors, the accuracy and robustness of business status assessment and prediction results are improved, providing a more objective and reliable quantitative basis for business decisions, thereby effectively enhancing the efficiency of business analysis and the quality of decision-making.

[0100] S103 performs cross-border e-commerce business-oriented analysis on structured input data, generates operational analysis results from the analysis model, and updates the business status vector based on the operational analysis results.

[0101] In some embodiments, based on the initial operational state vector, the analysis model further performs specialized analyses on the structured input data, targeting the characteristics of cross-border e-commerce businesses. Operational analysis may include, but is not limited to: market trend analysis, competitor analysis, price sensitivity analysis, promotional effectiveness analysis, and regional sales difference analysis.

[0102] The above analysis generates corresponding operational analysis results, which are then mapped to the corresponding dimensions of the business status vector. The business status vector is then updated to more accurately reflect the true business status of goods in the current cross-border e-commerce environment.

[0103] See Figure 3 The flowchart shown in this embodiment of the invention illustrates an update process for an operational state vector. Figure 3 As shown, it may include:

[0104] S1031, parse the product image data in the structured input data and extract the product display feature set.

[0105] In some embodiments, the structured input data includes image data corresponding to the product, which is used to characterize the product's appearance, visual elements, and presentation on the display page. During this step, image parsing processing can be performed on the product image data to extract multi-dimensional feature information that reflects the product's display characteristics, thereby forming a product display feature set.

[0106] In this embodiment, the product display features may include, but are not limited to: the product's appearance outline features, color distribution features, texture features, composition layout features, key visual element position features, and the scene features in which the product is located. By extracting features from the product image, the original unstructured image data can be transformed into a structured feature representation that can be used for subsequent calculations.

[0107] It should be noted that the specific types and quantities of the aforementioned product display features are not limited, as long as they can be used to characterize product information at the display level. In different embodiments, different image parsing methods and feature extraction strategies can be selected according to actual application needs.

[0108] S1032, Semantic parsing is performed on the user comment data in the structured input data to extract the set of user demand features.

[0109] In some embodiments, the structured input data also includes product-related user review data, which reflects users' subjective feelings and preferences during actual use or experience of the product. During this step, semantic parsing can be performed on the user review data to identify user demand information implicit in the review text, thereby extracting a set of user demand features.

[0110] In this embodiment, user demand characteristics may include, but are not limited to: users' focus on product functions, their evaluation tendency on product quality, their preferences for product appearance or user experience, price sensitivity, and their demand characteristics for after-sales service. By performing semantic analysis on user review data, scattered and unstructured text information can be summarized into a unified representation of demand characteristics.

[0111] It should be noted that different semantic parsing methods may be used in different embodiments, such as rule-based parsing or model-based parsing, and the embodiments of the present invention do not limit this.

[0112] S1033, construct a set of feature pairs based on the set of product display features and the set of user demand features.

[0113] In some embodiments, after obtaining the product display feature set and the user demand feature set respectively, they can be combined based on the correspondence between them to construct a feature pair set. Each feature pair in the feature pair set represents the correspondence between a certain product display feature and a certain user demand feature.

[0114] In this embodiment, feature pairs can be represented in the form of <product display features, user demand features>, used to characterize whether the product's display meets the user's concerns at the demand level. By constructing a set of feature pairs, basic data can be provided for subsequent consistency calculations, enabling product display information and user demand information to be matched within the same analytical framework.

[0115] It should be noted that the feature pairs can be constructed in a one-to-one, one-to-many, or many-to-many combination, depending on the granularity of the product display features and user demand features. This embodiment of the invention does not impose any restrictions on this.

[0116] S1034, perform consistency calculation on the feature pair set to generate initial matching results.

[0117] In some embodiments, after obtaining the set of feature pairs, a consistency calculation operation can be performed on each feature pair to evaluate the degree of matching between the product display features and the corresponding user demand features. By comprehensively analyzing the consistency calculation results of multiple feature pairs, an initial matching result between the product and user demand can be generated.

[0118] In this embodiment, the result of the consistency calculation can be represented numerically to reflect the degree to which the product display information meets user needs. Based on the consistency calculation result, a preliminary matching relationship between the product at the display level and user needs can be determined, thereby forming an initial matching result.

[0119] It should be noted that the specific method of consistency calculation is not a limitation, as long as it can reflect the degree of correlation between product display features and user demand features.

[0120] In one example, step S1034 may include: mapping product display features and user demand features to a unified semantic space, and calculating a semantic consistency score between product display features and user demand features based on the unified semantic space; calculating an evidential consistency score between product display features and user demand features based on the detection results of functional structures in product image data and functional parameter information in product text descriptions; calculating a conflict penalty factor for corresponding feature pairs based on the sentiment polarity distribution and degree of disagreement of user demand features in historical user reviews; calculating a scene consistency score based on the matching relationship between the usage scenario information corresponding to user demand features and scene elements in product display features; calculating competitor matching results based on the display features of competitor products and user demand features, and comparing the competitor matching results with the consistency calculation results of feature pairs to generate a relative consistency score; and performing a fusion calculation on the feature pair set based on the semantic consistency score, evidential consistency score, conflict penalty factor, scene consistency score, and relative consistency score to generate an initial matching result.

[0121] In one specific embodiment, suppose a user inputs the following requirements into the platform: suitable for subway commuting, good noise cancellation, comfortable to wear, long battery life, and not pinching the ears. Based on this requirement text and the user's browsing and purchasing history, user requirement characteristics are extracted, including requirements for active noise cancellation strength, wearing comfort, long battery life, and commuting usage scenarios.

[0122] Meanwhile, the system extracted product display features for a wireless active noise-canceling headphone from the candidate products. These features included: parameters in the product description such as hybrid active noise cancellation up to 45dB and a battery life of 40 hours; functional structural features identified in the product image, such as the closed-back earcup structure, microphone array structure, lightweight headband, and memory foam ear pads; and subway commuting scene elements appearing in the product display image.

[0123] In the feature matching stage, the system first maps product display features and user demand features to a unified semantic representation space. By vectorizing semantic expressions in user demands such as "good noise reduction" and "commuting use" with descriptions in product displays such as "45dB active noise cancellation" and "commuting noise-canceling headphones," and calculating the semantic similarity between the two, a semantic consistency score for the corresponding feature pair is obtained. This score reflects the degree to which the product display information semantically meets user needs.

[0124] Subsequently, the system calculates the consistency of evidence between product display features and user demand features based on product image data and functional information in product text descriptions. Specifically, for the noise reduction requirement raised by the user, the system detects a closed-back earcup structure and a multi-microphone array structure in the product image and obtains a clear active noise reduction intensity value in the product text parameters. This confirms that the product provides objective support for this requirement at both the structural and parameter levels, thus generating a high consistency of evidence score.

[0125] Furthermore, the system calculates conflict penalty factors for corresponding user needs based on their performance in historical user reviews. For example, regarding the need for comfortable wear and no ear pinching, the system analyzes historical reviews related to wearing comfort and finds that while most reviews are positive, a certain percentage are negative, indicating disagreements about user experience. Therefore, based on the distribution of sentiment polarity and the degree of disagreement, the system introduces a corresponding conflict penalty factor for this feature pair to reduce its weight in the final matching result.

[0126] Furthermore, the system also combines the usage scenario information implicit in user demand characteristics to perform matching analysis on scenario elements in product display characteristics. For the subway commuting scenario explicitly stated by the user, the system detected visual elements such as subway carriages and commuters wearing headphones in the product display images, and descriptions such as commuting noise reduction and subway suitability appeared in the product copy, thus determining that the product display scenario and the user demand scenario have a high degree of consistency, and generating a scenario consistency score accordingly.

[0127] Building upon this foundation, the system introduces a competitor comparison mechanism. It selects competing products within the same category as the target product and calculates the matching results between the product's display features and the user's needs. By comparing the target product with competing products in dimensions such as noise cancellation strength, battery life, and wearing experience, the system generates a relative consistency score to reflect the target product's advantages over competitors in meeting user needs.

[0128] Finally, based on semantic consistency score, evidence consistency score, conflict penalty factor, scenario consistency score, and relative consistency score, the system performs weighted fusion calculations on each feature pair to obtain an initial matching result between product display features and user demand features. This initial matching result is used to characterize the overall degree of matching between the target product and the user's current needs, and can serve as the input basis for subsequent recommendation ranking or decision-making modules.

[0129] S1035, based on historical conversion data, reverse-verify the initial matching results to obtain corrected matching results, and generate operational analysis results based on the corrected matching results.

[0130] In some embodiments, after obtaining the initial matching result, historical conversion data can be used to reverse-verify the initial matching result. Historical conversion data reflects the historical performance of a product from display to conversion during actual operation. By comparing and analyzing the initial matching result with the historical conversion data, the initial matching result can be corrected, thereby obtaining a corrected matching result.

[0131] In this embodiment, the corrected matching results can more accurately reflect the actual matching effect between product display features and user demand features. Based on this, operational analysis results can be generated from the corrected matching results to assist operational decisions, such as optimizing product display strategies, adjusting product recommendation order, or improving product description content.

[0132] It should be noted that the specific format of historical conversion data and the reverse verification method are not restrictions, as long as they can serve to verify and correct the initial matching results.

[0133] In one example, step S1035 may include: constructing a validation dataset containing initial matching results, historical conversion data, and page change information based on the time dimension, and aligning the changes in the initial matching results with the changes in the historical conversion data in terms of time; constructing a conversion prediction model based on the validation dataset, and calculating the deviation between the predicted conversion results and the actual historical conversion data to generate matching effectiveness evaluation parameters; updating the initial matching results with weights based on the matching effectiveness evaluation parameters, and reducing the weight of the corresponding initial matching results when the matching effectiveness evaluation parameters indicate that the corresponding initial matching results have a negative impact on the conversion; and using the updated matching results as corrected matching results.

[0134] In one embodiment, step S103 focuses on "core operational data insights," aiming to help operators "understand the current state of the product," primarily achieved through two modules:

[0135] Keyword traffic structure analysis:

[0136] Identify the product's primary keywords (these keywords account for approximately 80% of organic traffic), display keyword popularity (e.g., "high-frequency words" tags), and available optimization tools (e.g., "AI insights"). For example, keywords like "wasserhahn bad" in the image clearly show the core sources of traffic, helping operators focus on key traffic entry points and optimize keyword strategies.

[0137] Demand discovery and opportunity identification:

[0138] Extract "unmet needs" (i.e., market opportunities) from user feedback (your own reviews, competitor reviews, etc.) and provide targeted optimization suggestions:

[0139] Based on user preferences, such as "stylish appearance + high-quality workmanship + high cost performance" and "simple installation + clear instructions", we provide practical suggestions, including visual design (such as main image / sub-image text) and installation guide optimization (such as "three-step installation diagram" and "German instruction manual + QR code video"), to amplify the product's advantages.

[0140] For areas of user dissatisfaction / concern, such as "sealing issues (leakage / unstable installation)" and "dirt / cleaning disagreements (black easily leaves water stains / fingerprints)," solutions should be provided based on risk perception (such as clearly stating sealing information on the page, optimizing cleaning instructions, upgrading processes, etc.) to reduce negative reviews / return rates and make up for product shortcomings.

[0141] Overall, this feature provides end-to-end data support for product operations, from "traffic keyword positioning" to "in-depth user needs analysis (including opportunities and problems)," helping operators accurately grasp the current state of the product, explore market opportunities, and solve user pain points.

[0142] This in-depth optimization guide comprises two parts: operational strategy and AI-powered deep insights. The "Operational Strategy" provides weekly planning based on product positioning (upgrading from "low-priced alternative" to "quiet, durable, entry-level high-spec" in the diagram, using verifiable selling points to avoid price wars) and traffic structure (establishing a natural traffic base through "long-tail keywords + scenario-based keywords" rather than aggressively targeting broad keywords), helping to improve competitiveness in the medium to long term. The "AI-powered deep insights" analyze problems: for example, keywords not ranking indicate "insufficient weight and trust," high-priced competitor ads appearing on the page are seen as "low-price traffic interception," and inconsistent review information is a "return / negative review trigger," providing specific improvement suggestions for each problem to help users understand the big picture and clarify optimization directions and methods.

[0143] The technical solution provided in this invention involves parsing product image data from structured input data to extract a set of product display features, and performing semantic parsing on user review data to extract a set of user demand features. A feature pair set is then constructed based on the product display feature set and the user demand feature set. Consistency calculation is performed on the feature pair set to generate an initial matching result. This initial matching result is then verified and corrected using historical conversion data to generate operational analysis results. This technical solution achieves multi-dimensional matching and verification between product display information and actual user needs. It not only reduces matching bias caused by a single feature or data source but also makes the matching results closer to actual conversion effects, effectively improving the accuracy and reliability of operational analysis results. This provides more objective and effective data support for product display optimization and operational decision-making.

[0144] S104, monitor changes in the business status vector, and when the business status vector meets the preset trigger conditions, generate the corresponding status event through the analysis model.

[0145] In some embodiments, the operating status vector is continuously monitored, and its current status is compared with historical status or a preset threshold. When a change in the operating status vector is detected and a preset trigger condition is met, a status event is triggered.

[0146] The preset trigger conditions can be single-dimensional or a combination of multiple dimensions. As a non-restrictive example: a certain business dimension decreases by more than a threshold within a preset time; multiple business dimensions show abnormal fluctuations at the same time; the overall trend of the business status vector reverses.

[0147] When the triggering conditions are met, the analysis model generates a state event corresponding to the change, which indicates that the commodity operation status has undergone a change that requires intervention or attention.

[0148] See Figure 4 The diagram shown is a schematic representation of a state event generation process provided by an embodiment of the present invention. Figure 4 As shown, the following steps can be performed:

[0149] S1041, periodically sample the operation status vector within a preset time window to generate a historical status sequence.

[0150] In some embodiments, the operating status vector is used to characterize the overall operating status of an enterprise or system at a certain moment. It can be composed of multiple dimensions of status indicators. As a non-limiting example, status indicators may include, but are not limited to, capacity utilization rate, order completion rate, inventory turnover rate, energy consumption level, equipment utilization rate, and employee attendance rate. In this embodiment, a time window is pre-defined (e.g., in minutes, hours, days, or weeks), and the operating status vector is sampled within this time window according to a fixed or variable sampling period, thereby obtaining operating status vectors corresponding to multiple time points.

[0151] By arranging the operational state vectors corresponding to each sampling point in chronological order, a historical state sequence can be formed. It should be noted that the historical state sequence reflects the evolution of operational status over a period of time, providing a data foundation for subsequent trend analysis.

[0152] S1042, perform trend modeling on the historical state sequence to obtain state change trend parameters.

[0153] In some embodiments, after obtaining a historical state sequence, trend modeling can be performed on the sequence to extract the overall trend characteristics of the business status changing over time. As a non-limiting example, trend modeling can be implemented using linear regression models, exponential smoothing models, multinomial fitting models, time series models, or machine learning models.

[0154] In trend modeling, by fitting or learning the relationship between various state vectors or key indicators in a historical state sequence over time, trend parameters that characterize the trend of state change can be obtained, such as trend slope, growth rate, decay rate, period parameters, or prediction residuals. These state change trend parameters are used to depict whether the business state is in an upward, downward, or relatively stable trend.

[0155] In one example, step S1042 may include: collecting business status vectors at multiple time points to form corresponding historical status sequences, and performing time alignment, missing value repair, and outlier cleaning on the historical status sequences; based on the processed historical status sequences, performing trend anomaly detection, structural change point detection, and multivariate joint anomaly detection respectively to characterize the change features of the business status vectors within a preset time window, and generating a candidate anomaly set to characterize the status change trend parameters.

[0156] This invention, through systematic trend modeling and anomaly analysis of historical state sequences, can accurately depict the changing patterns of operational state vectors across multiple time scales, effectively improving the accuracy and timeliness of identifying operational anomalies. By introducing time alignment, missing value repair, and outlier cleaning, the completeness and consistency of historical state data are enhanced, reducing the interference of data noise on trend judgment, thereby improving the reliability of trend modeling results.

[0157] S1043, calculate the trend deviation based on the state change trend parameter.

[0158] In some embodiments, after obtaining the state change trend parameters, the actual operating state at the current or latest moment can be compared with the state predicted or expected based on the trend model to calculate the trend deviation. The trend deviation is used to quantify the degree of deviation of the actual state from the historical trend.

[0159] As a non-limiting example, trend deviation can be calculated using the difference between the actual value and the trend forecast, the absolute value of the difference, the standardized deviation, or the weighted deviation. When the business situation experiences abnormal fluctuations or sudden changes in a short period of time, the corresponding trend deviation will increase significantly.

[0160] In one example, step S1043 may include: performing root cause inference on the candidate anomaly set based on the root cause map describing the causal relationships between the state dimensions in the operational state vector, and determining the potential root cause and its confidence level for each candidate anomaly; generating gating parameters based on the inventory availability status, page content change status, and advertising placement status, and determining whether the potential root cause is executable based on the potential root cause and its confidence level; generating a corresponding state event when the potential root cause meets the preset gating conditions, and classifying the state event according to the impact degree and confidence level of the potential root cause, and determining the corresponding execution strategy type according to the level of the state event; and generating the state event. Then, the state events are arranged with other state events according to preset dependencies to form an event execution sequence. Within a preset cooldown time window, state events of the same type are merged. When the correction result of the operating state vector indicates that the anomaly has been mitigated, the triggering of repeated state events is suppressed. After the execution strategy corresponding to the state event is executed, the execution result is mapped to the state correction amount of the corresponding state dimension in the operating state vector, and the operating state vector is updated based on the state correction amount. Based on the updated operating state vector, the candidate anomaly is re-evaluated to see if it still exists. When the evaluation result indicates that the anomaly has not met the expected improvement conditions, the state event is generated again to enter the next round of decision-making process.

[0161] This invention further combines trend anomaly detection, structural change point detection, and multivariate joint anomaly detection to characterize the changes in the business state vector within a preset time window from multiple perspectives. This enables the comprehensive and detailed discovery of potential anomaly patterns, avoiding the problems of missed or false detections caused by a single detection method, and providing a more sufficient set of anomaly candidates for subsequent decision-making.

[0162] By inferring root causes from candidate anomaly sets based on root cause mapping, this invention effectively identifies the potential root causes and their confidence levels for each anomaly, achieving automated mapping from anomalies to their causes and significantly reducing manual analysis costs. Simultaneously, by combining inventory availability status, page content change status, and advertising placement status to generate gating parameters, the executability of potential root causes is determined. This avoids triggering invalid or conflicting execution strategies when objective conditions are not met, improving the overall controllability and security of the system's execution.

[0163] After generating state events, this invention achieves fine-grained control over the intensity of exception handling by classifying state events and matching them with different execution strategy types; and through event orchestration, merging cooldown time windows, and exception mitigation and suppression mechanisms, it effectively reduces repeated triggering and invalid execution, thereby improving the overall efficiency of event execution and system stability.

[0164] Furthermore, after the execution strategy is completed, this invention quantifies and maps the execution result into a state correction amount for the operational state vector, and performs closed-loop evaluation based on the corrected operational state vector. This enables continuous verification of the anomaly handling effect and achieves multi-round adaptive decision-making and optimization. Therefore, this invention constructs an operational state management mechanism with self-feedback and self-correction capabilities, significantly improving the accuracy, sustainability, and intelligence level of anomaly management.

[0165] S1044, based on the comparison between the trend deviation and the historical fluctuation threshold range, generates a state event when the trend deviation exceeds the historical fluctuation threshold range.

[0166] In some embodiments, the fluctuation range of the operating status under normal operating conditions can be pre-statistically calculated based on historical state sequences, thereby determining the historical fluctuation threshold range. The historical fluctuation threshold range is used to characterize the degree of normal fluctuation of the operating status within an acceptable range.

[0167] In this embodiment, the trend deviation calculated in step S1043 is compared with the historical fluctuation threshold range: when the trend deviation is within the threshold range, it indicates that the operating status is still within the normal fluctuation range; when the trend deviation exceeds the historical fluctuation threshold range, it indicates that the operating status has deviated significantly from the existing trend, and at this time, the corresponding status event is generated.

[0168] It should be noted that status events can be used to trigger subsequent processing procedures such as alarms, risk warnings, scheduling adjustments, or management decisions, thereby improving the timeliness of identification and response capabilities for operational anomalies.

[0169] By employing the above technical solution, the evolution of business status is continuously characterized by periodically sampling the business status vector within a preset time window and constructing a historical status sequence. Based on this, trend modeling is performed to obtain state change trend parameters, transforming state change monitoring from discrete monitoring to trend-based and structured analysis, thus improving the accuracy and foresight of status identification. Furthermore, by calculating the trend deviation and comparing it with historical fluctuation threshold ranges, normal fluctuations and abnormal changes can be effectively distinguished. This allows for the timely generation of status events when the trend deviation exceeds a reasonable range, achieving accurate identification and early warning of abnormal business status, reducing false alarm rates, and improving the reliability and practicality of monitoring and decision-making.

[0170] S105, based on state events, executes content optimization operations, inventory decision operations, and / or re-analysis operations corresponding to the products, and feeds the execution results back to the analysis model to update the business state vector.

[0171] In some embodiments, the generated status event may trigger one or more business response operations. Business response operations may include, but are not limited to: content optimization operations, such as optimizing product titles, descriptions, images, or keywords; inventory decision operations, such as adjusting replenishment strategies, inventory allocation, or clearance strategies; and re-analysis operations, such as conducting in-depth analysis of anomalies or introducing new data sources for re-evaluation.

[0172] After each operation is completed, the results will be fed back to the analysis model as new input data to further update the business status vector, thus forming a closed-loop optimization mechanism.

[0173] In one embodiment, step S105 may include:

[0174] B1) Based on the current operating status vector, generate a set of execution constraint parameters. The set of execution constraint parameters is used to constrain the execution scope and adjustment range of content optimization operations, inventory decision operations, and / or reanalysis operations.

[0175] In this embodiment, the business status vector consists of at least one of the following: historical sales data of the product, inventory level, content exposure data, conversion rate metric, and user behavior data. The analysis model parses the business status vector and generates a set of execution constraint parameters that match the current business status.

[0176] The set of execution constraint parameters includes at least: the maximum adjustment ratio of content parameters, the upper and lower thresholds for inventory replenishment or reduction, the minimum trigger interval and / or trigger confidence threshold for reanalysis operations, which are used to constrain the execution range and adjustment magnitude of content optimization operations, inventory decision operations and / or reanalysis operations.

[0177] B2) Based on the set of execution constraint parameters, determine the adjustable range of the corresponding operation, wherein the adjustable range of the operation is used to limit the adjustment range of the content parameters, the adjustment magnitude of the inventory decision and / or the triggering conditions of the reanalysis operation.

[0178] In this embodiment, the system determines the corresponding adjustable range for content optimization, inventory decision-making, and / or reanalysis operations based on the set of execution constraint parameters. The adjustable range defines the adjustment range for content parameters (including but not limited to title keywords, main image weight, and display order), the adjustment magnitude for inventory decisions (including replenishment quantity and clearance ratio), and the triggering conditions for reanalysis operations.

[0179] B3) Within the adjustable range of operations, perform content optimization operations, inventory decision operations, and / or re-analysis operations corresponding to the product to achieve targeted adjustments to the product's operating status.

[0180] In this embodiment, without exceeding the adjustable range of operations, at least one business adjustment operation is performed on the goods to achieve targeted adjustment of the goods' business status and avoid adverse effects on the stability of goods operations due to excessive adjustments or frequent analysis.

[0181] B4) Record the execution results of the operation and the corresponding changes in business status, and input the execution results and status change information as feedback data into the analysis model to update the business status vector, providing a basis for the identification of subsequent status events and business decisions.

[0182] In this embodiment, the execution results of the operation are recorded, including adjusted sales performance, inventory changes, changes in user interaction indicators, etc. At the same time, the corresponding business status change information is recorded, and the execution results and status change information are used as feedback data to input into the analysis model to update the business status vector, thereby providing a basis for the identification of subsequent business status events and business decisions.

[0183] In this embodiment, the content optimization operation may refer to the built-in graphic assistant, which is a graphic optimization tool designed for cross-border sellers.

[0184] Specifically, the system receives the original image and a text description, automatically segments out the main product element, and translates your requirements into AI instructions. Furthermore, through a built-in Amazon rules library, it ensures that the generation process automatically avoids illegal elements and complies with platform requirements.

[0185] In this way, based on the conditions of "perfectly preserving the main product" + "demand instructions" + "Amazon rules", only the background, lighting, and atmosphere are intelligently redrawn and optimized to generate multiple solutions.

[0186] Furthermore, it can enhance the image quality and standardize the size of the generated images, directly outputting usable images. The entire process requires no professional graphic design skills; through a simple interaction of "uploading the original image + describing the requirements," it can quickly generate highly attractive main images and scene images that comply with Amazon's rules, helping sellers significantly reduce shooting costs and increase click-through rate (CTR).

[0187] When the operations assistant provides suggestions for image optimization, you can use those suggestions to make intelligent adjustments and redraws in the image and text assistant, in conjunction with the original images.

[0188] Inventory decision-making operations refer to the built-in intelligent replenishment system. This system is not only a visual dashboard for logistics but also an AI-powered replenishment expert. Through in-depth analysis of historical and real-time logistics data using AI algorithms, it provides insights into global parcel flows and consumer trends.

[0189] First, the system identifies products and timelines, as well as sales volume, inventory, and coverage blind spots in each warehouse. It then automatically calculates replenishment priorities and recommended quantities, warns of low inventory risks, and recommends optimal warehouse layouts to store best-selling products nearby. Finally, it guides merchants to initiate transfers with a single click and simultaneously suggests adjusting advertising to areas with available stock, achieving precise "inventory-driven traffic" coordination. The entire process is data-driven, from visual analysis to automated decision-making, ensuring maximum inventory efficiency and sales opportunities. By analyzing global parcel flows, it automatically outputs replenishment priorities, inventory level warnings, and warehouse load suggestions, ensuring your best-selling products are always in the warehouse closest to consumers.

[0190] Based on the recommendations and data-driven direction established during the implementation of the operations assistant, and relying on the operations assistant's gradual guidance at the process and decision-making levels, we have achieved rapid deployment and application of the intelligent inventory replenishment function. By integrating global logistics and sales dynamics, the system automatically completes the entire process of decision-making, from product selection analysis and inventory assessment to replenishment recommendations, helping merchants maintain precise inventory matching and efficient operational responsiveness in a rapidly changing market.

[0191] Thus, a restocking order is initiated based on the "suggested replenishment" quantity provided by the AI. Simultaneously, in conjunction with the AI's suggestion to "expand coverage," the Amazon advertising targeting area is adjusted to drive traffic from inventory.

[0192] Through the above steps, a closed-loop adjustment mechanism based on dynamic feedback of operational status is formed. Specifically, by generating a set of execution constraint parameters based on the current operational status vector, and thereby limiting the adjustable range of content optimization operations, inventory decision operations, and / or re-analysis operations, various operational operations are executed within a controlled range, thus avoiding operational risks caused by excessive adjustments or frequent analyses. At the same time, by updating the feedback on operation execution results and operational status changes, a closed-loop adjustment mechanism for operational status is constructed, improving the pertinence, stability, and adaptability of operational decisions, thereby improving the overall efficiency and long-term profitability of merchandise operation and management.

[0193] S106, generate operational monitoring results based on the updated operational status vector, and trigger the status event again when an abnormal change is detected.

[0194] In some embodiments, after the business status vector is updated, corresponding business monitoring results are generated to display the current business performance and trends of the product. The system continuously monitors the updated business status vector, and when abnormal changes are detected again and preset trigger conditions are met, the status event is re-triggered, and the next round of analysis and optimization process begins.

[0195] For example, step S106 may include:

[0196] C1) Generate operational monitoring results based on the current operational status vector, and detect whether there are any abnormal changes in operational status based on the operational monitoring results.

[0197] In some embodiments, the operating status vector is used to characterize the comprehensive operating status of the current operating entity across multiple status dimensions. These status dimensions may include, but are not limited to, operating revenue, cost expenditure, inventory level, cash flow status, order fulfillment rate, equipment utilization rate, and human resource workload. Each status dimension can be quantified using corresponding numerical or range values, thereby forming a multi-dimensional operating status vector.

[0198] In this embodiment, based on the current operational status vector, corresponding operational monitoring results can be generated through a preset monitoring model or rule system. The operational monitoring results may include real-time status values ​​for each status dimension, the degree of deviation from historical benchmark values ​​or target thresholds, the rate of change, and other information.

[0199] Furthermore, by analyzing the operational monitoring results, it can be determined whether there have been any abnormal changes in the operational status. As a non-limiting example, when the value of a certain status dimension exceeds a preset threshold range, or when its trend fluctuates significantly within a preset time window, it can be determined that there has been an abnormal change in the operational status.

[0200] It should be noted that the method for determining abnormal changes can be based on rule-based judgment, statistical analysis, or machine learning models, and the embodiments of the present invention do not limit this.

[0201] C2) In response to the detection of abnormal changes, a state event is generated and the corresponding decision-making process is entered to obtain the execution result for the state event.

[0202] In some embodiments, when an abnormal change in the operating status is detected in step C1, the abnormal change can be abstracted into a corresponding status event. The status event is used to describe information such as the type of abnormal change, the location of occurrence (i.e., the corresponding status dimension), the degree of abnormality, and the time of occurrence.

[0203] In this embodiment, different types of status events can correspond to different decision-making processes. For example, a status event of abnormal cost increase can lead to a cost control decision-making process; a status event of inventory backlog can lead to an inventory optimization decision-making process.

[0204] By executing decision-making processes that match the status events, corresponding execution results can be generated. These results can include adjustments to strategies, control parameters, and resource allocation plans, used to intervene in the current operational status.

[0205] C3) Maps the execution result to the state correction amount of the corresponding state dimension in the business state vector.

[0206] In some embodiments, the execution result itself may exist in the form of a strategy or rule, which is difficult to directly affect the business state vector. Therefore, it is necessary to further quantify it into a state correction quantity.

[0207] In this embodiment, the adjustment magnitude, direction of action, and scope of influence involved in the execution result can be mapped to a state correction amount for one or more state dimensions in the business state vector. The state correction amount can be a positive correction or a negative correction, used to represent the improvement or suppression effect on the corresponding state dimension.

[0208] As a non-restrictive example, if the result is "reduce purchasing frequency", it can be mapped to a negative correction for the inventory level dimension and a positive correction for the cash flow dimension.

[0209] C4) Update the operating state vector based on the state correction amount to obtain the updated operating state vector.

[0210] In some embodiments, after obtaining the state correction amount, the state correction amount can be applied to the corresponding state dimension to update the original operating state vector.

[0211] In this embodiment, the update operation may include methods such as vector weighting, incremental update, or replacement update to obtain the updated operating status vector. The updated operating status vector is used to reflect the latest operating status after the execution of the decision-making process.

[0212] It should be noted that different update rules can be adopted according to different state dimensions. For example, fast updates can be adopted for short-term sensitive indicators, and smooth updates can be adopted for long-term stable indicators. This embodiment of the invention does not limit this.

[0213] C5) Based on the updated business state vector, recalculate the state change trend parameter that reflects the trend of business state change.

[0214] In some embodiments, based on the updated operating state vector, state change trend parameters can be further calculated to characterize the changing trend of the operating state. These state change trend parameters can be used to describe the direction of evolution, rate of change, and stability of the operating state over time.

[0215] In this embodiment, the state change trend parameters may include, but are not limited to, the slope value, fluctuation amplitude, and trend confidence level of the state dimension. By using the state change trend parameters, it can be determined whether the current operating state is stabilizing, improving, or further deteriorating.

[0216] C6) In response to the state change trend parameter satisfying the triggering condition again, if it is determined that the abnormal change has not been eliminated or has further evolved, a state event is generated again to enter the next round of decision-making process.

[0217] In some embodiments, when the state change trend parameter indicates that the abnormal change has not been effectively eliminated or there is a risk of further evolution, it can be determined that the re-triggering condition is met.

[0218] In this embodiment, the conditions for triggering again may include situations such as the trend not returning to the normal range, the degree of abnormality continuing to expand, or the duration of the abnormal state exceeding a preset duration.

[0219] When the triggering conditions are met again, the corresponding status event can be generated again, and the next round of decision-making process can begin, thus forming a closed-loop operational status monitoring and decision-making adjustment mechanism. Through multiple rounds of iterative execution, the operational status can be gradually guided towards stability or the target direction.

[0220] In this embodiment, the operational monitoring results can refer to an AI dashboard, a multi-dimensional operational analysis tool specifically tailored for sellers. It integrates core data such as sales, profits, costs, and product rankings, and transforms massive reports into intuitive strategic suggestions through AI insights, assisting in making rapid operational decisions.

[0221] Unlike AI tools, AI dashboards provide a comprehensive view of multi-dimensional data, specifically in the following ways:

[0222] First, a comprehensive physical examination.

[0223] Data integration: Aggregates core data from multiple dimensions, including sales (sales amount, order volume), profit (profit margin, profit amount), cost (cost structure, percentage), and ranking (product ranking changes).

[0224] Visual presentation: Use line charts (such as "Key Indicator Trends" charts) to show how data changes over time (such as the trend of sales and profits), and use number cards to display key indicators (such as total sales, total profit, and profit margin) to help sellers "see the big picture at a glance".

[0225] AI Insights: By using algorithms to identify trend inflection points (such as a sudden decline in sales) and abnormal indicators (such as profit margins deviating from expectations), we can anchor the direction for subsequent analysis.

[0226] Second, dismantling losses (cost control).

[0227] Objective: To identify the root causes of high costs and optimize them accordingly (e.g., reducing logistics and procurement costs). Implementation logic:

[0228] Cost structure breakdown: Use a funnel chart (such as "cost composition funnel") to show the hierarchical distribution of costs (such as "total cost → logistics cost → warehousing cost"), and use a bar chart (such as "cost item percentage") to show the amount / percentage of each cost item, clearly indicating "where costs are high".

[0229] Attribution analysis: Combine business scenarios (such as "sales", "logistics", "procurement") to analyze the reasons for high costs (e.g., are high logistics costs due to increased shipping costs or high return rates?).

[0230] Visual aids: Use pie charts (such as "income / expenditure structure") to show the composition of income and expenditure, and use Sankey diagrams / flow diagrams to show the flow of costs (such as the cost loss path of "purchasing → inventory → sales") to help sellers "accurately locate loss points".

[0231] Third, the bottom-ranking elimination system (with a defined plan).

[0232] Objective: Eliminate inefficient products / businesses and optimize resource allocation (e.g., stop promoting low-conversion products and remove high-cost, low-volume products). Implementation logic:

[0233] Performance evaluation: Use leaderboards (such as "product ranking" or "profit contribution ranking") or matrix diagrams (such as "sales-profit" matrix) to identify "inefficient objects" (such as products with low sales, negative profits, and declining rankings).

[0234] Decision recommendations: Based on the analysis results, output "elimination / optimization" suggestions (such as "This product has a profit margin of -5% and ranks in the bottom 10% of sales, it is recommended to remove it from the market") to help sellers "quickly determine a solution".

[0235] This invention achieves continuous tracking and dynamic intervention of operational anomalies by uniformly modeling operational status as an operational status vector and introducing a closed-loop processing mechanism of anomaly detection, event-driven decision-making, status correction, and trend reassessment. Compared with traditional static monitoring or one-time decision-making methods, it can more accurately reflect the real-time changes and evolution trends of operational status, improve the timeliness of anomaly identification and the pertinence of decision response, thereby effectively enhancing the level of intelligence in business management and risk control capabilities.

[0236] Through the above steps, continuous monitoring, dynamic analysis, and closed-loop feedback optimization of the product operation status are achieved, thereby improving the accuracy and timeliness of product operation decisions in cross-border e-commerce business.

[0237] The above describes the cross-border e-commerce decision-making method based on the entire supply chain in detail through some embodiments. In order to enable those skilled in the art to better understand and implement it, the corresponding system is also described in detail below through some embodiments.

[0238] See Figure 5 The embodiment of the present invention shown provides a structural diagram of a cross-border e-commerce decision-making system based on the entire supply chain, as illustrated below. Figure 5 As shown, the cross-border e-commerce decision-making system 500 based on the entire supply chain may include: an analysis model 510, which is adapted to use any of the cross-border e-commerce decision-making methods based on the entire supply chain in the aforementioned examples to execute operational decisions.

[0239] For more details on cross-border e-commerce decision-making systems based on the entire supply chain, please refer to the descriptions in the aforementioned examples.

[0240] In this embodiment, the analysis model can be a multi-business data intelligent decision-making system based on hybrid retrieval and large language model. It adopts a hierarchical and structured processing pipeline, and achieves fully automatic decision-making from fuzzy user requests to accurate cross-business responses through a combination of vector retrieval screening and large language model fine judgment, prompt engineering-driven feature extraction, structured JSON feature fusion, and output generation with scores and reasons.

[0241] The input layer (intent recognition and routing module based on hybrid retrieval) adopts a two-stage hybrid retrieval architecture to accurately parse user intent and locate relevant business data sources. It receives unstructured user natural language queries, including:

[0242] Phase 1: Vector Semantic Retrieval: Using pre-trained vector models (such as text2vec, BGE, etc.), user queries are encoded into high-dimensional semantic vectors.

[0243] The query vector is compared with a pre-built business knowledge vector library for similarity calculation (e.g., cosine similarity). This knowledge library contains standardized vectorized representations of all known business intents and their descriptions.

[0244] Output the Top-K candidate business intents that are most similar to the user's query semantics and their associated data source identifiers.

[0245] Phase 2: Precise decision-making using a large language model: The user's original query and the K candidate intents retrieved in the first phase are input into a large language model (such as QWEN) that has been fine-tuned by instructions.

[0246] Specific prompts (such as "Please select the most accurate one from the following candidate intents based on the user's question, and explain your reasoning") guide the model to make accurate judgments and selections.

[0247] Finally, the output and routing: the model outputs the final determined business intent, and then automatically initiates data call requests to the databases or APIs of one or more corresponding business lines based on this intent. The technical effect of this layer is that it combines the breadth of vector retrieval with the depth of understanding of large language models, significantly improving the accuracy and robustness of intent recognition under complex and fuzzy queries.

[0248] Feature Extraction Layer (hinted at the engineering-driven structured summary module): This layer performs automated and structured information refinement on the acquired raw business data.

[0249] Input: Raw response data from each business line.

[0250] Core processing: For each business line data, a large language model (such as QWEN, GPT) is invoked, and driven by a carefully designed prompt engineering.

[0251] Example prompt template: "Please analyze the following business data, extract all key information directly related to the user intent '[intent name]', and output it as a structured JSON object. This JSON must contain the following fields: summary (core summary), key_data_points (list of key data), relevance_score (relevance score to this intent, 0-10), and selection_reason (brief reason for selecting this data)."

[0252] Output: Data from each business line is transformed into a structured JSON object conforming to a predetermined pattern. The technical effect of this layer is that, through standardized and interpretable structured summaries, unstructured or semi-structured raw data is uniformly transformed into high-quality, machine-processable feature units containing metadata (scores, reasons), laying a solid foundation for subsequent fusion.

[0253] The fusion layer (the module for integrating and reasoning structured JSON objects) is the intelligent core of the system, responsible for deep correlation and comprehensive reasoning of structured features from various business lines.

[0254] Input: Multiple structured JSON objects from the feature extraction layer.

[0255] Core processing: Employs a context-based JSON fusion strategy.

[0256] Data assembly: Combine all input JSON objects, initial user queries, and identified business intents into a complete contextual information package.

[0257] Large Language Model Fusion Reasoning: Input this information package into a powerful fusion model (such as QWEN). Drive the model to perform cross-business logic reasoning through instructions (such as "Please comprehensively analyze the structured information provided by all the following business lines, understand the relationships between them, and form a global and coherent understanding.").

[0258] Structured Output Generation: The model is required to output a new, comprehensive, structured JSON object as the fusion result. This JSON contains fields such as global_summary, integrated_facts, conflict_analysis (if any), and decision_basis (basis for subsequent decisions).

[0259] Output: A high-level structured fusion object representing the system's overall understanding. The technical effect of this layer is to ensure the rigor and traceability of the fusion process through mandatory structured input and output; at the same time, it utilizes the reasoning capabilities of large language models to extract unified, deep insights from multi-source heterogeneous data that support the final decision.

[0260] Output layer (decision response generation module with explanation): This layer generates the final executable and interpretable output based on the global cognition after deep fusion.

[0261] Input: The global structured fusion object output by the fusion layer.

[0262] Core processing: Input the fused object and the user's original query together into the large language model generator (such as QWEN, GPT).

[0263] The final response is generated based on all evidence using a prompt-word instruction model, and the response must include:

[0264] final_decision / answer: final decision or answer; confidence_score: overall confidence score of the system; supporting_evidence: key supporting evidence cited (specific data points that can be linked back to the original business line JSON); reasoning_chain: brief chain of reasoning logic.

[0265] The output is also formatted as a final response JSON object, or rendered as user-friendly natural language text based on that object.

[0266] Output: A complete and interpretable response package containing the final conclusion, score, reasons, and evidence. The technical advantage of this layer lies in providing not only the results, but also a transparent explanation and quantified confidence level, greatly enhancing the system's credibility, auditability, and user experience.

[0267] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0268] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications, substitutions, and improvements without departing from the concept of the present invention, and these should all be covered within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the claims.

Claims

1. A cross-border e-commerce decision-making method based on the entire supply chain, characterized in that, include: Acquire multi-source heterogeneous data related to the product, and perform entity alignment and time-series synchronization on the multi-source heterogeneous data based on the product's unique identifier to generate structured input data; The structured input data is analyzed and processed by a pre-built analysis model to construct and maintain an operating status vector corresponding to the product. The operating status vector is used to characterize the comprehensive operating status of the product in terms of traffic, conversion, reputation, inventory and profit. The structured input data is subjected to cross-border e-commerce business-oriented analysis, the analysis model generates operational analysis results, and the business status vector is updated based on the operational analysis results; The changes in the business status vector are monitored, and when the business status vector meets the preset triggering conditions, a corresponding status event is generated through the analysis model. Based on the state events, content optimization operations, inventory decision operations, and / or reanalysis operations corresponding to the products are executed respectively, and the execution results are fed back to the analysis model to update the business state vector; The updated operational status vector is used to generate operational monitoring results, and the status event is triggered again when an abnormal change is detected.

2. The decision-making method according to claim 1, characterized in that, The generation of structured input data includes: Acquire product data from multiple different data platforms, including product image data, product text description data, user review data, and user behavior data; Semantic parsing of product data from different data platforms is performed to generate a set of platform semantic fields; A cross-platform field mapping relationship is established based on the platform's semantic field set, and the product data is reconstructed. The system performs entity consistency verification on the reconstructed product data based on the product's unique identifier, and performs time-series consistency verification on the product data that passes the entity consistency verification based on the timestamp. The product data that simultaneously satisfies both entity consistency verification and time-series consistency verification is used as the structured input data.

3. The decision-making method according to claim 1, characterized in that, The construction and maintenance of the operating status vector corresponding to the product includes: Based on the structured input data, traffic status feature set, conversion status feature set, reputation status feature set, and inventory status feature set are constructed respectively. Feature compression processing is performed on each set of state features to generate corresponding state sub-vectors; Based on the stability performance of the structured input data over different time periods, initial weights are assigned to each state sub-vector. The initial weights are adaptively adjusted based on the prediction error of the operating status vector within the historical period. The operating state vector is generated by combining each of the state subvectors with adjusted weights.

4. The decision-making method according to claim 1, characterized in that, The step of performing cross-border e-commerce business-oriented analysis on the structured input data, generating operational analysis results from the analysis model, and updating the business status vector based on the operational analysis results includes: The product image data in the structured input data is parsed to extract a set of product display features; Semantic parsing is performed on the user comment data in the structured input data to extract a set of user demand features; Construct a set of feature pairs based on the product display feature set and the user demand feature set; A consistency calculation is performed on the feature set to generate an initial matching result; The initial matching result is verified by reverse verification based on historical conversion data to obtain a corrected matching result, and the operational analysis result is generated based on the corrected matching result.

5. The decision-making method according to claim 4, characterized in that, Meet one or more of the following conditions: The step of performing consistency calculation on the feature pair set to generate an initial matching result includes: mapping the product display features and the user demand features to a unified semantic space, and calculating the semantic consistency score between the product display features and the user demand features based on the unified semantic space; calculating the evidence consistency score between the product display features and the user demand features based on the detection results of functional structures in product image data and functional parameter information in product text descriptions; calculating the conflict penalty factor for the corresponding feature pair based on the sentiment polarity distribution and degree of divergence of user demand features in historical user reviews; calculating the scene consistency score based on the matching relationship between the usage scenario information corresponding to the user demand features and the scene elements in the product display features; calculating the competitor matching result based on the display features of competitor products and the user demand features, and comparing the competitor matching result with the consistency calculation result of the feature pair to generate a relative consistency score; and performing a fusion calculation on the feature pair set based on the semantic consistency score, evidence consistency score, conflict penalty factor, scene consistency score, and relative consistency score to generate the initial matching result. The step of reverse-validating the initial matching result based on historical conversion data to obtain a corrected matching result includes: constructing a validation dataset containing the initial matching result, historical conversion data, and page change information based on the time dimension, and aligning the changes in the initial matching result with the changes in the historical conversion data in terms of time; constructing a conversion prediction model based on the validation dataset, and calculating the deviation between the predicted conversion result and the actual historical conversion data to generate a matching effectiveness evaluation parameter; updating the initial matching result with a weighted average based on the matching effectiveness evaluation parameter, and reducing the weight of the corresponding initial matching result when the matching effectiveness evaluation parameter indicates that the corresponding initial matching result has a negative impact on the conversion; and using the updated matching result as the corrected matching result.

6. The decision-making method according to claim 1, characterized in that, The monitoring of changes in the operational status vector, when the operational status vector meets preset trigger conditions, generates a corresponding status event through the analysis model, including: The operational status vector is periodically sampled within a preset time window to generate a historical status sequence; Trend modeling is performed on the historical state sequence to obtain state change trend parameters; Calculate the trend deviation based on the aforementioned state change trend parameters; Based on the comparison between the trend deviation and the historical fluctuation threshold range, the state event is generated when the trend deviation exceeds the historical fluctuation threshold range.

7. The decision-making method according to claim 6, characterized in that, Meet one or more of the following conditions: The step of trend modeling the historical state sequence to obtain state change trend parameters includes: collecting business state vectors at multiple time points to form corresponding historical state sequences, and performing time alignment, missing value repair, and outlier cleaning on the historical state sequences; based on the processed historical state sequences, performing trend anomaly detection, structural change point detection, and multivariate joint anomaly detection to characterize the change features of the business state vectors within a preset time window, and generating a candidate anomaly set to represent the state change trend parameters. The calculation of trend deviation based on the state change trend parameters includes: performing root cause inference on the candidate anomaly set based on the root cause map describing the causal relationships between the state dimensions in the business state vector, and determining the potential root cause and its confidence level for each candidate anomaly; generating gating parameters based on the inventory availability status, page content change status, and advertising placement status, and determining whether the potential root cause is executable based on the potential root cause and its confidence level; when the potential root cause meets the preset gating conditions, generating the corresponding state event, and classifying the state event based on the impact degree of the potential root cause and the confidence level, and determining the corresponding execution strategy type according to the level of the state event; in the After a state event is generated, it is arranged with other state events according to a preset dependency relationship to form an event execution sequence. Within a preset cooldown time window, state events of the same type are merged. When the correction result of the operating state vector indicates that the anomaly has been mitigated, the triggering of repeated state events is suppressed. After the execution strategy corresponding to the state event is executed, the execution result is mapped to the state correction amount of the corresponding state dimension in the operating state vector, and the operating state vector is updated based on the state correction amount. Based on the updated operating state vector, the candidate anomaly is re-evaluated to see if it still exists. When the evaluation result indicates that the anomaly has not met the expected improvement conditions, a state event is generated again to enter the next round of decision-making process.

8. The decision-making method according to claim 1, characterized in that, The step of performing content optimization, inventory decision-making, and / or re-analysis operations corresponding to the product based on the state event, and feeding the execution results back to the analysis model to update the operating state vector, includes: Based on the current operating status vector, an execution constraint parameter set is generated. The execution constraint parameter set is used to constrain the execution scope and adjustment range of the content optimization operation, inventory decision operation and / or reanalysis operation. Based on the set of execution constraint parameters, the adjustable range of the corresponding operation is determined, wherein the adjustable range of the operation is used to limit the adjustment range of the content parameters, the adjustment magnitude of the inventory decision and / or the triggering conditions of the reanalysis operation. Within the adjustable range of the operation, content optimization operation, inventory decision operation and / or re-analysis operation corresponding to the product are performed to achieve targeted adjustment of the product's operating status; The execution results of the operation and the corresponding changes in business status are recorded, and the execution results and changes in status are input into the analysis model as feedback data to update the business status vector, providing a basis for the identification of subsequent status events and business decisions.

9. The decision-making method according to claim 8, characterized in that, The process of generating operational monitoring results based on the updated operational status vector and triggering the status event again when an abnormal change is detected includes: Based on the current operating status vector, an operating monitoring result is generated, and the operating status is detected to determine whether any abnormal changes have occurred based on the operating monitoring result; In response to the detection of the abnormal change, a state event is generated and the corresponding decision-making process is initiated to obtain the execution result for the state event; The execution result is mapped to a state correction amount for the corresponding state dimension in the business state vector; The operating state vector is updated based on the state correction amount to obtain the updated operating state vector; Based on the updated business status vector, the state change trend parameters reflecting the changing trend of business status are recalculated. In response to the state change trend parameter satisfying the triggering condition again, it is determined that the abnormal change has not been eliminated or has further evolved, and a state event is generated again to enter the next round of decision-making process.

10. A cross-border e-commerce decision-making system based on the entire supply chain, characterized in that, include: An analysis model, which is adapted to perform operational decisions using the end-to-end cross-border e-commerce decision-making method according to any one of claims 1 to 9.

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