Intelligent assessment management system and method for cross-border e-commerce platform

Abstract

The invention relates to the field of cross-border e-commerce, and discloses an intelligent assessment management system and method for a cross-border e-commerce platform, and the method comprises the steps: collecting transaction data, user behavior data and merchant operation behavior data on the platform in real time; performing feature extraction and analysis on the preprocessed data by using a machine learning model, and automatically judging whether an illegal behavior exists in combination with a compliance policy embedded in a rule engine; according to the violation analysis report, the system automatically generates notification information attached with detailed violation basis, and after the merchant submits a complaint request through a specified interface, the system transfers complaint content to a manual auditing terminal and updates an auditing result in combination with violation judgment logic; summarizing the assessment data according to a preset period, generating a structured assessment report, and synchronously storing all judgment processes, notification records and appeal processing results in an audit database; based on feedback data of merchants and users, performance bottlenecks of an existing system are analyzed. The method has the advantages of being transparent and explainable.

Description

Technical Field

[0001] The present invention relates to the field of cross-border e-commerce, and specifically to an intelligent assessment management system and method for a cross-border e-commerce platform. Background Art

[0002] With the in-depth development of globalization, the cross-border e-commerce industry has rapidly emerged and become an important part of international trade. However, in the operation of cross-border e-commerce platforms, the application of intelligent assessment management systems still faces many challenges. Especially in the defect of poor algorithm transparency and interpretability, the problem is particularly prominent. The existing intelligent assessment management systems for cross-border e-commerce platforms mainly rely on big data analysis and machine learning algorithms to monitor and evaluate the behaviors of merchants. These systems automatically detect whether merchants have violated regulations, such as false transactions, brushing orders, infringement, etc., through complex algorithm models. However, due to the complexity and black-box characteristics of the algorithm models, merchants often cannot understand the specific reasons for violations and the basis for penalties. This opaque decision-making process not only increases the confusion and dissatisfaction of merchants, but also may lead to misjudgments and unfair penalties, seriously affecting the credibility of the platform and the enthusiasm of merchants. In addition, poor algorithm transparency and interpretability also bring regulatory and legal risks. When regulatory authorities review cross-border e-commerce platforms, they often require the platforms to provide detailed assessment bases and penalty reasons. If the platforms cannot provide clear explanations, they may face the risks of regulatory penalties and legal lawsuits. Therefore, it is necessary to design an intelligent assessment management system and method for cross-border e-commerce platforms that can provide transparency and interpretability. Summary of the Invention

[0003] (1) Technical Problems to be Solved

[0004] Aiming at the deficiencies of the prior art, the present invention provides an intelligent assessment management system and method for a cross-border e-commerce platform, which has the advantages of providing detailed reasons for violations and penalty bases to merchants, enhancing the credibility of the platform and the trust of merchants, and at the same time meeting the requirements of supervision and law, and solves the problems in the above background art.

[0005] (2) Technical Solutions

[0006] To achieve the above object of providing detailed reasons for violations and penalty bases to merchants, enhancing the credibility of the platform and the trust of merchants, and at the same time meeting the requirements of supervision and law, the present invention provides the following technical solutions: An intelligent assessment management method for a cross-border e-commerce platform, comprising the following steps:

[0007] S1: Real-time collect transaction data, user behavior data and merchant operation behavior data on the platform, and perform preprocessing such as data cleaning, duplicate removal and format standardization on the data.

[0008] Preferably, the S1 further collects transaction data, including order records, payment information, and logistics status, and counts the number of violation orders TP and FP; collects user behavior data, including browsing trajectories, click records, and evaluation content, and records the misjudgment situations TN and FN; collects merchant operation data, including product listing, price adjustment, and order processing records, and analyzes its impact on the violation probability.

[0009] S2: Use a machine learning model to perform feature extraction and analysis on the preprocessed data, combine the compliance policies embedded in the rule engine, automatically determine whether there are any violation behaviors, and generate a violation analysis report.

[0010] Preferably, the S2 further includes performing feature extraction on the preprocessed data to extract violation feature vectors; using a machine learning model to calculate the violation probability and generate a preliminary violation determination result, and using the XGBoost model for violation prediction. The core is to calculate the probability that a sample belongs to the violation category. The formula is as follows: Where P(X) is the probability that the sample X belongs to the violation category, and F(X) is the decision function value calculated by XGBoost, that is, the weighted sum of multiple decision trees. The formula is as follows:

[0011]

[0012] Where T is the number of decision trees, G t (X) is the prediction output of the t-th decision tree, and X includes merchant operation data; w t is the weight of the decision tree;

[0013] Set the violation determination threshold, which is determined through statistical analysis. The formula is as follows:

[0014] τ = arg max(TPR - FPR)

[0015]

[0016] Where TP is that the violation sample is correctly determined as a violation, and FN is that the violation sample is misjudged as compliant;

[0017]

[0018] Where FP is that the compliant sample is misjudged as a violation, and TN is that the compliant sample is correctly determined as compliant;

[0019] Divide the violation determination results according to the risk level. Different thresholds can be set based on the 20% quantile of the sample size, and the determined impact risks are divided into three levels: low risk, medium risk, and high risk. Calculate the 20% quantile, 50% quantile, and 80% quantile from the historical violation probability dataset, which respectively correspond to the thresholds of low risk, medium risk, and high risk:

[0020] τ low = P 20

[0021] τ mid = P 50

[0022] τ hi g h = P 80

[0023] where P 20 is the 20th percentile of the sample violation probability, that is, 20% of the violation sample probabilities are lower than this value; P 50 is the 50th percentile, that is, half of the sample probabilities are higher than this value; P 80 is the 80th percentile, that is, 80% of the violation sample probabilities are lower than this value, and only 20% of the violation sample probabilities are higher than this value.

[0024] Perform compliance matching in combination with the rule engine, and apply the rule library to identify violation behaviors; generate a violation analysis report, marking the violation type, risk level, and judgment basis.

[0025] S3: According to the violation analysis report, the system automatically generates notification information with detailed violation bases and triggers the appeal handling mechanism. After the merchant submits an appeal request through the specified interface, the system transfers the appeal content to the manual review end and updates the review result in combination with the violation judgment logic.

[0026] Preferably, the S3 further includes generating notification information containing violation type codes according to the violation analysis report; pushing the notification information to the merchant through in-site messages, emails, and API interfaces; receiving the merchant's appeal request and automatically comparing the appeal materials with the original transaction data; triggering the manual review process and conducting appeal review in combination with the violation judgment logic; updating the review result and notifying the merchant of the appeal handling conclusion.

[0027] S4: Summarize the assessment data according to the preset cycle, generate a structured assessment report, and synchronously store all judgment processes, notification records, and appeal handling results in the audit database.

[0028] Preferably, the S4 further includes summarizing the violation data within the assessment cycle, calculating the scope of violation impact; generating an assessment report containing KPI indicators and providing violation trend analysis; recording the violation judgment process, appeal handling records, and review conclusions; using blockchain evidence storage technology to store the audit logs and ensuring the data cannot be tampered with.

[0029] S5: Based on the feedback data of the merchant and the user, analyze the performance bottlenecks of the existing system, and continuously optimize the machine learning model, rule engine strategy, and data collection process.

[0030] Preferably, S5 further includes collecting merchant and user feedback data, and performing text sentiment analysis; identifying system function defects and violation rule application problems; generating system optimization suggestions, adjusting machine learning model parameters; updating rule engine policies, optimizing violation judgment logic; and adjusting data collection processes.

[0031] A cross-border e-commerce platform intelligent assessment and management system, including a data collection module, an intelligent analysis module, a notification and appeal processing module, a report and audit module, and a feedback optimization module;

[0032] The data collection module is used to collect multi-source heterogeneous data of the platform in real time, including transaction data, user behavior data, and merchant operation records.

[0033] The intelligent analysis module extracts features from the collected data through machine learning algorithms, establishes a violation behavior prediction model, and performs violation determination based on the rule engine of the platform rule library.

[0034] Preferably, the intelligent analysis module further includes standardizing and cleaning the collected data, extracting violation feature vectors, including but not limited to the follow-up selling frequency threshold, logistics timeliness deviation value, and picture repetition rate; using the XGBoost algorithm to perform supervised learning on historical violation data to generate a classification model with behavior prediction function; converting the platform rule library into an executable decision tree, including a three-level judgment logic, namely, the first-level basic compliance check, the second-level risk probability assessment, and the third-level manual review trigger; and outputting a visual analysis report containing a violation association graph, marking the risk propagation path and influence range.

[0035] The notification and appeal processing module is used to generate an electronic notice containing a violation type code, provide a visual appeal interface and an evidence upload port, and implement full-process tracking of the appeal status.

[0036] Preferably, the notification and appeal processing module further includes automatically matching a notification template according to the violation level, pushing through three channels of in-site message + email + API interface; providing a structured appeal form, implementing automatic comparison of appeal materials and original transaction data; establishing a multi-level review mechanism to conduct multi-dimensional evaluation of controversial cases; generating an appeal hot spot graph, identifying high-frequency controversial types, and outputting rule optimization suggestions to the platform policy department.

[0037] The report and audit module generates a multi-dimensional assessment report containing KPI indicators and establishes an audit log chain with blockchain evidence storage.

[0038] The feedback optimization module processes merchant and user feedback data through NLP sentiment analysis technology and outputs system function iteration suggestions to the supply chain management side and the marketing strategy side.

[0039] (3) Beneficial effects

[0040] Compared with the prior art, the present invention provides an intelligent assessment and management system and method for a cross-border e-commerce platform, having the following beneficial effects:

[0041] Through real-time data collection and intelligent analysis, the present invention constructs a full-process automated violation monitoring and handling mechanism; relying on the synergistic effect of a machine learning model and a rule engine, it realizes efficient and accurate identification of violation behaviors, reduces the cost of manual review while ensuring dynamic adaptation of compliance policies; through the organic combination of automatic notification, appeal interface and manual review, a closed-loop risk control system is formed to ensure the transparency and fairness of the handling process. By strengthening the supervision and traceability ability through periodic assessment reports and audit data archiving, and continuously optimizing the model and strategy based on feedback data, the safety, compliance and user trust of platform operation are effectively improved, and finally the systematic upgrade of platform governance efficiency is driven. Description of the Drawings

[0042] Figure 1 It is a schematic structural diagram of the present invention;

[0043] Figure 2 It is a schematic diagram of the method of the present invention. Specific Embodiments

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] The present invention provides a technical solution: an intelligent assessment and management method for a cross-border e-commerce platform, including the following steps:

[0046] S1: Real-time collect transaction data, user behavior data and merchant operation behavior data on the platform, and perform preprocessing such as data cleaning, duplicate removal and format standardization on the data.

[0047] Collect multi-dimensional data from the platform in real time, such as order information, payment methods, amounts, transaction statuses, timestamps, etc. User behavior data includes users' browsing behaviors, click records, search keywords, adding to the shopping cart, payment paths, etc. Merchant operation behavior data includes merchants' listing products, price adjustments, promotional activities, inventory changes, etc. Support high-concurrency and multi-channel data collection, be able to handle large-scale concurrent requests on the platform, and ensure the real-time and stability of the data collection process. Automatically identify and remove duplicate transaction records, user behaviors, or merchant operation records. For example, deduplicate through fields such as transaction numbers, user IDs, timestamps, etc., to ensure the uniqueness of each piece of data. Fill, interpolate, or delete missing data to ensure data integrity. For example, use techniques such as mean filling and forward / backward value interpolation to fill in missing data. Use machine learning methods to automatically identify and process outliers to prevent abnormal data from affecting the analysis results. Convert data from different sources and formats into a unified format for subsequent analysis. For example, standardize the date format, unify the currency unit, and convert timestamps to a unified time zone, etc. Convert the collected data according to a unified specification. For example, standardize the field names, data types, etc. of user behavior data, transaction data, and merchant data into a unified format for subsequent processing. For transaction data and user behavior data, perform time series processing to ensure the continuity and consistency of the data on the time axis. For example, convert timestamps to a specific time format, generate time windows, etc. Tag the data, such as classification tags, risk levels, etc., for convenient subsequent model training and analysis. Store the processed data in a distributed database to support large-scale data storage and efficient query. Use a stream processing platform for real-time data stream access, processing, and analysis to ensure data real-time.

[0048] Through cleaning, deduplication, and standardization, the quality of the platform data has been significantly improved, reducing the impact of redundant and incorrect data, and providing a reliable data foundation for subsequent analysis and decision-making. The real-time data collection module ensures the ability to track transactions and user behaviors on the platform in real time, supports rapid response to changes in platform dynamics and user needs, and reduces data latency and lag issues. Through the deduplication function, the processing and storage of duplicate data are avoided, optimizing the utilization of data storage space and computing resources, and enhancing the performance and efficiency of the platform. By means of missing data handling, outlier detection, etc., the integrity and consistency of the data can be effectively ensured, preventing incorrect data from having an adverse impact on subsequent models or analysis results. The standardization and conversion of data formats ensure that different types, sources, and structures of data can be uniformly processed, enabling the smooth integration and collaborative analysis of diverse data sources. The distributed storage and stream processing platform provides powerful data processing capabilities, ensuring that the real-time nature and stability of data collection and processing can still be maintained when the platform traffic surges. The processing of real-time data streams and the fast feedback mechanism enable the platform to obtain timely changes in user behaviors and transaction trends, assisting the platform in making rapid decisions and enhancing the user experience and platform operation efficiency. The unified data storage and management mode ensure the consistency, accessibility, and maintainability of the data, making subsequent analysis, mining, and report generation more efficient and standardized. The standardized and cleaned data can be directly used for subsequent data analysis, behavior prediction model training, etc., improving the accuracy and efficiency of the models and providing more intelligent services for the platform. Through automated data cleaning, deduplication, and format standardization, manual intervention is reduced, the data processing cost is lowered, and at the same time, the accuracy and efficiency of data processing are improved.

[0049] S2: Use a machine learning model to perform feature extraction and analysis on the preprocessed data, and combine the compliance policies embedded in the rule engine to automatically determine whether there are any violations and generate a violation analysis report.

[0050] Perform feature extraction on the preprocessed data to extract violation feature vectors; use a machine learning model to calculate the violation probability and generate a preliminary violation determination result. Use the XGBoost model for violation prediction. The core is to calculate the probability that a sample belongs to the violation category. The formula is as follows: Among them, P(y = 1|X) is the probability that sample X belongs to the violation category, and F(X) is the decision function value calculated by XGBoost, that is, the weighted sum of multiple decision trees. The formula is as follows:

[0051]

[0052] Among them, T is the number of decision trees, G t (X) is the prediction output of the t-th decision tree, w tis the weight of the decision tree;

[0053] Set the violation determination threshold, which is determined through statistical analysis. The formula is as follows:

[0054] τ = arg max(TPR - FPR)

[0055]

[0056] Among them, TP means that the violated samples are correctly determined as violated, and FN means that the violated samples are misjudged as compliant;

[0057]

[0058] Among them, FP means that the compliant samples are misjudged as violated, and TN means that the compliant samples are correctly determined as compliant;

[0059] Divide the violation determination results according to the risk level. Different thresholds can be set based on the 20th percentile of the sample size, and the impact risks of the determinations are divided into three levels: low risk, medium risk, and high risk. Calculate the 20th percentile, 50th percentile, and 80th percentile from the historical violation probability dataset, which correspond to the thresholds of low risk, medium risk, and high risk respectively:

[0060] τ low = P 20

[0061] τ mid = P 50

[0062] τ hi g h = P 80

[0063] Among them, P 20 is the 20th percentile of the sample violation probability, that is, the violation probabilities of 20% of the samples are lower than this value; P 50 is the 50th percentile, that is, the probabilities of half of the samples are higher than this value; P 80 is the 80th percentile, that is, the violation probabilities of 80% of the samples are lower than this value, and only the violation probabilities of 20% of the samples are higher than this value.

[0064] The system has a built-in rule engine that automatically matches according to preset compliance standards and business rules. The rule engine uses logical judgment and conditional screening to conduct real-time checks on merchants' behaviors, transaction data, etc. to ensure compliance with the platform's compliance requirements. The rule base contains various violation definitions, compliance judgment standards, and execution conditions. The engine automatically executes the corresponding rules based on the actual scenario and data, and identifies potential violations through rule matching and judgment. The system uses the rule base to conduct a comprehensive scan of merchants' operations and transaction data and automatically identify violations. Violations include but are not limited to transaction fraud, illegal marketing, false advertising, and violation of platform agreements. The rules and standards in the rule base are constantly updated and expanded according to the platform's compliance requirements. The engine can quickly adapt to changes in business scenarios and dynamically adjust rule applications. The system automatically generates a violation analysis report based on the analysis results of the rule engine. The report lists the specific circumstances of the violation in detail, including the type of violation, the triggered rules, the time when the violation occurred, and the participating merchants or users. The report will mark the risk level of each violation to help decision makers prioritize high-risk violations. At the same time, the report will also attach the basis for violation judgment to provide a transparent violation analysis process. In the violation analysis report, the system will classify each violation, indicate the violation type and assign a risk level to the violation according to the degree of impact and severity. The risk level assessment is based on factors such as the historical frequency of the violation, the scope of impact, and potential losses, helping the platform determine the necessary treatment measures.

[0065] With the help of the automated compliance matching function of the rule engine, the system can monitor and automatically identify violations in real time, reducing the workload of manual review and improving the efficiency and accuracy of violation identification. The flexibility of the rule engine allows the platform to quickly adapt to changing regulations and business needs, ensuring seamless compliance and no compliance loopholes due to outdated rules. The system accurately identifies violations by matching the rule base with the engine, reducing missed and misjudgment. The continuous updating and optimization of the rule base ensures the timely adaptation of the system to new violations. The automatically generated violation reports enable the platform to respond to violations more quickly and take corresponding measures immediately to reduce the risks and losses caused by violations. The violation analysis report provides a clear and detailed description of the violation and the basis for judgment, which enhances the transparency of violation handling. Merchants and users can understand the causes and specific rules of violations, which helps to establish the fairness and trust of the platform. Detailed annotation of violation events enables the platform to effectively monitor the trend of violations and provide data support for future strategy adjustments and rule optimization.

[0066] S3: Based on the violation analysis report, the system automatically generates notification information with detailed violation bases and triggers the appeal handling mechanism. After the merchant submits an appeal request through the specified interface, the system transfers the appeal content to the manual review terminal and updates the review results in combination with the violation determination logic.

[0067] The system automatically extracts the violation types based on the information in the violation analysis report and generates notification information containing violation type codes. The violation types are automatically identified through the data analysis module to generate a standardized notification format, avoiding manual intervention. The generated notification information includes violation type codes, violation descriptions, penalty measures, etc., ensuring the integrity and accuracy of the notification information. The notification information is pushed to the merchant through the in-site message module of the platform. The notification information is sent by email, supporting the email address provided by the merchant. The content of the email is the same as the in-site message notification to ensure information synchronization. An API interface is provided to support third-party systems or merchant systems to receive violation notifications. The merchant can obtain violation information by calling the API interface and perform secondary processing according to requirements. The notification channels are diversified to ensure that the merchant receives notifications through multiple methods, reducing the risk of information omission. The merchant can submit an appeal request through multiple methods such as in-site messages, emails, and API interfaces. The system will automatically receive and organize the merchant's appeal materials, including transaction data, appeal explanations, etc., and archive them in the appeal management system. A flexible interface is provided to support the merchant to upload appeal files in different formats, ensuring the diversity and processability of the appeal request. The system automatically detects whether there are differences by comparing the appeal materials provided by the merchant with the original transaction data. Data comparison algorithms are used to ensure the quick identification of inconsistencies between the appeal materials and the transaction data, reducing the burden of manual review. For the inconsistent parts, the system automatically marks the suspicious points and generates a report for the reference of manual review personnel. After the system automatically compares, if there are inconsistencies between the appeal materials and the transaction data, or there are significant doubts, the system automatically triggers the manual review process. The manual review personnel will conduct a review according to the violation determination logic. During the review process, the system provides real-time auxiliary tools, such as violation determination criteria, historical case libraries, etc., to help the review personnel make more accurate judgments.

[0068] The system's automatic generation of violation type codes, notification information push, and appeal material comparison functions greatly reduce the need for manual operations and improve work efficiency. The combination of automated review process and manual review reduces the error rate of manual review and avoids omissions and omissions. Intelligent data comparison and appeal review can quickly discover problems, reduce manual intervention, and improve the timeliness and accuracy of problem solving. The multi-channel notification system ensures that merchants can receive violation notifications and appeal handling results in a timely manner on different platforms, improving the coverage and accuracy of information communication. Even if merchants do not log in to the platform regularly, email notifications and API push ensure that merchants will not miss important information. Automatically comparing appeal materials with original transaction data ensures the objectivity and fairness of appeal handling. The review standards and judgment logic provided by the system ensure the uniformity and fairness of the review process, reduce the impact of human factors, and enhance the trust of merchants. The automated appeal handling process and notification push reduce merchants' waiting time and doubts, and improve the overall experience of merchants. Fast and accurate feedback of violation information and appeal handling reduce merchants' complaints and dissatisfaction with the platform, and improve merchants' satisfaction and platform operation efficiency. The clear logic of violation determination and the automated review process ensure that each appeal review complies with the platform's standardized process, which helps to ensure the consistency of platform operations. The generation of automated tools and audit records helps to trace and optimize the work of auditors and improves the manageability of the entire system.

[0069] S4: Summarize the assessment data according to the preset period, generate a structured assessment report, and synchronously store all judgment processes, notification records and appeal handling results in the audit database.

[0070] The system automatically aggregates the violation data within the preset assessment period. The content of data aggregation includes: violation type, violating merchant, number of violations, penalty situation, appeal situation, etc. Calculation of the violation impact scope: The system automatically calculates its impact scope according to the type and severity of the violation event, such as the number of affected merchants, users, transaction amount, etc., and evaluates the overall impact of the violation event on the platform. During the calculation process, the system will combine factors such as the time and frequency of the violation data to evaluate the overall impact of each type of violation behavior on the platform. The system automatically calculates the KPI indicators of the platform or merchant according to the aggregated data, such as the frequency of violation events, appeal passing rate, penalty timeliness, violation impact scope, etc. The generated assessment report will contain these KPI indicators, and the report format is structured for easy understanding and analysis. The report content includes: classifying and statistically analyzing the violation data by type, merchant, and time period; showing the violation handling performance, efficiency, and compliance of the platform during the assessment period; the system provides trend analysis based on historical data to help predict possible future violation patterns. The system uses data analysis and machine learning algorithms to conduct trend analysis on the aggregated violation data, identify the periodicity, regularity, and potential risks of violation events. By analyzing historical data, the system can predict future violation hotspots to help management personnel formulate countermeasures in advance and optimize platform rules. The system presents the violation trend in the form of charts, line charts, etc., making the analysis results more intuitive and helping decision-makers understand the violation patterns and risk points. The system will record the detailed judgment process of each violation event, including the judgment criteria, basis, and violation type. All merchant appeal records and handling results will also be stored in detail, including the time of appeal submission, appeal content, system automatic comparison results, and manual review process. The final review conclusion will be recorded in the system to ensure that all review operations can be traced and provide a basis for subsequent audits.

[0071] Functions such as automatically summarizing violation data, calculating the scope of violation impact, and generating KPI assessment reports have greatly reduced the time for manual data collation and report generation, improving the overall work efficiency of the platform. Through the automatically generated assessment reports, relevant personnel can quickly obtain key indicators and violation trends, reducing the complexity of data processing and analysis. The violation trend analysis function enables the platform to proactively identify potential violation risks and take proactive risk control and optimization measures through in-depth mining and prediction of historical data. The prediction of violation patterns and trends helps managers formulate more targeted response strategies for different types of violations, avoiding greater impacts on the platform caused by violation incidents. Recording the detailed violation determination process, appeal handling records, and audit conclusions ensures transparency and fairness in the platform's violation handling process. The storage of audit logs allows all determination and handling processes to be traceable, ensuring the compliance of platform behavior and increasing the trust of merchants and users. Through blockchain evidence storage technology, the platform can ensure that all audit logs, violation records, and appeal data are tamper-proof, meeting data protection and compliance requirements. Blockchain storage gives each audit record a clear timestamp and historical verification path, enhancing compliance and legal effectiveness. For industries with strict compliance requirements, it can provide strong data support and evidence preservation. Structured assessment reports and violation trend analysis provide in-depth insights, helping the platform identify the root causes, frequent occurrence periods, and impact scopes of various violations, thereby adjusting strategies. The prediction of violation trends not only helps the platform avoid potential violation risks but also helps optimize platform rules, adjust merchant management and penalty policies, and improve operational efficiency.

[0072] S5: Analyze the performance bottlenecks of the existing system based on the feedback data from merchants and users, and continuously optimize the machine learning model, rule engine strategy, and data collection process.

[0073] The system applies natural language processing and sentiment analysis algorithms to perform sentiment analysis on the collected feedback data. It uses a sentiment analysis model to analyze the sentiment tendency of the feedback content, identify negative sentiment feedback for priority processing. The text content is analyzed through methods such as sentiment scores and keyword extraction to quantify the sentiment attitudes of users and merchants, providing a basis for subsequent optimization and decision-making. The system automatically classifies the feedback data to identify system function defects and issues related to the application of violation rules. For system function defects, the system automatically generates defect reports based on the feedback data and aggregates them into a list to be repaired. For issues related to the application of violation rules, the system automatically identifies potential problems in rule execution based on the complaints and suggestions in the feedback and marks the rule logic that needs to be adjusted. Based on the collected feedback from merchants and users and the results of text sentiment analysis, the system automatically generates optimization suggestions, including: improving the user experience, enhancing system stability, etc.; adjusting or optimizing existing violation rules to reduce false positives and improve execution efficiency; giving an interface optimization plan according to the interface usability issues feedback by users. The suggestions generated by the system through data analysis can help the development team more accurately locate areas that need improvement. According to the feedback from users and merchants and the results of sentiment analysis, the system identifies deficiencies or biases in the model and timely adjusts the parameters of the machine learning model. For example, the violation detection model can adjust the threshold according to the feedback data, optimize the accuracy of the algorithm, and reduce missed detections and misjudgments. When the feedback accumulates to a certain amount, the system will trigger an automated retraining process to improve the performance of the model through newly collected data. According to the feedback from merchants and users and the results of sentiment analysis, the system will adjust and optimize the existing violation rule engine. The updated content includes: reducing over-punishment and false positives by optimizing rule parameters; adjusting the conditions for rule application to ensure the fairness and reasonableness of rule application; optimizing the response speed of the rule engine to improve the real-time and accuracy of violation determination. The updated rules will be verified through methods such as A / B testing to facilitate further adjustment. According to sentiment analysis and system feedback, optimize the violation determination logic to ensure that the system can accurately and efficiently determine the violation behavior of merchants. The optimization content includes: optimizing the condition judgment of the rules to avoid rule conflicts and duplicate judgments; optimizing the threshold of violation detection to reduce false alarms and missed reports; simplifying the determination process according to the feedback to improve the determination efficiency and accuracy.

[0074] A smart assessment and management system for cross-border e-commerce platforms, including a data collection module, a smart analysis module, a notification and appeal processing module, a reporting and auditing module, and a feedback optimization module;

[0075] The data collection module is used to collect multi-source heterogeneous data of the platform in real time, including transaction data, user behavior data, and merchant operation records.

[0076] The intelligent analysis module extracts features from the collected data through machine learning algorithms, establishes a prediction model for violation behaviors, and conducts violation determination based on the rule engine of the platform rule library.

[0077] The notification and appeal handling module is used to generate an electronic notice containing the violation type code, provide a visual appeal interface and an evidence upload port, and implement full-process tracking of the appeal status.

[0078] The report and audit module generates a multi-dimensional assessment report containing KPI indicators and establishes an audit log chain with blockchain evidence storage.

[0079] The feedback and optimization module processes the feedback data of merchants and users through NLP sentiment analysis technology and outputs system function iteration suggestions to the supply chain management side and the marketing strategy side.

[0080] Automatically collect and analyze feedback data from merchants and users to help the platform quickly identify potential problems and improve the response speed of problem handling. Through sentiment analysis, the platform can prioritize the handling of feedback with negative emotions, improve user satisfaction, and enhance merchants' trust. By identifying and analyzing feedback on rule application issues, the system can identify loopholes in the implementation of violation rules, promptly adjust the rule engine, and reduce the situation of misjudgment and abuse of rules. By optimizing the violation judgment logic and rule strategies, the violation judgment becomes more accurate and fair, avoiding unnecessary losses to merchants and users. By identifying system function defects and collected user feedback, the platform can quickly repair and optimize system functions, enhancing the user experience. The optimized system is more stable and user-friendly, increasing user stickiness and improving merchants' satisfaction with use. Adjust the parameters and algorithms of the machine learning model to continuously improve the accuracy and recall rate of the violation detection model, reducing missed reports and false alarms. The retraining and parameter optimization of the model can continuously improve the model's detection ability and adaptability to violation behaviors based on the latest feedback data. The system can automatically generate practical optimization suggestions based on feedback data, reducing the time and cost of manual analysis and directly connecting to the development team for implementation. The optimization suggestions cover multiple aspects such as functions, rules, and user interfaces, helping to comprehensively improve the service quality of the platform. By adjusting the data collection process, the platform can collect effective feedback from merchants and users more efficiently, ensuring the integrity, quality, and accuracy of the data. The optimized data cleaning and standardization process reduces data deviation and repetition, ensuring the accuracy of subsequent analysis. By quickly identifying and adapting to changes in the needs of merchants and users, the platform can promptly adjust strategies and enhance its responsiveness to market changes. The optimized rule engine, machine learning model, and data collection process enable the platform to better meet the changing business requirements and enhance the platform's competitiveness. The automated optimization process of the system promotes the continuous improvement of the product, enabling the platform to iterate and update according to feedback and enhancing the overall service quality. The long-term optimization process can gradually improve the functions and rules of the platform, enhancing the satisfaction of merchants and users, thereby driving the business growth of the platform.

[0081] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device.

[0082] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cross-border e-commerce platform intelligent assessment management method, characterized in that: The following steps are involved: S1: Collect transaction data, user behavior data and merchant operation behavior data on the platform in real time, and perform data cleaning, deduplication and format standardization preprocessing; S2: Use machine learning models to extract and analyze features of preprocessed data, combine with the compliance policies embedded in the rule engine, automatically determine whether there are any violations, and generate a violation analysis report; S3: Based on the violation analysis report, the system automatically generates a notification message with detailed violation evidence and triggers the appeal handling mechanism. After the merchant submits an appeal request through the designated interface, the system transfers the appeal content to the manual review end and updates the review results based on the violation judgment logic; S4: Summarize the assessment data according to the preset period, generate a structured assessment report, and synchronously store all the determination processes, notification records and appeal handling results in the audit database; S5: Based on the feedback data from merchants and users, analyze the performance bottlenecks of the existing system and continuously optimize the machine learning model, rule engine strategy and data collection process.

2. According to claim 1, a cross-border e-commerce platform intelligent assessment management method is characterized in that ,The S1 further includes collecting transaction data, including order records, payment information, logistics status, and counting the number of illegal orders TP, FP; collecting user behavior data, including browsing ,tracks, click records, evaluation content, and recording the misjudgment situations TN, FN; Collect merchant operation data, including product listing, price adjustment, and order processing records, and analyze their impact on the probability of violations.

3. According to claim 1, a cross-border e-commerce platform intelligent assessment management method is characterized in that ,S2 further includes extracting features from the preprocessed data and extracting the violation feature vector; using the machine learning model to calculate the violation probability and generate a preliminary violation determination result; using the XGBoost model to predict violations, the core of which is to calculate the probability that the sample belongs to the violation category, and the formula is as follows: Among them, P(X) is the probability that sample X belongs to the violation category, and F(X) is the decision function value calculated by XGBoost, that is, the weighted sum of multiple decision trees. The formula is as follows: Where T is the number of decision trees, G t (X) is the prediction output of the t-th decision tree, X includes the merchant operation data; w t is the weight of the decision tree; Set the violation determination threshold and determine it through statistical analysis. The formula is as follows: τ=arg max(TPR-FPR) Among them, TP means that the violation sample is correctly judged as violation, and FN means that the violation sample is misjudged as compliance; Among them, FP means that the compliant sample is misjudged as noncompliant, and TN means that the compliant sample is correctly judged as compliant; The violation determination results are divided into risk levels. Different thresholds can be set based on the 20% quantile of the sample size, and the impact risk of the determination is divided into three levels: low risk, medium risk, and high risk. The 20% quantile, 50% quantile, and 80% quantile are calculated from the historical violation probability data set, corresponding to the low risk, medium risk, and high risk thresholds respectively: t low =P 20 t mid =P 50 t hi g h =P 80 Among them, P 20 is the 20% quantile of the sample violation probability, that is, the probability of 20% of the violation samples is lower than this value; P 50 is the 50% quantile, that is, half of the sample probability is higher than this value; P 80 is the 80% quantile, that is, 80% of the violation samples have a probability lower than this value, and only 20% of the violation samples have a probability higher than this value. Combine the rule engine for compliance matching and apply the rule base to identify violations; generate a violation analysis report, marking the violation type, risk level and judgment basis.

4. According to claim 1, a cross-border e-commerce platform intelligent assessment management method is characterized in that ,S3 further includes generating notification information including violation type code according to the violation analysis report; pushing notification information to merchants through internal letters, emails and API interfaces; receiving merchant appeal requests, and automatically comparing the appeal materials with the original transaction data; Trigger the manual review process and conduct appeal review based on the violation determination logic; Update the review results and notify the merchant of the appeal handling conclusion.

5. According to claim 1, a cross-border e-commerce platform intelligent assessment management method is characterized in that ,The S4 further includes summarizing the violation data within the assessment period, calculating the scope of violation impact; generating an assessment report containing KPI indicators, and providing violation trend analysis; recording the violation determination process, appeal handling records and audit conclusions; using blockchain evidence storage technology to store audit logs, and ensure that the data cannot be tampered with.

6. According to claim 1, a cross-border e-commerce platform intelligent assessment management method is characterized in that ,The S5 further includes collecting merchant and user feedback data and conducting text sentiment analysis; identifying system function defects and violation rule applicability issues; generating system optimization suggestions and adjusting machine learning model parameters; updating rule engine strategies and optimizing violation determination logic; and adjusting data collection processes.

7. A cross-border e-commerce platform intelligent assessment management system, applied to a cross-border e-commerce platform intelligent assessment management method as described in claims 1-6, characterized in that: It includes data collection module, intelligent analysis module, notification and complaint handling module, report and audit module, and feedback optimization module; The data collection module is used to collect multi-source heterogeneous data from the platform in real time, including transaction data, user behavior data, and merchant operation records; The intelligent analysis module extracts features from the collected data through machine learning algorithms, establishes a violation prediction model, and makes violation determinations based on the rule engine of the platform rule library; The notification and complaint processing module is used to generate an electronic notification form containing the violation type code, provide a visual complaint interface and evidence upload port, and implement full-process tracking of the complaint status; The report and audit module generates a multi-dimensional assessment report containing KPI indicators and establishes an audit log chain stored in the blockchain; The feedback optimization module processes merchant and user feedback data through NLP sentiment analysis technology, and outputs system function iteration suggestions to the supply chain management end and the marketing strategy end.

8. The cross-border e-commerce platform intelligent assessment management system according to claim 7, characterized in that: The intelligent analysis module further comprises: Standardize and clean the collected data, extract violation feature vectors, including but not limited to the frequency threshold of follow-up sales, logistics time deviation value, and image repetition rate; use the XGBoost algorithm to supervise the learning of historical violation data and generate a classification model with behavior prediction function; convert the platform rule base into an executable decision tree, which includes three-level judgment logic, first-level basic compliance inspection, second-level risk probability assessment, and third-level manual review trigger; output a visual analysis report containing a violation association map, marking the risk propagation path and impact range.

9. The cross-border e-commerce platform intelligent assessment management system according to claim 7, characterized in that: The notification and complaint handling module further includes: Automatically match notification templates according to violation levels and push notifications through three channels: in-site letter, email, and API interface. Provide structured complaint forms to automatically compare complaint materials with original transaction data. Establish a multi-level review mechanism to conduct multi-dimensional assessments of dispute cases. Generate complaint hotspot maps to identify high-frequency dispute types and output rule optimization suggestions to the platform policy department.

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