Cross-border e-commerce platform operation management system

Abstract

The invention discloses a cross-border e-commerce platform operation management system, which belongs to the technical field of cross-border e-commerce, and comprises a customer management module used for customer information management, classification, follow-up recording and data analysis; the product management module is used for managing product information, classifying, publishing and analyzing data; the supplier management module is used for managing supplier information, purchasing, evaluating and supply chain collaboration; the intelligent analysis engine is used for realizing data mining, prediction analysis and personalized recommendation; the data processing center is used for realizing data intercommunication and business process integration of the modules; and the interactive user interface is used for providing more intelligent and personalized user interaction experience. Enterprise import and export tracking and cross-border e-commerce product page management can be realized so that enterprise work efficiency can be enhanced. The intelligent level of the system can be improved; and the personalized application of the system can be improved through an interactive user interface, a customer behavior analysis subsystem and a personalized recommendation subsystem.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of cross-border e-commerce, and specifically to a cross-border e-commerce platform operation and management system. Background Art

[0002] Cross-border e-commerce refers to an international business activity in which trading entities in different customs territories reach transactions and make payment settlements through e-commerce platforms, and deliver goods and complete transactions through cross-border logistics. Cross-border e-commerce is developed based on the Internet. Compared with physical space, cyberspace is a new space, a virtual but objective world composed of URLs and passwords. The unique value standards and behavior patterns of cyberspace have a profound impact on cross-border e-commerce, making it different from traditional trading methods and presenting its own characteristics. With the rapid development of global e-commerce, cross-border e-commerce business is increasing. Traditional cross-border e-commerce systems have single functions and cannot meet these needs in a trading environment with increasingly diversified demands.

[0003] At present, a cross-border e-commerce platform operation and management system is disclosed in the patent document with application number 202011521597.6, including a cross-border mall operation and management module, a filing management module, a warehouse management module, and a consumer management module, which can ensure that the payment information, product information, and logistics information of cross-border goods can be managed in a timely manner, and the warehousing, outbound, and return information of cross-border goods can be traced in a timely manner. However, although the existing cross-border e-commerce operation and management system has improved business efficiency to a certain extent, it still needs to be improved in terms of intelligent and personalized services to better adapt to the rapidly changing cross-border e-commerce market. Summary of the invention

[0004] The technical solution of the present invention aims at the technical problem that the existing technical solutions are too single, and provides a solution that is significantly different from the existing technologies. It mainly provides a cross-border e-commerce platform operation and management system to solve the technical problems raised in the above background technology that the existing cross-border e-commerce operation and management system still needs to be improved in terms of intelligence and personalized services.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] A cross-border e-commerce platform operation management system, comprising:

[0007] Customer management module, used to manage customer information, classification, follow-up records and data analysis;

[0008] Product management module, used to manage product information, classification, release and data analysis;

[0009] Supplier management module, used to manage supplier information, procurement, evaluation and supply chain collaboration;

[0010] Intelligent analysis engine for data mining, predictive analysis and personalized recommendations;

[0011] Data processing center, used to realize data intercommunication among modules and integration of business processes;

[0012] Interactive user interface, used to provide a more intelligent and personalized user interaction experience.

[0013] Specifically, the intelligent analysis engine includes a market trend prediction subsystem, and the market trend prediction subsystem includes:

[0014] Data collection and preprocessing unit, used to collect historical sales data, customer behavior data, marketing activity data, social media data; and perform data cleaning, standardization and normalization, as well as data integration;

[0015] Feature engineering unit, used to identify and extract key features that influence market trends;

[0016] Model building and training unit, builds prediction models, trains models using historical data, and optimizes model parameters through cross-validation techniques;

[0017] The market trend prediction unit inputs the preprocessed real-time market data into the trained prediction model; the model outputs the market trend prediction results for a period of time in the future.

[0018] Furthermore, the intelligent analysis engine also includes a customer behavior analysis subsystem and a personalized recommendation subsystem.

[0019] Specifically, the customer behavior analysis subsystem includes:

[0020] Data integration unit, which is used to collect various behavioral data of customers on the platform and integrate data from different data sources;

[0021] Behavior pattern recognition unit divides customers into different groups and identifies different purchasing behavior patterns; discovers customers' purchase sequences and association rules; and predicts customers' potential behaviors;

[0022] The preference analysis unit is used to analyze the customer's purchase history and identify the customer's preferred product categories, brands, and price ranges; integrate the customer's personal attributes, behavioral characteristics, and preference information by building a customer profile; and use association rule analysis to discover the purchase correlation between different products;

[0023] The result output unit is used to output the analysis results and provide a basis for the formulation of marketing strategies; customer groups and preference information are imported into the personalized recommendation subsystem to support personalized recommendations.

[0024] Specifically, the personalized recommendation subsystem includes:

[0025] The recommendation model building unit is used to build a recommendation model based on the results of customer behavior analysis; and optimize model parameters through cross-validation and A / B testing to improve recommendation accuracy;

[0026] A recommendation list generating unit, used for generating a personalized recommendation list for each customer according to the recommendation model;

[0027] A recommendation result display unit, used to display recommendation results on the e-commerce platform;

[0028] The feedback loop unit is used to collect customer feedback on the recommendation results, use the feedback data to update the recommendation model, and continuously optimize the recommendation effect.

[0029] Specifically, the customer management module includes a customer information entry and query subsystem, a customer classification label management subsystem, a customer follow-up record management subsystem, and a customer transaction data analysis subsystem.

[0030] Specifically, the product management module includes a product information entry and query subsystem, a product classification setting subsystem, a multi-platform publishing management subsystem, and a product sales and evaluation analysis subsystem;

[0031] And / or, the supplier management module includes a supplier information entry and query subsystem, a purchase order creation and tracking subsystem, a supplier performance evaluation subsystem, and a supply chain information sharing subsystem.

[0032] Specifically, the data processing center includes:

[0033] Data integration processor, used to realize data exchange and integration between modules;

[0034] A data analysis processor, used for performing statistics and analysis on the integrated data;

[0035] The data storage unit is used to store system data and business data.

[0036] Specifically, the interactive user interface includes:

[0037] Speech recognition and interaction subsystem, used to realize speech command recognition and interaction;

[0038] Personalized dashboards to display customized business data and key indicators;

[0039] The dynamic prompt and decision support subsystem is used to provide real-time business prompts and decision support.

[0040] Furthermore, it also includes an automation task processor, and the automation task processor includes:

[0041] Automatic order processing subsystem, used to automatically process orders, shipments and logistics tracking;

[0042] An automatic inventory management subsystem to automatically monitor inventory levels and trigger replenishment reminders;

[0043] The report automatic generation subsystem is used to automatically generate business reports and analysis reports on a regular basis.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) The present invention is provided with a customer management module, a product management module and a supplier management module, which respectively manage and operate different management objects, thereby realizing the centralized management of customer, product and supplier information, and can realize enterprise import and export tracking and cross-border e-commerce product page management, which is beneficial to improving enterprise work efficiency; and can reduce labor costs.

[0046] (2) By introducing an intelligent analysis engine, the present invention can analyze historical data and real-time market information through the market trend prediction subsystem, predict future market trends and demand changes, provide a scientific basis for platform operation decisions, and improve the intelligence level of the system. In addition, through the customer behavior analysis subsystem and the personalized recommendation subsystem, it can provide the ability to gain in-depth insights into customer behavior and preferences, helping companies to better understand customers; personalized recommendations improve user experience, increase conversion rates and customer loyalty; the feedback loop mechanism ensures the continuous optimization of the recommendation system and its ability to adapt to market changes, which can optimize the user shopping experience and enhance user satisfaction and loyalty.

[0047] (3) The present invention can realize voice command recognition and interaction through an interactive user interface; it can display customized business data and key indicators, providing a more convenient and personalized user experience, thereby enhancing the personalized application of the system.

[0048] (4) The present invention can eliminate information islands through the data processing center, realize business process integration, and achieve the purpose of simplifying foreign trade workflow.

[0049] (5) The automated task processor in the optimization scheme of the present invention can greatly improve the intelligence level of business processes.

[0050] (6) The system provided by the present invention is highly scalable and user-friendly, adapting to the needs of enterprises of different sizes and able to adapt to the rapid changes in the cross-border e-commerce market.

[0051] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a structural block diagram of the present invention in the first embodiment;

[0053] Figure 2 It is a structural block diagram of the intelligent analysis engine of the present invention in the second embodiment;

[0054] Figure 3 This is a structural block diagram of the present invention in Embodiment 3.

[0055] Figure numerals: 1. Customer management module; 11. Customer information entry and query subsystem; 12. Customer classification label management subsystem; 13. Customer follow-up record management subsystem; 14. Customer transaction data analysis subsystem; 2. Product management module; 21. Product information entry and query subsystem; 22. Product classification setting subsystem; 23. Multi-platform publishing management subsystem; 24. Product sales and evaluation analysis subsystem; 3. Supplier management module; 31. Supplier information entry and query subsystem; 32. Purchase order creation and tracking subsystem; 33. Supplier performance evaluation subsystem; 34. Supply chain information sharing subsystem; 4. Intelligent analysis engine; 41. Market trend prediction subsystem; 411. Data collection and preprocessing unit; 412. Feature engineering unit; 413. Model building and training unit; 414 , market trend prediction unit 414; 42, customer behavior analysis subsystem; 421, data integration unit; 422, behavior pattern recognition unit; 423, preference analysis unit; 424, result output unit; 43, personalized recommendation subsystem; 431, recommendation model building unit; 432, recommendation list generation unit; 433, recommendation result display unit; 434, feedback loop unit; 5, data processing center; 51, data integration processor; 52, data analysis processor 52; 53, data storage unit 53; 6, interactive user interface; 61, speech recognition and interaction subsystem; 62, personalized dashboard; 63, dynamic prompt and decision support subsystem; 7, automated task processor; 71, order automatic processing subsystem; 72, inventory automatic management subsystem; 73, report automatic generation subsystem. DETAILED DESCRIPTION

[0056] To facilitate understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings, but the present invention can be implemented in different forms and is not limited to the embodiments described in the text. On the contrary, these embodiments are provided to make the content disclosed in the present invention more thorough and comprehensive.

[0057] Embodiment 1: A cross-border e-commerce platform operation and management system, the system architecture of which includes:

[0058] Multi-layer B / S architecture: including presentation layer, business logic layer, data access layer and data storage layer, ensuring efficient and stable operation of the system;

[0059] Microservice architecture: split the system into multiple independent and scalable microservices to improve the maintainability and scalability of the system;

[0060] Cloud service platform: deployed on cloud servers, supporting elastic scaling and cross-regional deployment.

[0061] The core functional modules of the system include:

[0062] Customer management module 1, used to manage customer information, classification, follow-up records and data analysis;

[0063] Product management module 2, used to manage product information, classification, release and data analysis;

[0064] Supplier Management Module 3, used to manage supplier information, procurement, evaluation and supply chain collaboration;

[0065] Intelligent analysis engine 4, used to implement data mining, predictive analysis and personalized recommendations;

[0066] Data processing center 5, used to realize data intercommunication among modules and integration of business processes;

[0067] Interactive user interface 6 is used to provide a more intelligent and personalized user interaction experience.

[0068] The application method of the above cross-border e-commerce platform operation management system includes the following steps:

[0069] S1, start the platform and enter the interactive user interface 6;

[0070] S2. Access the corresponding management module according to user authority;

[0071] S2-1, in the customer management module 1, perform customer information entry, query, classification and follow-up;

[0072] S2-2, in product management module 2, perform product information entry, query, classification, publishing and data analysis;

[0073] S2-3. In the supplier management module 3, perform supplier information entry, query, procurement, evaluation and supply chain collaboration;

[0074] S3, through the data processing center 5, realize the intercommunication of data of each module and the integration of business processes;

[0075] S4, using intelligent analysis engine 4 to predict market trends;

[0076] S5, voice recognition, personalized data display and dynamic prompts through interactive user interface 6;

[0077] S6. End the operation and exit the system.

[0078] Specifically:

[0079] The customer management module 1 includes a customer information entry and query subsystem 11 , a customer classification label management subsystem 12 , a customer follow-up record management subsystem 13 , and a customer transaction data analysis subsystem 14 .

[0080] The product management module 2 includes a product information entry and query subsystem 21 , a product classification setting subsystem 22 , a multi-platform publishing management subsystem 23 , and a product sales and evaluation analysis subsystem 24 .

[0081] The supplier management module 3 includes a supplier information entry and query subsystem 31 , a purchase order creation and tracking subsystem 32 , a supplier performance evaluation subsystem 33 , and a supply chain information sharing subsystem 34 .

[0082] The data processing center 5 includes: a data integration processor 51 for realizing data exchange and integration between modules; a data analysis processor 52 for performing statistics and analysis on the integrated data; and a data storage unit 53 for storing system data and business data.

[0083] The interactive user interface 6 includes: a voice recognition and interaction subsystem 61 for realizing voice command recognition and interaction; a personalized dashboard 62 for displaying customized business data and key indicators; a dynamic prompt and auxiliary decision subsystem 63 for providing real-time business prompts and decision support.

[0084] The intelligent analysis engine 4 includes: a market trend prediction subsystem 41, which aims to predict future market trends and demand changes by analyzing historical data and real-time market information, and provide data support for the operational decisions of the cross-border e-commerce platform.

[0085] The market trend forecasting subsystem 41 includes:

[0086] The data collection and preprocessing unit 411 collects historical sales data, customer behavior data, marketing activity data, social media data, etc. from internal databases and external data sources; uses data cleaning technology to remove invalid and erroneous data, and performs data standardization and normalization; and then uses data integration technology to integrate data from different sources and formats into the data input layer of the prediction model;

[0087] Feature Engineering Unit 412, identifies and extracts key features that affect market trends, such as seasonality, promotional activities, price changes, consumer sentiment, etc.; uses feature selection techniques to select the feature combinations that have the greatest impact on the prediction results; enhances the expressiveness of features through feature transformation techniques, such as using principal component analysis (PCA) to reduce dimensionality;

[0088] Model building and training unit 413, select appropriate prediction models, such as time series analysis (ARIMA, SARIMA), machine learning algorithms (random forest, gradient boosting machine), deep learning models (recurrent neural network RNN, long short-term memory network LSTM); use historical data to train the model and optimize model parameters through cross-validation technology; evaluate model performance and select the optimal model for subsequent prediction;

[0089] The market trend prediction unit 414 inputs the preprocessed real-time market data into the trained prediction model; the model outputs the market trend prediction results for a period of time in the future, including sales forecasts, demand change trends, etc.; and generates a visual report based on the prediction results, such as a trend chart, a prediction interval chart, etc.

[0090] When the above-mentioned market trend forecasting subsystem is applied, the forecasting results can be combined with actual business to guide decisions such as inventory management, product pricing, and marketing strategies; the forecasting model can be updated regularly to adapt to market changes and improve forecasting accuracy; the forecasting results can be used for risk assessment and to formulate plans to deal with market fluctuations.

[0091] The advantages of the above market trend forecasting subsystem are: combining multiple forecasting models to improve the accuracy and robustness of the forecast; feature engineering and model optimization processes improve the interpretability of the forecast results; the application of forecast results helps companies to plan the market in advance and reduce operational risks; the system has good adaptability and scalability and can cope with changes in different market environments.

[0092] Embodiment 2: This embodiment differs from Embodiment 1 in that:

[0093] The intelligent analysis engine 4 also includes: a customer behavior analysis subsystem 42 for analyzing customer purchasing behavior and preferences; and a personalized recommendation subsystem 43 for recommending suitable products and services to customers.

[0094] The customer behavior analysis subsystem 42 includes:

[0095] The data integration unit 421 is used to collect various behavior data of customers on the platform, including purchase records, browsing history, click behavior, evaluation feedback, etc.; integrate data from different data sources to build a customer behavior data set in a unified format;

[0096] The behavior pattern recognition unit 422 applies data mining techniques, such as cluster analysis, to classify customers into different groups and identify different purchase behavior patterns; uses sequence pattern mining techniques, such as frequent item set mining, to discover customers' purchase sequences and association rules; uses machine learning algorithms, such as classification algorithms, to predict customers' potential behaviors;

[0097] The preference analysis unit 423 is used to analyze the customer's purchase history and identify the customer's preferred product category, brand, price range, etc.; integrate the customer's personal attributes, behavioral characteristics and preference information by building a customer profile; and use association rule analysis to discover the purchase correlation between different products;

[0098] The result output unit 424 is used to output the analysis results in the form of reports, charts, etc. to provide a basis for formulating marketing strategies; and to import customer groups and preference information into the personalized recommendation subsystem 43 to support personalized recommendations.

[0099] The personalized recommendation subsystem 43 includes:

[0100] The recommendation model building unit 431 selects a suitable recommendation algorithm, such as collaborative filtering, content-based recommendation, hybrid recommendation, etc., based on the customer behavior analysis results; uses machine learning techniques, such as matrix decomposition and deep learning, to build a recommendation model; optimizes model parameters through cross-validation and A / B testing to improve recommendation accuracy;

[0101] The recommendation list generation unit 432 generates a personalized recommendation list for each customer based on the recommendation model; considers diversity and novelty to ensure that the recommendation list contains products of different categories and characteristics; dynamically adjusts the recommendation list based on the customer's real-time behavior to achieve real-time recommendation;

[0102] The recommendation result display unit 433 displays the recommendation results on different touch points of the e-commerce platform, such as the home page, the shopping cart page, the search results page, etc.; uses visualization techniques, such as carousels and recommendation cards, to attract customers' attention; provides reasons for recommendation to enhance customers' trust and willingness to buy;

[0103] Feedback loop unit 434 collects customer feedback on recommendation results, such as clicks, purchases, and reviews; uses feedback data to update the recommendation model and continuously optimize the recommendation effect; and regularly evaluates the performance of the recommendation system to ensure the quality of recommendations.

[0104] The advantages of the above-mentioned customer behavior analysis subsystem and personalized recommendation subsystem are: providing the ability to gain in-depth insights into customer behavior and preferences, helping companies to better understand customers; personalized recommendations improve user experience, increase conversion rates and customer loyalty; and the feedback loop mechanism ensures the continuous optimization of the recommendation system and its ability to adapt to market changes.

[0105] The rest is the same as the first embodiment.

[0106] Embodiment 3: This embodiment differs from Embodiment 2 in that:

[0107] In this embodiment, the cross-border e-commerce platform operation and management system also includes an automated task processor 7, which is used to execute preset business process automation tasks.

[0108] The automated task processor 7 includes:

[0109] An automatic order processing subsystem 71 is used for automatically processing orders, shipments and logistics tracking;

[0110] an automatic inventory management subsystem 72 for automatically monitoring inventory levels and triggering replenishment reminders;

[0111] The report automatic generation subsystem 73 is used to automatically generate business reports and analysis reports on a regular basis.

[0112] The rest is the same as the second embodiment.

[0113] The above is an exemplary description of the present invention in combination with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as such non-substantial improvements are made by adopting the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A cross-border e-commerce platform operation and management system, characterized by: include: Customer management module (1), used to manage customer information, classification, follow-up records and data analysis; Product management module (2), used to manage product information, classification, release and data analysis; Supplier management module (3), used to manage supplier information, procurement, evaluation and supply chain collaboration; Intelligent analysis engine (4), used to implement data mining, predictive analysis and personalized recommendations; Data processing center (5), used to realize data intercommunication among modules and integration of business processes; An interactive user interface (6) is used to provide a more intelligent and personalized user interaction experience.

2. A cross-border e-commerce platform operation and management system according to claim 1, characterized in that: The intelligent analysis engine (4) comprises a market trend prediction subsystem (41), and the market trend prediction subsystem (41) comprises: A data collection and preprocessing unit (411) is used to collect historical sales data, customer behavior data, marketing activity data, and social media data; and perform data cleaning, standardization and normalization processing, as well as data integration; A feature engineering unit (412) for identifying and extracting key features that influence market trends; A model building and training unit (413) builds a prediction model, trains the model using historical data, and optimizes model parameters using cross-validation techniques; The market trend prediction unit (414) inputs the pre-processed real-time market data into the trained prediction model; the model outputs the market trend prediction results for a period of time in the future.

3. A cross-border e-commerce platform operation and management system according to claim 2, characterized in that: The intelligent analysis engine (4) also includes a customer behavior analysis subsystem (42) and a personalized recommendation subsystem (43).

4. A cross-border e-commerce platform operation and management system according to claim 3, characterized in that: The customer behavior analysis subsystem (42) comprises: A data integration unit (421), used to collect various behavioral data of customers on the platform and integrate data from different data sources; The behavior pattern recognition unit (422) divides customers into different groups, identifies different purchase behavior patterns, discovers the purchase sequence and association rules of customers, and predicts the potential behavior of customers; The preference analysis unit (423) is used to analyze the customer's purchase history and identify the customer's preferred product category, brand, and price range; integrate the customer's personal attributes, behavioral characteristics, and preference information by building a customer profile; and use association rule analysis to discover the purchase correlation between different products; The result output unit (424) is used to output the analysis results to provide a basis for formulating marketing strategies; and to import customer group and preference information into the personalized recommendation subsystem (43) to support personalized recommendations.

5. A cross-border e-commerce platform operation and management system according to claim 3, characterized in that: The personalized recommendation subsystem (43) includes: A recommendation model building unit (431) is used to build a recommendation model based on the customer behavior analysis results; and optimize the model parameters through cross-validation and A / B testing to improve the accuracy of recommendations; A recommendation list generating unit (432), used to generate a personalized recommendation list for each customer according to the recommendation model; A recommendation result display unit (433), used to display the recommendation results on the e-commerce platform; The feedback loop unit (434) is used to collect customer feedback on the recommendation results, and use the feedback data to update the recommendation model to continuously optimize the recommendation effect.

6. A cross-border e-commerce platform operation and management system according to claim 1, characterized in that: The customer management module (1) comprises a customer information entry and query subsystem (11), a customer classification label management subsystem (12), a customer follow-up record management subsystem (13), and a customer transaction data analysis subsystem (14).

7. A cross-border e-commerce platform operation and management system according to claim 1, characterized in that: The product management module (2) includes a product information entry and query subsystem (21), a product classification setting subsystem (22), a multi-platform publishing management subsystem (23), and a product sales and evaluation analysis subsystem (24); And / or, the supplier management module (3) includes a supplier information entry and query subsystem (31), a purchase order creation and tracking subsystem (32), a supplier performance evaluation subsystem (33), and a supply chain information sharing subsystem (34).

8. A cross-border e-commerce platform operation and management system according to claim 1, characterized in that: The data processing center (5) comprises: A data integration processor (51), used to realize data exchange and integration between modules; A data analysis processor (52), used for performing statistics and analysis on the integrated data; The data storage unit (53) is used to store system data and business data.

9. A cross-border e-commerce platform operation and management system according to claim 1, characterized in that: The interactive user interface (6) comprises: A speech recognition and interaction subsystem (61), used to realize speech command recognition and interaction; Personalized dashboards (62) for displaying customized business data and key indicators; The dynamic prompt and decision support subsystem (63) is used to provide real-time business prompts and decision support.

10. A cross-border e-commerce platform operation and management system according to any one of claims 1 to 9, characterized in that: It also includes an automation task processor (7), wherein the automation task processor (7) includes: An automatic order processing subsystem (71) for automatically processing orders, shipments and logistics tracking; An automatic inventory management subsystem (72) for automatically monitoring inventory levels and triggering replenishment reminders; The report automatic generation subsystem (73) is used to automatically generate business reports and analysis reports on a regular basis.

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