E-commerce intelligent operation management system and ecological construction method

Abstract

The invention discloses an e-commerce intelligent operation management system and an ecological construction method. According to the invention, through mining and analyzing a large amount of user behavior data and market trends, accurate market positioning and strategic decision support can be provided for an e-commerce platform. By identifying the user behavior mode, predicting the sales trend and evaluating the marketing activity effect, the e-commerce platform is helped to optimize resource configuration, and the operation efficiency is improved. And the user portrait analysis sub-module constructs a detailed user portrait, so that the e-commerce platform can implement personalized marketing and service, and the satisfaction and loyalty of the user are improved. And the competitive product analysis sub-module monitors and analyzes products, prices, marketing strategies and the like of competitors to help the e-commerce platform to formulate effective competition strategies and maintain market advantages, so that the e-commerce platform can better understand the market and users, thereby making more intelligent decisions and improving the overall operation performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of e-commerce operation, and specifically relates to an e-commerce intelligent operation management system and an ecological construction method. Background Art

[0002] With the rapid development of Internet technology and the popularization of e-commerce, e-commerce platforms have become an important channel for consumers' daily shopping. However, with the intensification of market competition, how to improve operation efficiency, reduce costs, and enhance user experience has become a key challenge faced by e-commerce platforms. The traditional operation management mode of e-commerce platforms often relies on manual operations and empirical judgments, suffering from problems such as low efficiency, insufficient data utilization, and lack of scientific basis for decision-making. Therefore, an e-commerce operation management system is needed.

[0003] However, although the existing e-commerce operation management systems have achieved information management to a certain extent, they lack intelligent data analysis and decision support modules and cannot provide accurate market forecasts and personalized user services for the operation team. Summary of the Invention

[0004] The purpose of the present invention is to provide an e-commerce intelligent operation management system and an ecological construction method to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: An e-commerce intelligent operation management system, the system includes: a data collection module, a data processing module, a business processing module, a data analysis and decision module, a system integration and interface module, and a user interface module; The data analysis and decision module is internally provided with a data analysis sub-module, a user portrait analysis sub-module, a competitor analysis sub-module, and an intelligent recommendation sub-module; The data collection module, as the front-end antenna of the e-commerce intelligent operation management system, is responsible for real-time capturing of original data such as product information, user behavior, and market dynamics from various data sources and transmitting it to the data processing module.

[0006] After receiving the data, the data processing module converts the original data into high-quality, structured information through operations such as data cleaning, standardization, integration, and sampling, laying a solid foundation for subsequent business processing and data analysis.

[0007] The business processing module then executes daily operation tasks such as order management, customer service, and inventory control based on the processed data, and at the same time feeds back business data to the data processing module for further processing.

[0008] The data analysis and decision-making module uses the processed data to provide decision-making support for the management layer through means such as descriptive analysis, association analysis, trend analysis, and user portrait analysis. The output results of this module directly affect the strategy adjustment of the business processing module.

[0009] The system integration and interface module, as a bridge, is responsible for seamlessly connecting the e-commerce intelligent operation management system with external systems (such as payment, logistics, CRM, etc.) and other internal systems (such as finance, procurement, etc.), ensuring unobstructed data flow.

[0010] The user interface module provides an intuitive and easy-to-operate interface for operators, through which they can access data analysis results, execute business operations, and monitor system status.

[0011] In a preferred embodiment, the data collection module collects raw data from multiple data sources. First, it uses methods such as web crawlers, API interfaces, and log files to capture data such as user browsing behaviors, transaction records, and product information in real time. The data collection module uses web crawler technology to regularly capture product price information from e-commerce platforms. The crawler will access web pages according to a preset list of URLs, parse the HTML content, extract the product price fields, and store them in the database. Next, these data are preliminarily formatted and classified to ensure the integrity and accuracy of the data, providing a basis for subsequent data processing.

[0012] In a preferred embodiment, the data processing module is responsible for cleaning, transforming, and integrating the collected raw data for subsequent analysis. First, data cleaning is performed. Regular expressions are used to remove invalid characters, and missing value handling is used to fill or delete missing data. Then, through data normalization methods such as min-max normalization, the data is scaled to a fixed range for easy comparison and analysis.

[0013] In a preferred embodiment, when an order is created in the business processing module, the system will first verify the validity of the order information, such as checking whether the inventory quantity meets the order requirements. Then, through the order management process, the inventory records are updated, a shipping note is generated, and the logistics system is notified to prepare for shipment. For example, transaction processing methods are used to ensure the atomicity of order status updates and avoid data inconsistencies. At the same time, the business processing module will also trigger subsequent financial processes such as invoice generation and payment confirmation to ensure the smooth progress of the entire transaction process.

[0014] In a preferred embodiment, the data analysis sub-module divides data points into K clusters through the K-means clustering algorithm, minimizing the sum of the squared distances between each point and its assigned cluster center. The specific steps include: S1. Select the value of K: Determine the number of clusters to be divided.

[0015] S2. Initialize the center points: Randomly select K data points as the initial cluster centers.

[0016] S3. Assign data points: Assign each data point to the nearest cluster center.

[0017] S4. Update the cluster centers: Calculate the average value of the data points in each cluster and update the cluster centers.

[0018] S5. Repeat steps S3 and S4: Until the change in the cluster centers is less than a certain threshold or the preset number of iterations is reached; The formula for calculating the distance between a data point and a cluster center using the Euclidean distance formula is: ; where p and q are two data points, and n is the dimension of the data points; For each cluster k, the update formula for its center point is: ; where c_k is the center point of cluster k, S_k is the set of all data points in cluster k, and |S_k| is the number of data points in cluster k.

[0019] In a preferred embodiment, the user profile analysis sub-module uses a decision tree algorithm to classify or predict data through a series of judgment rules, constructs a user profile based on the user's purchase history, browsing behavior, and personal information, and predicts the user's purchase tendency. The steps include: S1. Data preparation: Collect and organize data such as the user's purchase records, browsing logs, and personal information.

[0020] S2. Feature selection: Select features related to the user's purchase behavior, such as purchase frequency, purchase amount, browsing duration, etc.

[0021] S3. Decision tree construction: Use the feature data to construct a decision tree model.

[0022] S4. User classification: Classify users according to the decision tree.

[0023] S5. Profile generation: Generate a profile description for each classified user.

[0024] The information gain calculation formula for selecting the optimal feature is: ; where IG(T, a) is the information gain given by feature a to set T, H(T) is the entropy of set T, and T_v is the subset of T where the value of feature a is v; The calculation formula for measuring the purity entropy of a data set is: Among them, \(p_i\) is the probability that the \(i\)-th type of sample appears in the set \(T\).

[0025] In a preferred embodiment, the competitor analysis sub-module compares the products on the e-commerce platform with competitors in terms of multiple characteristics, including price, performance, and user ratings. The specific steps are as follows: S1. Data collection: Collect multi-dimensional characteristic data of the e-commerce platform and competitors.

[0026] S2. Data standardization: Standardize the data to the same scale.

[0027] S3. Covariance matrix calculation: Calculate the covariance matrix of the standardized data.

[0028] S4. Eigenvalue and eigenvector calculation: Calculate the eigenvalues and eigenvectors of the covariance matrix.

[0029] S5. Principal component selection: Select the principal component with the highest cumulative contribution rate.

[0030] S6. Competitor comparison: Conduct competitor comparison in the principal component space.

[0031] The covariance matrix calculation formula is: ; Among them, \(\sum\) is the covariance matrix, \(x_i\) is the eigenvector of the \(i\)-th sample, \(\bar{x}\) is the average value of the eigenvectors, and \(n\) is the number of samples.

[0032] The calculation formulas for eigenvalues and eigenvectors are: \(\det(\sum - \lambda I) = 0\); where \(\lambda\) is the eigenvalue and \(I\) is the identity matrix.

[0033] In a preferred embodiment, the steps for the intelligent recommendation sub-module to recommend products for a user through the item-based collaborative filtering algorithm include: S1. Collect user rating data: Construct a user-product rating matrix.

[0034] S2. Calculate product similarity: Calculate the similarity between products.

[0035] S3. Generate a recommendation list: Predict the ratings of unrated products for the user based on the user's historical ratings and product similarities, and recommend the products with the highest ratings; The cosine similarity calculation formula for product similarity is: ; Among them, \(\text{sim}(i,j)\) is the similarity between product \(i\) and \(j\), \(r_{ui}\) is the rating of product \(i\) by user \(u\), \(\bar{r}_i\) is the average rating of product \(i\), and \(U\) is the set of users who have rated product \(i\) or \(j\); The predicted score calculation predicts the score that a user gives to an unrated item based on the similarity of items and the user's historical scores. The calculation formula is as follows: ; Among them, r^ui is the predicted score that user u gives to item i, rˉu is the average score of user u, and N_i^k is the set of the k items most similar to item i.

[0036] In a preferred embodiment, the system integration and interface module first establishes a connection with the payment platform through the API interface and uses the HTTPS protocol to ensure the security of data transmission. Then, it defines the interface parameters and the return data format, such as the data structures of payment requests and responses. When the user completes the payment, the payment platform will notify the e-commerce platform of the payment result through a preset callback interface. The e-commerce platform updates the order status based on this information and notifies the user that the payment is successful.

[0037] When using the user interface module, after the user enters keywords in the search box, the system captures the input through the front-end JavaScript code and sends an asynchronous request (AJAX) to the back-end server. After the server processes the request, it returns a list of items that meet the conditions, and the front-end then renders this data onto the page and displays it to the user.

[0038] A method for constructing the ecosystem of an e-commerce intelligent operation management system includes the following steps: S1: Determine the data requirements, implement the data collection module, and obtain key business data from various data sources in real time, including user behavior, transaction records, product information, etc., and ensure the diversity and comprehensiveness of the data.

[0039] S2: Construct the data processing module, clean, transform, and integrate the collected data to ensure data quality and usability, and at the same time establish a data warehouse for subsequent data analysis and mining.

[0040] S3: Design the business processing module to automate the core business processes of the e-commerce platform, such as order management, inventory control, and customer service, to improve operation efficiency and reduce human errors.

[0041] S4: Develop the system integration and interface module to achieve seamless docking and data exchange with other systems, and ensure the information flow and collaborative work of the entire e-commerce ecosystem.

[0042] S5: Create the user interface module to provide an intuitive and easy-to-use operation interface, enabling the operation team to easily manage and monitor the e-commerce platform, and at the same time support customized reports and real-time data analysis to assist in decision-making.

[0043] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are: 1. In the present invention, by mining and analyzing a large amount of user behavior data and market trends, it is possible to provide accurate market positioning and strategic decision-making support for e-commerce platforms. It helps e-commerce platforms optimize resource allocation and improve operational efficiency by identifying user behavior patterns, predicting sales trends, and evaluating the effectiveness of marketing activities. By analyzing the user purchase path, e-commerce platforms can optimize product layouts, enhance the user experience, and thus increase conversion rates. In addition, the data analysis sub-module can also timely detect potential operational problems, such as inventory backlogs or supply chain bottlenecks, and take measures in advance to avoid losses.

[0044] 2. In the present invention, the user portrait analysis sub-module enables e-commerce platforms to implement personalized marketing and services and improve user satisfaction and loyalty by constructing detailed user portraits. The competitor analysis sub-module helps e-commerce platforms formulate effective competitive strategies and maintain market advantages by monitoring and analyzing the products, prices, marketing strategies, etc. of competitors. The intelligent recommendation sub-module provides accurate product recommendations for users based on user portraits and product similarities, which not only enhances the user shopping experience but also significantly increases cross-selling and repurchase rates. In summary, the effective operation of the data analysis and decision-making module enables e-commerce platforms to better understand the market and users, thus making more informed decisions and improving overall operational performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is the overall system block diagram of the present invention; Figure 2 is the internal system block diagram of the data analysis and decision-making module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] Embodiment: Referring to Figure 1-2 , an e-commerce intelligent operation management system, the system includes: a data collection module, a data processing module, a business processing module, a data analysis and decision-making module, a system integration and interface module, and a user interface module; The data analysis and decision-making module internally is provided with a data analysis sub-module, a user portrait analysis sub-module, a competitor analysis sub-module, and an intelligent recommendation sub-module; As the front-end antenna of the e-commerce intelligent operation management system, the data collection module is responsible for real-time capturing of raw data such as product information, user behavior, and market dynamics from various data sources and transmitting it to the data processing module.

[0048] After receiving the data, the data processing module transforms the raw data into high-quality and structured information through operations such as data cleaning, standardization, integration, and sampling, laying a solid foundation for subsequent business processing and data analysis.

[0049] The business processing module then executes daily operation tasks such as order management, customer service, and inventory control based on the processed data, and at the same time feeds back the business data to the data processing module for further processing.

[0050] The data analysis and decision-making module uses the processed data to provide decision support for the management through means such as descriptive analysis, association analysis, trend analysis, and user profiling analysis. The output results of this module directly affect the strategy adjustment of the business processing module.

[0051] As a bridge, the system integration and interface module is responsible for seamlessly connecting the e-commerce intelligent operation management system with external systems (such as payment, logistics, CRM, etc.) and other internal systems (such as finance, procurement, etc.), ensuring unobstructed data flow.

[0052] The user interface module provides an intuitive and easy-to-operate interface for operators, through which they can access the data analysis results, execute business operations, and monitor the system status.

[0053] The data collection module collects raw data from multiple data sources. First, it uses methods such as web crawlers, API interfaces, and log files to capture data such as user browsing behaviors, transaction records, and product information in real time. The data collection module uses web crawler technology to regularly capture product price information from e-commerce platforms. The crawler will visit web pages according to a preset list of URLs, parse the HTML content, extract the product price fields, and store them in the database. Next, these data are preliminarily formatted and classified to ensure the integrity and accuracy of the data, providing a basis for subsequent data processing.

[0054] The data processing module is responsible for cleaning, transforming, and integrating the collected raw data for subsequent analysis. First, data cleaning is performed, using regular expressions to remove invalid characters and using missing value handling to fill or delete missing data. Then, through data standardization methods such as min-max standardization, the data is scaled to a fixed range for easy comparison and analysis.

[0055] When the business processing module is in use and an order is created, the system will first verify the validity of the order information, such as checking whether the inventory quantity meets the order requirements. Then, through the order management process, the inventory records are updated, a shipping note is generated, and the logistics system is notified to prepare for shipment. For example, the transaction processing method is used to ensure the atomicity of order status updates and avoid data inconsistencies. At the same time, the business processing module will also trigger subsequent financial processes, such as invoice generation and payment confirmation, to ensure the smooth progress of the entire transaction process.

[0056] The data analysis sub-module divides the data points into K clusters through the K-means clustering algorithm, making the sum of the squared distances between each point and its assigned cluster center the smallest. The specific steps include: S1. Select the value of K: Determine the number of clusters to be divided.

[0057] S2. Initialize the center points: Randomly select K data points as the initial cluster centers.

[0058] S3. Assign data points: Assign each data point to the nearest cluster center.

[0059] S4. Update the cluster centers: Calculate the average value of the data points in each cluster and update the cluster centers.

[0060] S5. Repeat steps S3 and S4: Until the change in the cluster centers is less than a certain threshold or the preset number of iterations is reached; The formula for calculating the distance between a data point and a cluster center using the Euclidean distance formula is: ; where p and q are two data points, and n is the dimension of the data points; For each cluster k, the update formula for its center point is: ; where c_k is the center point of cluster k, S_k is the set of all data points in cluster k, and |S_k| is the number of data points in cluster k.

[0061] The user portrait analysis sub-module uses the decision tree algorithm to classify or predict data through a series of judgment rules, constructs a user portrait based on the user's purchase history, browsing behavior, and personal information, and predicts the user's purchase tendency. The steps include: S1. Data preparation: Collect and organize data such as the user's purchase records, browsing logs, and personal information.

[0062] S2. Feature selection: Select features related to the user's purchase behavior, such as purchase frequency, purchase amount, browsing duration, etc.

[0063] S3. Decision tree construction: Use the feature data to construct a decision tree model.

[0064] S4. User Classification: Classify users according to the decision tree.

[0065] S5. Portrait Generation: Generate portrait descriptions for users in each classification.

[0066] The information gain calculation formula for selecting the optimal feature is as follows: ; where IG(T, a) is the information gain given by feature a to set T, H(T) is the entropy of set T, and T_v is the subset of T where the value of feature a is v; The calculation formula for measuring the purity entropy of the data set is: where p_i is the probability that the i-th class of samples appears in set T.

[0067] The competitor analysis sub-module compares the performance of the products on the e-commerce platform with those of competitors in multiple features, including price, performance, and user ratings. The specific steps are as follows: S1. Data Collection: Collect multi-dimensional feature data of the e-commerce platform and competitors.

[0068] S2. Data Standardization: Standardize the data to the same scale.

[0069] S3. Covariance Matrix Calculation: Calculate the covariance matrix of the standardized data.

[0070] S4. Eigenvalue and Eigenvector Calculation: Calculate the eigenvalues and eigenvectors of the covariance matrix.

[0071] S5. Principal Component Selection: Select the principal component with the highest cumulative contribution rate.

[0072] S6. Competitor Comparison: Conduct competitor comparison in the principal component space.

[0073] The covariance matrix calculation formula is: ; where Σ is the covariance matrix, x_i is the feature vector of the i-th sample, xˉ is the average of the feature vectors, and n is the number of samples.

[0074] The calculation formulas for eigenvalues and eigenvectors are: det(Σ−λI)=0; where λ is the eigenvalue and I is the identity matrix The steps for the intelligent recommendation sub-module to recommend products for a user through the item-based collaborative filtering algorithm include: S1. Collect User Rating Data: Construct a user-product rating matrix.

[0075] S2. Calculate Product Similarity: Calculate the similarity between products.

[0076] S3. Generate a recommendation list: Based on the user's historical ratings and item similarity, predict the ratings of unrated items by the user, and recommend the items with the highest ratings. The cosine similarity calculation formula for item similarity is as follows: ; where sim(i, j) is the similarity between item i and j, r_ui is the rating of item i by user u, rˉi is the average rating of item i, and U is the set of users who have rated item i or j. The predicted rating is calculated based on item similarity and the user's historical ratings to predict the rating of an unrated item by the user. The calculation formula is as follows: ; where r^ui is the predicted rating of item i by user u, rˉu is the average rating of user u, and N_i^k is the set of the k items most similar to item i.

[0077] The system integration and interface module first establishes a connection with the payment platform through the API interface and uses the HTTPS protocol to ensure the security of data transmission. Then, it defines the interface parameters and the return data format, such as the data structures of payment requests and responses. When the user completes the payment, the payment platform will notify the e-commerce platform of the payment result through a preset callback interface. The e-commerce platform updates the order status based on this information and notifies the user that the payment is successful.

[0078] When the user interface module is in use, after the user enters keywords in the search box, the system captures the input through the front-end JavaScript code and sends an asynchronous request (AJAX) to the back-end server. After the server processes the request, it returns a list of items that meet the conditions, and the front-end then renders this data onto the page and displays it to the user.

[0079] A method for constructing the ecosystem of an e-commerce intelligent operation management system includes the following steps: S1: Determine the data requirements, implement the data collection module, and obtain key business data from various data sources in real time, including user behavior, transaction records, product information, etc., and ensure the diversity and comprehensiveness of the data.

[0080] S2: Construct the data processing module, clean, transform, and integrate the collected data to ensure data quality and usability, and at the same time establish a data warehouse for subsequent data analysis and mining.

[0081] S3: Design the business processing module to automate the core business processes of the e-commerce platform, such as order management, inventory control, and customer service, to improve operation efficiency and reduce human errors.

[0082] S4: Develop the system integration and interface module to achieve seamless docking and data exchange with other systems, ensuring information circulation and collaborative work across the entire e-commerce ecosystem.

[0083] S5: Create the user interface module to provide an intuitive and easy-to-use operation interface, enabling the operation team to easily manage and monitor the e-commerce platform. Meanwhile, support customized reports and real-time data analysis to assist in decision-making.

[0084] In this invention, by mining and analyzing a large amount of user behavior data and market trends, it is possible to provide accurate market positioning and strategic decision-making support for the e-commerce platform. It helps the e-commerce platform optimize resource allocation and improve operational efficiency by identifying user behavior patterns, predicting sales trends, and evaluating the effectiveness of marketing activities. By analyzing the user purchase path, the e-commerce platform can optimize the product layout, enhance the user experience, and thus increase the conversion rate. In addition, the data analysis sub-module can also promptly detect potential operational problems, such as inventory backlogs or supply chain bottlenecks, and take measures in advance to avoid losses.

[0085] In this invention, the user profile analysis sub-module enables the e-commerce platform to implement personalized marketing and services, improving user satisfaction and loyalty by constructing detailed user profiles. The competitor analysis sub-module helps the e-commerce platform formulate effective competitive strategies and maintain a market advantage by monitoring and analyzing the products, prices, marketing strategies, etc. of competitors. The intelligent recommendation sub-module provides accurate product recommendations for users based on user profiles and product similarities, which not only enhances the user shopping experience but also significantly increases cross-selling and repurchase rates. In summary, the effective operation of the data analysis and decision-making module enables the e-commerce platform to better understand the market and users, thus making more informed decisions and enhancing the overall operational performance.

[0086] 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" 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. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article, or device including the element.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An e-commerce intelligent operation management system, characterized in that: The system includes: a data collection module, a data processing module, a business processing module, a data analysis and decision-making module, a system integration and interface module, and a user interface module; Inside the data analysis and decision-making module, there are a data analysis sub-module, a user portrait analysis sub-module, a competitor analysis sub-module, and an intelligent recommendation sub-module; As the front-end antenna of the e-commerce intelligent operation management system, the data collection module is responsible for real-time capturing of raw data on product information, user behavior, and market dynamics from various data sources and transmitting it to the data processing module; After receiving the data, the data processing module converts the raw data into high-quality, structured information through data cleaning, standardization, integration, and sampling operations; The business processing module then executes daily operation tasks such as order management, customer service, and inventory control based on the processed data, and at the same time feeds back the business data to the data processing module for further processing; The data analysis and decision-making module uses the processed data to provide decision support for the management through descriptive analysis, association analysis, trend analysis, and user portrait analysis means; As a bridge, the system integration and interface module is responsible for seamlessly connecting the e-commerce intelligent operation management system with external systems and other internal systems; The user interface module provides an intuitive and easy-to-operate interface for operation personnel to access data analysis results, execute business operations, and monitor system status through this interface.

2. The e-commerce intelligent operation management system according to claim 1, characterized in that: The data collection module collects raw data from multiple data sources; first, it real-time captures user browsing behavior, transaction records, and product information data through web crawlers, API interfaces, and log files; the data collection module uses web crawler technology to regularly capture product price information from e-commerce platforms. The crawler will access web pages according to a preset URL list, parse the HTML content, extract the product price fields, and store them in the database; next, these data are preliminarily formatted and classified to ensure data integrity and accuracy, providing a basis for subsequent data processing.

3. The e-commerce intelligent operation management system according to claim 1, characterized in that: The data processing module is responsible for cleaning, transforming, and integrating the collected raw data for subsequent analysis; first, data cleaning is performed, using regular expressions to remove invalid characters and using missing value processing to fill or delete missing data; then the data is scaled to a fixed range through data standardization methods for easy comparison and analysis.

4. An e-commerce intelligent operation management system according to claim 1, characterized in that: When an order is created during the use of the business processing module, the system first verifies the validity of the order information, and then updates the inventory record, generates a shipping note, and notifies the logistics system to prepare for shipment through the order management process; the transaction processing method is used to ensure the atomicity of order status updates and avoid data inconsistencies; at the same time, the business processing module will also trigger subsequent financial processes.

5. An e-commerce intelligent operation management system according to claim 1, characterized in that: The data analysis sub-module divides data points into K clusters through the K-means clustering algorithm, minimizing the sum of the squared distances between each point and its assigned cluster center. The specific steps include: S1. Select the value of K: Determine the number of clusters to be divided; S2. Initialize the center points: Randomly select K data points as the initial cluster centers; S3. Assign data points: Assign each data point to the nearest cluster center; S4. Update cluster centers: Calculate the average of the data points in each cluster and update the cluster centers; S5. Repeat steps S3 and S4: Until the change in the cluster centers is less than a certain threshold or the preset number of iterations is reached; The formula for calculating the distance between a data point and the cluster center using the Euclidean distance formula is: ; where p and q are two data points, and n is the dimension of the data points; For each cluster k, the update formula for its center point is: ； where \(c_k\) is the center point of cluster k, \(S_k\) is the set of all data points in cluster k, and \(|S_k|\) is the number of data points in cluster k.

6. An e-commerce intelligent operation management system according to claim 1, characterized in that: The user portrait analysis sub-module uses the decision tree algorithm to classify or predict data through a series of judgment rules, constructs a user portrait based on the user's purchase history, browsing behavior, and personal information, and predicts the user's purchase tendency. The steps include: S1. Data preparation: Collect and organize the user's purchase records, browsing logs, and personal information data; S2. Feature selection: Select features related to the user's purchase behavior; S3. Decision tree construction: Use the feature data to construct a decision tree model; S4. User classification: Classify users according to the decision tree; S5. Portrait generation: Generate a portrait description for each classified user; The information gain calculation formula for selecting the optimal feature is: ； where \(IG(T, a)\) is the information gain given by feature a to set T, \(H(T)\) is the entropy of set T, and \(T_v\) is the subset of T where the value of feature a is v; The calculation formula for measuring the purity entropy of the data set is: Among them, \(p_i\) is the probability that the \(i\)-th type of sample appears in the set \(T\).

7. The e-commerce intelligent operation management system according to claim 1, wherein: The competitor analysis sub-module compares the performance of the products on the e-commerce platform with those of competitors in multiple characteristics, including price, performance, and user ratings. The specific steps are: S1. Data collection: Collect multi-dimensional characteristic data of the e-commerce platform and competitors; S2. Data standardization: Standardize the data to the same scale; S3. Covariance matrix calculation: Calculate the covariance matrix of the standardized data; S4. Eigenvalue and eigenvector calculation: Calculate the eigenvalues and eigenvectors of the covariance matrix; S5. Principal component selection: Select the principal component with the highest cumulative contribution rate; S6. Competitor comparison: Conduct competitor comparison in the principal component space; The covariance matrix calculation formula is: ； where \(\Sigma\) is the covariance matrix, \(x_i\) is the feature vector of the \(i\)-th sample, \(\bar{x}\) is the average of the feature vectors, and \(n\) is the number of samples; The calculation formulas for eigenvalues and eigenvectors are: \(det(\Sigma - \lambda I) = 0\); where \(\lambda\) is the eigenvalue and \(I\) is the identity matrix.

8. An e-commerce intelligent operation management system according to claim 1, characterized in that: The intelligent recommendation sub-module recommends products for a user through the item-based collaborative filtering algorithm. The steps include: S1. Collect user rating data: Construct a user-product rating matrix; S2. Calculate product similarity: Calculate the similarity between products; S3. Generate a recommendation list: Predict the user's rating of unrated products based on the user's historical ratings and product similarity, and recommend the products with the highest ratings; The cosine similarity calculation formula for product similarity calculation is: ； where \(sim(i, j)\) is the similarity between products \(i\) and \(j\), \(r_{ui}\) is the rating of product \(i\) by user \(u\), \(\bar{r}_i\) is the average rating of product \(i\), and \(U\) is the set of users who have rated product \(i\) or \(j\); The prediction score calculation predicts the score that a user gives to an unrated item based on the similarity of items and the user's historical scores. The calculation formula is as follows: ; Among them, \(r^{ui}\) is the predicted rating of user \(u\) for item \(i\), \(\bar{r}_u\) is the average rating of user \(u\), and \(N_i^k\) is the set of the \(k\) most similar items to item \(i\).

9. An e-commerce intelligent operation management system according to claim 1, characterized in that: The system integration and interface module first establishes a connection with the payment platform through the API interface and uses the HTTPS protocol to ensure the security of data transmission; then defines the interface parameters and the return data format; When the user interface module is in use, after the user enters keywords in the search box, the system captures the input through the front-end JavaScript code and sends an asynchronous request to the back-end server; after the server processes the request, it returns a list of items that meet the conditions, and the front-end then renders this data onto the page and displays it to the user.

10. A method for constructing the ecosystem of an e-commerce intelligent operation management system, characterized in that: It includes the following steps: S1: Determine the data requirements, implement the data collection module, and obtain key business data from various data sources in real time, including user behavior, transaction records, and product information, and ensure the diversity and comprehensiveness of the data; S2: Build the data processing module, clean, transform, and integrate the collected data to ensure data quality and usability, and at the same time establish a data warehouse for subsequent data analysis and mining; S3: Design the business processing module to automate the core business processes of the e-commerce platform, including order management, inventory control, and customer service, to improve operational efficiency and reduce human errors; S4: Develop the system integration and interface module to achieve seamless docking and data exchange with other systems, and ensure the information flow and collaborative work of the entire e-commerce ecosystem; S5: Create the user interface module to provide an intuitive and easy-to-use operation interface, enabling the operation team to easily manage and monitor the e-commerce platform, and at the same time support customized reports and real-time data analysis to assist in decision-making.

Images