An e-commerce operation data processing analysis interaction platform

Abstract

The application discloses an e-commerce operation data processing analysis interaction platform, and relates to the technical field of e-commerce platforms.The e-commerce operation data processing analysis interaction platform comprises a data interaction management center, which is communicatively connected with a data collection and integration module, a multi-dimensional evaluation system construction module, an evaluation index trend analysis module, an inventory management analysis module, a monitoring and early warning module and a data visualization module, wherein the modules are connected through electrical signals; the data collection and integration module is used for collecting multi-dimensional data of the e-commerce platform.The application can capture various key data on the e-commerce platform in real time through real-time data monitoring, and convert the key data into intuitive visual charts.According to preset early warning rules and thresholds, an early warning mechanism is automatically triggered.Once the data fluctuates abnormally, an early warning notice is sent to relevant personnel, so that the operation team can quickly respond to market changes and timely adjust the operation strategy, thereby significantly improving the operation efficiency of the e-commerce platform.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of e-commerce platforms, and in particular to an e-commerce operation data processing and analysis interactive platform. BACKGROUND

[0002] With the continuous progress of Internet technology and the change of consumer shopping habits, the e-commerce industry is showing an explosive growth trend, and the traditional offline shopping mode is gradually being replaced by online shopping. E-commerce platforms have become one of the main channels for consumers to shop. This trend has driven the demand for data processing and analysis of e-commerce enterprises to increase in order to better understand market dynamics, consumer behavior, and competitor situations. E-commerce platforms, as a huge data source, generate massive amounts of data every day, including user behavior data, transaction data, product data, etc. These data contain huge commercial value and can help enterprises gain a deep understanding of the market, analyze user needs, and optimize operation and sales strategies.

[0003] In the prior art, the operation status of e-commerce platforms is easily affected by changes in market environment, making it difficult to identify business fluctuations in a timely manner, which affects the accuracy of operation early warning. Therefore, how to comprehensively evaluate multi-dimensional evaluation indicators to improve the evaluation accuracy of e-commerce operation status and respond to potential market risks in advance is a problem we need to solve. For this reason, an e-commerce operation data processing and analysis interactive platform is proposed. SUMMARY

[0004] The present application aims to provide an e-commerce operation data processing and analysis interactive platform to solve the problems raised in the background art.

[0005] To solve the above technical problems, the technical solution adopted by the present application is:

[0006] An e-commerce operation data processing and analysis interactive platform, comprising a data interaction management center, the data interaction management center being communicatively connected with a data collection and integration module, a multi-dimensional evaluation system construction module, an evaluation index trend analysis module, a inventory management analysis module, a monitoring and early warning module, and a data visualization module, wherein the modules are electrically connected;

[0007] The data collection and integration module is used to collect multi-dimensional data of e-commerce platforms, including user behavior data (browsing, searching, purchasing, etc.), transaction data, inventory data, advertising data, product information, and customer service data, and to integrate and obtain operation data sequences;

[0008] The multi-dimensional evaluation system construction module, based on e-commerce operation requirements, clearly defines multi-dimensional evaluation indicators for operation status evaluation, including transaction evaluation indicators and user activity indicators, providing comprehensive and in-depth operation status evaluation to help operators quickly identify business fluctuations and potential risks;

[0009] The evaluation index trend analysis module is used for analyzing the multi-dimensional evaluation indexes of the operation state evaluation one by one, and determining the change trend of each evaluation index of the operation state;

[0010] The inventory management analysis module monitors and analyzes inventory data, analyzes sales demand trends, optimizes inventory management strategies, reduces inventory costs, improves inventory turnover rates, and ensures the timeliness and stability of commodity supply;

[0011] The monitoring and early warning module monitors and analyzes real-time data, combines the change trend of each evaluation index and the inventory analysis result, identifies the abnormality and risk of e-commerce operation, and triggers the early warning mechanism to identify abnormal fluctuations in advance and take measures to reduce risks;

[0012] The data visualization module is used for displaying the analysis and early warning results through data visualization tools, helping managers quickly understand the business status, regularly generating data reports, providing decision support for management, and helping to comprehensively understand various data indicators of e-commerce operation.

[0013] The further improvement of the technical scheme of the present application is that the data collection and integration module specifically comprises:

[0014] According to the needs of e-commerce operation, the types of data to be collected are determined, including user behavior data, transaction data, inventory data, advertising data, product information and customer service data;

[0015] Based on the data structure of the target e-commerce platform, configure data collection parameters and execute data collection tasks, and obtain the latest data from each data source of e-commerce operation through API interface;

[0016] The collected data is preprocessed, including data cleaning, data conversion and data summarization steps, wherein the repeated records are removed, the error data is corrected, and the missing values are filled through data cleaning, the data is converted to a unified format and standard through data conversion, and the data from different sources is merged to facilitate subsequent processing, improve the quality and consistency of the data, and reduce the analysis deviation caused by data problems;

[0017] According to the unique identifier of the user ID, the information from different data sources is associated, and the associated data is merged into a comprehensive data set to form a complete operation data sequence;

[0018] Based on the MySQL relational database, a data warehouse is built to store e-commerce operation data related to the operation data sequence, and a data backup plan is made to regularly back up data.

[0019] The further improvement of the technical scheme of the present application is that the multi-dimensional evaluation system construction module specifically comprises:

[0020] The related data of the operation data sequence is traversed, and the multi-dimensional evaluation indexes of the operation situation evaluation are determined in combination with the e-commerce operation demand, including transaction evaluation indexes and user activity indexes;

[0021] The transaction evaluation indexes are analyzed, the sub-transaction evaluation indexes including order conversion rate, order transaction volume, return rate and repeat purchase rate are determined, and the standard values of the sub-transaction evaluation indexes are preset in combination with the transaction evaluation index data of the last evaluation period, wherein the order conversion rate is the proportion of the users completing purchase among the users accessing the website, the order transaction volume is the total number of orders completed in the evaluation period, the return rate is the proportion of orders returned in the evaluation period, and the repeat purchase rate is the proportion of users repeatedly purchasing in the evaluation period;

[0022] The user activity indexes are analyzed, the sub-user activity indexes including user average access frequency, new user growth rate and active user number are determined, and the standard values of the sub-user activity indexes are preset in combination with the user activity index data of the last evaluation period, wherein the user average access frequency is the average access number of each user in the evaluation period, the new user growth rate is the growth speed of the new users in the evaluation period, and the active user number is the number of users having activity records in the evaluation period;

[0023] Based on the determined sub-transaction evaluation indexes and sub-user activity indexes, the associated data of the sub-transaction evaluation indexes and the sub-user activity indexes of the current evaluation period and the last evaluation period is grabbed from the operation data sequence.

[0024] The further improvement of the technical scheme of the present application is that the evaluation index trend analysis module specifically comprises:

[0025] According to the multi-dimensional evaluation index data of the current evaluation period and the last evaluation period grabbed, comparative analysis is respectively performed;

[0026] For the transaction evaluation indexes, the sub-transaction evaluation indexes of the two evaluation periods and the preset standard values of the sub-transaction evaluation indexes are analyzed, the transaction situation evaluation index is calculated, and the change trend of the transaction evaluation indexes of the current evaluation period is analyzed;

[0027] For the user activity indexes, the sub-user activity indexes of the two evaluation periods and the preset standard values of the sub-user activity indexes are analyzed, the user activity evaluation index is calculated, and the change trend of the user activity indexes of the current evaluation period is analyzed;

[0028] The values of the transaction evaluation indexes and the user activity indexes in the current evaluation period and the last evaluation period are plotted into a time sequence graph, and the change trend is intuitively displayed, so that the upward, downward or stable trend of each index is identified.

[0029] A further improvement to the technical solution of this invention is that the expression for the transaction status assessment index is:

[0030] ;

[0031] In the formula, As an index for assessing transaction conditions, For the current evaluation period, the [number]th The actual values ​​of individual transaction evaluation indicators For the preset first Standard values ​​for individual transaction evaluation indicators This serves as an index for sub-transaction evaluation metrics. These represent order conversion rate, order volume, return rate, and repurchase rate, respectively. The order conversion rate for the current evaluation period. This refers to the order volume during the current evaluation period. The return rate for the current assessment period. The repurchase rate for the current evaluation period. The preset order conversion rate standard value, The preset standard value for order volume. The preset return rate standard value, The preset repurchase rate standard value, The value range is between 0 and 1;

[0032] The expression for the user activity assessment index is:

[0033] ;

[0034] In the formula, Assess user activity index For the current evaluation period, the [number]th The actual value of each sub-user activity metric For the preset first Standard values ​​for individual user activity metrics An index for sub-user activity metrics. These represent the average frequency of user visits, the growth rate of new users, and the number of active users, respectively. This represents the average frequency of user visits during the current evaluation period. The new user growth rate for the current evaluation period. The number of active users in the current evaluation period. The preset average user access frequency standard value, The preset new user growth rate standard value, The preset standard value for the number of active users, The value range is between 0 and 1.

[0035] A further improvement to the technical solution of this invention lies in that: the inventory management analysis module specifically includes:

[0036] Extract inventory data from the current assessment period and the previous period from the data warehouse, including inventory quantity, inbound records, outbound records, and product sales data, and calculate the current inventory quantity, classifying and summarizing it by inventory holding unit;

[0037] Analyze the inventory changes between the previous and current assessment periods, analyze the inventory turnover rate of each inventory unit, identify long-term unsold inventory units, mark them, gain a comprehensive understanding of the current inventory status, identify potential problems, and provide a basis for subsequent optimization measures.

[0038] Based on inventory changes, analyze the sales volume and average inventory level of each inventory holding unit within the current assessment period, and calculate the inventory trend index by combining the average sales volume of all inventory holding units. Analyze sales demand trends, identify shortcomings in inventory management, and if some inventory holding units have low inventory turnover and large fluctuations in sales volume, it indicates that the inventory holding unit may have an inventory backlog problem. If some inventory holding units have high inventory turnover but are frequently out of stock, it indicates that the inventory holding unit may need to increase inventory or adjust its replenishment strategy.

[0039] Based on the analysis of sales demand trends and inventory levels, optimize inventory management strategies. For identified slow-moving products, propose handling suggestions including promotional activities, discounts, or returns to suppliers. For high-turnover products, prioritize ensuring sufficient inventory to reduce the risk of stockouts.

[0040] A further improvement to the technical solution of this invention lies in the fact that the expression for the inventory trend index is:

[0041] ;

[0042] In the formula, For inventory trend index, The number of units held in inventory. For the first The number of sales per inventory unit during the assessment period. For the first The average inventory level of each inventory unit during the assessment period. The average sales quantity of all inventory units held during the assessment period. The value of is between 0 and 1. When the sales quantity of each inventory unit is close to its average inventory level and the sales quantity fluctuates little, each term in the formula will approach 1. Approaching 1.

[0043] The further improvement of the technical scheme of the present application is that the monitoring and early warning module specifically comprises:

[0044] Real-time extraction of the latest data from various data sources of the e-commerce platform, and obtaining of transaction condition evaluation indexes, user activity evaluation indexes and inventory trend indexes in the current evaluation period, analysis of the change trend of each index, and comprehensive understanding of the overall operation status of the e-commerce platform;

[0045] According to the importance and influence degree of each index, different weights are assigned to the transaction condition evaluation indexes, the user activity evaluation indexes and the inventory trend indexes, and the transaction condition evaluation indexes, the user activity evaluation indexes and the inventory trend indexes are combined with their respective weights to calculate an abnormal early warning coefficient;

[0046] Based on the analysis result of the abnormal early warning coefficient in the last evaluation period, a warning threshold T is set, the abnormal early warning coefficient is compared with the warning threshold, and the risk level of the current operation status is determined;

[0047] When the abnormal early warning coefficient deviates from the warning threshold, an early warning mechanism is automatically triggered, early warning notices are sent to relevant personnel in the form of emails, short messages and message centers, the early warning notices contain early warning names, early warning details and early warning time information, after receiving the early warning notices, the relevant personnel quickly analyze the problem causes, formulate targeted solutions according to the problem causes, and implement them as soon as possible.

[0048] The further improvement of the technical scheme of the present application is that the expression of the abnormal early warning coefficient is:

[0049] ;

[0050] In the formula, is the abnormal early warning coefficient, is the transaction condition evaluation index in the current period, is the transaction condition evaluation index standard value in the last evaluation period, is the weight of the transaction condition evaluation index, reflecting its importance in the overall risk assessment, is the user activity evaluation index in the current period, is the user activity evaluation index standard value in the last evaluation period, is the weight of the user activity evaluation index, reflecting its importance in the overall risk assessment, is the inventory trend index in the current period, is the inventory trend index standard value in the last evaluation period, is the weight of the inventory trend index, reflecting its importance in the overall risk assessment, is an adjustment factor, taking a positive value, used to control the change rate of the index function, The value range of the transaction condition evaluation index is between 0 and 1, when each index is very close to the standard value of the last evaluation period, each item in the formula will tend to 1, so Close to 1, indicating that the operation state is very ideal.

[0051] Further improvement of the technical scheme of the application is that the data visualization module specifically includes

[0052] Obtain the relevant data of the transaction condition evaluation index, user activity evaluation index and inventory trend index of the current evaluation period, and use Tableau to create a comprehensive dashboard to centrally display the index-related data and charts, highlight the abnormal points in the current evaluation period, and provide detailed information and suggestions;

[0053] Add a filter to the dashboard to allow users to select the corresponding time period, commodity category and region conditions, dynamically update the chart content, and support users to click the data points in the chart to further view detailed sub-data or associated data;

[0054] According to the business requirements, set the generation cycle of the operation report, including the summary part, detailed analysis part and early warning and suggestion part, use the automatic report generation function of Tableau to automatically generate and send the report.

[0055] Due to the adoption of the above technical scheme, the application has the following technical progress compared with the prior art:

[0056] 1. The application provides an e-commerce operation data processing and analysis interactive platform, which can capture various key data on the e-commerce platform in real time through real-time data monitoring, and convert it into intuitive visual charts, automatically trigger the early warning mechanism according to the preset early warning rules and threshold, and send early warning notifications to relevant personnel once the data appears abnormal fluctuation, so that the operation team can quickly respond to market changes and timely adjust the operation strategy, thereby significantly improving the operation efficiency of the e-commerce platform.

[0057] 2. The application provides an e-commerce operation data processing and analysis interactive platform, which can obtain the latest business data in real time through the construction of a comprehensive data processing and analysis interactive platform, and make decisions quickly, according to the abnormal early warning coefficient calculated comprehensively, help to identify potential operation risks, and automatically trigger the early warning mechanism combined with the detected abnormal fluctuation or risk, to ensure the smooth operation of the e-commerce platform. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0059] Figure 1 The system function module schematic diagram of the present application;

[0060] Figure 2 The workflow schematic diagram of the evaluation index trend analysis module of the present application;

[0061] Figure 3 The workflow schematic diagram of the monitoring and early warning module of the present application. DETAILED DESCRIPTION

[0062] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0063] Embodiment 1, as shown in Figure 1 , Figure 2 The present application provides an e-commerce operation data processing and analysis interactive platform, which comprises a data interaction management center, a data collection and integration module, a multi-dimensional evaluation system construction module, an evaluation index trend analysis module, an inventory management analysis module, a monitoring and early warning module and a data visualization module are communicatively connected to the data interaction management center, wherein the modules are connected by electrical signals.

[0064] The data collection and integration module is used to collect various dimension data of the e-commerce platform, including user behavior data (browsing, searching, purchasing, etc.), transaction data, inventory data, advertising data, product information, and customer service data, and to integrate and obtain operation data sequences. According to the needs of e-commerce operation, the types of data to be collected are determined, including user behavior data, transaction data, inventory data, advertising data, product information, and customer service data. Based on the data structure of the target e-commerce platform, data collection parameters are configured and data collection tasks are executed. The latest data is obtained in real time from various data sources of e-commerce operation through API interface. The collected data is preprocessed, including data cleaning, data conversion, and data aggregation. Through data cleaning, duplicate records are removed, incorrect data is corrected, and missing values are filled. Through data conversion, the data is converted to a unified format and standard, and data from different sources is merged to facilitate subsequent processing, improve data quality and consistency, reduce analysis bias caused by data problems, and associate information from different data sources based on the unique identifier of the user ID. The associated data is merged into a comprehensive data set to form a complete operation data sequence. Based on the MySQL relational database, a data warehouse is built to store e-commerce operation data related to operation data sequences, and a data backup plan is developed for regular data backup.

[0065] The multi-dimensional evaluation system construction module is based on the needs of e-commerce operation to determine the multi-dimensional evaluation indicators for operation status evaluation, including transaction evaluation indicators and user activity indicators, to provide comprehensive and in-depth operation status evaluation, and to help operators quickly identify business fluctuations and potential risks. The relevant data of the operation data sequence is traversed, and the multi-dimensional evaluation indicators for operation status evaluation are determined based on the needs of e-commerce operation, including transaction evaluation indicators and user activity indicators. The transaction evaluation indicators are analyzed to determine sub-transaction evaluation indicators, including order conversion rate, order transaction volume, return rate, and repeat purchase rate. The standard values of each sub-transaction evaluation indicator are preset based on the transaction evaluation indicator data of the previous evaluation period. The user activity indicators are analyzed to determine sub-user activity indicators, including user average visit frequency, new user growth rate, and active user number. The standard values of each sub-user activity indicator are preset based on the user activity indicator data of the previous evaluation period. Based on the determined sub-transaction evaluation indicators and sub-user activity indicators, the associated data of the sub-transaction evaluation indicators and sub-user activity indicators of the current evaluation period and the previous evaluation period is grabbed from the operation data sequence.

[0066] The evaluation indicator trend analysis module is used to analyze the multi-dimensional evaluation indicators of operational status one by one, determine the changing trend of each evaluation indicator of operational status, and conduct comparative analysis based on the multi-dimensional evaluation indicator data captured in the current evaluation period and the previous evaluation period. For transaction evaluation indicators, it analyzes the sub-transaction evaluation indicators of the two evaluation periods and the preset standard values ​​of each sub-transaction evaluation indicator, calculates the transaction status evaluation index, and analyzes the changing trend of transaction evaluation indicators in the current evaluation period. For user activity indicators, it analyzes the sub-user activity indicators of the two evaluation periods and the preset standard values ​​of each sub-user activity indicator, calculates the user activity evaluation index, and analyzes the changing trend of user activity indicators in the current evaluation period. The values ​​of transaction evaluation indicators and user activity indicators in the current evaluation period and the previous evaluation period are plotted into time series graphs to intuitively display their changing trends and identify the upward, downward or stable trends of each indicator.

[0067] Furthermore, the expression for the transaction condition assessment index is as follows:

[0068] ;

[0069] In the formula, As an index for assessing transaction conditions, For the current evaluation period, the [number]th The actual values ​​of individual transaction evaluation indicators For the preset first Standard values ​​for individual transaction evaluation indicators This serves as an index for sub-transaction evaluation metrics. These represent order conversion rate, order volume, return rate, and repurchase rate, respectively. The order conversion rate for the current evaluation period. This refers to the order volume during the current evaluation period. The return rate for the current assessment period. The repurchase rate for the current evaluation period. The preset order conversion rate standard value, The preset standard value for order volume. The preset return rate standard value, The preset repurchase rate standard value, The value range is between 0 and 1. When each sub-transaction evaluation indicator is very close to its preset standard value, each term in the formula will approach 1. A value close to 1 indicates a very ideal trading situation. When one or more sub-trading evaluation indicators deviate from their preset standard values, the corresponding terms in the formula will approach 0, leading to... A value close to 0 indicates a poor trading situation;

[0070] The expression of the user activity evaluation index is:

[0071] ;

[0072] In the formula, is the user activity evaluation index, is the actual value of the i-th sub-user activity index in the current evaluation period, is the preset standard value of the i-th sub-user activity index, is the index of the sub-user activity index, , respectively represent the user average access frequency, the new user growth rate, and the active user number, is the user average access frequency in the current evaluation period, is the new user growth rate in the current evaluation period, is the active user number in the current evaluation period, is the preset user average access frequency standard value, is the preset new user growth rate standard value, is the preset active user number standard value, the value range of is between 0 and 1, when each sub-user activity index is very close to its preset standard value, and the actual value is not lower than the standard value, each term in the formula will tend to 1, so that is close to 1, indicating that the user activity is very ideal, when one or more sub-user activity indexes deviate from their preset standard values, or the actual values are lower than the standard values, the corresponding terms in the formula will tend to 0, resulting in is close to 0, indicating that the user activity is poor;

[0073] ​​The inventory management analysis module monitors and analyzes inventory data, analyzes sales demand trends, optimizes inventory management strategies, reduces inventory costs, improves inventory turnover rates, ensures the timeliness and stability of commodity supply, extracts current evaluation period and last period inventory data from the data warehouse, including inventory quantity, warehouse-in records, warehouse-out records, commodity sales data, etc., and counts the current inventory quantity, classifies and summarizes it by inventory holding units, analyzes the inventory changes in the last evaluation period and the current evaluation period, and analyzes the inventory turnover rate of each inventory holding unit, identifies long-term unsold commodity units in inventory, and then marks them, comprehensively understands the current inventory status, identifies potential problems, and provides a basis for subsequent optimization measures, according to the inventory changes, analyzes the sales quantity and average inventory quantity of each inventory holding unit in the current evaluation period, and calculates the inventory trend index combined with the average sales quantity of all inventory holding units, analyzes the sales demand trend, and identifies the short board of inventory management, if the inventory turnover rate of some inventory holding units is low and the sales quantity fluctuates greatly, it means that there may be inventory accumulation problems in this inventory holding unit, if the inventory turnover rate of some inventory holding units is high but often out of stock, it means that this inventory holding unit may need to increase inventory or adjust the replenishment strategy, based on the analysis results of sales demand trend and inventory level, optimize the inventory management strategy, for the identified slow-moving products, propose processing suggestions including promotion activities, discount sales or return to suppliers, for high turnover rate commodities, preferentially ensure sufficient inventory to reduce the risk of stockout;

[0074] Further, the expression of the inventory trend index is:

[0075] ;

[0076] In the formula, is the inventory trend index, is the number of inventory holding units, is the sales quantity of the th inventory holding unit in the evaluation period, is the average inventory quantity of the th inventory holding unit in the evaluation period, is the average sales quantity of all inventory holding units in the evaluation period, The value range of is between 0 and 1, when the sales quantity of each inventory holding unit is close to its average inventory quantity, and the sales quantity fluctuation is small, each term in the formula will tend to 1, so tends to 1, indicating that the inventory management is in very good condition, when the sales quantity of one or more inventory holding units is much lower than its average inventory quantity, or the sales quantity fluctuation is large, the corresponding term in the formula will tend to 0, resulting in tends to 0, indicating that the inventory management is poor;

[0077] The monitoring and early warning module monitors and analyzes real-time data, combines the changing trends of various evaluation indicators with inventory analysis results, identifies anomalies and risks in e-commerce operations, and triggers an early warning mechanism to identify abnormal fluctuations in advance and take measures to reduce risks quickly.

[0078] The data visualization module is used to display the analysis and early warning results through data visualization tools, helping managers to quickly understand the current business situation, generate data reports regularly, provide decision support for management, and help them fully understand various data indicators of e-commerce operations.

[0079] Example 2, as Figure 3 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, the monitoring and early warning module specifically includes:

[0080] The system extracts the latest data in real time from various data sources on the e-commerce platform and obtains the transaction status assessment index, user activity assessment index, and inventory trend index for the current assessment period. It analyzes the changing trends of each index to gain a comprehensive understanding of the overall operation of the e-commerce platform. Based on the importance and impact of each index, different weights are assigned to them. The transaction status assessment index, user activity assessment index, and inventory trend index are combined with their respective weights to calculate the anomaly warning coefficient. Based on the analysis results of the anomaly warning coefficient of the previous assessment period, a warning threshold T is set. The anomaly warning coefficient is compared with the warning threshold to determine the risk level of the current operation. When the anomaly warning coefficient deviates from the warning threshold, the warning mechanism is automatically triggered, and a warning notification is sent to relevant personnel via email, SMS, and message center. The warning notification includes the warning name, warning details, and warning time information. After receiving the warning notification, relevant personnel quickly analyze the cause of the problem, formulate targeted solutions based on the cause, and implement them as soon as possible.

[0081] Furthermore, the expression for the abnormal warning coefficient is:

[0082] ;

[0083] In the formula, This is the abnormal warning coefficient. An index used to assess the trading conditions of the current period. The standard value of the transaction status assessment index for the previous assessment period. The weighting of the trading condition assessment index reflects its importance in the overall risk assessment. This is a user activity assessment index for the current period. The standard value of the user activity evaluation index for the previous evaluation period. The weighting of the user activity assessment index reflects its importance in the overall risk assessment. the inventory trend index for the current period, the inventory trend index standard value for the last evaluation period, the weight of the inventory trend index, reflecting its importance in the overall risk assessment, the adjustment factor, taking a positive value, used to control the rate of change of the exponential function, the value range of is between 0 and 1, when each index is very close to its standard value in the last evaluation period, each term in the formula will tend to 1, so close to 1, indicating that the operation state is very ideal, when one or more indexes are much lower than its standard value in the last evaluation period, the corresponding term in the formula will tend to 0, resulting in close to 0, indicating that there is a greater risk of operation;

[0084] The data visualization module specifically includes:

[0085] Obtain the relevant data of the transaction condition evaluation index, user activity evaluation index and inventory trend index for the current evaluation period, and use Tableau to create a comprehensive dashboard that centrally displays the index-related data and charts, highlights the abnormal points in the current evaluation period, provides detailed information and recommended measures, adds filters in the dashboard to allow users to select the corresponding time period, commodity category and regional conditions, dynamically update the chart content, and support users to click the data points in the chart to further view detailed sub-data or associated data, set the generation period of the operation report according to business needs, including the overview section, detailed analysis section and early warning and suggestion section, use the automatic report generation function of Tableau to automatically generate and send the report.

[0086] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An e-commerce operation data processing, analysis, and interaction platform, comprising a data interaction management center, characterized in that: The data interaction management center has communication connections to a data acquisition and integration module, a multi-dimensional evaluation system construction module, an evaluation indicator trend analysis module, an inventory management analysis module, a monitoring and early warning module, and a data visualization module, wherein the modules are connected by electrical signals. The data acquisition and integration module is used to collect data from various dimensions of the e-commerce platform and integrate and obtain operational data sequences. The multi-dimensional evaluation system construction module, based on e-commerce operation needs, clarifies multi-dimensional evaluation indicators for operational status assessment, namely transaction evaluation indicators and user activity indicators, specifically including: Traverse the relevant data in the operational data sequence and, in conjunction with the needs of e-commerce operations, clarify the multi-dimensional evaluation indicators for operational status assessment, including transaction evaluation indicators and user activity indicators; Analyze the transaction evaluation indicators to determine sub-transaction evaluation indicators, including order conversion rate, order volume, return rate, and repurchase rate. Combine the transaction evaluation indicator data from the previous evaluation period to preset the standard values ​​for each sub-transaction evaluation indicator. Analyze user activity metrics to determine sub-user activity metrics including average user access frequency, new user growth rate, and number of active users, and preset standard values ​​for each sub-user activity metric based on user activity metric data from the previous evaluation period. Based on the determined sub-transaction evaluation indicators and sub-user activity indicators, the correlation data of the sub-transaction evaluation indicators and sub-user activity indicators between the current evaluation period and the previous evaluation period are extracted from the operational data sequence. The evaluation indicator trend analysis module is used to analyze the multi-dimensional evaluation indicators of operational status one by one, and determine the changing trends of each evaluation indicator of operational status, specifically including: Comparative analysis is conducted based on the multi-dimensional evaluation indicator data captured for the current evaluation period and the previous evaluation period. For transaction evaluation indicators, analyze the sub-transaction evaluation indicators of the two evaluation periods and the preset standard values ​​of each sub-transaction evaluation indicator, calculate the transaction status evaluation index, and analyze the changing trend of transaction evaluation indicators in the current evaluation period. For user activity metrics, analyze the sub-user activity metrics of each sub-user activity metric in two evaluation periods and the preset standard values ​​of each sub-user activity metric, calculate the user activity evaluation index, and analyze the changing trend of user activity metrics in the current evaluation period. The transaction evaluation indicators and user activity indicators are plotted as a time series graph between the current evaluation period and the previous evaluation period to visually show their changing trends and identify whether each indicator is rising, falling or remaining stable. The expression for the transaction status assessment index is as follows: In the formula, As an index for assessing transaction conditions, For the current evaluation period, the [number]th The actual values ​​of individual transaction evaluation indicators For the preset first Standard values ​​for individual transaction evaluation indicators This serves as an index for sub-transaction evaluation metrics. These represent order conversion rate, order volume, return rate, and repurchase rate, respectively. The value range is between 0 and 1; The expression for the user activity assessment index is: In the formula, Assess user activity index For the current evaluation period, the [number]th The actual value of each sub-user activity metric For the preset first Standard values ​​for individual user activity metrics An index for sub-user activity metrics. These represent the average frequency of user visits, the growth rate of new users, and the number of active users, respectively. The value range is between 0 and 1; The inventory management and analysis module monitors and analyzes inventory data, analyzes sales demand trends, and optimizes inventory management strategies. The monitoring and early warning module monitors and analyzes real-time data, combines the changing trends of various evaluation indicators with inventory analysis results, identifies anomalies and risks in e-commerce operations, and triggers an early warning mechanism. The data visualization module is used to display the analysis and early warning results through data visualization tools.

2. The e-commerce operation data processing, analysis, and interaction platform according to claim 1, characterized in that: The data acquisition and integration module specifically includes: Based on the needs of e-commerce operations, the types of data to be collected should be clearly defined, including user behavior data, transaction data, inventory data, advertising data, product information, and customer service data. Based on the data structure of the target e-commerce platform, configure data collection parameters and execute data collection tasks to obtain the latest data in real time from data sources of various data types in e-commerce operations through API interfaces; The collected data is preprocessed, including data cleaning, data transformation, and data summarization. Information from different data sources is linked based on the unique identifier of the user ID, and the linked data is merged into a comprehensive dataset to form a complete operational data sequence; A data warehouse is built based on a MySQL relational database to store e-commerce operational data related to operational data sequences, and a data backup plan is developed to perform regular data backups.

3. The e-commerce operation data processing, analysis, and interaction platform according to claim 1, characterized in that: The inventory management analysis module specifically includes: Extract inventory data from the current assessment period and the previous period from the data warehouse, including inventory quantity, inbound records, outbound records, and product sales data, and calculate the current inventory quantity, classifying and summarizing it by inventory holding unit; Analyze the inventory changes between the previous assessment period and the current assessment period, analyze the inventory turnover rate of each inventory unit, identify long-term unsold commodity units in the inventory, and then mark them. Based on inventory changes, analyze the sales volume and average inventory level of each inventory unit within the current assessment period, and calculate the inventory trend index by combining the average sales volume of all inventory units to analyze sales demand trends and identify shortcomings in inventory management. Based on the analysis of sales demand trends and inventory levels, optimize inventory management strategies.

4. The e-commerce operation data processing, analysis, and interaction platform according to claim 3, characterized in that: The expression for the inventory trend index is: In the formula, For inventory trend index, The number of units held in inventory. For the first The number of sales per inventory unit during the assessment period. For the first The average inventory level of each inventory unit during the assessment period. The average sales quantity of all inventory units held during the assessment period. The value range is between 0 and 1.

5. The e-commerce operation data processing, analysis, and interaction platform according to claim 4, characterized in that: The monitoring and early warning module specifically includes: The latest data is extracted in real time from various data sources of e-commerce platforms, and the transaction status assessment index, user activity assessment index and inventory trend index for the current assessment period are obtained. The changing trends of each index are analyzed to gain a comprehensive understanding of the overall operation of the e-commerce platform. Based on the importance and impact of each index, different weights are assigned to them. The transaction status assessment index, user activity assessment index, and inventory trend index are combined with their respective weights to calculate the abnormal warning coefficient. Based on the analysis results of the abnormal warning coefficient in the previous assessment period, a warning threshold T is set, and the abnormal warning coefficient is compared with the warning threshold to determine the risk level of the current operation status. When the abnormal warning coefficient deviates from the warning threshold, the warning mechanism is automatically triggered, and warning notifications are sent to relevant personnel via email, SMS and message center.

6. The e-commerce operation data processing, analysis, and interaction platform according to claim 5, characterized in that: The expression for the abnormal warning coefficient is: In the formula, This is the abnormal warning coefficient. An index used to assess the trading conditions of the current period. The standard value of the transaction status assessment index for the previous assessment period. The weighting of the index to assess trading conditions. This is a user activity assessment index for the current period. The standard value of the user activity evaluation index for the previous evaluation period. The weighting of the index for evaluating user activity. This is the inventory trend index for the current cycle. The standard value of the inventory trend index for the previous assessment period. As the weight of the inventory trend index, This is a positive adjustment factor used to control the rate of change of the exponential function. The value range is between 0 and 1.

7. The e-commerce operation data processing, analysis, and interaction platform according to claim 6, characterized in that: The data visualization module specifically includes: Obtain relevant data on the transaction status assessment index, user activity assessment index, and inventory trend index for the current assessment period, and use Tableau to create a comprehensive dashboard that centrally displays relevant data and charts for each index, highlights outliers in the current assessment period, and provides detailed information and suggested measures. Add filters to the dashboard to allow users to select the corresponding time period, product category and regional conditions, dynamically update the chart content, and support users to click on data points in the chart to view more detailed sub-data or related data; Set the generation cycle of operation reports according to business needs, including an overview section, a detailed analysis section, and an alert and recommendation section. Utilize Tableau's automatic report generation function to automatically generate and send reports.

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