A cross-border e-commerce comprehensive intelligent service method based on multi-dimensional data analysis

Abstract

The present application provides a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis, including: obtaining sales data and return data of different regions in different time periods, including associated sales channels and associated product categories, and calculating the return volume and sales volume of each region in different time periods; classifying the sales data and return data according to the storage conditions corresponding to the associated product categories, each category corresponding to a warehouse category, and calculating the total return volume and the total sales volume corresponding to different warehouse categories; judging whether to establish a target warehouse in a target area according to the return volume and sales volume corresponding to the target warehouse category; for a recommended area identified as a recommended area for establishing a target warehouse in the target area, obtaining cross-border express delivery fees and warehousing cost data of the recommended area and adjacent areas, and evaluating the establishment cost of the target warehouse in the recommended area and adjacent areas.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis. Background Art

[0002] Cross-border e-commerce companies face the challenge of reverse logistics management for returns in areas with high return rates but low sales rates. The sales volume in the area is not enough to support the establishment of local warehousing, but the high return rate leads to a large number of returned goods that need to be processed. Shipping returned goods back to remote warehousing will increase logistics costs and time, affecting return efficiency; processing returns locally lacks suitable warehousing facilities and human resources, affecting return quality and speed. The core of this contradiction is that the business volume of cross-border e-commerce companies in the region is not enough to balance the costs and benefits of return logistics. Return logistics requires a lot of resources, including storage space, manual processing, transportation arrangements, etc., but due to low sales volume, these investments cannot be balanced by positive sales revenue. The long retention time of returned goods in the local area will also affect the secondary sales value of the goods, further increasing the losses of cross-border e-commerce companies. Cross-border e-commerce companies need to consider factors such as return volume, return reasons, product attributes, local logistics infrastructure, etc. according to the specific conditions of the region. They need to cooperate with local logistics service providers to utilize their warehousing and transportation networks, while optimizing the return process and improving manual processing efficiency. Cross-border e-commerce needs to strengthen return data analysis and adjust strategies in a timely manner to adapt to market changes, conduct in-depth analysis of business scenarios, optimize logistics strategies, and cooperate with all parties to jointly improve the level of return logistics management. Summary of the invention

[0003] The present invention provides a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis, which mainly includes:

[0004] Obtain sales and return data for different regions in different time periods, including associated sales channels and associated product categories, and calculate the return volume and sales volume for different time periods in each region;

[0005] For the target region, the correlation analysis module is used to analyze sales data and return data, including analyzing the impact of sales and return-related sales channels on return volume and sales volume, classifying sales channels into two categories, including positive sales channels and negative sales channels, and generating sales channel improvement reports based on sales channel categories;

[0006] According to the storage conditions corresponding to the associated product categories, the sales data and return data are classified. Each category corresponds to a warehouse category, and the total return volume and sales volume corresponding to different warehouse categories are calculated;

[0007] Determine whether to establish a target warehouse in the target area based on the return volume and sales volume corresponding to the target warehouse category;

[0008] For the target warehouses identified as recommended areas in the target areas, cross-border express delivery fees and warehousing cost data for the recommended areas and adjacent areas are obtained to evaluate the cost of establishing the target warehouses in the recommended areas and adjacent areas;

[0009] Analyze the sales trend of the sales data based on the sales data of all products in the recommended region, adjacent regions, and target warehouse categories, and calculate the comprehensive scores of the recommended region and adjacent regions based on the establishment cost and sales trend to determine the best region for establishing the target warehouse;

[0010] A linear programming model is established, and the linear programming model is used to classify the returned products corresponding to the target warehouse, including classifying them into returnable and non-returnable products based on whether the cost of transporting them back to the target warehouse is lower than the cost of processing the return locally. For returnable products, they are transported back to the target warehouse, and for non-returnable products, the return is processed locally, including selling them at a reduced price locally.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The invention provides a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis. Through detailed analysis of sales and return data in different time periods and regions, it can obtain more accurate insights into market demand, timely adjust inventory and sales strategies, reduce unnecessary inventory backlogs and return losses, and improve overall operational efficiency. It can identify positive and negative sales channels through correlation analysis, which helps companies focus on efficient channels and optimize resource allocation. At the same time, it can take improvement measures or elimination strategies for channels with negative impacts to improve sales performance and customer satisfaction. It can ensure that warehouse resources match market demand based on the matching of product categories and storage conditions and the comprehensive analysis of sales and return data. Matching, reducing logistics costs and improving response speed; by setting thresholds and ratio analysis, combined with cross-border express fees and warehousing cost considerations, the solution can more accurately judge the economic rationality of warehouse construction, avoid blind investment, and achieve the optimal balance between cost and benefit; establish a linear programming model to classify and process returned products, consider cost-effectiveness, effectively solve the cost problem in return processing, reduce processing time and cost, and improve customer experience; accurate calculation of return logistics paths, especially consideration of cross-border logistics costs, helps companies better manage logistics costs in global operations, reduce unnecessary cross-border transportation, and improve the overall efficiency of the logistics chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flowchart of a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis.

[0014] Figure 2A schematic diagram of a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis of the present invention.

[0015] Figure 3 This is another schematic diagram of a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis of the present invention. DETAILED DESCRIPTION

[0016] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0017] like Figure 1-3 In this embodiment, a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis may specifically include:

[0018] S101. Obtain sales data and return data from different regions in different time periods, including associated sales channels and associated product categories, and calculate the return volume and sales volume in different time periods in each region.

[0019] Obtain sales data and return data for each sales region in different sales cycles, and associate sales channels and product category information to form a multi-dimensional data set. Perform exploratory analysis on the original data, and use statistical methods to analyze the distribution, outliers, and missing values ​​of the data; after analysis, clean and preprocess the data set to remove abnormal data, and select corresponding processing methods based on the proportion and business meaning of missing values, including deleting missing value records, filling with fixed values, and filling with mean or median values; divide the data according to the time dimension, and aggregate it according to different time periods to obtain sales and return data for each sales region in different time periods, including years, quarters, and months; analyze sales trends in different time periods, and use time series analysis methods to determine long-term trends, seasonal changes, and emergencies in sales and return volumes; introduce regional dimensions, conduct horizontal comparisons of sales and return data for each sales region, use sales volume, sales volume, and return rate for comparison, and use visualization to display the comparison results, analyze sales information differences between different regions, and identify sales hot spots and potential problem areas, including visualization methods bar charts, line charts and maps; in combination with the sales channels and product category information, vertical drilling of sales and return volumes of different channels and products, analyzing the sales composition and return factors of each channel and product, using correlation analysis to determine the correlation between different indicators, using regression analysis to determine the significance of influencing factors, and using decision trees to analyze the impact rules of different factors on sales and return volumes; by calculating the ratio of return volume to sales volume, obtaining the return rate index of each sales region in different time periods as an important factor for evaluating sales quality and customer satisfaction; combining the calculation of return rate index with sales channel and product category analysis to evaluate the overall information of sales business; generating optimization suggestions based on data analysis, and proposing specific optimization measures based on the analysis results, including adjusting sales strategies, optimizing product structure and improving customer service, and instructing business practices; comprehensively analyzing sales cycles, sales regions, sales channels and product categories, mining the correlation and influencing factors between data, and forming sales business reports.

[0020] For example, after obtaining the original data set containing multi-dimensional information such as sales cycle, sales region, return data, sales channel and product category, Python's pandas library is first used to perform exploratory analysis on the data. The statistical characteristics of the data, such as minimum, maximum, mean, standard deviation, etc., are calculated by the describe function, and it is found that there are many missing values ​​and outliers in the sales volume field. Then the Z-score method is used to identify the outliers in the sales volume field, and the records with Z-score absolute values ​​greater than 3 are marked as outliers and removed. For missing values, the fields with a missing ratio of more than 20% are directly deleted, and other missing values ​​are processed by the mean filling method. Then, the groupby function is used to aggregate the data according to the time dimension, and the sales and return volumes of each region are calculated in months. Then the seasonal_decompose function is used to seasonally decompose the aggregated time series data to identify the long-term trend, seasonal factors and random fluctuations of sales and return volumes. The matplotlib library is used to draw a line chart to intuitively show the trend of sales and return volumes in different regions over time. In terms of the regional dimension, the pivot_tab le function is used to summarize the sales volume and return volume, and the pyecharts library is used to draw bar charts and maps to compare the sales performance of different regions. Furthermore, the pearsonr function is used to calculate the correlation coefficient between sales channels and product categories and sales volume and return volume. It is found that the return rate of online channels is significantly higher than that of offline channels, and the return rate of home appliances is the highest. Using the logistic regression model, with sales channels, product categories and other factors as independent variables and return status as the dependent variable, a return prediction model is established. The accuracy of the model reached 85%, and the key factors affecting returns were identified. Finally, the return rate of each region and month was calculated and compared with the industry benchmark value, and early warning and optimization suggestions were put forward for regions and product categories that were higher than the benchmark value. Through data analysis, it was found that the return rate of home appliances in East China continued to be higher than that in other regions. It is recommended to strengthen the after-sales service management in this region, improve customer satisfaction, and optimize the return process of online channels to reduce return costs.

[0021] S102. For the target area, a correlation analysis module is used to analyze sales data and return data, including analyzing the impact of sales channels associated with sales and returns on return volume and sales volume, and classifying sales channels into two categories, including positive sales channels and negative sales channels; and generating a sales channel improvement report based on the categories of sales channels.

[0022] The sales data and return data of the target area, including sales channels, sales volume and return volume, are obtained to form a data set; the data set is preprocessed to check the data quality, handle missing values ​​and outliers, and ensure the accuracy and consistency of the data; the correlation analysis method is used to calculate the correlation between the sales channel and the sales volume and the return volume; the Pearson correlation coefficient is used to calculate the linear correlation between the variables, and the correlation coefficient range is [-1, 1]. If the correlation coefficient is greater than 0.25, it indicates a positive correlation, and if the correlation coefficient is less than -0.25 indicates negative correlation, and a correlation coefficient of 0 indicates no correlation; if the data volume is greater than the preset threshold or there is a nonlinear relationship between the variables, the Spearman rank correlation coefficient or the Kendall rank correlation coefficient is used; the sales channels are classified into two categories according to the size and significance level of the correlation coefficient, and the sales channels with significant positive correlation to the sales volume are screened out and marked as positive sales channels, and the sales channels with significant negative correlation to the sales volume are marked as negative sales channels; the positive sales channels and the negative sales channels are analyzed, and the key indicators of each channel are calculated, and the key indicators include sales amount, sales proportion and return rate; Matplotlib is used to calculate the sales channels. The lib library draws a bar chart to compare the sales amount and sales proportion of different sales channels, and shows the sales scale and importance of each channel; the Seaborn library is used to draw a multi-series line chart to show the return rate change trend of the positive sales channel and the negative sales channel in different time dimensions, and judge the stability of the positive sales channel and the negative sales channel; the pyecharts library is used to draw pie charts and radar charts to analyze the advantages and disadvantages of the positive sales channel and the negative sales channel in terms of the key indicators; according to the analysis results of the positive sales channel and the negative sales channel, combined with the business scenario information and the market environment information, a sales channel improvement report is generated, and the sales channel improvement report includes optimizing positive sales channel measures and transforming negative sales channel reports; the optimization of positive sales channel measures includes increasing marketing investment, improving service system and upgrading product experience; the transformation of negative sales channel reports includes adjusting commission policies, Optimize the return process and strengthen quality control; establish a monitoring indicator system for sales channels, obtain channel indicators, including sales amount, return rate and customer complaint rate, and set early warning thresholds; use data visualization panels to monitor the channel indicators of each sales channel in real time. When the channel indicators exceed the early warning threshold, trigger the early warning mechanism and take timely response measures; regularly conduct statistical analysis on monitoring indicator data, identify abnormal fluctuations and change trends in sales channels, adjust optimization strategies, and achieve dynamic management; integrate the measures to optimize positive sales channels and the report on transforming negative sales channels into the sales channel management module, adjust resource allocation, optimize business processes, improve the sales efficiency of positive sales channels, and control the return risk of negative sales channels; continuously monitor the sales data and return data of sales channels, regularly conduct correlation analysis and binary classification, dynamically adjust sales channel strategies, and achieve continuous optimization and growth of sales business. .

[0023] For example, first obtain the sales data and return data of the target area in the last year from the sales system and return system, including fields such as sales channel, commodity category, sales amount, sales quantity, return amount, and return quantity. Use Python's Pandas library to read the data file in CSV format to obtain a data set containing 100,000 records. Use the info method to view the basic information of the data set, and find that there are 5% missing values ​​in the sales amount field, and there are individual abnormal values ​​greater than 10,000 in the return quantity field. Remove missing values ​​by deleting records, use the quartile method to detect abnormal values ​​in the return quantity field, and replace values ​​exceeding Q3+1.5IQR with Q3. Then use the preprocessing module of the sklearn library to standardize the data so that the numerical range of each field is unified to between 0 and 1 to eliminate the dimension effect. Then, the Pearson correlation coefficient is used to calculate the correlation between sales channels and sales volume and return volume. The results show that the sales volume correlation coefficient of online channels is 0.8, and the return volume correlation coefficient is 0.6; the sales volume correlation coefficient of offline channels is 0.2, and the return volume correlation coefficient is -0.3. Combined with the significance level p value less than 0.05, the online channel is judged to be a positive sales channel, and the offline channel is a negative sales channel. Further analysis of the sales amount, return rate and other indicators of the two types of channels is carried out. Matplotlib is used to draw a bar chart, and it is found that the average monthly sales amount of online channels accounts for 60%, but the return rate is as high as 15%; the average monthly sales amount of offline channels accounts for 40%, and the return rate is only 5%. Using Seaborn to draw a line chart to analyze the channel sales trend, it is found that the return rate of online channels increases significantly during promotion nodes such as Double 11 and 618, and the return rate of offline channels is relatively stable. Based on the above analysis, an optimization report is proposed: First, strengthen the return management of online channels, introduce AI algorithms to optimize return policies, use logistic regression models to predict the probability of returns, and take incentives for high-risk orders, such as providing after-sales coupons, etc., which is expected to reduce the overall return rate by 3 percentage points; second, strengthen customer service in offline channels, analyze user reviews through NLP technology, improve product experience, and enhance customer satisfaction, which is expected to increase sales conversion rate by 5%. Establish a channel monitoring system based on real-time data streams, use Kafka to collect data from all links, use Redis to store warning thresholds, automatically alarm when the return rate exceeds 20%, and use AI rflow to trigger emergency plans.

[0024] S103. Classify the sales data and return data according to the storage conditions corresponding to the associated product categories. Each category corresponds to a warehouse category. The total return volume and total sales volume corresponding to different warehouse categories are calculated.

[0025] Obtain the corresponding relationship data between product categories and storage conditions, associate the product category field with sales data and return data, and form a data set including product category fields, storage condition fields, sales volume fields, and return volume fields; define corresponding warehouse categories according to different values ​​of the storage condition fields; set mapping rules between storage conditions and warehouse categories, including dividing into normal temperature warehouses, refrigerated warehouses, and frozen warehouses according to temperature conditions, dividing into dry warehouses and moisture-proof warehouses according to humidity conditions, and dividing into hazardous chemical warehouses and cool warehouses according to special storage requirements; divide into corresponding warehouse categories according to sales and return records; group the data set according to warehouse categories, use aggregate functions to sum the sales volume fields and the return volume fields, obtain the total sales volume and the total return volume under different warehouse categories, and form summary data of warehouse categories, sales volume, and return volume.

[0026] For example, first obtain the product master data, including product category and storage condition attributes, and associate it with sales and return data through product codes to form a data set containing 10 million records. Then divide the warehouse categories according to the storage conditions. The temperature below -18℃ is divided into frozen warehouses, the temperature between -18℃ and 0℃ is divided into refrigerated warehouses, and the rest are divided into normal temperature warehouses; the humidity below 50% is divided into dry warehouses, and the rest are divided into moisture-proof warehouses; warehouses with special requirements such as explosion-proof and light-proof are divided into special warehouses. Use Python's NumPy library to vectorize the data set, and quickly complete the warehouse category division through Boolean indexing, which improves the processing efficiency by 10 times. Group and summarize by warehouse category, it is obtained that the normal temperature warehouse has a sales volume of 8 million pieces and a return volume of 200,000 pieces, the refrigerated warehouse has a sales volume of 1.5 million pieces and a return volume of 50,000 pieces, and the special warehouse has a sales volume of 500,000 pieces and a return volume of 10,000 pieces. Selecting monthly as the time granularity, using Pandas's resample function to aggregate monthly, generating 24 monthly time series data for 2 years, it is found that the sales volume of normal temperature warehouses drops significantly in summer, the sales volume of refrigerated warehouses increases slightly in winter, and the special warehouses fluctuate greatly due to holidays. Combined with the statistical indicators of product categories, the average monthly sales volume of food products is 500,000 pieces, accounting for 60%, and the return rate is 3%; the average monthly sales volume of electronic products is 100,000 pieces, accounting for 20%, and the return rate is 8%, which are listed as key focus objects. Matplotlib is used to draw a stacked bar chart to intuitively display the monthly sales changes of the three warehouse categories. The color matching adopts the colorblindsafe scheme, which is suitable for color blind and color weak people. Finally, the sales and return data are cross-analyzed in multiple dimensions by product category, warehouse category, and month to identify the key influencing factors of sales and return volume, form a visual report, and provide data support for sales strategy optimization and inventory management. According to business needs and data volume, select the time granularity, including monthly, quarterly and annual; use the groupby function to group the data according to the time granularity, and then use aggregate functions such as sum to calculate the sales and return volumes of each warehouse category in different time periods, generate time series data, and analyze the dynamic changes in warehouse category sales performance. Combined with product category attributes, subdivide the data under different warehouse categories, and calculate the sales share and return share of each product category in different warehouse categories. Sort by sales volume, the top 20% of products are defined as key products; sort by return rate (return volume / sales volume), and the top 10% of products are defined as problem products. Identify key products and problem products in each warehouse category to provide a reference for inventory management. Use data visualization technology to draw bar charts, pie charts and other charts to intuitively display the differences in sales and return volumes of different warehouse categories, as well as the distribution of each product category in the warehouse category, so as to facilitate comparative analysis and decision optimization.

[0027] S104. Determine whether to establish a target warehouse in the target area based on the return volume and sales volume corresponding to the target warehouse category.

[0028] Obtain the return volume and sales volume of the target warehouse category in the target area, collect data on the population size, consumption level and competition landscape of the target area, and evaluate the market demand of the target area; calculate the inventory turnover rate and inventory cost of the target warehouse category in the target area based on the return volume and sales volume of the target warehouse category; the formula for the inventory turnover rate is: inventory turnover rate = sales cost / average inventory, where average inventory = (beginning inventory + ending inventory) / 2; the formula for the inventory cost is: inventory cost = ordering cost + storage cost + out-of-stock cost, where ordering cost includes fixed ordering cost and unit ordering cost, storage cost includes warehousing fee and capital cost, and out-of-stock cost includes sales loss and customer churn; analyze the necessity and feasibility of establishing a warehouse based on the calculation results and compare them with the preset values; analyze the logistics distance and transportation cost between the target area and the existing warehouses, and evaluate the impact of establishing a warehouse in the target area on distribution efficiency and logistics cost; quantitatively evaluate the logistics benefits of establishing a warehouse by comparing the distribution efficiency indicators and logistics cost indicators before and after the establishment of the warehouse, and the distribution efficiency indicators include the order fulfillment cycle , on-time delivery rate and delivery frequency, the logistics cost indicators include transportation cost, warehousing cost, loading and unloading cost and packaging cost; combined with the logistics conditions of the target area, optimize the warehouse site selection, the logistics conditions include traffic conditions and delivery radius; according to the land information, labor information and energy conditions of the target area, evaluate the construction cost and operation cost of establishing a warehouse, and judge whether it is economically feasible to establish a warehouse; compare the costs and benefits of the two options of establishing a warehouse and maintaining the status quo in the target area, and calculate the net present value and internal rate of return based on the cash flow forecast of the warehouse project; when the net present value is greater than 0 or the internal rate of return is greater than the project capital cost, accept the warehouse project; when the net present value is less than 0 or the internal rate of return is less than the project capital cost, reject the warehouse project; when the net present value is equal to 0 or the internal rate of return is equal to the project capital cost, reject the warehouse project; derive the financial feasibility of the warehouse based on the net present value and internal rate of return; combined with the market demand, the logistics conditions, the economic feasibility and the financial feasibility, evaluate the necessity and feasibility of establishing a warehouse in the target area, and form a decision report.

[0029] For example, first obtain the monthly sales and return volume data of the target warehouse category in the target area in the past year, and at the same time obtain the permanent population of 3 million, per capita disposable income of 30,000 yuan, e-commerce penetration rate of 30%, and the scale and share data of 3 major competitors in the area; by calculating indicators such as per capita consumption, market capacity and market concentration, it is judged that the market potential of the target area is 80 million yuan / year, and the customer demand growth rate is 10%; then, based on sales and return volume, the inventory turnover rate of the target warehouse category is calculated to be 5 times / year, while the benchmark value of the same industry is 8 times / year; the inventory cost is calculated using the activity-based cost method, in which the ordering cost is 20%. The cost of the project is 200,000 yuan, the storage cost is 500,000 yuan, and the out-of-stock cost is 100,000 yuan, totaling 800,000 yuan, which is higher than the industry average. Then, by calling Baidu Map API to calculate the logistics distance and transportation cost, it is found that the target area is 500 kilometers away from the existing warehouse, and after the establishment of the new warehouse, the distribution radius can be shortened to 100 kilometers, the distribution frequency can be increased from 2 times a week to 5 times, the order fulfillment cycle can be shortened from 3 days to 1 day, and the logistics cost can be reduced by 30%. Then, by crawling the 58.com website to obtain local land transfer information and labor wage levels, and combined with the design scale of the new warehouse, the construction cost is calculated to be 10 million yuan and the annual operating cost is 2 million yuan. Finally, the numpy_financial library of Python is used to calculate the financial indicators. Assuming that the project cycle is 10 years, the after-tax discount rate is 8%, the average annual cash inflow of the new warehouse is 6 million yuan, the net present value is 15 million yuan, and the internal rate of return is 20%, all of which exceed the capital cost of the project and are financially feasible. Therefore, a new warehouse is established in the target area to improve supply chain efficiency and meet market demand.

[0030] If the sales volume is greater than or equal to the first threshold, establishing a target warehouse in the target area is recommended; if the sales volume is less than the first threshold and greater than the second threshold, whether to establish a target warehouse in the target area is determined based on the ratio of the return volume to the sales volume. If the ratio is greater than the ratio threshold, establishing a target warehouse in the target area is recommended, otherwise it is not recommended; if the sales volume is less than the second threshold, establishing a target warehouse in the target area is not recommended.

[0031] Obtain the sales volume in the target area, set a first threshold and a second threshold, and determine the degree of recommendation for establishing a warehouse in the target area based on the comparison results of the sales volume with the first threshold and the second threshold; if the sales volume is greater than or equal to the first threshold, mark the degree of recommendation for establishing a warehouse in the target area as recommended, output the recommendation result in JSON format, including the target area ID, recommendation flag and timestamp, and write the output result to the Kafka message queue; if the sales volume is less than the first threshold and greater than the second threshold, obtain the return volume of the target area in the most recent year; compare the ratio of the return volume to the sales volume with the preset ratio threshold; adopt a dynamic threshold strategy to adjust the ratio threshold in real time according to business seasonality and promotion activity data; if the ratio is greater than the ratio threshold, The recommendation level for establishing a warehouse in the target area is marked as recommended, otherwise it is marked as not recommended; if the sales volume is less than the second threshold, the recommendation level for establishing a warehouse in the target area is marked as not recommended; a decision tree algorithm is introduced to generate a judgment rule for warehouse establishment recommendation based on the sales volume, the return volume and the ratio; a time series model is used to predict the sales volume and return volume of the target area; the attribute data of the target area is associated with the judgment rule and the predicted sales volume and return volume, and an association rule mining algorithm is used to analyze the association pattern between the attribute data and the warehouse establishment recommendation result, discover the decision-making rules, and form multi-dimensional decision-making reference information, wherein the attribute data includes geographical location, population size and consumption level; the recommendation judgment result and the association analysis result are stored in a database and displayed through a visualization panel.

[0032] For example, clustering algorithms can also be used to analyze historical data and divide the ratio threshold into high and low intervals; first, the monthly sales volume data of the target region in the past year is obtained from the database, and the first threshold is set to 1 million pieces and the second threshold is set to 500,000 pieces. If the sales volume is greater than or equal to 1 million pieces, the region is directly marked as "recommended warehouse building", and a JSON result containing fields such as region ID, recommendation flag, and timestamp is generated, written to the "recom_result" topic of the Kafka message queue, and the log is recorded in Elasticsearch to release Redis cache resources. If the sales volume is between 500,000 and 1 million, the return volume data is extracted from the ODS layer of HDFS through Hi veSQL, and the return volume and sales volume are summarized and the ratio is calculated using Spark's DataFrame API, and the Z-score anomaly detection algorithm is used to filter out outliers with a ratio exceeding 3 times the standard deviation, and then the K-means clustering algorithm is used to divide the historical data of the ratio to obtain a high return rate cluster with a threshold of 15%. According to the comparison between the current return rate and the threshold, the label of "recommended" or "not recommended" is dynamically generated and stored in the recommendation result table of HBase. If the sales volume is less than 500,000, it is directly marked as "not recommended", and the JSON result is generated and logged to release resources. While making the recommendation judgment, the Prophet time series library is used to predict the sales volume and return volume of each region in the next three months, and the CART decision tree algorithm is used to generate the recommendation rule tree as a supplementary basis for the judgment result. In parallel, the POI data and consumption level data of the target area are extracted from Redis, and the FP-Growth association rule mining algorithm is used to obtain the association rules between each attribute and the recommendation result. The rules with a confidence of more than 80% are added to the Neo4j knowledge graph as effective knowledge. Finally, the recommendation results, prediction data, rule trees, association rules, etc. are generated into a visual report, pushed to the Tableau portal of the business department, and a real-time query API is provided through the FlaskWeb service to indicate the warehouse site selection and network optimization.

[0033] S105. For the target warehouse to be established in the target area, the recommended area is identified as a recommended area, cross-border express delivery fees and warehousing cost data for the recommended area and adjacent areas are obtained, and the cost of establishing the target warehouse in the recommended area and adjacent areas is evaluated.

[0034] Obtain the recommendation result data for establishing warehouses in the target area, and extract the list of areas marked as recommended; query the geographical location and boundary range of the recommended area through the geographic information system module, and set the adjacent distance threshold according to the timeliness requirements of the logistics business and the speed of the transportation tools, and determine the list of adjacent areas for each recommended area; collect cross-border express delivery cost data for the recommended area and adjacent areas, including express delivery prices for different logistics companies, different types of goods, and different weight ranges, as well as import and export customs clearance fees and tariffs, to form an express delivery cost data set; collect storage cost data for the recommended area and adjacent areas, including warehouse rent, labor costs, equipment depreciation and energy consumption; combine the above The land price factors, labor market factors and logistics infrastructure factors of the target area are used as storage cost estimation parameters, wherein the land price factor can introduce the average price or rental level data of industrial land in the target area; the labor market factor introduces the local employment number and average wage level to estimate the labor cost; the logistics infrastructure factor introduces the highway accessibility rate and railway freight volume to evaluate the convenience of logistics; based on the express cost dataset and the storage cost estimation parameters, the initial investment cost of establishing warehouses in the recommended area and adjacent areas is evaluated, including land acquisition costs, construction project costs and equipment procurement costs, to form a warehouse construction cost dataset.

[0035] For example, for an order that is required to be delivered within 24 hours, the delivery radius is generally not more than 300 kilometers, so the adjacent distance threshold can be set to 300 kilometers; clustering algorithms can also be used to analyze historical order data, and the adjacent distance threshold can be automatically divided according to the distribution of delivery distance and time efficiency; first, a list of target areas marked as "recommended" is extracted from the recommendation result database, and then the latitude and longitude boundaries of these areas are obtained by calling the Amap API, and then the DBSCAN clustering algorithm is used to cluster the delivery distance and time efficiency in the historical order data, setting the eps parameter to 50 kilometers and the min_sample parameter to 100, and the optimal adjacent distance threshold is 200 kilometers. Based on this, the association relationship between the target area and the adjacent area is established in the Neo4j graph database. Next, the express price list of different regions and different types of goods is crawled from the logistics company website, and the key fields are extracted using regular expressions, and stored in the MySQL database after cleaning. At the same time, the data of industrial land price, employment, average salary, etc. in the target area are obtained from the Wi nd database, and dimensionless processing is performed using the Z-score normalization method; the highway accessibility rate and railway freight volume data are obtained from the Ministry of Transport website, and mapped to the 0-1 interval using the Min-Max normalization method. Then, according to the warehouse design parameters, the construction cost of each region is calculated using the packing algorithm and cost estimation formula, and stored in the PostgreSQL database.

[0036] S106. Analyze the sales trend of the sales data based on the sales data of all products in the associated recommended region, adjacent regions, and associated target warehouse categories; calculate the comprehensive scores of the recommended region and adjacent regions based on the establishment cost and sales trend, and determine the best region for establishing the target warehouse.

[0037] According to the associated recommended areas and adjacent areas, and the associated target warehouse categories, the historical sales data of all products are obtained, and the trend analysis of the sales data is performed using the time series analysis method to obtain the sales trend of each product; through the geographical location information of the associated recommended areas and adjacent areas, the establishment cost data of the associated recommended areas and adjacent areas are obtained, and the comprehensive score of each area is calculated using the weighted calculation method in combination with the sales trend characteristics; according to the comprehensive score, the areas with the highest scores are sorted and compared, and the best area for establishing the target warehouse is determined; according to the geographical location coordinates of the best area, the customer distribution information of the best area is obtained through the spatial clustering algorithm, and combined with the purchasing power level of the customers data, judge the market capacity and growth potential of the optimal region; if the market capacity and growth potential are lower than the preset threshold, obtain the product data of the associated target warehouse category, and use the association rule mining algorithm to find the product category with the highest matching degree with the customer demand in the optimal region as the key development category; according to the characteristics of the key development category, obtain the warehouse construction parameters that meet the requirements of the key development category through the classification algorithm, including warehouse area, number of shelves and automation equipment configuration, and obtain the construction plan; according to the market capacity, the growth potential, the key development category and the optimal region, generate a feasibility analysis report for establishing the target warehouse through visualization, and determine the final construction plan based on the feasibility analysis report.

[0038] For example, in the analysis of the target warehouse site selection, we first start with the historical sales data and use the ARI MA time series analysis method to predict the sales trends of each product in different regions. For example, by analyzing the monthly sales data of the past five years, we can predict the sales trend of the next year. Then, combined with the geographical location information of the region, the construction cost data of the relevant region, such as land price, labor cost, etc., are collected. By calculating the sales trend score and construction cost score of each region, the weighted average method is used to calculate the comprehensive score of each region, such as the sales trend weight is 70% and the construction cost weight is 30%. By comparing these scores, the region with the highest score is selected as the best location for building a warehouse. Further, the K-means spatial clustering algorithm is used to analyze the customer distribution in the region, and the market capacity and growth potential are evaluated in combination with the customer's purchasing power data, such as per capita income and consumption level. Assuming that the market capacity and growth potential are lower than the preset threshold, such as the annual growth rate is lower than 5%, the needs of customers in the region are further analyzed. By using an association rule mining algorithm, such as the Aprior i algorithm, customer purchase data is analyzed to find the product category with the highest matching degree with customer needs. If the analysis results show that the demand for household appliances is growing steadily, it will be identified as a key development category. Finally, based on the characteristics of this key development category, the decision tree classification algorithm is used to determine the construction parameters of the warehouse, including the area required for the warehouse, the number of shelves, and the configuration of automated equipment. If the focus is on the development of household appliances, a larger storage space and an efficient logistics distribution system may be required. These analysis results are displayed in a feasibility analysis report through visualization technology. The report will show in detail the market capacity, growth potential, key development categories, and construction plans, providing a basis for the final construction decision.

[0039] S107. Establish a linear programming model, wherein the linear programming model is used to classify the returned products corresponding to the target warehouse, including classifying them into returnable and non-returnable products based on whether the cost of transporting them back to the target warehouse is lower than the cost of processing the return locally; for returnable products, they are transported back to the target warehouse; for non-returnable products, the return is processed locally, including selling them at a reduced price locally.

[0040] Obtain the return product data corresponding to the target warehouse, including product type, quantity, price and transportation distance, and establish a basic data set for return products; preset the decision variables of the linear programming model, assuming that there are m types of return products and n return regions, and the decision variable x(i, j) represents the processing method of the i-th product in the j-th region, i=1, 2, ..., m; j=1, 2, ..., n; x(i, j) is a 0-1 variable, when x(i, j)=1, it means that the i-th product is transported from the j-th region back to the target warehouse, and when x(i, j)=0, it means that the i-th product is processed locally in the j-th region; collect the transportation cost and local processing cost data of each return product in different regions, assuming that the unit transportation cost of the i-th product in the j-th region is c1(i, j), and the unit local processing cost is c2(i, j); the calculation formula of c1(i, j) is: c1(i, j) = 1; when x(i, j) = 0, it means that the i-th product is processed locally in the j-th region; collect the transportation cost and local processing cost data of each return product in different regions, and assume that the unit transportation cost of the i-th product in the j-th region is c1(i, j), and the unit local processing cost is c2(i, j); the calculation formula of c1(i, j) is:

[0041] j)=p×d(j)+q×w(i)+r×v(i), where p is the unit distance transportation cost, d(j) is the distance from the jth region to the target warehouse; q is the unit weight transportation cost, w(i) is the unit weight of the i-th product; r is the unit volume transportation cost, v(i) is the unit volume of the i-th product; the calculation formula of c2(i, j) is: c2(i, j)=s(j)+t×h(i)+u×l(i), where s(j) is the storage cost of the jth region; t is the unit price reduction loss , h(i) is the unit price reduction of the i-th product; u is the unit labor processing cost, l(i) is the unit processing time of the i-th product; thus, a parameter matrix of transportation cost and local processing cost is formed; the objective function of the linear programming model is constructed, the goal is to minimize the total cost, including the transportation cost back to the target warehouse and the cost of local processing of returns, let Q(i, j) be the number of returns of the i-th product in the j-th region, then the objective function can be expressed as: minZ = ∑(i = 1)^m∑(j = 1)^n[c1(i,

[0042] j)×x(i, j)+c2(i, j)×(1-x(i, j))]×Q(i, j); According to the actual business constraint information, set the constraints of the linear programming model: ∑(i=1)^mx(i, j)×Q(i, j)≤P(j), indicating that the total amount of returns shipped back to the target warehouse from the jth region does not exceed P(j); ∑(j=1)^nx(i, j)×Q(i,

[0043] j)≤V(i), indicating that the total amount of the ith product shipped back to the target warehouse does not exceed V(i); x(i, j)=0 or 1, indicating the constraint on the value of the decision variable; the simplex method is used to solve the linear programming model, and the returned products are classified according to the solution results. When x(i, j)=1, the ith product in the jth region is marked as a returnable product, and when x(i, j)=0, it is marked as a non-returnable product; the returnable goods are shipped back to the target warehouse; the non-returnable goods are processed for return locally, including selling at a reduced price locally.

[0044] Exemplarily, the specific steps of solving the linear programming model are to convert the linear programming model into a standard form and add slack variables; construct an initial simplex table and select an initial basis feasible solution; determine whether the optimal condition is met, if not, select the variable to be swapped in and the variable to be swapped out, perform elementary row transformation, and obtain a new simplex table; repeat the steps to determine whether the optimal condition is met, if not, select the variable to be swapped in and the variable to be swapped out, perform elementary row transformation, and obtain a new simplex table until the optimal condition is met and the optimal solution, i.e., the value of the decision variable x(i, j), is obtained. In order to obtain the return product data corresponding to the target warehouse, the return records of the most recent quarter were extracted from the ERP system, including 7 types of return products from 5 different return regions across the country. The quantity, price and other information of each return product were summarized to form a basic data set. Next, the decision variables of the linear programming model were preset, where x(i, j) represents the processing method of the i-th product in the j-th region. When x(i, j) = 1, it means that the product is transported from the region back to the target warehouse; when x(i, j) = 0, it means that it is processed locally. In order to calculate the transportation cost and local processing cost, the distance data from each return region to the target warehouse, as well as the weight and volume data of the product are collected. The unit distance transportation cost is set to 5 yuan / km, the unit weight transportation cost is 2 yuan / kg, and the unit volume transportation cost is 1 yuan / cubic meter. At the same time, data such as storage costs, product price reductions, and processing hours in each region are also collected to calculate local processing costs. When constructing the objective function, the function objective is to minimize the total cost, so the transportation cost and the local processing cost are added and multiplied by the corresponding number of returns to obtain the complete objective function expression. In addition, the constraints of the model are set according to the actual business constraints, such as the total amount of returns shipped back to the target warehouse in each region cannot exceed the local storage capacity, and the total amount of each product shipped back to the target warehouse cannot exceed its acceptable maximum value. When solving the linear programming model, it is first converted into a standard form and slack variables are added. Then, a set of initial feasible solutions was selected and the initial simplex table was constructed. Next, by continuously selecting variables to be swapped in and out and performing elementary row transformations, a new simplex table was obtained until the optimal condition was reached. Finally, the optimal value of the decision variable x(i, j) was obtained. According to the solution results, the returned products were classified. For returnable products, they were arranged to be transported back to the target warehouse from the return area; for non-returnable products, they were processed locally through price reduction sales and other methods, which effectively reduced the total cost of return processing and improved the operational efficiency of the supply chain.

[0045] Based on the return address and the location of the target warehouse in the best area, determine the return logistics route and calculate the logistics cost of transporting the goods back to the target warehouse; if the return logistics route is cross-border logistics, the logistics cost of transporting the goods back to the target warehouse includes cross-border logistics fees.

[0046] Obtain the return address information of the returned product, including the country, province, city, district and detailed address, and convert the return address information into longitude and latitude coordinates and store them in the return address data table. Obtain the location coordinates of the target warehouse from the warehouse site selection results, and calculate the straight-line distance from each return address to the target warehouse; obtain the API document provided by the third-party logistics service provider, obtain the interface calling method, identity authentication, request parameters and response format, write code to call the API interface, and query the logistics path and mileage; parse and extract the JSON or XML format data returned by the API, and map the logistics path, mileage and estimated time in the return logistics path data table; determine whether the return logistics path is cross-border based on the country where the return address and the target warehouse are located. If the return address and the target warehouse are located in different countries, mark it as cross-border logistics; for non-cross-border logistics, collect and maintain the carrier's freight standard table, and promptly update the pricing method and unit price in the system. Data, obtain the corresponding pricing method and unit price, including the starting price, unit mileage price and weight unit price, calculate the logistics cost of transporting back to the target warehouse according to the mileage of the logistics route and the weight of the goods; for temporary freight discounts or discounts, notify the business department through SMS to adjust the billing rules to avoid calculation errors; use the inquiry and comparison module to select the most cost-effective carrier and logistics solution to achieve dynamic optimization of logistics costs; for the cross-border logistics, obtain the additional costs of the cross-border logistics link, the additional costs include tariffs, customs clearance fees and international transportation fees, add the additional costs to the return logistics cost, and obtain the total cross-border logistics cost of the return; according to the cross-border return logistics path and the total cross-border logistics cost of the return, use visual display to generate a path map and cost report.

[0047] For example, in order to obtain the return address information of the returned product, the system will call the Amap API and convert the return address into longitude and latitude coordinates through the address resolution interface, such as "No. 10, Shangdi 10th Street, Haidian District, Beijing". The converted coordinates are 11326014, 4050552, and then the return address, longitude, latitude and other information are stored in the return address data table. The system obtains the longitude and latitude coordinates of the target warehouse from the warehouse site selection result table, such as 11460983, 3937574, and uses the spherical distance formula in mathematics to calculate the straight-line distance from each return address to the target warehouse. Next, the system selects logistics providers from the third-party logistics service provider database whose service scope covers the return address and target warehouse, whose timeliness meets the requirements such as delivery within 48 hours, whose price is reasonable such as 5 yuan per kilometer, and whose reputation is good such as user ratings above 5. After weighted scoring, the top 3 logistics providers are selected as partners. The system obtains the API documents provided by the three logistics providers, writes Java code to call the path planning interface, passes in the return address and target warehouse coordinates as parameters, obtains the returned JSON format data, extracts the logistics path coordinate point sequence, mileage, estimated time and other information, and maps and stores them in the return logistics path data table. If the return address and target warehouse are in different countries, the return path is marked as cross-border logistics. For non-cross-border logistics, the system regularly synchronizes the freight standard table from the carrier system, updates the starting price such as 10 yuan, unit mileage price such as 5 yuan / kilometer, weight unit price such as 1 yuan / kilogram and other billing rules. When the logistics path mileage is 30 kilometers and the cargo weight is 5 kilograms, the freight = 10 + 30 * 5 + 5 * 1 = 30 yuan. If there is a temporary discount, such as a 10% discount, the system reminds the relevant personnel through SMS and writes the discount rate 9 into the database. At the same time, the price inquiry and comparison module is started, 100 path planning simulations are performed, and the average value is taken to update the freight. For cross-border logistics, the system obtains additional tariffs such as 12%, customs clearance fees such as 1,000 yuan, and international transportation fees such as 2,000 yuan, which are included in the total cost of return logistics. Finally, the system calls visualization tools such as HighCharts, draws a logistics route map with the return address as the starting point and the target warehouse as the end point, and generates a return logistics cost report to intuitively present the optimized return logistics plan.

[0048] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis, characterized in that: The method comprises: Obtain sales and return data for different regions in different time periods, including associated sales channels and associated product categories, and calculate the return volume and sales volume for different time periods in each region; For the target regions, the correlation analysis module is used to analyze sales data and return data, including analyzing the impact of sales and return-related sales channels on return volume and sales volume, classifying sales channels into two categories, including positive sales channels and negative sales channels, and generating sales channel improvement reports based on sales channel categories; According to the storage conditions corresponding to the associated product categories, the sales data and return data are classified. Each category corresponds to a warehouse category, and the total return volume and sales volume corresponding to different warehouse categories are calculated; Determine whether to establish a target warehouse in the target area based on the return volume and sales volume corresponding to the target warehouse category; For the target warehouses identified as recommended areas in the target areas, cross-border express delivery fees and warehousing cost data for the recommended areas and adjacent areas are obtained to evaluate the cost of establishing the target warehouses in the recommended areas and adjacent areas; Analyze the sales trend of the sales data based on the sales data of all products in the recommended region, adjacent regions, and target warehouse categories, and calculate the comprehensive scores of the recommended region and adjacent regions based on the establishment cost and sales trend to determine the best region for establishing the target warehouse; A linear programming model is established, and the linear programming model is used to classify the returned products corresponding to the target warehouse, including classifying them into returnable and non-returnable products based on whether the cost of transporting them back to the target warehouse is lower than the cost of processing the return locally. For returnable products, they are transported back to the target warehouse, and for non-returnable products, the return is processed locally, including selling them at a reduced price locally, including: Obtain the return product data corresponding to the target warehouse, including product type, quantity, price, and transportation distance, and establish a basic data set for return products; The decision variables of the preset linear programming model are: there are m types of returned products and n return regions; the decision variable x(i, j) represents the treatment method of the i-th product in the j-th region, i=1, 2, ..., m; j = 1, 2, ..., n; x(i, j) is a 0-1 variable. When x(i, j) = 1, it means that the i-th product is transported from the j-th region to the target warehouse. When x(i, j) = 0, it means that the i-th product is processed locally in the j-th region. Collect the transportation cost and local handling cost data of each returned product in different regions. Suppose the unit transportation cost of the i-th product in the j-th region is c1(i, j), and the unit local handling cost is c2(i, j); The calculation formula of c1(i, j) is: c1(i, j) = p×d(j)+q×w(i)+r×v(i), where p is the unit distance transportation cost and d(j) is the distance from the jth region to the target warehouse; q is the unit weight transportation cost, w(i) is the unit weight of the i-th product; r is the unit volume transportation cost, v(i) is the unit volume of the i-th product; The calculation formula of c2(i, j) is: c2(i, j) = s(j) + t×h(i) + u×l(i), where s(j) is the storage cost of the jth region; t is the unit price reduction sales loss, h(i) is the unit price reduction of the i-th product; u is the unit labor processing cost, l(i) is the unit processing labor time of the i-th product; This forms a parameter matrix of transportation costs and local handling costs; Construct the objective function of the linear programming model. The goal is to minimize the total cost, including the transportation cost back to the target warehouse and the cost of handling returns locally. Let Q(i, j) be the number of returns of the i-th product in the j-th region. The objective function can be expressed as: minZ = ∑(i = 1)^m∑(j = 1)^n[c1(i, j) × x(i, j) + c2(i, j) × (1-x(i, j))] × Q(i, j); According to the actual business constraint information, set the constraint condition of the linear programming model: ∑(i=1)^mx(i,j)×Q(i,j)≤P(j), which means that the total amount of returned goods shipped back to the target warehouse from the jth region does not exceed P(j); ∑(j=1)^nx(i,j)×Q(i,j)≤V(i), indicating that the total amount of the i-th product shipped back to the target warehouse does not exceed V(i); x(i, j) = 0 or 1, indicating the constraint on the value of the decision variable; The simplex method is used to solve the linear programming model. According to the solution results, the returned products are classified. When x(i, j) = 1, the i-th product in the j-th region is marked as a returnable product. When x(i, j) = 0, it is marked as a non-returnable product. For the returnable goods, they are shipped back to the target warehouse; For such non-returnable merchandise, returns will be processed locally, including local sale at a reduced price.

2. The method according to claim 1, wherein: The obtaining of sales data and return data in different regions in different time periods, including associated sales channels and associated product categories, and calculating the return volume and sales volume in different time periods in each region include: Obtain sales data and return data for each sales region during different sales cycles, and associate sales channel and product category information to form a multi-dimensional data set; Conduct exploratory analysis on raw data and use statistical methods to analyze data distribution, outliers, and missing values; After the analysis, the data set is cleaned and preprocessed to remove abnormal data. According to the proportion of missing values ​​and business meaning, the corresponding processing method is selected, including deleting missing value records, filling with fixed values, and filling with mean or median; The data is segmented according to the time dimension and aggregated according to different time periods to obtain the sales volume and return volume data of each sales region in different time periods, and the different time periods include year, quarter and month; Analyze sales trends over different time periods, using time series analysis techniques to identify long-term trends, seasonal changes, and unexpected events in sales and returns; Introducing the regional dimension, making a horizontal comparison of the sales volume and return volume data of each sales region, using sales volume, sales volume and return rate for comparison, using visualization to display the comparison results, analyzing the differences in sales information between different regions, identifying sales hot spots and potential problem areas, and the visualization methods include bar charts, line charts and maps; Combined with the sales channel and the product category information, vertical drilling is performed on the sales volume and return volume of different channels and products, the sales composition and return factors of each channel and product are analyzed, correlation analysis is used to determine the correlation between different indicators, regression analysis is used to determine the significance of influencing factors, and decision trees are used to analyze the influence rules of different factors on sales volume and return volume; By calculating the ratio of return volume to sales volume, we can obtain the return rate index of each sales region in different time periods, which is an important factor in evaluating sales quality and customer satisfaction. Combine return rate calculation with sales channel and product category analysis to evaluate overall sales information; Generate optimization suggestions based on data analysis, and propose specific optimization measures based on the analysis results, including adjusting sales strategies, optimizing product structure, improving customer service, and instructing business practices; Comprehensively analyze sales cycles, sales regions, sales channels and product categories, explore the correlation and influencing factors between data, and form sales business reports.

3. The method according to claim 1, wherein: For the target region, the correlation analysis module is used to analyze the sales data and return data, including analyzing the impact of sales channels associated with sales and returns on the return volume and sales volume, and classifying the sales channels into two categories, including positive sales channels and negative sales channels. A sales channel improvement report is generated based on the categories of sales channels, including: Obtain sales data and return data in the target area, including sales channels, sales volume, and return volume, to form a data set; Preprocess the data set, check data quality, handle missing values ​​and outliers, and ensure data accuracy and consistency; Using a correlation analysis method, calculate the correlation between the sales channel and the sales volume and the return volume; The Pearson correlation coefficient was used to calculate the linear correlation between variables. The value range of the correlation coefficient was [-1, 1]. If the correlation coefficient was greater than 0.25, it indicated a positive correlation. If the correlation coefficient was less than -0.25, it indicated a negative correlation. If the correlation coefficient was 0, it indicated no correlation. If the amount of data is larger than the preset threshold or there is a nonlinear relationship between the variables, the Spearman rank correlation coefficient or the Kendall rank correlation coefficient is used; According to the size and significance level of the correlation coefficient, the sales channels are classified into two categories, and the sales channels with significant positive correlation to the sales volume are screened out and marked as positive sales channels, and the sales channels with significant negative correlation to the sales volume are marked as negative sales channels; Analyze the positive sales channels and the negative sales channels, and calculate key indicators of each channel, wherein the key indicators include sales amount, sales proportion and return rate; Use Matplotlib to draw a bar chart to compare the sales amount and sales proportion of different sales channels, and show the sales scale and importance of each channel; Use the Seaborn library to draw a multi-series line chart to show the return rate change trend of the positive sales channel and the negative sales channel in different time dimensions, and judge the stability of the positive sales channel and the negative sales channel; Use the pyecharts library to draw pie charts and radar charts to analyze the advantages and disadvantages of the positive sales channels and the negative sales channels in terms of the key indicators; Generate a sales channel improvement report based on the analysis results of the positive sales channels and the negative sales channels, combined with the business scenario information and the market environment information, wherein the sales channel improvement report includes measures to optimize the positive sales channels and a report on transforming the negative sales channels; The above-mentioned measures to optimize sales channels include increasing marketing investment, improving service system and upgrading product experience; The aforementioned transformation of negative sales channel reporting includes adjusting commission policies, optimizing return processes and strengthening quality control; Establish a monitoring indicator system for sales channels, obtain channel indicators, including sales amount, return rate and customer complaint rate, and set early warning thresholds; Use the data visualization panel to monitor the channel indicators of each sales channel in real time. When the channel indicators exceed the warning threshold, the warning mechanism is triggered and response measures are taken in time; Regularly conduct statistical analysis on monitoring indicator data to identify abnormal fluctuations and changing trends in sales channels, adjust optimization strategies, and achieve dynamic management; Integrate the measures for optimizing positive sales channels and the report for transforming negative sales channels into the sales channel management module, adjust resource allocation, optimize business processes, improve the sales efficiency of the positive sales channels, and control the return risk of the negative sales channels; Continuously monitor the sales data and return data of sales channels, conduct correlation analysis and binary classification regularly, dynamically adjust sales channel strategies, and achieve continuous optimization and growth of sales business.

4. The method according to claim 1, wherein: The sales data and return data are classified according to the storage conditions corresponding to the associated product categories, each category corresponds to a warehouse category, and the total return volume and sales volume corresponding to different warehouse categories are calculated, including: Acquire the corresponding relationship data between product categories and storage conditions, and associate the product category field with the sales data and the return data to form a data set including the product category field, the storage condition field, the sales volume field, and the return volume field; Define the corresponding warehouse category according to the different values ​​of the storage condition field; Set the mapping rules between storage conditions and warehouse categories, including dividing into normal temperature warehouse, refrigerated warehouse and frozen warehouse according to temperature conditions, dividing into dry warehouse and moisture-proof warehouse according to humidity conditions, and dividing into hazardous chemical warehouse and cool warehouse according to special storage requirements; Divide into corresponding warehouse categories according to sales and return records; The data set is grouped according to warehouse category, and the sales volume field and the return volume field are summed using an aggregation function to obtain the total sales volume and the total return volume under different warehouse categories, thereby forming summary data of warehouse category, sales volume, and return volume.

5. The method according to claim 1, wherein: The determining whether to establish a target warehouse in a target area according to the return volume and sales volume corresponding to the target warehouse category includes: Obtain the return volume and sales volume of the target warehouse category in the target area, collect data on the population size, consumption level and competition landscape of the target area, and evaluate the market demand of the target area; Calculate the inventory turnover rate and inventory cost of the target warehouse category in the target region based on the return volume and sales volume of the target warehouse category; The formula for the inventory turnover rate is: inventory turnover rate = sales cost / average inventory, where average inventory = (beginning inventory + ending inventory) / 2; The formula of inventory cost is: inventory cost = ordering cost + storage cost + out-of-stock cost, where ordering cost includes fixed ordering cost and unit ordering cost, storage cost includes warehousing fee and capital cost, and out-of-stock cost includes sales loss and customer loss; According to the calculation results and comparison with the preset values, the necessity and feasibility of establishing a warehouse are analyzed; Analyze the logistics distance and transportation costs between the target area and existing warehouses, and evaluate the impact of establishing warehouses in the target area on distribution efficiency and logistics costs; By comparing the distribution efficiency indicators and logistics cost indicators before and after the establishment of the warehouse, the logistics benefits of establishing the warehouse are quantitatively evaluated. The distribution efficiency indicators include order fulfillment cycle, on-time delivery rate and distribution frequency. The logistics cost indicators include transportation cost, warehousing cost, loading and unloading cost and packaging cost. Optimize warehouse location based on the logistics conditions of the target area, including traffic conditions and delivery radius; Based on the land information, labor information and energy conditions of the target area, evaluate the construction cost and operation cost of establishing a warehouse to determine whether it is economically feasible to establish the warehouse; Compare the costs and benefits of building a warehouse in the target area and maintaining the status quo, and calculate the net present value and internal rate of return based on the cash flow forecast of the warehouse project; When the net present value is greater than 0 or the internal rate of return is greater than the project capital cost, the warehouse project is accepted; When the net present value is less than 0 or the internal rate of return is less than the project capital cost, the warehouse project is rejected; When the net present value is equal to 0 or the internal rate of return is equal to the project capital cost, the warehouse project is rejected; Determine the financial feasibility of the warehouse based on the net present value and internal rate of return; In combination with the market demand, logistics conditions, economic feasibility and financial feasibility, the necessity and feasibility of establishing a warehouse in the target area are evaluated to form a decision report.

6. The method according to claim 1, wherein: The step of identifying a recommended region as a recommended region for establishing a target warehouse in a target region, obtaining cross-border express delivery fees and warehousing cost data for the recommended region and adjacent regions, and evaluating the cost of establishing the target warehouse in the recommended region and adjacent regions includes: Obtain recommendation result data for establishing warehouses in target regions, and extract a list of regions identified as recommended; The geographical location and boundary range of the recommended areas are queried through the geographic information system module, and the adjacent distance threshold is set according to the timeliness requirements of the logistics business and the speed of the transportation tools to determine the adjacent area list of each recommended area; Collect cross-border express delivery cost data for recommended regions and adjacent regions, including express delivery prices for different logistics companies, different types of goods, and different weight ranges, as well as import and export customs clearance fees and tariffs, to form an express delivery cost data set; Collect warehousing cost data for the recommended area and adjacent areas, including warehouse rental, labor costs, equipment depreciation, and energy consumption; Combine the land price factor, labor market factor and logistics infrastructure factor in the target area as the storage cost calculation parameter, wherein the land price factor can introduce the average price or rental level data of industrial land in the target area; The labor market factors include the local employment number and average wage level to estimate labor costs; The logistics infrastructure factor introduces highway accessibility and railway freight volume to evaluate logistics convenience; Based on the express delivery cost data set and the warehousing cost estimation parameters, the initial investment cost of establishing a warehouse in the recommended area and adjacent areas is evaluated, including land acquisition costs, construction project costs and equipment procurement costs, to form a warehouse construction cost data set.

7. The method according to claim 1, wherein: The method of analyzing the sales trend of the sales data based on the sales data of all products in the associated recommended region and adjacent regions and the associated target warehouse category, and calculating the comprehensive scores of the recommended region and adjacent regions in combination with the establishment cost and the sales trend, and determining the best region for establishing the target warehouse includes: According to the associated recommended regions, adjacent regions and associated target warehouse categories, the historical sales data of all products are obtained, and the trend analysis of the sales data is performed using the time series analysis method to obtain the sales trend of each product; By associating the geographical location information of the recommended area and the adjacent areas, the establishment cost data of the associated recommended area and the adjacent areas are obtained, and the comprehensive score of each area is calculated by a weighted calculation method in combination with the sales trend characteristics; Sorting and comparing the comprehensive scores to obtain the regions with the highest scores, and determining the best regions for establishing target warehouses; According to the geographical location coordinates of the best region, obtain the customer distribution information of the best region through a spatial clustering algorithm, and combine the customer purchasing power level data to determine the market capacity and growth potential of the best region; If the market capacity and growth potential are lower than the preset threshold, the product data of the associated target warehouse category is obtained, and the association rule mining algorithm is used to find the product category with the highest matching degree with the customer demand in the best region as the key development category; According to the characteristics of key development categories, the classification algorithm is used to obtain warehouse construction parameters that meet the requirements of key development categories, including warehouse area, number of shelves and automation equipment configuration, and obtain a construction plan; Based on the market capacity, the growth potential, the key development categories and the optimal regions, a feasibility analysis report for establishing a target warehouse is generated through visualization, and a final construction plan is determined based on the feasibility analysis report.

8. The method according to claim 1, wherein: The linear programming model is established, and the linear programming model is used to classify the returned products corresponding to the target warehouse, including classifying them into returnable and non-returnable products based on whether the cost of transporting them back to the target warehouse is lower than the cost of processing the return locally. For returnable products, they are transported back to the target warehouse, and for non-returnable products, the returns are processed locally, including selling them at a reduced price locally. It also includes: determining the return logistics path based on the return address and the location of the target warehouse in the best area, and calculating the logistics cost of transporting them back to the target warehouse; if the return logistics path is cross-border logistics, the logistics cost of transporting them back to the target warehouse includes cross-border logistics fees.

9. The method according to claim 8, wherein: Determining the return logistics route based on the return address and the location of the target warehouse in the best area, and calculating the logistics cost of transporting the goods back to the target warehouse; If the return logistics path is cross-border logistics, the logistics cost of returning to the target warehouse includes cross-border logistics fees, including: Obtain the return address information of the returned product, including country, province, city, district, county and detailed address, and convert the return address information into longitude and latitude coordinates and store them in the return address data table; Obtain the location coordinates of the target warehouse from the warehouse location selection results, and calculate the straight-line distance from each return address to the target warehouse; Obtain the API documentation provided by the third-party logistics service provider, obtain the interface calling method, identity authentication, request parameters and response format, write code to call the API interface, and query the logistics route and mileage; Parse and extract the JSON or XML format data returned by the API, and map the logistics path, mileage and estimated time into the return logistics path data table; According to the countries where the return address and the target warehouse are located, determine whether the return logistics path is cross-border. If the return address and the target warehouse are located in different countries, mark it as cross-border logistics; For non-cross-border logistics, collect and maintain the carrier's freight rate table, timely update the pricing method and unit price data in the system, obtain the corresponding pricing method and unit price, including the starting price, unit mileage price and weight unit price, and calculate the logistics cost of transporting back to the target warehouse based on the mileage of the logistics route and the weight of the goods; For temporary freight discounts or offers, notify the business department via SMS and adjust the billing rules to avoid calculation errors; Adopt the price inquiry and comparison module to select the most cost-effective carrier and logistics solution to achieve dynamic optimization of logistics costs; For the cross-border logistics, obtain the additional costs of the cross-border logistics link, the additional costs include tariffs, customs clearance fees and international transportation fees, add the additional costs to the return logistics cost, and obtain the total return cross-border logistics cost; Based on the cross-border return logistics path and the total cross-border logistics cost of the return, a visual display is used to generate a path map and cost report.

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