An e-commerce system based on big data

Abstract

The present invention discloses an e-commerce system based on big data, relates to the technical field of e-commerce, and is implemented by a data collection module, a data sorting module, a prediction analysis module, and a monitoring optimization module. The present invention uses big data to perform in-depth mining and prediction analysis on the complex e-commerce sales data, and extracts factors related to sales volume to build an accurate sales volume prediction system, and connects and combines sales volume prediction with inventory management, so as to automatically adjust the inventory level according to the prediction results, ensure that the inventory matches the market demand to reduce the inventory cost, and improve the inventory turnover rate and enhance the operational efficiency of the enterprise, and can build a comprehensive profit prediction system and comprehensively consider market fluctuations, cost changes, sales strategies and other factors, so that the prediction of this system is accurate and can provide excellent forward-looking management for e-commerce enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce, and in particular to an e-commerce system based on big data. Background Art

[0002] With the rapid development of e-commerce, e-commerce companies are also facing increasingly fierce market competition and rapid changes in consumer demand. In order to maintain their competitive advantage, e-commerce companies need to accurately predict product sales and profits, so as to formulate effective market strategies and operation plans.

[0003] Currently, existing e-commerce systems rely on simple statistics and empirical judgments to predict sales, which makes it difficult to accurately capture market dynamics and changes in consumer behavior and habits. In addition, the connection between sales forecasts and inventory management is not strong, which may lead to inventory levels being difficult to match with market demand, resulting in inventory backlogs or shortages. The lack of refined management and optimization strategies for inventory costs may lead to excessively high inventory costs, affecting corporate profitability. In addition, there is a lack of comprehensive analysis of factors affecting profits, making it difficult to accurately assess a company's future profitability and risk level. Summary of the invention

[0004] The purpose of the present invention is to provide an e-commerce system based on big data, which solves the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following e-commerce system based on big data, including a data collection module, a data sorting module, a prediction analysis module and a monitoring optimization module;

[0006] The data collection module is used to obtain sales data from the historical sales records of the e-commerce platform;

[0007] The data sorting module is used to clean and pre-process the collected sales data and process the data into a unified format;

[0008] The prediction analysis module is used to calculate and obtain the predicted sales value of the e-commerce company in the next period , optimized inventory and the optimized profit forecast value ;

[0009] The monitoring optimization module is used to establish a real-time monitoring system to predict the sales value of the e-commerce company in the next period. , optimized inventory and the optimized profit forecast value Conduct real-time monitoring and generate sales reports, inventory reports and profit reports regularly.

[0010] Optionally, the forecasting and analysis module includes a sales forecasting unit, an inventory optimization unit, an inventory optimization profit forecasting unit and an e-commerce management adjustment and optimization unit.

[0011] Optionally, the forecasting process of the sales forecasting unit is as follows:

[0012] ;

[0013] ;

[0014] ;

[0015] in:

[0016] Predict sales value for the next period for e-commerce;

[0017] is the current sales value of the e-commerce company, The sales value of the e-commerce company in the previous period;

[0018] is the current month number;

[0019] is the total number of months in a year, The value of is 12;

[0020] is the current sales growth trend value, is the impact value of sales volume change speed, is the seasonal fluctuation impact value, is the nonlinear influence coefficient value of sales volume itself;

[0021] is the impact of current sales on next period sales, is the change in sales volume;

[0022] It is a periodic function, which represents the seasonal fluctuation value of sales volume in a year;

[0023] By entering the current sales value of the e-commerce , Sales volume’s own nonlinear impact coefficient value and seasonal fluctuations , in order to comprehensively capture the various factors that affect sales and output the next period of e-commerce sales forecast value .

[0024] Optionally, the optimization process of the inventory optimization unit is as follows:

[0025] ;

[0026] ;

[0027] ;

[0028] in:

[0029] To optimize the inventory level;

[0030] a is the proportional adjustment factor of one, which is used to adjust the inventory according to the predicted sales volume;

[0031] b is the proportional adjustment coefficient 2, which is used to control the impact of sales volume change rate on inventory;

[0032] c is an adjustment factor used to avoid division by zero;

[0033] Forecast sales and inventory optimization values ​​for the next period, is the impact of sales volume change on the optimal inventory level;

[0034] By inputting the current sales value of the e-commerce in the sales forecast unit The next period of forecast sales value of e-commerce As the basis for inventory optimization, the optimized inventory volume is obtained , to achieve dynamic optimization of inventory.

[0035] Optionally, the forecasting process of the inventory optimization profit forecasting unit is as follows:

[0036] ;

[0037] ;

[0038] ;

[0039] in:

[0040] is the predicted value of profit after optimization;

[0041] is the unit commodity holding cost value, is the value of inventory fixed costs, including warehouse rent and insurance;

[0042] u is the sales profit margin, which is the ratio of the profit obtained from each unit of goods sold to the sales price;

[0043] is the expected total profit, is the inventory cost value;

[0044] Based on the optimized inventory in the inventory optimization unit , and the next period forecast sales value of e-commerce in the sales forecast unit , get the optimized profit forecast value , the profit forecast value after optimization It relies on the inventory optimization unit and the sales forecast unit, while the sales forecast unit provides the basis for revenue and the inventory optimization unit is part of determining costs to fully predict and evaluate profits.

[0045] Optionally, the adjustment and optimization process of the e-commerce management adjustment and optimization unit is as follows:

[0046] Set target profit first , and then compare it with the optimized profit forecast value Make a comparison and adjust the current sales growth trend value in the sales forecast unit based on the comparison results. , in order to explore market potential, increase sales and profits, and avoid inventory backlogs and capital occupation caused by overproduction and procurement, thereby reducing risks.

[0047] Optionally, the data collection module uses a web crawler to capture data on the e-commerce web page, and uses a database to store the collected data.

[0048] Optionally, the data sorting module uses data cleaning software and data quality monitoring software, and the data quality monitoring software is used to monitor the data quality during the data cleaning process to ensure the accuracy and completeness of the data.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention obtains the predicted sales value of the e-commerce in the next period through the sales forecast unit, which solves the shortcomings of the existing technology in dealing with complex sales changes and improves the accuracy and reliability of the forecast. The sales forecast unit provides the predicted value of future sales and provides a forward-looking management effect for the e-commerce platform.

[0051] 2. The present invention obtains the optimized inventory through the inventory optimization unit, which can reduce the risk of excess inventory caused by market fluctuations, thereby improving the operational stability of the enterprise. By introducing sales forecasts and sales changes as the basis for inventory optimization, dynamic optimization of inventory is achieved, thereby improving the efficiency and accuracy of inventory management, and thereby reducing inventory costs and risks.

[0052] 3. The present invention obtains the optimized profit forecast value through the inventory optimization profit forecast unit, which can comprehensively consider various factors and realize the comprehensive prediction and evaluation of profits. This prediction method is close to the actual operating conditions, thereby improving the accuracy and practicality of the prediction results. At the same time, the results of profit prediction can also be fed back to sales forecast and inventory optimization to guide future business decisions.

[0053] 4. The present invention iterates and updates the current sales growth trend value through the e-commerce management adjustment and optimization unit, and accurate sales forecasting is the key to optimizing resource allocation. Through iterative adjustment, the overall system can reasonably arrange various links such as production, procurement, inventory and logistics according to the prediction results, so as to improve resource utilization efficiency. The e-commerce management adjustment and optimization unit can dynamically and intelligently adapt to market changes, improve the practicality and effectiveness of the algorithm, and improve the accuracy and efficiency of prediction and optimization, thereby enhancing the stability and adaptability of the system, bringing greater market competitiveness and profitability to the e-commerce platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flowchart of the steps of the e-commerce system based on big data;

[0055] Figure 2 This is a schematic diagram of the overall structure of the e-commerce system based on big data;

[0056] Figure 3 This is a schematic diagram of the structure of the prediction analysis module. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] This e-commerce system based on big data is different from existing e-commerce systems. Existing e-commerce systems often rely on simple statistics and experience to make judgments, which makes it difficult to accurately capture market dynamics and changes in consumer behavior, and there are deficiencies in inventory management and sales forecasting. It may be difficult for inventory levels to match market demand, resulting in inventory backlogs and shortages. The algorithm unit of this e-commerce system conducts in-depth mining and analysis of massive sales data based on big data, and can build an accurate sales forecasting system. It also comprehensively considers various factors such as market fluctuations, cost changes, and sales strategies to achieve accurate predictions of the company's future profitability. Example

[0059] See also Figures 1 to 3 ,This implementation provides an e-commerce system based on big data, including a data collection module, a data ,collation module, a prediction analysis module and a monitoring optimization module;

[0060] The data collection module is used to obtain sales data from the historical sales records of the e-commerce platform. The data collection module uses a web crawler to capture data on the e-commerce web pages and uses a database to store the collected data.

[0061] During the data collection process, the web crawler software is SCRAPY, which is used to crawl market reports and competitor prices from web pages. The web crawler software can automatically crawl data from various sources, thereby reducing manual intervention and improving efficiency. The database provides efficient data storage and query capabilities to support subsequent data analysis and processing.

[0062] The data sorting module is used to clean and pre-process the collected sales data and process the data in a unified format. The data sorting module uses data cleaning software and data quality monitoring software. The data quality monitoring software is used to monitor the data quality during the data cleaning process to ensure the accuracy and completeness of the data.

[0063] In the process of data organization, ETL software is first used for data extraction, rotation and loading. Then data cleaning software such as Python is used to write customized data cleaning logic. The data quality during the data cleaning process is monitored by data quality monitoring software. ETL software can automate the data processing process, thereby reducing human errors to improve processing efficiency. Data cleaning scripts provide flexible data processing capabilities. Data quality monitoring software can ensure the accuracy and completeness of data during the cleaning process, providing a reliable data foundation for subsequent analysis.

[0064] The forecast analysis module is used to calculate and obtain the next period's forecast sales value of e-commerce , optimized inventory and the optimized profit forecast value .

[0065] The monitoring optimization module is used to establish a real-time monitoring system to predict the sales value of the e-commerce in the next period. , optimized inventory and the optimized profit forecast value Conduct real-time monitoring and generate sales reports, inventory reports and profit reports regularly.

[0066] The forecasting and analysis module includes sales forecasting unit, inventory optimization unit, inventory optimization profit forecasting unit and e-commerce management adjustment optimization unit. The forecasting and analysis process of the forecasting and analysis module is as follows:

[0067] Enter the current sales value of the e-commerce company , Sales volume’s own nonlinear impact coefficient value and seasonal fluctuations To the sales forecast unit, to comprehensively capture the various factors that affect sales and output the next period of e-commerce sales forecast value ;

[0068] Enter the current sales value of the e-commerce company in the sales forecast unit The next period of forecast sales value of e-commerce To the inventory optimization unit, the current sales value of the e-commerce company The next period of forecast sales value of e-commerce As the basis for inventory optimization, the optimized inventory volume is obtained ;

[0069] Through the inventory optimization profit forecast unit and based on the optimized inventory in the inventory optimization unit , and the next period forecast sales value of e-commerce in the sales forecast unit , get the optimized profit forecast value ;

[0070] Set target profit first by adjusting and optimizing the e-commerce management unit , and then compare it with the optimized profit forecast value Make a comparison and adjust the current sales growth trend value in the sales forecast unit based on the comparison results. .

[0071] In this embodiment, the e-commerce system based on big data forms a complete prediction and optimization system based on the mutual cooperation and association of multiple computing units. By comprehensively considering multiple factors, the scientificity and accuracy of enterprise decision-making are greatly improved. For example, e-commerce enterprises can adjust production plans according to the predicted sales value of the next period of e-commerce, adjust procurement strategies according to the optimized inventory, and formulate pricing and promotion strategies according to the optimized profit forecast value;

[0072] Through inventory optimization, e-commerce companies can reduce unnecessary inventory backlogs and out-of-stock risks, thereby reducing inventory costs. Accurate sales and profit forecasts can also help companies better manage cash flow and financial risks. The prediction and optimization system based on big data enables e-commerce companies to respond to market changes more quickly and seize market opportunities. By optimizing inventory and pricing strategies, customer satisfaction and loyalty can be improved, thereby enhancing market competitiveness.

[0073] Compared with traditional forecasting and optimization system methods, this system is a forecasting and optimization system based on big data, which is more comprehensive, accurate and flexible. It can not only consider more influencing factors, but also adjust the forecasting and optimization strategies in real time according to market changes. This innovative method provides enterprises with more powerful decision-making support tools, promotes the improvement of enterprise management level and enhances market competitiveness.

[0074] See also Figures 1 to 3 , the forecasting process of the sales forecasting unit is as follows:

[0075] ;

[0076] ;

[0077] ;

[0078] in:

[0079] Predict sales value for the next period for e-commerce;

[0080] The current sales value of the e-commerce company;

[0081] The sales value of the e-commerce company in the previous period;

[0082] is the current month number;

[0083] is the total number of months in a year, The value of is 12;

[0084] is the current sales growth trend value;

[0085] is the impact value of sales volume change speed;

[0086] is the seasonal fluctuation impact value;

[0087] is the nonlinear influence coefficient value of sales volume itself;

[0088] The value of the impact of current sales on next period’s sales;

[0089] is the change in sales volume;

[0090] It is a periodic function, which represents the seasonal fluctuation of sales volume in a year.

[0091] In this embodiment: the calculation unit takes into account various factors based on sales volume and seasonal fluctuations, so that the calculation unit can more comprehensively capture various factors affecting sales volume, thereby improving the accuracy of sales volume forecasting. The calculation is based on historical data and current trends for prediction, which can provide e-commerce with forward-looking information on future sales and help enterprises make production and sales plans in advance. Compared with the existing technology, which may only rely on simple linear regression and time series analysis, and ignore the complexity and nonlinear characteristics of sales changes, as well as the important influence of seasonal factors, resulting in inaccurate prediction results, this operation unit can effectively solve the shortcomings of the existing technology in dealing with complex sales changes by introducing nonlinear terms and seasonal factors, and improve the accuracy and reliability of the prediction. This operation unit provides the predicted value of future sales as the basis for subsequent inventory optimization and profit forecasting, and the accuracy of sales forecasting will directly affect the accuracy of inventory optimization and profit forecasting. If the sales forecast deviates from the actual, the inventory and profit management strategies based on the forecast may also fail.

[0092] See also Figures 1 to 3 , the inventory optimization process of the inventory optimization unit is as follows:

[0093] ;

[0094] ;

[0095] ;

[0096] in:

[0097] To optimize the inventory level;

[0098] a is the proportional adjustment factor of one, which is used to adjust the inventory according to the predicted sales volume;

[0099] b is the proportional adjustment coefficient 2, which is used to control the impact of sales volume change rate on inventory;

[0100] c is an adjustment factor used to avoid division by zero;

[0101] Forecast sales and inventory optimization values ​​for the next period;

[0102] is the impact of sales volume change on the optimal inventory level.

[0103] In this embodiment: based on , and Then calculate the optimized inventory Therefore, unnecessary inventory backlog and out-of-stock risks can be reduced, thereby reducing inventory costs. Inventory optimization strategies can respond to market changes more quickly, increase inventory when sales forecasts show growth, and reduce inventory when sales decline, maintaining a balance between inventory and market demand. By considering the impact of sales changes on inventory, the risk of excess or shortage inventory caused by market fluctuations can be reduced, and the operational stability of the enterprise can be improved. Compared with existing inventory optimization methods that may only rely on simple inventory thresholds or fixed inventory turnover rates, they lack the ability to dynamically respond to market changes and are prone to inventory backlogs or out-of-stock phenomena. This calculation unit introduces sales forecasts and sales changes as the basis for inventory optimization, thereby achieving dynamic optimization of inventory, improving the efficiency and accuracy of inventory management, and reducing inventory costs and risks.

[0104] See also Figures 1 to 3 , the forecasting process of the inventory optimization profit forecasting unit is as follows:

[0105] ;

[0106] ;

[0107] ;

[0108] in:

[0109] is the predicted value of profit after optimization;

[0110] is the unit commodity holding cost value;

[0111] is the value of inventory fixed costs, including warehouse rent and insurance;

[0112] u is the sales profit margin, which is the ratio of the profit obtained from each unit of goods sold to the sales price;

[0113] is the expected total profit;

[0114] is the inventory cost value.

[0115] In this embodiment: through comprehensive consideration 、u、 and This operation unit can provide the best profit forecast for the enterprise, which helps the enterprise to formulate production and sales strategies that maximize profits. The form of profit forecast provides an important decision-making basis for e-commerce platforms, which helps e-commerce make more scientific and reasonable decisions in market competition, such as pricing strategies and promotional activities. By predicting profits under different sales and inventory levels, enterprises can evaluate the risks and benefits of different strategies to select the best solution. The existing profit forecasting method may focus on the simple relationship between sales and costs, while ignoring the impact of inventory costs, fixed costs and market changes on profits, resulting in incomplete and inaccurate forecast results. This operation unit achieves a comprehensive forecast and evaluation of profits by comprehensively considering multiple factors. This forecasting method is closer to the actual operation of the enterprise, thereby improving the accuracy and practicality of the forecast results, and providing enterprises with more powerful decision-making support. The profit forecast depends on the results of sales forecast and inventory optimization. Sales forecast provides the basis for income, while inventory optimization determines part of the cost. At the same time, the results of profit forecast can also be fed back to sales forecast and inventory optimization to guide future business decisions.

[0116] See also Figures 1 to 3 , the adjustment and optimization process of the e-commerce management adjustment and optimization unit is as follows:

[0117] Set target profit first and the adjustment factor is 0.02;

[0118] like , then increase the current sales growth trend value ,and then , and use Replace the formula in ;

[0119] like , then reduce the current sales growth trend value ,and then , and use Replace the formula in ;

[0120] like , then keep constant.

[0121] In this embodiment, through this iterative form, a forecasting and optimization system is formed between the sales forecasting unit and the inventory optimization profit forecasting unit. The value of can make the whole system more flexible and accurate to respond to market changes, improve the prediction and optimization effects. This operation unit can make the algorithm more dynamic and intelligent to adapt to market changes by introducing an iterative adjustment mechanism, improve the practicality and effectiveness of the algorithm, and To iteratively adjust the sales forecast unit Value has significant effects and significance in e-commerce. It can not only improve the accuracy and efficiency of prediction and optimization, but also enhance the stability and adaptability of the system, bringing greater market competitiveness and profitability to enterprises.

[0122] Market environment, consumer preferences, competitive situation and other factors are constantly changing, all of which will affect sales and profits, and then through iterative adjustments The system can dynamically adapt to these changes to make the prediction more accurate. Higher than When, to increase value, can further explore market potential, increase sales and profits, when Lower than When, reduce Value can avoid inventory backlog and capital occupation caused by overproduction or over-purchasing, thus reducing risks;

[0123] The accurate sales forecast of this operation unit is the key to optimizing resource allocation. Through iterative adjustment, the overall system can reasonably arrange various links such as production, procurement, inventory and logistics according to the forecast results to improve resource utilization efficiency. When it is predicted that future sales will increase, inventory and production capacity can be increased in advance. When it is predicted that sales will decrease, inventory and production plans can be reduced to avoid waste.

[0124] Based on the iteratively adjusted sales forecast and profit forecast, enterprises can make more scientific and reasonable decisions, such as formulating sales strategies, adjusting pricing strategies and optimizing product structures. These decisions will be based on more accurate data and forecast results, which will help improve market competitiveness and profitability. The iterative adjustment mechanism helps the system maintain stability in the face of emergencies or abnormal data. Iterative adjustment is an ongoing process. With the accumulation of data and experience, the system can continuously optimize and adjust strategies to improve the accuracy and efficiency of prediction and optimization.

[0125] In the specific implementation process, the three algorithm units in the method are used to form an e-commerce system based on big data. By substituting the current sales value of the e-commerce company and the current month number into the sales forecast unit, the predicted sales value of the e-commerce company for the next period is calculated, thereby effectively solving the shortcomings of the existing technology in dealing with complex sales changes and improving the accuracy and reliability of the prediction. By substituting the predicted sales value of the e-commerce company for the next period and the current sales value of the e-commerce company into the inventory optimization unit, the optimized inventory is calculated, and then the dynamic optimization of the inventory is achieved by introducing the sales forecast and the sales change as the basis for inventory optimization, thereby improving the efficiency and accuracy of inventory management and reducing inventory costs and risks. By substituting the optimized inventory and the predicted sales value of the e-commerce company for the next period into the inventory optimization unit, the optimized inventory is calculated. In the profit forecasting unit, the optimized profit forecast value is calculated, and then a comprehensive forecast and evaluation of profits is achieved by comprehensively considering various factors. This forecasting method is closer to the actual operation of the enterprise, thereby improving the accuracy and practicality of the forecast results, and providing e-commerce enterprises with more powerful decision-making support. In addition, the optimization unit based on e-commerce management adjustment can link the sales forecasting unit and the inventory optimization profit forecasting unit together, so that the system composed of the entire algorithm unit can be cyclically and iteratively optimized, so that e-commerce enterprises can improve their market competitiveness and profitability through more accurate data and forecasting results, and the overall multiple groups of algorithm units can cooperate with each other in calculation, and can also perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0126] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An e-commerce system based on big data, characterized in that: include: The data collection module is used to obtain sales data from the historical sales records of the e-commerce platform; The data sorting module is used to clean and pre-process the collected sales data and process the data into a unified format; Prediction analysis module, used to calculate and obtain the next period of e-commerce sales forecast value , optimized inventory and the optimized profit forecast value ; The monitoring optimization module is used to establish a real-time monitoring system to predict the sales value of the e-commerce in the next period. , optimized inventory and the optimized profit forecast value Conduct real-time monitoring and generate sales reports, inventory reports and profit reports regularly; The forecasting and analysis module includes a sales forecasting unit, an inventory optimization unit, an inventory optimization profit forecasting unit, and an e-commerce management adjustment and optimization unit; The prediction process of the sales volume prediction unit is as follows: ； ； ； in: Predict sales value for the next period for e-commerce; is the current sales value of the e-commerce company, The sales value of the e-commerce company in the previous period; is the current month number; is the total number of months in a year, The value of is 12; is the current sales growth trend value, is the impact value of sales volume change speed, is the seasonal fluctuation impact value, is the nonlinear influence coefficient value of sales volume itself; is the impact of current sales on next period sales, is the change in sales volume; It is a periodic function, which represents the seasonal fluctuation value of sales volume in a year; By entering the current sales value of the e-commerce , Sales volume’s own nonlinear impact coefficient value and seasonal fluctuations , in order to comprehensively capture the various factors that affect sales and output the next period of e-commerce sales forecast value .

2. The e-commerce system based on big data according to claim 1, characterized in that: The inventory optimization process of the inventory optimization unit is as follows: ； ； ； in: To optimize the inventory level; a is the proportional adjustment factor of one, which is used to adjust the inventory according to the predicted sales volume; b is the proportional adjustment coefficient 2, which is used to control the impact of sales volume change rate on inventory; c is an adjustment factor used to avoid division by zero; Forecast sales and inventory optimization values ​​for the next period, is the impact of sales volume change on the optimal inventory level; By inputting the current sales value of the e-commerce in the sales forecast unit And the next period of e-commerce sales forecast As the basis for inventory optimization, the optimized inventory volume is obtained , to achieve dynamic optimization of inventory.

3. The big data-based e-commerce system according to claim 2, characterized in that: The forecasting process of the inventory optimization profit forecasting unit is as follows: ； ； ； in: is the predicted value of profit after optimization; is the unit commodity holding cost value, is the value of inventory fixed costs, including warehouse rent and insurance; u is the sales profit margin, which is the ratio of the profit obtained from each unit of goods sold to the sales price; is the expected total profit, is the inventory cost value; Based on the optimized inventory in the inventory optimization unit , and the next period forecast sales value of e-commerce in the sales forecast unit , get the optimized profit forecast value , the profit forecast value after optimization It relies on the inventory optimization unit and the sales forecast unit, while the sales forecast unit provides the basis for revenue and the inventory optimization unit is part of determining costs to fully predict and evaluate profits.

4. The e-commerce system based on big data according to claim 3, characterized in that: The adjustment and optimization process of the e-commerce management adjustment and optimization unit is as follows: Set target profit first , and then compare it with the optimized profit forecast value Make a comparison and adjust the current sales growth trend value in the sales forecast unit based on the comparison results. , in order to explore market potential, increase sales and profits, and avoid inventory backlogs and capital occupation caused by overproduction and procurement, thereby reducing risks.

5. The big data-based e-commerce system according to claim 1, characterized in that: The data collection module uses a web crawler to capture data on the e-commerce web page, and uses a database to store the collected data.

6. The big data-based e-commerce system according to claim 1, characterized in that: The data sorting module uses data cleaning software and data quality monitoring software. The data quality monitoring software is used to monitor the data quality during the data cleaning process to ensure the accuracy and completeness of the data.

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