An AI-driven e-commerce demand prediction and inventory management method and system

Abstract

This invention provides an AI-driven e-commerce demand forecasting and inventory management method and system. It collects data from e-commerce platforms and social media using web crawling technology, combines image recognition to generate product shape and texture feature data, and optimizes the product tag database. Multiple algorithms are used to verify data and analyze shopping cart interactions. A Bayesian dynamic linear model and convolutional neural network are combined to generate medium- to long-term demand forecasts, predicting popular color schemes or pattern trends. Inventory allocation is optimized by using genetic algorithms and adaptive large-scale neighborhood search algorithms to plan logistics routes and schedule resources. Data visualization and algorithm adjustments enable system feedback optimization, synchronously sending forecast results to the production line to guide dynamic capacity adjustments, achieving intelligent control of the entire e-commerce process.

Description

Technical Field

[0001] This invention relates to the field of demand forecasting and inventory management technology, specifically to an AI-driven e-commerce demand forecasting and inventory management method and system. Background Technology

[0002] In e-commerce operations, traditional demand forecasting and inventory management models have many limitations. They rely heavily on manual experience or single data sources, failing to comprehensively collect user feedback search data from e-commerce platforms and relevant discussion data from social media. They also cannot effectively analyze product visual characteristics, resulting in a lack of comprehensive data support for demand forecasting, low accuracy, and difficulty in predicting consumer trends such as popular colors or patterns. This leads to unreasonable inventory allocation, resulting in either stockpiled goods tying up capital or stockouts and lost customers. Furthermore, traditional models suffer from chaotic product labeling, lacking a standardized label management system, compromising data consistency, and failing to fully leverage the value of shopping cart interaction data to quantify product demand. Logistics route planning is often based on fixed routes, without dynamic optimization based on real-time traffic conditions and resource distribution. This results in inefficient resource scheduling and disconnects between different parts of the supply chain, making it impossible to synchronize demand forecasting results to the production line, guide dynamic capacity adjustments, and adapt to rapid market changes. In addition, traditional systems lack effective feedback and optimization mechanisms, failing to dynamically adjust operational strategies based on user behavior data, hindering operational efficiency improvements.

[0003] Existing technologies for e-commerce demand forecasting and inventory management still have significant shortcomings. Most technologies can only manage a single stage, lacking the ability for collaborative control across the entire process. Data collection is limited, failing to fully integrate data from e-commerce platforms and social media, and lacking the integration of image recognition technology to mine product visual features, resulting in inaccurate product feature data. The product tag optimization mechanism is also incomplete, affecting the accuracy of subsequent demand analysis. Demand forecasting often relies on single algorithms, failing to achieve multi-algorithm collaborative computation, thus failing to account for both short-term fluctuations and long-term trends, making it difficult to generate accurate medium- to long-term demand forecasts or effectively predict popular trends. Inventory optimization algorithms are simplistic and unable to dynamically adapt to demand fluctuations. Logistics route planning lacks efficient optimization algorithms, resulting in insufficient flexibility in resource scheduling. Furthermore, existing technologies lack comprehensive data visualization and system feedback optimization mechanisms, making it impossible to intuitively grasp user behavior patterns, dynamically adjust model parameters, or dynamically adjust production lines. The lack of seamless integration between stages makes it difficult to solve core problems in e-commerce operations such as unreasonable inventory, inefficient logistics, and delayed demand forecasting, failing to meet the needs of large-scale and intelligent e-commerce operations. Summary of the Invention

[0004] To address the aforementioned technical shortcomings, the purpose of this invention is to provide an AI-driven e-commerce demand forecasting and inventory management method and system that ensures the comprehensiveness, standardization, and accuracy of the data.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An AI-driven e-commerce demand forecasting and inventory management method includes the following steps:

[0007] S1. Data collection and preprocessing: Use web crawling technology to obtain user feedback data, search data and product tag data from social media platforms of e-commerce platforms; use image recognition technology to generate shape and texture feature data; optimize product tags and generate a product tag database.

[0008] S2. Based on the preprocessed data, perform demand forecasting and analysis, verify the consistency between visual data and text analysis results, analyze shopping cart interaction data to calculate tag weight scores, and combine multiple data to generate medium- and long-term demand forecasting results through Bayesian dynamic linear model and convolutional neural network algorithm.

[0009] S3. Based on the prediction results, optimize and adjust the inventory, update the inventory and user preference data, use adaptive filtering algorithm and Bayesian optimization algorithm to process the feedback data, and use convolutional neural network to optimize the inventory configuration.

[0010] S4. Conduct logistics route planning and resource scheduling. Generate an initial path solution set based on a genetic algorithm, evaluate the fitness, and then use an adaptive large-scale neighborhood search algorithm to optimize the path and dynamically schedule logistics resources.

[0011] S5. Conduct system feedback and optimization, analyze the efficiency of the recommendation system and user satisfaction, display user behavior data through data visualization, adjust model parameters and optimize the efficiency of each link in the supply chain.

[0012] Preferably, in step S1, data collection and preprocessing specifically includes: using web crawling technology to selectively acquire user feedback data, search data, and product tag data from e-commerce platforms and social media websites, ensuring the comprehensiveness and real-time nature of data acquisition; using image recognition technology to analyze the texture and shape of product images, using image processing algorithms to identify and classify the texture types in product images, using edge detection technology to draw the shape outline of the product, and generating standardized shape and texture feature data; based on product tag correlation scoring, optimizing the descriptiveness and accuracy of tags, adjusting the classification and description information of tags, eliminating redundant and incorrect tags, and integrating effective tags to generate a standardized product tag database for subsequent demand prediction analysis.

[0013] Preferably, in step S2, the demand forecasting and analysis specifically includes: based on product feature identification information, using data verification algorithms to compare the consistency between visual data and text analysis results and verify the data between the two; evaluating the correlation between visual features and text descriptions through a logistic regression model; making targeted adjustments to inconsistent data points to ensure data accuracy; based on shopping cart interaction analysis data, using statistical methods and machine learning algorithms to accurately analyze the relationship between the increase / decrease patterns of products in the shopping cart and product tags; calculating the weight score of each tag by comprehensively referring to the increase and decrease frequency of the tag products, and quantifying the demand popularity of the products corresponding to the tags; based on Bayesian dynamic linear model and convolutional neural network algorithm, importing short-term forecast results, historical order data, market trend data, and environmental factors; and generating accurate medium- and long-term demand forecast results through the collaborative operation of the two algorithms to clarify the demand trends of each product.

[0014] Preferably, in step S3, inventory optimization and adjustment specifically includes: real-time collection of seller product inventory information and user shopping pattern data, synchronously updating product inventory information and user preference data to ensure the real-time nature of inventory and user data; processing of feedback data using adaptive filtering and Bayesian optimization algorithms, wherein the adaptive filtering algorithm is used to dynamically adjust the inventory strategy, setting the filter order and adjustment step size, and the Bayesian optimization algorithm ensures the accuracy of prediction when demand fluctuates greatly by adjusting the feedback frequency and confidence interval; based on medium- and long-term demand forecast results, using convolutional neural networks to help identify long-term trends in inventory data, combining product demand popularity and user preferences to optimize inventory configuration and rationally allocate the inventory quantity of each product.

[0015] Preferably, in step S4, the logistics route planning and resource scheduling specifically includes: generating an initial path solution set based on a genetic algorithm, setting the population size to ensure the diversity and rationality of the initial solution set; establishing a fitness evaluation system based on three core indicators—path length, traffic conditions, and estimated transportation time—to conduct a comprehensive fitness evaluation of the initial path solution set; using an adaptive large-scale neighborhood search algorithm for path optimization, iteratively optimizing the evaluated path for complex transportation demands and resource constraints to obtain the optimal logistics path; and collecting the current logistics resource status in real time, including the number of vehicles, vehicle locations, and transportation capacity, and combining this with the optimal logistics path to achieve dynamic scheduling of vehicles and other logistics resources and rational allocation of transportation tasks.

[0016] Preferably, in step S5, system feedback and optimization specifically include: real-time collection of user response data and purchase behavior data for recommended products; statistical analysis of the efficiency and user satisfaction of the recommendation system to identify its shortcomings; data visualization processing using Kibana to transform user behavior data into intuitive visual charts that clearly demonstrate user behavior patterns and demand characteristics; dynamic adjustment of model parameters for each unit based on feedback data using a Bayesian optimization algorithm to optimize algorithm efficiency and improve prediction and management accuracy; and linkage of all links in the supply chain through an intelligent collaboration mechanism to optimize workflows and resource allocation at each link, thereby improving the overall operational efficiency of the supply chain.

[0017] Preferably, the data acquired by the web crawler technology includes product reviews, user questions, search keyword search frequency on e-commerce platforms, and product mentions in user comments and discussions on social media; the image processing algorithm includes texture feature extraction algorithm and texture classification algorithm, and the edge detection technology uses an edge detection operator to ensure the accuracy of product shape contour drawing, and the generated shape and texture feature data is associated with the product tag database.

[0018] Preferably, the Bayesian dynamic linear model is used to process historical order data and environmental factors to capture the dynamic trend of data changes, while the convolutional neural network is used to analyze visual data and short-term prediction results to mine hidden features in the data. When the two algorithms work together, the output of the Bayesian dynamic linear model is used as the input parameter of the convolutional neural network, and the results of the two operations are combined to generate medium- and long-term demand prediction results.

[0019] An AI-driven e-commerce demand forecasting and inventory management method includes a data acquisition and preprocessing module, a demand forecasting and analysis module, an inventory optimization and adjustment module, a logistics route planning module, a system feedback optimization module, a data storage module, and a control system module. The data acquisition and preprocessing module is used for data acquisition, image recognition, and product tag database generation. The demand forecasting and analysis module is used for data verification, shopping cart interaction analysis, and demand forecasting result generation. The inventory optimization and adjustment module is used for inventory and user preference data update feedback data processing and inventory configuration optimization. The logistics route planning module is used for initial route generation, route optimization, and logistics resource scheduling. The system feedback optimization module is used for recommendation efficiency analysis, data visualization, parameter adjustment, and supply chain collaborative optimization. The data storage module stores various types of data and calculation results. The control system module is connected to each module and controls the modules to collaboratively complete the entire demand forecasting and inventory management process.

[0020] Preferably, the data acquisition and preprocessing module includes a crawler unit, an image recognition unit, and a label optimization unit. The crawler unit is used to acquire various types of data, the image recognition unit is used to generate shape and texture feature data, and the label optimization unit is used to optimize product labels and generate a product label database. The demand prediction and analysis module includes a data verification unit, a shopping cart analysis unit, and a prediction algorithm unit. The data verification unit is used to verify data consistency, the shopping cart analysis unit is used to calculate label weight scores, and the prediction algorithm unit is used to run a Bayesian dynamic linear model and a convolutional neural network algorithm to generate prediction results.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] The data acquisition and preprocessing module uses web crawling technology to comprehensively collect various relevant data from e-commerce platforms and social media. Combined with image recognition technology, it analyzes the texture and shape of products, generates standardized shape and texture feature data, optimizes product labels, establishes a standardized product label database, and eliminates redundant and erroneous labels to ensure the comprehensiveness, standardization, and accuracy of the data, providing a reliable data foundation for subsequent demand forecasting and analysis.

[0023] The demand forecasting and analysis module effectively verifies the consistency between visual data and text analysis results through data verification algorithms and logistic regression models, adjusts inconsistent data points, and ensures data accuracy. By analyzing shopping cart interaction data to quantify the popularity of product demand, and combining Bayesian dynamic linear models and convolutional neural networks for collaborative computation, it can accurately generate medium- and long-term demand forecast results, effectively predict popular color schemes or pattern trends, and provide a clear basis for adjusting production line capacity.

[0024] The inventory optimization and adjustment module can update product inventory information and user preference data in real time. It uses adaptive filtering algorithm and Bayesian optimization algorithm to process feedback data in a collaborative manner, dynamically adjust inventory strategy, and combine convolutional neural network to identify long-term inventory trends, optimize inventory configuration, and reasonably allocate the inventory quantity of each product to avoid inventory backlog or stockout problems, thereby achieving dynamic and intelligent inventory management.

[0025] The logistics route planning module generates diverse initial path solutions through a genetic algorithm, establishes a fitness evaluation system by combining path length, traffic conditions, and estimated transportation time, and iteratively optimizes the paths using an adaptive large-scale neighborhood search algorithm. It dynamically schedules logistics resources in real time, rationally allocates transportation tasks, improves logistics transportation efficiency, and reduces logistics operating costs.

[0026] The system feedback optimization module can analyze the efficiency of the recommendation system and user satisfaction in real time. It can intuitively display user behavior patterns through data visualization, dynamically adjust the parameters of each unit model in combination with the Bayesian optimization algorithm to optimize the algorithm's running efficiency, and at the same time, link all links in the supply chain through the intelligent collaboration mechanism to optimize the workflow and resource allocation of each link, thereby achieving closed-loop optimization of the entire process.

[0027] The entire system achieves intelligent control of the entire process, including data collection, demand forecasting, inventory optimization, logistics scheduling, and system feedback, through the collaborative work of its various modules. It synchronizes demand forecasting results to the production line, guides the production line to dynamically adjust its capacity, connects e-commerce operations with production processes, achieves precise matching of supply and demand, and improves the overall operational efficiency and intelligence level of e-commerce.

[0028] The establishment and optimization of a product tag database can improve the accuracy of product classification and retrieval, providing precise support for demand forecasting and product recommendation. At the same time, in-depth analysis of shopping cart interaction data can accurately capture user demand preferences, further improving the targeting and accuracy of demand forecasting. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method of the present invention;

[0030] Figure 2 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0031] The invention will now be further described with reference to the accompanying drawings.

[0032] like Figure 1 An AI-driven e-commerce demand forecasting and inventory management method includes the following steps:

[0033] S1. Data collection and preprocessing: Use web crawling technology to obtain user feedback data, search data and product tag data from social media platforms of e-commerce platforms; use image recognition technology to generate shape and texture feature data; optimize product tags and generate a product tag database.

[0034] Specifically, web scraping technology is used to selectively acquire user feedback data, search data, and product tag data from e-commerce platforms and social media websites, ensuring the comprehensiveness and real-time nature of data acquisition. Image recognition technology is used to analyze the texture and shape of product images, image processing algorithms are used to identify and classify texture types in product images, edge detection technology is used to draw the shape outline of products, and standardized shape and texture feature data is generated. Based on product tag relevance scoring, the descriptiveness and accuracy of tags are optimized, the classification and description information of tags are adjusted, redundant and incorrect tags are eliminated, and effective tags are integrated to generate a standardized product tag database for subsequent demand forecasting and analysis.

[0035] The data acquired by web crawling technology includes product reviews, user questions, search keywords and search frequency from e-commerce platforms, and user comments, topics, and related product mentions on social media. Image processing algorithms include texture feature extraction algorithms and texture classification algorithms. Edge detection technology uses edge detection operators to ensure the accuracy of product shape contour drawing. The generated shape and texture feature data is associated with the product tag database.

[0036] S2. Based on the preprocessed data, perform demand forecasting and analysis, verify the consistency between visual data and text analysis results, analyze shopping cart interaction data to calculate tag weight scores, and combine multiple data to generate medium- and long-term demand forecasting results through Bayesian dynamic linear model and convolutional neural network algorithm.

[0037] Specifically, based on product feature recognition information, data verification algorithms are used to compare the consistency between visual data and text analysis results and verify the data between the two. A logistic regression model is used to evaluate the correlation between visual features and text descriptions, and inconsistent data points are adjusted accordingly to ensure data accuracy. Based on shopping cart interaction analysis data, statistical methods and machine learning algorithms are used to accurately analyze the relationship between the increase / decrease patterns of items in the shopping cart and product tags. By comprehensively referencing the frequency of increase and decrease of tagged products, the weight score of each tag is calculated, quantifying the demand popularity of the corresponding product. Based on Bayesian dynamic linear models and convolutional neural network algorithms, short-term prediction results, historical order data, market trend data, and environmental factors are imported. Through the collaborative operation of the two algorithms, accurate medium- and long-term demand forecast results are generated, clarifying the demand trends of each product.

[0038] The Bayesian dynamic linear model is used to process historical order data and environmental factors to capture the dynamic trends of data changes. The convolutional neural network is used to analyze visual data and short-term prediction results to uncover hidden features in the data. When the two algorithms work together, the output of the Bayesian dynamic linear model is used as the input parameter of the convolutional neural network, and the results of the two operations are combined to generate medium- and long-term demand prediction results.

[0039] S3. Based on the prediction results, optimize and adjust the inventory, update the inventory and user preference data, use adaptive filtering algorithm and Bayesian optimization algorithm to process the feedback data, and use convolutional neural network to optimize the inventory configuration.

[0040] Specifically, the system collects seller inventory information and user shopping pattern data in real time, and updates inventory and user preference data synchronously to ensure the timeliness of inventory and user data. It employs adaptive filtering and Bayesian optimization algorithms to process the feedback data. The adaptive filtering algorithm dynamically adjusts the inventory strategy by setting the filter order and adjustment step size, while the Bayesian optimization algorithm adjusts the feedback frequency and confidence interval to ensure prediction accuracy even when demand fluctuates significantly. Based on medium- to long-term demand forecasts, a convolutional neural network is used to help identify long-term trends in inventory data. Combining product demand popularity and user preferences, the system optimizes inventory allocation and rationally distributes the inventory quantity of each product.

[0041] S4. Conduct logistics route planning and resource scheduling. Generate an initial path solution set based on a genetic algorithm, evaluate the fitness, and then use an adaptive large-scale neighborhood search algorithm to optimize the path and dynamically schedule logistics resources.

[0042] Specifically, an initial path solution set is generated based on a genetic algorithm, and the population size is set to ensure the diversity and rationality of the initial solution set. A fitness evaluation system is established, combining three core indicators: path length, traffic conditions, and estimated transportation time, to comprehensively evaluate the fitness of the initial path solution set. An adaptive large-scale neighborhood search algorithm is used for path optimization. For complex transportation demands and resource constraints, the evaluated paths are iteratively optimized to obtain the optimal logistics path. The current logistics resource status, including the number of vehicles, vehicle locations, and transportation capacity, is collected in real time. Combined with the optimal logistics path, dynamic scheduling of vehicles and other logistics resources is achieved, and transportation tasks are rationally allocated.

[0043] S5. Conduct system feedback and optimization, analyze the efficiency of the recommendation system and user satisfaction, display user behavior data through data visualization, adjust model parameters and optimize the efficiency of each link in the supply chain.

[0044] Specifically, the system collects real-time data on user responses to recommended products and their purchasing behavior. Through statistical analysis, it assesses the efficiency and user satisfaction of the recommendation system and identifies its shortcomings. Kibana is used for data visualization, transforming user behavior data into intuitive charts that clearly demonstrate user behavior patterns and needs. Bayesian optimization algorithms are combined to dynamically adjust model parameters for each unit based on feedback data, optimizing algorithm efficiency and improving prediction and management accuracy. Finally, an intelligent collaboration mechanism links all aspects of the supply chain, optimizing workflows and resource allocation at each stage to improve overall supply chain operational efficiency.

[0045] like Figure 2As shown, an AI-driven e-commerce demand forecasting and inventory management method includes a data acquisition and preprocessing module, a demand forecasting and analysis module, an inventory optimization and adjustment module, a logistics route planning module, a system feedback optimization module, a data storage module, and a control system module. The data acquisition and preprocessing module is used for data acquisition, image recognition, and product tag database generation. The demand forecasting and analysis module is used for data verification, shopping cart interaction analysis, and demand forecasting result generation. The inventory optimization and adjustment module is used for inventory and user preference data update feedback data processing and inventory configuration optimization. The logistics route planning module is used for initial route generation, route optimization, and logistics resource scheduling. The system feedback optimization module is used for recommendation efficiency analysis, data visualization, parameter adjustment, and supply chain collaborative optimization. The data storage module stores various types of data and calculation results. The control system module is connected to each module and controls the modules to collaboratively complete the entire demand forecasting and inventory management process.

[0046] Furthermore, the data acquisition and preprocessing module includes a web crawler unit, an image recognition unit, and a label optimization unit. The web crawler unit is used to acquire various types of data, the image recognition unit is used to generate shape and texture feature data, and the label optimization unit is used to optimize product labels and generate a product label database. The demand forecasting and analysis module includes a data verification unit, a shopping cart analysis unit, and a prediction algorithm unit. The data verification unit is used to verify data consistency, the shopping cart analysis unit is used to calculate label weight scores, and the prediction algorithm unit is used to run Bayesian dynamic linear models and convolutional neural network algorithms to generate prediction results.

[0047] Example 1

[0048] This embodiment is applied to an e-commerce platform for apparel, and the specific process is as follows:

[0049] The data collection and preprocessing module involves several steps. The web crawler unit uses web crawling technology to acquire user feedback data, search data, and product tag data from apparel e-commerce platforms, and user comments, discussions, and product mentions from social media. The image recognition unit analyzes the texture and shape of apparel product images using image recognition technology. Image processing algorithms identify and classify texture types in apparel images, and edge detection technology is used to draw the shape outlines of the apparel, generating standardized shape and texture feature data. The tag optimization unit optimizes the descriptiveness and accuracy of tags based on their relevance scoring, adjusts the classification and description information of tags, eliminates redundant and incorrect tags, integrates valid tags to generate a standardized apparel product tag database, and links the shape and texture feature data with the product tag database to provide data support for subsequent demand forecasting and analysis.

[0050] Demand forecasting and analysis are conducted. The data verification unit of the demand forecasting and analysis module uses clothing product feature recognition information and data verification algorithms to compare the consistency between clothing visual data and text analysis results and verify the data between the two. It evaluates the correlation between clothing visual features and text descriptions through a logistic regression model and makes targeted adjustments to inconsistent data points to ensure data accuracy. The shopping cart analysis unit uses shopping cart interaction analysis data and statistical methods and machine learning algorithms to accurately analyze the relationship between the increase and decrease patterns of clothing products in the shopping cart and product tags. By comprehensively referring to the increase and decrease frequency of tagged products, it calculates the weight score of each clothing tag and quantifies the demand popularity of the clothing products corresponding to the tags. The prediction algorithm unit uses Bayesian dynamic linear model and convolutional neural network algorithm. It imports short-term prediction results, historical order data, market trend data and environmental factors. The output of Bayesian dynamic linear model is used as the input parameter of convolutional neural network. Through the collaborative operation of the two algorithms, accurate medium- and long-term demand forecast results for clothing products are generated, clarifying the demand trends of various types of clothing and predicting the trend of popular colors and patterns.

[0051] Inventory optimization and adjustment are implemented. The inventory optimization and adjustment module collects sellers' apparel inventory information and user shopping pattern data in real time, and updates product inventory information and user preference data synchronously to ensure the real-time nature of inventory and user data. Adaptive filtering algorithm and Bayesian optimization algorithm are used to process feedback data. The adaptive filtering algorithm is used to dynamically adjust the apparel inventory strategy, and the Bayesian optimization algorithm ensures the accuracy of prediction when apparel demand fluctuates greatly by adjusting the feedback frequency and confidence interval. Based on the medium and long-term demand forecast results, convolutional neural networks are used to help identify long-term trends in inventory data. Combining the demand popularity of apparel products and user preferences, inventory configuration is optimized, and the inventory quantity of various types of apparel is reasonably allocated. At the same time, the prediction results of popular colors and patterns are synchronized to the production line to guide the production line to dynamically adjust the production capacity of corresponding apparel.

[0052] The system implements logistics route planning and resource scheduling. The logistics route planning module generates an initial set of logistics route solutions based on a genetic algorithm, setting a population size to ensure the diversity and rationality of the initial solutions. A fitness evaluation system is established, combining three core indicators: route length, traffic conditions, and estimated transportation time, to comprehensively evaluate the fitness of the initial route solutions. An adaptive large-scale neighborhood search algorithm is used for route optimization. Addressing the complex needs and resource constraints of apparel transportation, the evaluated routes are iteratively optimized to obtain the optimal logistics route. Real-time data collection of current logistics resource status, including vehicle quantity, vehicle location, and transportation capacity, combined with the optimal logistics route, enables dynamic scheduling of vehicles and other logistics resources, rationally allocating apparel transportation tasks and ensuring timely delivery of inventory apparel to designated locations.

[0053] The system feedback and optimization module collects real-time user response and purchase behavior data for recommended apparel products. Through statistical analysis of the data, it assesses the efficiency and user satisfaction of the recommendation system and identifies its shortcomings. Kibana is used for data visualization, transforming user behavior data into intuitive charts that clearly demonstrate user preferences and demand patterns for various apparel items. Combined with a Bayesian optimization algorithm, the model parameters of each unit are dynamically adjusted based on feedback data to optimize algorithm efficiency and improve prediction and management accuracy. An intelligent collaboration mechanism links all aspects of the supply chain, optimizing workflows and resource allocation at each stage to improve overall supply chain operational efficiency and achieve end-to-end optimization of demand forecasting and inventory management for apparel e-commerce.

[0054] Example 2

[0055] This embodiment is applied to a beauty e-commerce platform, and the specific process is as follows:

[0056] The data collection and preprocessing module involves several steps. The web crawler unit uses web crawling technology to acquire user feedback data, search data, and product tag data from beauty e-commerce platforms. This includes user reviews, questions, search keywords, and search frequencies related to beauty products. It also gathers user comments, discussions, and product mentions from social media. The image recognition unit analyzes the texture and shape of beauty product images using image recognition technology. Image processing algorithms identify and classify texture types in beauty product images, and edge detection technology is used to draw the shape outlines of beauty products, generating standardized shape and texture feature data. The tag optimization unit optimizes the descriptiveness and accuracy of tags based on their relevance scoring, adjusts tag classification and description information, removes redundant and incorrect tags, integrates valid tags to generate a standardized beauty product tag database, and links the shape and texture feature data with the product tag database to ensure data standardization and usability.

[0057] The demand forecasting and analysis module employs several methods. The data verification unit, based on cosmetic product feature recognition information, compares the consistency between visual data and text analysis results using data verification algorithms, and verifies the data between the two. A logistic regression model is used to evaluate the correlation between visual features and text descriptions, and inconsistencies are addressed to ensure data accuracy. The shopping cart analysis unit, based on shopping cart interaction analysis data, uses statistical methods and machine learning algorithms to precisely analyze the relationship between the increase / decrease patterns of cosmetic products in the shopping cart and product tags. By comprehensively referencing the frequency of increases and decreases in tagged products, a weight score is calculated for each cosmetic tag, quantifying the demand for the corresponding cosmetic product. The prediction algorithm unit, based on a Bayesian dynamic linear model and a convolutional neural network algorithm, imports short-term prediction results, historical order data, market trend data, and environmental factors. The Bayesian dynamic linear model processes historical order data and environmental factors to capture dynamic trends, while the convolutional neural network analyzes visual data and short-term prediction results to uncover hidden features. These two algorithms work together to generate accurate medium- and long-term demand forecasts for cosmetic products, clarifying demand trends for various cosmetic products and predicting popular shades and packaging designs.

[0058] Inventory optimization and adjustment are implemented. The inventory optimization and adjustment module collects sellers' beauty product inventory information and user shopping pattern data in real time, and updates product inventory information and user preference data synchronously to ensure the real-time nature of inventory and user data. Adaptive filtering algorithm and Bayesian optimization algorithm are used to process feedback data. The adaptive filtering algorithm is used to dynamically adjust the beauty inventory strategy, and the Bayesian optimization algorithm ensures the accuracy of prediction when beauty demand fluctuates greatly by adjusting the feedback frequency and confidence interval. Based on the medium and long-term demand forecast results, convolutional neural network is used to help identify long-term trends in inventory data. Combining the demand popularity of beauty products and user preferences, inventory configuration is optimized, and the inventory quantity of various beauty products is reasonably allocated. The prediction results of popular color numbers and packaging patterns are synchronized to the beauty production line to guide the production line to dynamically adjust the production capacity of corresponding beauty products and avoid inventory backlog and stockout problems.

[0059] The system performs logistics route planning and resource scheduling. The logistics route planning module generates an initial logistics route solution set based on a genetic algorithm, setting a population size to ensure the diversity and rationality of the initial solution set. Combining three core indicators—route length, traffic conditions, and estimated transportation time—a fitness evaluation system is established to comprehensively assess the fitness of the initial route solution set, with a focus on the fragile nature of cosmetics and emphasizing transportation safety-related indicators in the fitness evaluation. An adaptive large-scale neighborhood search algorithm is used for route optimization. Addressing the complex needs and resource constraints of cosmetics transportation, the evaluated routes are iteratively optimized to obtain the optimal logistics route. Real-time data collection of current logistics resource status, including vehicle quantity, vehicle location, transportation capacity, and refrigerated / freshness resource availability, combined with the optimal logistics route, enables dynamic scheduling of vehicles and other logistics resources, rationally allocating cosmetics transportation tasks to ensure the safe and timely delivery of cosmetics to designated locations.

[0060] The system feedback and optimization module collects real-time user response and purchase behavior data for recommended beauty products. Through statistical analysis of the data, it assesses the efficiency and user satisfaction of the recommendation system, identifying its shortcomings in beauty product recommendations. Kibana is used for data visualization, transforming user behavior data into intuitive charts that clearly demonstrate user preferences and demand characteristics for various beauty products. A Bayesian optimization algorithm dynamically adjusts model parameters for each unit based on feedback data, optimizing algorithm efficiency and improving the accuracy of beauty product demand forecasting and inventory management. An intelligent collaboration mechanism links all aspects of the supply chain, optimizing workflows and resource allocation at each stage, with a focus on optimizing the warehousing, preservation, and transportation of beauty products. This improves the overall operational efficiency of the supply chain and achieves intelligent control over the entire process of beauty e-commerce demand forecasting and inventory management.

Claims

1. An AI-driven method for e-commerce demand forecasting and inventory management, characterized in that, Includes the following steps: S1. Data collection and preprocessing: Use web crawling technology to obtain user feedback data, search data and product tag data from social media platforms of e-commerce platforms; use image recognition technology to generate shape and texture feature data; optimize product tags and generate a product tag database. S2. Based on the preprocessed data, perform demand forecasting and analysis, verify the consistency between visual data and text analysis results, analyze shopping cart interaction data to calculate tag weight scores, and combine multiple data to generate medium- and long-term demand forecasting results through Bayesian dynamic linear model and convolutional neural network algorithm. S3. Based on the prediction results, optimize and adjust the inventory, update the inventory and user preference data, use adaptive filtering algorithm and Bayesian optimization algorithm to process the feedback data, and use convolutional neural network to optimize the inventory configuration. S4. Conduct logistics route planning and resource scheduling. Generate an initial path solution set based on a genetic algorithm, evaluate the fitness, and then use an adaptive large-scale neighborhood search algorithm to optimize the path and dynamically schedule logistics resources. S5. Conduct system feedback and optimization, analyze the efficiency of the recommendation system and user satisfaction, display user behavior data through data visualization, adjust model parameters and optimize the efficiency of each link in the supply chain.

2. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 1, characterized in that, In step S1, data collection and preprocessing specifically include: using web crawling technology to selectively acquire user feedback data, search data, and product tag data from e-commerce platforms and social media websites, ensuring the comprehensiveness and real-time nature of data acquisition; using image recognition technology to analyze the texture and shape of product images, identifying and classifying texture types in product images through image processing algorithms, using edge detection technology to draw the shape outline of products, and generating standardized shape and texture feature data; based on product tag correlation scoring, optimizing the descriptiveness and accuracy of tags, adjusting the classification and description information of tags, eliminating redundant and incorrect tags, and integrating effective tags to generate a standardized product tag database for subsequent demand forecasting and analysis.

3. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 1, characterized in that, In step S2, demand forecasting and analysis specifically include: based on product feature identification information, using data verification algorithms to compare the consistency between visual data and text analysis results and verify the data between the two; evaluating the correlation between visual features and text descriptions through a logistic regression model; and making targeted adjustments to inconsistent data points to ensure data accuracy; based on shopping cart interaction analysis data, using statistical methods and machine learning algorithms to accurately analyze the relationship between the increase and decrease patterns of products in the shopping cart and product tags; calculating the weight score of each tag by comprehensively referring to the increase and decrease frequency of the tag products, and quantifying the demand popularity of the products corresponding to the tags; and based on Bayesian dynamic linear models and convolutional neural network algorithms, importing short-term forecast results, historical order data, market trend data, and environmental factors, and generating accurate medium- and long-term demand forecast results through the collaborative operation of the two algorithms, clarifying the demand trends of each product.

4. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 1, characterized in that, In step S3, inventory optimization and adjustment specifically include: real-time collection of seller product inventory information and user shopping pattern data, synchronously updating product inventory information and user preference data to ensure the real-time nature of inventory and user data; processing of feedback data using adaptive filtering and Bayesian optimization algorithms, where the adaptive filtering algorithm is used to dynamically adjust inventory strategies by setting the filter order and adjustment step size, and the Bayesian optimization algorithm ensures the accuracy of predictions when demand fluctuates significantly by adjusting the feedback frequency and confidence interval; based on medium- and long-term demand forecast results, using convolutional neural networks to help identify long-term trends in inventory data, and combining product demand popularity and user preferences to optimize inventory configuration and rationally allocate the inventory quantity of each product.

5. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 1, characterized in that, In step S4, logistics route planning and resource scheduling specifically include: generating an initial path solution set based on a genetic algorithm, setting the population size to ensure the diversity and rationality of the initial solution set; establishing a fitness evaluation system based on three core indicators—path length, traffic conditions, and estimated transportation time—to comprehensively evaluate the fitness of the initial path solution set; using an adaptive large-scale neighborhood search algorithm for path optimization, iteratively optimizing the evaluated path for complex transportation demands and resource constraints to obtain the optimal logistics path; and collecting the current logistics resource status in real time, including the number of vehicles, vehicle locations, and transportation capacity, and combining this with the optimal logistics path to achieve dynamic scheduling of vehicles and other logistics resources and rational allocation of transportation tasks.

6. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 1, characterized in that, In step S5, system feedback and optimization specifically include: real-time collection of user response data and purchase behavior data for recommended products; statistical analysis of the data to assess the efficiency and user satisfaction of the recommendation system and identify its shortcomings; data visualization processing using Kibana to transform user behavior data into intuitive visual charts that clearly demonstrate user behavior patterns and demand characteristics; dynamic adjustment of model parameters for each unit based on feedback data using Bayesian optimization algorithms to optimize algorithm efficiency and improve prediction and management accuracy; and intelligent collaboration mechanisms to link all links in the supply chain, optimize workflows and resource allocation at each stage, and improve the overall operational efficiency of the supply chain.

7. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 2, characterized in that, The data acquired by the web crawler technology includes product reviews, user questions, search keyword search frequency on e-commerce platforms, and related product mentions in user comments and discussions on social media. The image processing algorithm includes texture feature extraction algorithm and texture classification algorithm. The edge detection technology uses edge detection operators to ensure the accuracy of product shape contour drawing. The generated shape and texture feature data is associated with the product tag database.

8. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 3, characterized in that, The Bayesian dynamic linear model is used to process historical order data and environmental factors to capture the dynamic trends of data changes. The convolutional neural network is used to analyze visual data and short-term prediction results to uncover hidden features in the data. When the two algorithms work together, the output of the Bayesian dynamic linear model is used as the input parameter of the convolutional neural network, and the results of the two operations are combined to generate medium- and long-term demand prediction results.

9. The AI-driven e-commerce demand forecasting and inventory management method as described in claim 1, used to execute the method described in any one of claims 1 to 8, characterized in that, It includes a data acquisition and preprocessing module, a demand forecasting and analysis module, an inventory optimization and adjustment module, a logistics route planning module, a system feedback optimization module, a data storage module, and a control system module; The data acquisition and preprocessing module is used to realize data acquisition, image recognition, and product label database generation; the demand forecasting and analysis module is used to realize data verification, shopping cart interaction analysis, and demand forecasting results generation. The inventory optimization and adjustment module is used to process inventory and user preference data updates and feedback, and to optimize inventory configuration. The logistics route planning module is used to generate initial routes, optimize routes, and schedule logistics resources. The system feedback optimization module is used to realize the visualization of recommendation efficiency analysis data, parameter adjustment, and supply chain collaborative optimization; the data storage module is used to store various types of data and calculation results; the control system module is connected to each module to control the modules to work together to complete the entire process of demand forecasting and inventory management.

10. The AI-driven e-commerce demand forecasting and inventory management system as described in claim 9, characterized in that, The data acquisition and preprocessing module includes a crawler unit, an image recognition unit, and a label optimization unit. The crawler unit is used to acquire various types of data, the image recognition unit is used to generate shape and texture feature data, and the label optimization unit is used to optimize product labels and generate a product label database. The demand prediction and analysis module includes a data verification unit, a shopping cart analysis unit, and a prediction algorithm unit. The data verification unit is used to verify data consistency, the shopping cart analysis unit is used to calculate label weight scores, and the prediction algorithm unit is used to run a Bayesian dynamic linear model and a convolutional neural network algorithm to generate prediction results.

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