A fast iteration based commodity type supply decision method

Abstract

This invention discloses a product type supply decision-making method based on rapid iteration, aiming to address the pain points of long decision-making cycles, high risks, and inaccurate assessments in overseas operations. The method includes: receiving core information about overseas operations; screening target products through a multi-dimensional attribute matching strategy; deploying interactive probes to collect real-time interactive data from multiple platforms and integrating it to form an evaluation dataset; calculating value parameters such as expected sales volume, reasonable selling price, and expected decay period; quantifying three types of risk coefficients—freight, warehousing, and slow-moving goods—using time factors; constructing a risk-value fitting model to output an initial decision; dynamically correcting the decision by calling an overseas operation behavior database; and outputting the final solution after verifying that the deviation meets the target. This invention, through proactive data collection, dual-dimensional assessment, and dynamic correction mechanisms, accurately adapts to the characteristics of rapidly iterating products and the needs of overseas scenarios, improving the accuracy and timeliness of decision-making, reducing operational risks and resource waste, and helping enterprises conduct overseas business efficiently.

Description

Technical Field

[0001] This invention relates to the field of overseas e-commerce product supply, specifically to a product type supply decision-making method based on rapid iteration. Background Technology

[0002] In recent years, the overseas e-commerce market has continued to expand. Fast-iter products such as fast fashion, 3C digital accessories, and new beauty products have become core categories for overseas operations due to their high-frequency updates. However, existing supply decision-making technologies are ill-suited to the specific needs of these scenarios, resulting in numerous pain points. In overseas operations, the procedures for commodity transportation and warehousing are complicated, and cross-regional coordination of production and sales is required, resulting in a significantly longer supply decision-making cycle than the domestic direct sales model, making it impossible to respond to market changes in a timely manner. At the same time, overseas transportation and warehousing costs are high, and existing technologies lack a systematic assessment of related risks, which can easily lead to inventory backlog or supply shortages due to decision-making errors, resulting in significant economic losses and waste of resources.

[0003] Furthermore, most enterprises lack comprehensive multi-channel information collection and accurate evaluation capabilities. Traditional decision-making relies heavily on historical sales data or subjective judgment, with data sources being singular and outdated, making it difficult to accurately predict target customer demand and market trends. Especially for rapidly iterating products, existing technologies have not established decision-making mechanisms adapted to their decay characteristics, resulting in a severe imbalance between the product decay rate and the decision-making cycle, further exacerbating the risk of unsold inventory or missed market opportunities. In summary, existing supply decision-making methods cannot simultaneously meet the demands of the complexity of overseas scenarios, the timeliness of rapidly iterating products, and the accuracy of decision-making, necessitating a targeted intelligent decision-making solution.

[0004] Therefore, in order to address the problems existing in the prior art, this application proposes a commodity type supply decision-making method based on rapid iteration. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a product type supply decision-making method based on rapid iteration.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A product type supply decision-making method based on rapid iteration includes: The target product confirmation step involves receiving core information about overseas operations, including product origin, overseas sales location, store attributes, sales preferences, and budget constraints. Based on this core information, target products are selected using an overseas product attribute matching strategy. The market data integration step involves deploying interaction probes on overseas traffic and sales platforms for the target product to collect real-time user interaction data. The collected interaction data is then preprocessed and integrated with historical sales data and overseas market trend data to obtain a product evaluation dataset, which includes interaction popularity and demand distribution. The product valuation step involves calculating the expected sales volume, reasonable selling price, target customer profile, and expected decline period of the target product based on the product valuation dataset, and generating a product valuation report. The risk dynamic assessment process involves using a risk dynamic assessment strategy to calculate freight risk coefficients, warehousing risk coefficients, and unsold inventory risk coefficients for overseas operating scenarios, and generating dynamic risk assessment results. The supply decision generation step involves constructing a risk value fitting model, inputting the parameters of the commodity value assessment report and dynamic risk assessment results into the model, and outputting initial supply decision recommendations by setting the minimum comprehensive decision deviation as the objective function. The supply decision correction step involves calling the overseas business behavior database and dynamically correcting the initial supply decision suggestion in conjunction with the user's budget constraints. The deviation value of the corrected decision is verified. If the deviation value is less than a preset threshold, the final supply decision is output.

[0007] Preferably, the market data integration step includes: based on the overseas sales regions of the target product, selecting traffic platforms and sales platforms in the corresponding regions to deploy interaction probes; the interaction probes set the collection frequency according to the product iteration speed to collect real-time user interaction data; the real-time user interaction data includes browsing duration, page dwell path nodes, forwarding trigger times, keyword segmentation results for inquiries, pre-sale order submission behavior, and time spent adding items to the shopping cart; and removing abnormal data such as browsing duration less than a preset threshold and empty inquiries; standardizing the collected data from different platforms; grouping users by region and consumption preference tags using the k-means clustering algorithm to obtain different demand regions; calculating interaction heat based on browsing duration, forwarding trigger times, inquiry frequency, and pre-sale order volume; and obtaining the demand distribution based on the ratio of the collected data volume in each demand region to the total collected data volume.

[0008] Preferably, the product value assessment step includes: calculating a conversion coefficient based on the interaction popularity and historical interaction conversion data of the platform using a logistic regression algorithm; calculating a decay correction coefficient based on historical sales data of the same period and the product iteration rate; and obtaining the expected sales volume by multiplying the interaction popularity and conversion coefficient by the historical sales volume of the same period and the decay correction coefficient; collecting the historical average price of competitors in the target demand area; calculating the competitor's attention share by comparing the competitor's sales volume with the total sales volume of similar products in the demand area; calculating overseas warehousing costs and transportation unit costs; and calculating a reasonable selling price; obtaining a basic iteration cycle by statistically analyzing historical iteration data of similar products; calculating a market competition coefficient by collecting the update frequency of the target product and competitors; and calculating the expected decay cycle based on the basic iteration cycle and the market competition coefficient.

[0009] Preferably, the dynamic risk assessment step includes: calculating the probability of transportation delay using historical transportation data along the same route; determining the weight of the time factor based on the transportation cycle and decay cycle; obtaining a delay loss coefficient based on historical data statistics, wherein the delay loss coefficient is the proportion of sales loss caused by a one-day delay; determining the transportation loss rate and loss cost coefficient based on the transportation mode; and calculating the freight risk coefficient; calculating the probability of warehousing backlog using the warehousing cycle, decay cycle, and inventory turnover rate; calculating the backlog cost coefficient using overseas warehousing daily rental fees and warehousing cycle; determining the warehousing loss rate and loss cost coefficient based on the characteristics of the goods; and calculating the warehousing risk coefficient; calculating the supply-demand ratio; calculating the decay factor using the decay cycle and warehousing cycle; and calculating the unsold inventory risk coefficient based on the supply-demand ratio and decay factor.

[0010] Preferably, the risk value fitting model is constructed using a linear programming algorithm, with the objective function being the minimum overall decision bias. The objective function is expressed as MinΔ=|(V×(1-R))-(S×P-Ctotal)|, where V is the product value score, V=S×P×(1-v), v is the product iteration rate, S is the expected sales volume, P is the reasonable selling price, R is the total risk score, and Ctotal is the overall cost, including transportation costs, warehousing costs, and risk losses. The model also sets three types of constraints: budget constraints, inventory constraints, and timeliness constraints. Furthermore, the range of values ​​for the constraints needs to be dynamically adjusted in conjunction with the characteristics of overseas operating scenarios. The risk value fitting model solves by converting the expected sales volume of the goods, reasonable sales price, expected decay period, freight risk coefficient, warehousing risk coefficient, and unsold risk coefficient into standardized decision variables of a linear programming model, and iteratively solves the objective function using the simplex method. By gradually optimizing the values ​​of the decision variables, the optimal solution that satisfies all constraints is found. The optimal solution includes warehousing volume, transportation mode weights, and replenishment cycle. Based on the optimal solution, initial supply decision suggestions are generated, including operating decisions and specific execution parameters.

[0011] Preferably, the supply decision correction step includes retrieving historical business behavior data consistent with the target product category and sales region from the overseas business behavior database, matching keywords including transportation mode and risk of unsold goods, storage volume and backlog costs, interaction heat and sales volume, analyzing the correlation between behavior and result based on the matched historical behavior using a decision tree algorithm, adjusting the corresponding behavior in the initial decision if the impact of historical behavior on the result exceeds a preset threshold, dynamically adjusting the regional storage volume based on the association rules retrieved from the overseas business behavior database, and calculating the deviation value of the corrected decision using a mean squared error algorithm based on the difference between the actual expected return and the corrected expected return. If the deviation value is less than a preset threshold, the final supply decision is output; otherwise, the supply decision correction continues.

[0012] Preferably, the overseas product attribute matching strategy includes setting multi-dimensional screening indicators, including transportation feasibility, warehousing adaptability, policy compliance, and iterative characteristic adaptability. Each screening indicator is assigned a corresponding weight, and a quantitative standard is set for each screening indicator. The core information of overseas operations is compared and scored item by item with the product attributes in the product database. The comprehensive matching score of the products is calculated, and products with a comprehensive score greater than a preset threshold are selected as target products. If multiple products meet the conditions, they are sorted in descending order of comprehensive score, and the top 3 categories of products with the highest scores are selected to enter the subsequent evaluation stage.

[0013] Preferably, the dynamic risk assessment strategy also includes a real-time update mechanism. By connecting with overseas logistics platforms, warehouse management systems, and customs policy release platforms, it can obtain real-time information on transportation route congestion warnings, warehouse cost adjustment notices, and changes in import and export policies. When updated information is obtained, the risk coefficient is recalculated: the freight risk coefficient adjusts the delay probability according to the transportation route congestion level, the warehouse risk coefficient adjusts the backlog cost coefficient according to the latest daily warehouse rental fee, and the unsold inventory risk coefficient adjusts the supply-demand ratio according to the impact of policy changes on demand.

[0014] Preferably, the construction of the overseas business behavior database includes collecting historical data on the entire overseas business process, including transportation methods, warehousing volume, interaction intensity, sales volume, risk occurrence, and decision adjustment results. The data is then classified and archived according to behavior type, scenario characteristics, and result feedback. The correlation between each type of behavior and result is quantified, and the correlation weight is calculated using the Pearson correlation coefficient to obtain an overseas business behavior database that includes the correlation between several behaviors and results.

[0015] The beneficial effects of this invention are: (1) By matching multi-dimensional attributes and overseas-specific risk assessment system, the system avoids overseas-specific risks such as transportation, warehousing, and policy compliance. Combined with time factor to quantify the decay characteristics of goods, it effectively solves the pain points of long decision-making cycle and high cost overseas, and significantly reduces economic losses and resource waste.

[0016] (2) Relying on interactive probes to actively collect real-time interactive data from multiple platforms, and combining historical data with market trends to form a comprehensive evaluation dataset, it breaks through the limitations of traditional passive data collection, greatly improves the prediction accuracy of core parameters such as expected sales volume and customer profile, and provides reliable data support for decision-making.

[0017] (3) Construct a risk value fitting model with the goal of minimizing the comprehensive decision deviation. Integrate the dual-dimensional parameters of value and risk and multiple constraints to output a scientific and reasonable initial supply decision. Then, use the overseas business behavior database for dynamic correction to achieve a precise balance between returns and risks and avoid inventory backlog or supply shortage of rapidly iterating goods.

[0018] (4) Integrate real-time updates and dynamic adjustments to respond in real time to changes in the external environment such as logistics, warehousing, and policies, continuously optimize decision-making schemes, adapt to the market life cycle characteristics of rapidly iterating products, ensure the timeliness and flexibility of decision-making, improve the supply chain response speed and market competitiveness, and reduce the threshold for overseas operations. Attached Figure Description

[0019] Figure 1 This is a flowchart of a product type supply decision-making method based on rapid iteration according to the present invention.

[0020] Figure 2 This is a schematic diagram of the supply decision system according to an embodiment of the present invention.

[0021] Figure 3 This is a flowchart of the risk dynamic assessment steps in an embodiment of the present invention.

[0022] Figure 4 This is a flowchart illustrating the construction process of the risk value fitting model according to an embodiment of the present invention.

[0023] Figure 5 This is a flowchart of the supply decision correction steps in an embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom surface," "top surface," "inner," and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0025] This invention proposes a product type supply decision-making method based on rapid iteration, such as... Figures 1 to 5 As shown, it includes: In the target product confirmation process, the system receives core overseas business information input by the user, including key details such as product origin, overseas sales location, store attributes, sales preferences, and budget constraints. The core basis for selecting target products is an overseas product attribute matching strategy, which includes multi-dimensional screening indicators. Each indicator is assigned a corresponding importance weight based on the core needs of the overseas business scenario. The transportation feasibility indicator focuses on whether the product meets the transportation standards of the sales location and the accessibility of the transportation route. The warehousing adaptability indicator considers the degree to which the product size and storage conditions match overseas warehousing facilities. The policy compliance indicator covers relevant regulations such as tariff requirements and quality inspection standards required for import and export of goods. The iteration characteristic adaptability indicator judges the fit between the product iteration speed and the market acceptance pace of the sales location. The weighting is not fixed but dynamically adjusted according to the actual situation of the sales location. For example, the weight of the transportation feasibility indicator will be increased in areas with limited logistics, and the weight of the policy compliance indicator will be increased in areas with strict policy regulations. The system compares and evaluates core overseas operational information against product attributes in the product database, assigning corresponding scores. A comprehensive matching score is then calculated by combining the scores of each indicator with their corresponding weights. Products meeting the preset matching score are selected as target products. If multiple products meet the selection criteria, they are sorted from highest to lowest comprehensive matching score, with the top three categories prioritized for further evaluation, ensuring efficient and focused processing in subsequent stages.

[0026] It should be noted that the "collection of real-time user interaction data" involved in this step is conducted within the existing legal and compliant user data collection framework of overseas traffic platforms and sales platforms. Specifically, when using the aforementioned platforms, users have explicitly authorized and consented to the platforms collecting necessary behavioral data such as browsing, clicking, inquiries, and orders through the platform's user agreement or privacy policy, in order to obtain product information, complete transactions, or enjoy personalized services. The deployment and data collection of the interaction probe described in this application are achieved by calling the platform's open standardized interfaces or embedded programs. The data types, scope, and uses collected do not exceed the authorized scope declared in the platform's privacy policy, nor do they breach the boundary of personal information processing between users and the platform that is "necessary for entering into or performing a contract." Therefore, the data collection behavior in this method has a clear legal and contractual basis, complies with the provisions of the Personal Information Protection Law, and does not constitute the illegal acquisition of users' personal information.

[0027] In the market data integration step, for the selected target products, the system will deploy interaction probes on traffic platforms and mainstream sales platforms with high user activity in overseas sales regions, based on the distribution of these overseas sales regions. Overseas traffic platforms are social media platforms, such as X, and overseas sales platforms are e-commerce platforms, such as Amazon. The deployment of interaction probes is achieved through platform open interfaces or embedded programs, adapting to the data format requirements of different platforms. The collection frequency is flexibly adjusted according to the product iteration speed; a faster product iteration speed shortens the collection interval to ensure data real-time performance, while a slower product iteration speed appropriately extends the collection interval to reduce resource consumption. The interaction probes collect a wide variety of real-time user interaction data, including the duration of user browsing product pages, page dwell path nodes during browsing, the number of times products are forwarded, keyword segmentation results during inquiries, pre-sale order submission behavior, and dwell time after adding items to the shopping cart. The data preprocessing process follows a strict procedure, eliminating invalid data such as those with excessively short browsing time or empty consultation keywords to prevent such data from affecting the accuracy of the evaluation results. Next, data collected from different platforms is uniformly converted into standardized units to eliminate data format differences between platforms. Then, the K-means clustering algorithm is used to group users' geographic information and consumption preference tags, naturally dividing different demand areas. The calculation of interaction heat comprehensively considers the influence of multiple interactive behaviors, assigning corresponding weights according to the importance of different behaviors to product sales prediction. For example, products with strong social attributes will have more weight given to forwarding behavior, while functional products will have more weight given to consultation behavior. Interaction heat is obtained by weighted summation of data such as browsing time, number of forwarding triggers, consultation frequency, and pre-sale order volume. Demand distribution is determined by the ratio of the amount of data collected in each demand area to the total amount of data collected, clearly presenting the market demand heat of the target product in different regions. Simultaneously, the system integrates the preprocessed interaction data with historical sales data and overseas market trend data to form a comprehensive product evaluation dataset, providing solid data support for subsequent evaluations.

[0028] The product valuation process involves the system using a product valuation dataset to comprehensively calculate various value parameters of the target product and generate a product valuation report. The calculation of expected sales volume combines interaction popularity and historical sales data from the same period. The conversion coefficient, based on the correlation between similar interactions and actual purchases in the platform's history, is obtained through logistic regression analysis and reflects the ability of interaction popularity to translate into actual sales. The decay correction coefficient, calculated by referencing the decay trend of past sales over time and considering the current market competition environment, is used to correct historical sales data to better reflect the product's current iterative characteristics. The expected sales volume of the target product is obtained by multiplying the interaction popularity and conversion coefficient, and then adding the product of historical sales data and the decay correction coefficient. In determining a reasonable selling price, the system collects the historical average price of competing products within the target demand region, calculates the proportion of each competing product's sales volume to the total sales volume of similar products in that region as the competitor's attention share, and accurately calculates overseas warehousing costs and transportation unit costs. It then combines the competitor's price corresponding to the competitor's attention share with the price expectations reflected in user inquiries, and deducts the allocated warehousing and transportation costs to ultimately determine a reasonable selling price that is both competitive in the market and ensures operating profits. The construction of the target customer profile is based on clustered user geographic location, consumption preference tags, and other data, clearly outlining the distribution area, consumption capacity range, and purchasing decision preferences of the core customer group. The calculation of the expected decay period references the historical cycle of similar products from market launch to a significant decline in market demand, serving as the basic iteration cycle. By collecting the update frequency of the target product and competing products, the market competition coefficient is calculated. Combining the basic iteration cycle and the market competition coefficient yields the expected decay period of the target product, providing an important time-dimensional reference for subsequent decisions.

[0029] The dynamic risk assessment process employs a dynamic risk assessment strategy, comprehensively quantifying various risk coefficients by incorporating time factors to generate dynamic risk assessment results. The core function of the time factor is to correlate transportation cycles, warehousing cycles, and product decay cycles. The impact of the time dimension is reflected in the dynamic changes of risk coefficients, ensuring that the risk assessment adapts to the iterative characteristics of the product. The calculation of the freight risk coefficient integrates multiple factors. Historical transportation data for the same route is used to statistically analyze the probability of transportation delays, and the weight of the time factor is determined by combining the ratio of the transportation cycle to the decay cycle; the longer the transportation cycle, the higher the weight of the time factor. The delay loss coefficient is derived from historical data statistics, reflecting the proportion of loss to product sales due to a one-day delay. The transportation loss rate is determined based on the characteristics of the selected transportation mode, as the loss risk varies among different transportation modes. The loss cost coefficient is related to the unit price of the product. Through comprehensive consideration of these parameters, the risk level of the freight transportation process is comprehensively measured. The calculation of the warehousing risk coefficient is equally rigorous. The probability of warehousing backlog is calculated by combining the ratio of the warehousing cycle to the decay cycle and the inverse of the inventory turnover rate. The longer the warehousing cycle and the lower the inventory turnover rate, the higher the probability of backlog. The backlog cost coefficient is calculated based on the daily rental fee of overseas warehousing and the warehousing cycle, clearly reflecting the cost pressure caused by inventory backlog. The warehousing loss rate is determined based on the characteristics of the goods themselves; fragile and easily damaged goods have relatively high loss rates. The loss cost coefficient is set in conjunction with the unit price of the goods. The risk coefficient of the warehousing link is obtained by combining the factors. The calculation of the unsold inventory risk coefficient focuses on the supply and demand matching situation and the decay characteristics of the goods. The supply and demand ratio of expected sales volume and effective inventory is calculated, and then the decay factor is determined by the ratio of the decay cycle to the warehousing cycle. The supply and demand ratio reflects the degree of matching between inventory and demand, and the decay factor reflects the impact of the decay of the goods' value over time. The combination of the two yields the unsold inventory risk coefficient. When the supply and demand ratio is unbalanced and the decay factor is large, the unsold inventory risk coefficient increases significantly. Meanwhile, the dynamic risk assessment strategy includes a real-time update mechanism. The system connects to overseas logistics platforms, warehouse management systems, and customs policy release platforms to obtain real-time alerts for transportation route congestion, notifications of warehousing cost adjustments, and information on changes in import and export policies. When updated information is received, the system triggers a recalculation of risk coefficients. It adjusts the delay probability in the freight risk coefficient based on the level of transportation route congestion, adjusts the backlog cost coefficient in the warehousing risk coefficient based on the latest daily warehousing rental rate, and corrects the supply-demand ratio in the unsold goods risk coefficient based on the impact of policy changes on demand. This ensures that the risk assessment results respond promptly to changes in the external environment and maintain dynamic accuracy.

[0030] The supply decision generation process involves constructing a risk-value fitting model. This model takes various value parameters from the commodity valuation report and risk coefficients from the dynamic risk assessment results as input, and outputs initial supply decision recommendations with the core objective function of minimizing the overall decision deviation. The risk-value fitting model is constructed using a linear programming algorithm. The core logic of the objective function is to minimize the gap between the actual and ideal returns of the commodity. Actual returns are calculated by multiplying the expected sales volume and the reasonable selling price, minus transportation costs, warehousing costs, and risk losses. Ideal returns are determined based on the commodity value score and the total risk score. The commodity value score comprehensively considers expected sales volume, reasonable selling price, and commodity iteration rate. The total risk score is obtained by weighted summation of freight risk coefficients, warehousing risk coefficients, and unsold inventory risk coefficients. The model sets three types of constraints: a budget constraint to ensure the total investment of the decision plan does not exceed the user-set operating budget; an inventory constraint to limit warehousing volume to the maximum capacity of overseas warehousing facilities; and a timeliness constraint to require the transportation cycle to not exceed half of the commodity's depreciation cycle, preventing a significant drop in value due to depreciation during transportation. The specific value range of these constraints is dynamically adjusted according to the characteristics of the overseas operating scenario to adapt to different operating environments. During the model solving process, the system transforms value parameters such as expected sales volume, reasonable selling price, and expected decay period of the product, along with risk parameters such as freight risk coefficient, warehousing risk coefficient, and unsold inventory risk coefficient, into standardized decision variables for a linear programming model. The objective function is iteratively solved using the simplex method, gradually optimizing the values ​​of the decision variables until the optimal solution satisfying all constraints is found. Simultaneously, the model considers the user's budget constraints and business preferences, such as risk preference, return preference, and balance preference, setting multiple sets of constraint weight combinations to generate 3-5 optimal decision schemes that meet the deviation requirements. Each scheme corresponds to different combinations of warehousing volume, transportation mode weights, and replenishment cycles, allowing users to choose according to their actual business needs. The core parameters of the optimal solution, such as warehousing volume, transportation mode weights, and replenishment cycles, serve as an important component of the initial supply decision recommendations. The system also clearly provides a binary judgment on whether to operate the product and specific execution parameters, ensuring the practicality and operability of the decision recommendations.

[0031] The supply decision correction process involves the system accessing an overseas business behavior database and dynamically correcting initial supply decision recommendations based on user budget constraints. Rigorous deviation verification ensures the accuracy of the final decision. The overseas business behavior database is a collection of historical data from the entire overseas business process. This data includes various categories such as transportation mode selection, warehouse volume adjustments, changes in interaction intensity, actual sales data, risk occurrences, and decision adjustment results, categorized and archived according to behavior type, scenario characteristics, and result feedback. The correlation between behavior and results is quantified using algorithms, employing the Pearson correlation coefficient to calculate correlation weights, clearly demonstrating the impact of different behaviors on sales results and risk control, forming reusable association rules. During dynamic correction, the system retrieves historical business behavior data from the database that matches the target product category and sales region, using keywords such as transportation mode and unsold inventory risk, warehouse volume and backlog costs, and interaction intensity and sales volume to accurately match relevant historical data. Based on the matched historical behavior data, the system uses a decision tree algorithm to deeply analyze the correlation between behavior and outcome. If the impact of a certain type of historical behavior on sales revenue or risk control reaches a preset standard, the adjustment logic corresponding to that behavior is applied to the initial decision, optimizing the corresponding behavior in the initial decision. When the interaction probe subsequently detects significant fluctuations in the interaction heat of a certain area, the system retrieves the correlation rules between heat changes and sales adjustments from the behavior database, dynamically adjusting the warehouse inventory in that area to ensure real-time matching between inventory and market demand. Deviation verification is achieved through a mean squared error algorithm, calculating the deviation between the expected revenue and the actual possible revenue of the corrected decision. If the deviation value is less than a preset threshold, it indicates that the corrected decision meets the accuracy requirements, and the final supply decision is output. If the deviation value is greater than or equal to the preset threshold, the system returns to the behavior matching and decision adjustment stage, repeating the correction process until the deviation value meets the preset requirements, ensuring the reliability and accuracy of the final decision.

[0032] Specifically, such as Figures 1 to 5 As shown, the market data integration steps include, based on the overseas sales regions of the target products, the system first analyzes the user profile characteristics of the region and the overlap of user groups across platforms, filtering out traffic platforms and mainstream sales platforms with high user activity and a high degree of matching with the target product's customer base. Traffic platforms include popular regional social media and vertical interest communities, while sales platforms include cross-border e-commerce main sites and local specialty retail platforms, ensuring that data collection covers core potential users. The deployment of the interactive probe is achieved through standardized interfaces or lightweight embedded scripts opened by the platform. Before deployment, the platform's data format and interface call rules are adapted and parsed to ensure that the probe can stably capture data without affecting the normal operation of the platform.

[0033] The sampling frequency of the interactive probe is not a fixed value, but is dynamically adjusted according to the product iteration speed. The product iteration speed is judged by analyzing the product's historical update cycle, the iteration pattern of similar products, and the update frequency of competitors. If the product's historical update cycle is short and competitors' iterations are frequent, it indicates that the product iteration speed is fast. In this case, the sampling interval should be shortened to ensure timely capture of changes in market demand. If the product update pace is slow and market competition is stable, the sampling interval should be appropriately extended to reduce system resource consumption while ensuring data validity.

[0034] The collected real-time user interaction data includes various behavioral information that reflects user needs. Browsing duration is the time a user spends from opening a product page to closing it, directly reflecting the user's level of interest in the product. Page dwell path nodes record the user's browsing trajectory, including the entry point to the page, the sequence of subpages browsed, and the final page exited. This trajectory allows analysis of the core functions or information of the product that the user is interested in. The number of times a user forwards a product reflects its social dissemination potential; users actively forwarding a product indicates their approval and willingness to share it with others. The keyword segmentation results of inquiries break down the complete user inquiry into core words, such as product specifications, usage methods, and delivery time, facilitating the accurate extraction of the user's core needs. Pre-sale order submission behavior directly reflects the user's purchase intention and is an important indicator of demand intensity. The dwell time after adding items to the shopping cart helps determine the user's purchase decision stage; a longer dwell time may indicate that the user is comparing and considering options, while a shorter dwell time and no cancellation indicate a clearer purchase intention.

[0035] The data preprocessing stage follows strict quality control logic. Invalid data is eliminated. A preset threshold for browsing time is determined by analyzing the average effective browsing time of similar products in the industry and combining it with historical data of the target product. Browsing behavior below this threshold is judged as accidental clicks or invalid browsing. Empty consultation keywords indicate that the user has not expressed an actual need, and this type of data is filtered out. Subsequently, data standardization processing is performed. Different platforms may have different units of measurement for the same type of data; for example, some platforms may use seconds for browsing time, while others use minutes. Standardization processing converts all data to the same unit, eliminating differences in statistical methods across platforms and ensuring data comparability.

[0036] After data standardization, users are grouped using the k-means clustering algorithm. During algorithm application, a reasonable number of groups is determined based on the geographical characteristics of sales regions and the degree of difference in user consumption preferences. This avoids excessive grouping leading to dispersed demand or insufficient grouping leading to confused demand. The core basis for clustering is the user's geographic information and consumption preference tags. Geographic information includes geographical dimensions such as the user's country and city, while consumption preference tags include price sensitivity, functional needs, and purchase frequency. The algorithm automatically calculates the feature similarity between users, grouping users with similar features together to naturally form different demand regions. Users within each demand region have similar consumption habits and demand tendencies, providing a basis for subsequent regional supply decisions.

[0037] The calculation of interaction popularity employs a multi-dimensional weighted logic, with weight allocation dynamically adjusted based on product type and market characteristics. For example, for products with strong social attributes, forwarding behavior has a greater impact on sales dissemination, thus increasing the weight of forwarding trigger frequency; for functional products, users pay more attention to product details, and inquiry frequency more accurately reflects the intensity of demand, thus increasing the weight of inquiry frequency; pre-sale order submission behavior is directly related to actual purchase and always maintains a high weight. During the calculation process, each interaction data point is first normalized to eliminate the impact of differences in data volume, and then comprehensively summarized according to the weights of each data point to ultimately obtain an interaction popularity that comprehensively reflects the market attention and demand potential of the product.

[0038] Demand distribution is determined by the ratio of the effective data volume in each demand region to the total effective data volume. Effective data volume refers to the remaining data after preprocessing and removing invalid data. This ratio directly reflects the level of attention and demand for the target product in different demand regions. Regions with higher ratios indicate stronger market demand, providing accurate data support for subsequent regional warehousing planning and transportation resource allocation. Simultaneously, the system monitors the changes in interactive data across regions in real time. When a region experiences significant fluctuations in data volume, a secondary clustering verification is triggered to ensure the timeliness and accuracy of demand region segmentation, adapting to dynamic changes in market demand.

[0039] Specifically, such as Figures 1 to 5As shown, the product value assessment steps include calculating the expected sales volume based on interaction popularity and historical sales data from the same period, achieving accurate prediction through dual-coefficient correction. Interaction popularity directly reflects market demand potential, while the conversion coefficient quantifies the probability of this potential translating into actual sales volume. The conversion coefficient is calculated using a logistic regression algorithm, which learns patterns from historical interaction conversion data on the platform to establish a correlation model between interactive behavior and purchasing behavior. Historical interaction conversion data includes various user interactions and corresponding final purchase results over a certain period. The algorithm extracts and learns features from the data, such as analyzing the purchase conversion rate corresponding to different interaction popularity intervals, and then outputs a conversion coefficient that reflects the current interaction popularity's conversion capability. The range of conversion coefficient values ​​is closely related to product category, sales season, and user group characteristics. For example, the conversion coefficient for high-priced products is usually lower than that for fast-moving consumer goods, and the algorithm automatically adapts to these differences.

[0040] The attenuation correction coefficient dynamically adjusts historical sales data to better reflect the current product's iterative characteristics. The product iteration rate is the core basis for calculating this coefficient, determined by analyzing the product's historical sales trends over time, the iteration patterns of similar products, and the update frequency of competitors. A higher product iteration rate indicates a shorter market lifespan, lower reference value for historical sales data, a smaller attenuation correction coefficient, and a greater correction to historical sales. Conversely, a lower product iteration rate results in an attenuation correction coefficient closer to 1, with higher weighting for historical sales data. The final calculation logic for expected sales volume involves first calculating potential sales based on current market demand using interaction heat and conversion coefficients, then calculating reference sales adjusted for iterative characteristics using historical sales and the attenuation correction coefficient. Adding these two figures yields the expected sales volume, comprehensively reflecting market demand and product iteration characteristics. Simultaneously, the system fine-tunes the expected sales volume based on user business preferences. For example, users with higher risk tolerance can appropriately increase the estimated proportion of expected sales volume, while users with lower risk tolerance can appropriately decrease it, ensuring the prediction results align with the user's business strategy.

[0041] Determining a reasonable selling price requires comprehensive consideration of market competition and operating costs to achieve a balance between profitability and competitiveness. The historical average price data for competitors in the target demand region covers major sales platforms in that region. The collection period is flexibly set according to the product iteration speed; a faster product iteration speed shortens the collection period to ensure the timeliness of price data, while a slower product iteration speed appropriately extends the collection period to ensure data representativeness. The competitor's attention share is obtained by statistically analyzing the percentage of a competitor's sales volume in the total sales volume of similar products in the demand region. This percentage reflects the competitor's popularity and market share. The higher the percentage, the greater the influence of its price on the market, and the higher its reference weight in pricing.

[0042] Overseas warehousing costs are calculated across multiple aspects, including warehousing facility rental fees, management fees during storage, and costs associated with product loss during warehousing. All costs are calculated per unit of product. Transportation unit costs include transportation costs from the production site to the overseas sales destination, insurance costs during transportation, and import / export tariffs and clearance fees, also calculated per unit of product. The calculation logic for a reasonable selling price is based on a weighted average of competitors' historical average prices and their corresponding market attention percentages. Adjustments are made based on price expectations reflected in user inquiries, and then the overseas warehousing and transportation unit costs allocated per unit of product are deducted. This results in a reasonable selling price that is both competitive in the market and ensures operating profits. When policies in the target demand region change, such as tariff adjustments or changes in subsidy policies, the system automatically recalculates transportation unit costs or related expenses and adjusts the reasonable selling price in real time to ensure dynamic price rationality.

[0043] The calculation of the expected decay cycle focuses on the product's market life cycle, determined by combining historical patterns and the market competition environment. The basic iteration cycle is obtained by statistically analyzing historical data of similar products; specifically, it is the average time from the official launch of a similar product to a significant decline in market demand. This data is adjusted according to the market characteristics of different sales regions. For example, a certain product may have a shorter basic iteration cycle in regions with strong purchasing power and a longer basic iteration cycle in regions with weak purchasing power. The market competition coefficient is calculated based on the update frequency of the target product and its competitors. The higher the update frequency of competitors, the more intense the market competition, and the easier it is to compress the market life cycle of the target product, resulting in a higher market competition coefficient. Conversely, the lower the update frequency of competitors, the closer the market competition coefficient is to 1, and the smaller its impact on the basic iteration cycle. The final determination of the expected decay cycle is achieved through the calculation of the basic iteration cycle and the market competition coefficient. The result directly reflects the expected market life cycle of the product under the current market competition environment, providing a crucial time dimension reference for subsequent inventory planning and replenishment cycle setting. Meanwhile, the system will monitor changes in the market competition landscape in real time. When the frequency of competitor updates fluctuates significantly, it will automatically recalculate the market competition coefficient and expected decay cycle to ensure the timeliness and accuracy of decision-making.

[0044] Specifically, such as Figures 1 to 5 As shown, the dynamic risk assessment steps include: calculating the freight risk coefficient around the core risk points of the entire overseas transportation process, with each parameter determined by combining historical data and scenario characteristics to achieve precise quantification; calculating the transportation delay probability based on historical transportation data for the same route, including all transportation records for that route within a certain period, such as transportation date, whether there was a delay, delay duration, and reason for delay; the system categorizes and statistically analyzes the data, removing extreme delay cases caused by force majeure, and calculates the ratio of the number of delays to the total number of transportations to obtain the basic delay probability; simultaneously, the system adjusts the basic probability based on influencing factors such as season, weather, and port congestion patterns. For example, the delay probability will be adjusted upwards during busy port periods before and after holidays, and the adjustment range will be increased during seasons with frequent severe weather to ensure that the probability calculation closely matches the actual transportation scenario.

[0045] The core of setting the time factor weight is to relate the transportation cycle to the intrinsic relationship between the product's depreciation cycle. Because products continuously consume their market lifespan during transportation, the higher the proportion of the transportation cycle to the depreciation cycle, the shorter the time the product can be sold after arriving at the sales destination, and the greater the risk. Therefore, the time factor weight is determined by the proportional relationship between the two: the longer the transportation cycle and the shorter the product's depreciation cycle, the larger the weight value, and the more significant the impact on the freight risk coefficient. For example, if the depreciation cycle of a fast-evolving product is 30 days, and sea freight has a transportation cycle of 20 days, the time factor weight will be significantly higher than if air freight (transportation cycle of 5 days) is chosen, thus highlighting the amplifying effect of long transportation cycles on the risk of fast-evolving products.

[0046] The delay loss coefficient is a key parameter reflecting the impact of transportation delays on sales, and its determination is based on statistical analysis of a large amount of historical sales data. The system extracts product sales change data corresponding to different delay days, such as the sales decline percentages corresponding to delays of 1 day, 3 days, and 5 days, and obtains the daily average delay loss percentage through data fitting. This coefficient is not a fixed value but is dynamically adjusted according to product type. Fast-iterating products experience significantly higher sales losses due to delays than slow-iterating products because market demand changes rapidly. Seasonal products also have higher delay loss coefficients during peak sales seasons than during off-seasons, ensuring that the coefficient accurately matches the market characteristics of the product.

[0047] The determination of transportation loss rate is directly related to the characteristics of the transportation mode. Different transportation modes have different transportation environments and loading and unloading processes, resulting in varying loss rates. Sea freight has a relatively high loss rate due to its long transportation time and significant ship turbulence; air freight has a lower loss rate due to its short transportation time and stable environment; land freight is greatly affected by road conditions, with a loss rate falling in between. Furthermore, the transportation loss rate is adjusted based on the characteristics of the goods. Fragile items and precision instruments have higher loss rates than ordinary daily necessities, and the loss rate for liquids and powders is also adjusted based on packaging protection. The calculation of the loss cost coefficient needs to comprehensively consider the purchase cost, maintenance cost, or residual value after scrapping of the goods, rather than simply based on the unit price. For example, the loss cost coefficient for high-value goods will include maintenance costs, while for low-value goods it mainly considers scrapping losses, ensuring the accuracy of cost accounting. Through the comprehensive calculation of the above parameters, the system ultimately obtains a freight risk coefficient that comprehensively reflects the risks of overseas transportation.

[0048] The calculation of the warehousing risk coefficient focuses on the risks of backlog and loss in overseas warehousing. The determination of each parameter also follows the principles of scenario adaptation and data support. The calculation of the probability of warehousing backlog integrates three core indicators: warehousing cycle, decay cycle, and inventory turnover rate. Inventory turnover rate reflects the efficiency of commodity inventory turnover. The more turnovers, the smoother the sales and the lower the risk of backlog; the fewer turnovers, the slower the inventory turnover and the higher the risk of backlog. The system obtains the probability of warehousing backlog by multiplying the ratio of warehousing cycle to decay cycle by the inverse of inventory turnover rate. When the warehousing cycle is close to or exceeds the decay cycle and the inventory turnover rate is low, the probability of backlog will increase significantly, intuitively reflecting the risk level of inventory backlog.

[0049] The calculation of the stockpiling cost coefficient is based on the daily rental fee of overseas warehousing. The system connects to the overseas warehousing management system to obtain the rental fee standards of warehousing facilities in real time, including basic rental fees, management fees, insurance fees, etc. The total costs are then multiplied by the warehousing cycle to obtain the total warehousing cost for that batch of goods. This total warehousing cost is then converted to the unit price of the goods, which is the stockpiling cost coefficient per unit. For goods that are large in volume and occupy a lot of warehousing space, the system will additionally consider the space occupancy fee, correspondingly increasing its stockpiling cost coefficient to ensure the comprehensiveness of cost accounting.

[0050] The determination of the storage loss rate is primarily based on the physical characteristics and storage requirements of the goods. Fragile, moisture-sensitive, and perishable goods have relatively high storage loss rates, while durable and structurally stable goods have lower rates. The system adjusts the rate based on the goods' storage requirements, such as whether constant temperature, moisture-proof, and light-proof storage is necessary, as well as the actual conditions of overseas warehousing facilities. If the warehousing facilities cannot meet the specific storage requirements of the goods, the storage loss rate will be appropriately increased. The calculation of the storage loss cost coefficient is similar to that of the transportation loss cost coefficient, comprehensively considering the goods' procurement costs, repair costs, or residual value to ensure accurate quantification of loss costs. Through the integrated calculation of the above parameters, the system obtains a storage risk coefficient, comprehensively reflecting the potential risks in the overseas warehousing process.

[0051] The core of calculating the unsold inventory risk coefficient is to measure the degree of supply and demand matching of goods and their suitability to the market life cycle. The supply-demand ratio is determined by the ratio of expected sales volume to effective inventory. Effective inventory refers to the actual sellable inventory after deducting storage losses and inventory in transit. When this ratio is less than 1, it indicates that inventory exceeds demand, posing a risk of unsold inventory; when the ratio is greater than 1, it indicates that inventory can meet demand, and the risk of unsold inventory is low. The decay factor is determined by the ratio of the decay period to the storage period. This ratio reflects the degree of decay in the market value of goods during the storage period. The smaller the ratio, the larger the proportion of the storage period to the decay period, the higher the risk of depreciation during storage, and the greater the possibility of unsold inventory. Through the comprehensive calculation of the supply-demand ratio and the decay factor, the system obtains the unsold inventory risk coefficient, accurately quantifying the risk of unsold inventory caused by supply-demand imbalance and value decay.

[0052] Specifically, such as Figures 1 to 5 As shown, the risk value fitting model is constructed using a linear programming algorithm, with the objective function being the minimum overall decision bias. The objective function is expressed as MinΔ=|(V×(1-R))-(S×P-Ctotal)|, where V is the product value score, V=S×P×(1-v), v is the product iteration rate, S is the expected sales volume, P is the reasonable selling price, R is the total risk score, and Ctotal is the comprehensive cost, including transportation costs, warehousing costs, and risk losses. The model also sets three types of constraints: budget constraints, inventory constraints, and timeliness constraints. Furthermore, the range of values ​​for the constraints needs to be dynamically adjusted in conjunction with the characteristics of overseas operating scenarios. The risk value fitting model solves by converting the expected sales volume of the goods, reasonable sales price, expected decay period, freight risk coefficient, warehousing risk coefficient, and unsold risk coefficient into standardized decision variables of a linear programming model, and iteratively solves the objective function using the simplex method. By gradually optimizing the values ​​of the decision variables, the optimal solution that satisfies all constraints is found. The optimal solution includes warehousing volume, transportation mode weights, and replenishment cycle. Based on the optimal solution, initial supply decision suggestions are generated, including operating decisions and specific execution parameters.

[0053] The model employs a linear programming algorithm as its core framework. The algorithm's key advantage lies in its ability to find the optimal decision-making scheme under multiple constraints, adapting to the complex scenarios of multi-factor interactions in overseas supply decisions. The model's core objective is to minimize overall decision-making bias, essentially ensuring that the actual operating profit of the goods is as close as possible to the ideal profit level. The ideal profit level is determined by combining the goods' value score and the overall risk score. The goods' value score comprehensively considers expected sales volume, reasonable selling price, and goods iteration rate. Goods with faster iteration rates experience faster value decay, resulting in a lower score. The overall risk score is obtained by weighted summation of freight risk coefficients, warehousing risk coefficients, and unsold goods risk coefficients. The weight of each risk coefficient is dynamically adjusted based on the risk characteristics of the sales location. For example, areas with weak logistics infrastructure will have a higher weight for freight risk coefficients, and areas with high warehousing costs will have a higher weight for warehousing risk coefficients. Actual operating profit is the net profit after deducting transportation costs, warehousing costs, and risk losses from total sales revenue. Risk losses are calculated by correlating the overall risk score with total sales revenue; higher risk results in greater losses.

[0054] The model employs three types of constraints to ensure the feasibility and rationality of decision-making solutions. The budget constraint sets a clear upper limit on the total investment, including costs for goods procurement, transportation, and warehousing, which must not exceed the user's overseas operating budget to avoid excessive investment and operational pressure. The inventory constraint is determined based on the actual capacity of overseas warehousing facilities, specifying the maximum storage volume for a single batch of goods. It also considers the storage characteristics of different goods; for example, the storage capacity limit for large-volume goods will be reduced to avoid storage chaos or additional costs due to insufficient storage space. The timeliness constraint focuses on the iterative characteristics of goods, requiring that the transportation cycle not exceed half of the product's decay cycle. This is because goods continuously consume their market lifecycle during transportation; excessively long transportation times significantly shorten the marketable period after arrival, increasing the risk of unsold inventory. This constraint ensures that goods arrive at the sales location promptly during periods of high market demand. The values ​​of these three constraints are not fixed but are adjusted dynamically according to changes in the overseas operating scenario. For example, inventory constraints can be appropriately relaxed during peak sales seasons to meet market demand, while budget constraints can be moderately adjusted when funds are sufficient to seize prime business opportunities.

[0055] The model solution process follows a standardization and iterative optimization logic. First, the system converts value parameters such as expected sales volume, reasonable selling price, and expected decay period, along with risk parameters such as freight risk coefficient, warehousing risk coefficient, and unsold inventory risk coefficient, into standardized decision variables identifiable by the linear programming model. This conversion process eliminates unit and magnitude differences between different parameters; for example, parameters with tangible units, such as sales volume and price, are converted into dimensionless relative values, ensuring that all parameters can be calculated within the same dimension. Subsequently, the system uses the simplex method to iteratively solve the objective function. This method continuously calculates the objective function by gradually adjusting the values ​​of the decision variables, while simultaneously checking whether the current variable combination satisfies all constraints. If the current variable combination does not satisfy the constraints, the direction of the variable values ​​is adjusted; if the constraints are satisfied, the variable values ​​are further optimized to reduce the overall decision bias until the variable combination that minimizes the overall decision bias is found. This combination is the optimal solution of the model.

[0056] The optimal solution includes three core execution parameters: inventory level, transportation mode weight, and replenishment cycle. Inventory level is determined by comprehensively considering expected sales volume, safety stock, and risk losses. Safety stock is set based on the overall risk score; the higher the risk, the larger the safety stock ratio, ensuring the ability to cope with inventory shortages caused by sudden demand or risks. Transportation mode weight clarifies the proportion of different transportation modes in the total transportation volume. For example, some goods are transported by air to shorten transit time, while others are transported by sea to reduce costs. Weight allocation is based on a balance between risk coefficient and cost. The replenishment cycle is closely related to the product's decay cycle. Products with fast iteration speeds have shorter replenishment cycles to ensure timely inventory updates and avoid unsold inventory due to decay; products with slow iteration speeds can have appropriately longer replenishment cycles to reduce replenishment costs. The initial supply decision recommendations generated based on the optimal solution not only clearly provide a binary judgment on whether to operate the product, but also list the specific values ​​of each execution parameter in detail. At the same time, it generates 3-5 differentiated optimal solutions based on the user's business preferences. For example, the risk-seeking solution focuses on increasing expected returns, the balanced solution takes into account both returns and risks, and the conservative solution focuses on controlling risk losses. Users can choose according to their actual business needs, ensuring the flexibility and practicality of the decision recommendations.

[0057] Specifically, such as Figures 1 to 5As shown, the supply decision correction steps include an overseas business behavior database, which serves as the core data foundation for decision correction. This database stores historical business behavior data that has been meticulously categorized and archived, including multi-dimensional information such as transportation mode selection, warehousing capacity allocation, changes in interaction activity, actual sales feedback, risk occurrence records, and decision adjustment trajectories. When retrieving historical data matching the target product, the system employs a two-dimensional precise matching logic. On one hand, it filters based on product category to ensure that the matched historical behavior is consistent with the attribute characteristics and iteration patterns of the target product; for example, fast fashion products only match historical business data of other fast fashion products. On the other hand, it filters based on sales region, focusing on common factors such as the market environment, consumption habits, and logistics characteristics of the target sales location to avoid correction deviations caused by cross-regional data differences.

[0058] The matching logic of keywords has a clear correspondence. The matching of transportation mode and unsold inventory risk focuses on the impact of the arrival time of goods under different transportation modes on the probability of unsold inventory. For example, the unsold inventory risk data corresponding to sea freight and air freight are respectively associated with the transportation planning of the target goods. The matching of storage volume and backlog cost revolves around the backlog cost data such as storage fees and capital occupation costs corresponding to different storage volume ranges, providing a reference for adjusting the storage volume of the target goods. The matching of interaction popularity and sales volume explores the correspondence between different interaction popularity levels and the final actual sales volume, supporting sales prediction and decision adjustment based on real-time interaction data.

[0059] The application of decision tree algorithms in correlation analysis follows a clear technical logic. The algorithm first decomposes the matched historical business behavior data into feature attributes and outcome attributes. Feature attributes include behavior-related features such as transportation mode type, storage volume range, and interaction intensity level. Outcome attributes include result feedback features such as sales volume change, reduction in unsold inventory risk, and savings in backlog costs. The algorithm constructs multi-layered decision nodes, each corresponding to a feature attribute's classification standard. For example, the transportation mode node is divided into sea, air, and land transportation, and the storage volume node is divided into low, medium, and high ranges. It generates outcome paths corresponding to different feature combinations by traversing historical data. The quantification of correlation is achieved through path weight calculation. The weight of each path is allocated based on the historical data sample size and the degree of influence of the outcome; a higher path weight indicates a stronger correlation between the corresponding behavior and the outcome. When the weight of a certain path reaches the preset standard, that is, when it is determined that the impact of the historical behavior corresponding to the path on the result exceeds the preset threshold, the system will extract the adjustment logic of the behavior and apply it to the corresponding link in the initial decision. For example, if a certain path shows that the weight of reducing the risk of unsold goods corresponding to air freight meets the standard, then the sea freight method in the initial decision will be adjusted to air freight.

[0060] When the interaction probe detects that the interaction intensity in a certain area exceeds a preset threshold, the system initiates a dynamic adjustment mechanism. The preset threshold is not a fixed value, but rather a result of comprehensive calculations based on historical interaction intensity data for similar products in that area, market demand capacity, and product iteration speed. For example, in areas with rapid product iteration, the threshold is set relatively low to ensure timely response to changes in demand. The system retrieves the correlation rules between interaction intensity and warehouse volume adjustments from the overseas business behavior database. These rules are based on a summary of patterns in historical data regarding the fluctuation range of different interaction intensity levels and the corresponding warehouse volume adjustment ratios. For instance, if the interaction intensity increases by 30% from the baseline value, the warehouse volume needs to increase by 15% to match the demand. During the dynamic adjustment process, the system not only refers to a single correlation rule but also combines the region's risk assessment results, warehouse facility capacity, and transportation replenishment capabilities for comprehensive calculations to ensure that the adjusted warehouse volume meets market demand without triggering new inventory buildup risks or supply shortages.

[0061] The deviation verification process employs a mean squared error (MSE) algorithm. Its core purpose is to measure the difference between the expected return of the corrected decision and the actual potential return. The actual expected return is determined based on the initial decision's return calculation, combined with the return deviation patterns of historical correction cases. The corrected expected return is the return result recalculated after applying the adjustment logic, taking into full account changes in transportation costs, warehousing costs, and risk losses after the adjustment. The algorithm calculates the deviation value by averaging the squared differences between the two types of returns across multiple samples. The smaller this value, the closer the corrected decision is to the actual optimal return level. The preset threshold is dynamically adjusted based on the user's risk appetite and business objectives. Users with high risk appetite can set higher thresholds, allowing for a certain degree of return fluctuation; users with low risk appetite set lower thresholds, pursuing a more precise return match. If the deviation value is less than the preset threshold, it indicates that the corrected decision meets the accuracy requirements, and the system outputs the final supply decision. If the deviation value does not meet the standard, the system returns to the behavioral data matching and correlation analysis stage, re-screens historical behaviors, optimizes the adjustment logic, and repeats the correction process until the deviation value meets the preset requirements.

[0062] Meanwhile, the system incorporates dynamic updates to the behavior database and manual intervention mechanisms during the supply decision-making correction process. The behavior database automatically collects new overseas operational data weekly, including feedback on actual results after decision adjustments. It recalculates the weights of association rules using algorithms, eliminating invalid rules with extremely low correlation to ensure the timeliness and accuracy of the data. When significant changes occur in the market environment corresponding to historical behaviors, such as adjustments to sales location policies or substantial fluctuations in logistics costs, leading to a decrease in the applicability of association rules, the system triggers a manual review process. This allows users to manually correct association rules or supplement new adjustment logic, ensuring that decision-making corrections can adapt to complex and ever-changing overseas operational scenarios.

[0063] Specifically, such as Figures 1 to 5 As shown, the overseas commodity attribute matching strategy includes setting multi-dimensional screening indicators to comprehensively adapt to overseas business scenarios. Transportation feasibility indicators focus on the adaptability and accessibility of commodity transportation, including core considerations such as the stability of transportation routes, the compatibility of transportation tools with commodity characteristics, and the simplification of customs clearance procedures; warehousing adaptability indicators focus on the matching of commodity storage needs with overseas warehousing facilities, including the fit between commodity size and storage space, and the fulfillment of special storage conditions (such as constant temperature and moisture protection); policy compliance indicators cover relevant clauses such as tariff standards, quality inspection requirements, and prohibited / restricted transport regulations required for commodity import and export; and iterative characteristic adaptability indicators measure the degree of matching between the commodity update speed and the iterative pace of market demand in the sales location.

[0064] The weighting of each screening indicator is dynamically adjusted based on the actual overseas operating scenarios. For example, the weight of the transportation feasibility indicator will be increased in areas with weak logistics infrastructure, while the weight of the policy compliance indicator will be increased in areas with strict policy regulations. Clear quantitative standards are set for each screening indicator, using a 100-point scoring system: In the transportation feasibility indicator, goods with stable routes and convenient customs clearance receive high scores, while goods with transportation restrictions or complex customs clearance have points deducted according to the degree of impact; in the policy compliance indicator, goods that fully meet all requirements receive full marks, while those that partially meet requirements have their scores adjusted proportionally based on compliance; the remaining indicators are scored according to the actual degree of matching.

[0065] The system compares core overseas operational information with product attributes in the product database one by one, assigning a score to each indicator according to quantitative standards. The overall product matching score is then obtained by weighted summation of each indicator's score and its corresponding weight. A preset threshold is determined based on the average matching level of similar products in the industry and user risk preferences. Users with high risk preferences can have the threshold appropriately lowered, while those with low risk preferences can have it raised. Products with overall matching scores exceeding the preset threshold are selected as target products. If multiple products meet the criteria, they are sorted from highest to lowest overall matching score, with the top three categories prioritized for subsequent evaluation, ensuring both efficiency and accuracy.

[0066] Specifically, such as Figures 1 to 5 As shown, the dynamic risk assessment strategy also includes a real-time update mechanism. This involves connecting to overseas logistics platforms, warehouse management systems, and customs policy release platforms via open API interfaces to establish a stable data synchronization channel. Data synchronization employs a dual-mode of "scheduled checks + real-time push." ​​Scheduled checks acquire data from each platform at fixed intervals, while real-time pushes receive key information notifications proactively sent by the platforms, ensuring timely data updates.

[0067] When a transportation route congestion warning is received, the system categorizes the congestion level into different grades: mild congestion corresponds to a slight increase in the probability of delays, moderate congestion corresponds to a moderate increase, and severe congestion corresponds to a significant increase. The adjustment range is determined based on the correlation between historical congestion data and delay probabilities. When a notification of warehousing cost adjustments is received, the system updates overseas warehousing daily rental data in real time and recalculates the stockpiling cost coefficient per unit of goods; if costs increase, this coefficient is increased accordingly. When information on changes in import and export policies is captured, the system assesses the impact on market demand based on the policy type (such as tariff adjustments or restrictions on smuggling). Tariff increases will reduce market demand and thus correct the supply-demand ratio, while favorable policies will increase demand expectations and adjust the supply-demand ratio. After each update, the system automatically triggers a recalculation of the risk coefficient to ensure that the risk assessment results remain synchronized with changes in the overseas operating environment.

[0068] Specifically, such as Figures 1 to 5 As shown, the construction of the overseas business behavior database includes the comprehensive collection of data from the entire overseas business process, covering all aspects such as commodity procurement, transportation, warehousing, sales, and after-sales service. Data types include records of transportation mode selection, warehousing quantity adjustment trajectory, interaction popularity change data, actual sales feedback, details of risk events and decision adjustment results, etc.

[0069] Data is categorized and archived according to three dimensions: Behavior type, including categories such as transportation management, warehouse configuration, promotion execution, and pricing adjustments; Scenario characteristics, including product category, sales region, season, and market competition intensity; and Result feedback, including revenue change, risk reduction percentage, and sales fluctuation. The correlation between each behavior and its corresponding result is quantified, using the Pearson correlation coefficient to measure the linear correlation. The correlation weights are adjusted based on the data sample size; a larger sample size and stronger correlation result in higher weights.

[0070] The behavior database adopts a dynamic update mechanism, regularly collecting newly added overseas business data, recalculating correlation weights, and removing invalid behavior data with extremely low correlation. When the feedback of a certain type of behavior in a new scenario deviates significantly from the historical correlation pattern, a manual review process is triggered to verify the cause of the deviation and update the correlation rules, ensuring the timeliness and accuracy of the behavior database data and providing reliable support for decision correction.

[0071] The foregoing has illustrated and described the basic features, principles, and advantages of the present invention. It should be noted that the present invention is not limited to the above embodiments, but only to some embodiments. Any improvements and additions made without departing from the spirit and scope of the present invention are considered to be within the scope of protection of the present invention.

Claims

1. A product type supply decision-making method based on rapid iteration, characterized in that, include: The target product confirmation step involves receiving core overseas business information, including the product's place of origin and overseas sales location, and filtering target products based on overseas product attribute matching strategies. The market data integration step involves deploying interaction probes on overseas traffic and sales platforms for the target product, collecting real-time user interaction data, and integrating it with historical sales data and overseas market trend data to obtain a product evaluation dataset, including interaction popularity and demand distribution. The product valuation process involves calculating the expected sales volume, reasonable selling price, target customer profile, and expected decline period of the target product based on the product valuation dataset, and generating a product valuation report. The risk dynamic assessment step involves calculating freight risk coefficients, warehousing risk coefficients, and unsold inventory risk coefficients for overseas operating scenarios using a risk dynamic assessment strategy, thereby generating dynamic risk assessment results. The supply decision generation process involves constructing a risk value fitting model, inputting parameters from the commodity value assessment report and dynamic risk assessment results into the model, and outputting initial supply decision recommendations by setting the goal of minimizing the overall decision deviation. The supply decision correction step involves calling the overseas business behavior database and dynamically correcting the initial supply decision recommendations in conjunction with budget constraints. It then verifies the deviation value of the corrected decision. If the deviation value is less than a preset threshold, the final supply decision is output.

2. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The market data integration steps include: accessing core overseas business information, which includes store attributes, sales preferences, and budget constraints; deploying interaction probes on traffic and sales platforms in the corresponding regions based on the overseas sales regions of the target products; setting the collection frequency of the interaction probes according to the product iteration speed; collecting real-time user interaction data, which includes browsing duration, page dwell path nodes, forwarding trigger counts, keyword segmentation results for inquiries, pre-sale order submission behavior, and time spent adding items to the shopping cart; removing abnormal data such as browsing duration less than a preset threshold and empty inquiries; standardizing the collected data from different platforms; grouping users by region and consumption preference tags using a k-means clustering algorithm to obtain different demand regions; calculating interaction heat based on browsing duration, forwarding trigger counts, inquiry frequency, and pre-sale order volume; and obtaining demand distribution based on the ratio of collected data volume in each demand region to the total collected data volume.

3. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The product value assessment steps include: calculating a conversion coefficient based on the interaction popularity and historical interaction conversion data of the platform using a logistic regression algorithm; calculating a decay correction coefficient based on historical sales data of the same period and the product iteration rate; and obtaining the expected sales volume by multiplying the interaction popularity and conversion coefficient by the historical sales volume of the same period and the decay correction coefficient; collecting the historical average price of competitors in the target demand area; calculating the competitor's attention share by comparing competitor sales with the total sales of similar products in the demand area; calculating overseas warehousing costs and transportation unit costs; and obtaining a reasonable selling price; statistically analyzing historical iteration data of similar products to obtain the basic iteration cycle; calculating the market competition coefficient by collecting the update frequency of the target product and competitors; and calculating the expected decay cycle based on the basic iteration cycle and the market competition coefficient.

4. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The dynamic risk assessment steps include: calculating the probability of transportation delays using historical transportation data along the same route; determining the weight of the time factor based on the transportation cycle and decay cycle; obtaining a delay loss coefficient based on historical data statistics, where the delay loss coefficient is the proportion of sales loss caused by a one-day delay; determining the transportation loss rate and loss cost coefficient based on the transportation method; and calculating the freight risk coefficient. The steps also include: calculating the probability of warehousing backlog using the warehousing cycle, decay cycle, and inventory turnover rate; calculating the backlog cost coefficient using overseas warehousing daily rental fees and warehousing cycle; determining the warehousing loss rate and loss cost coefficient based on the characteristics of the goods; and calculating the warehousing risk coefficient. The supply-demand ratio is calculated, and the decay factor is calculated using the decay period and storage period. The unsold risk coefficient is then calculated based on the supply-demand ratio and the decay factor.

5. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The construction of the value at risk fitting model includes using a linear programming algorithm, with the objective function being the minimum overall decision bias. The objective function is expressed as MinΔ=|(V×(1-R))-(S×PC) total )|, where V is the product value score, V=S×P×(1-v), v is the product iteration rate, S is the expected sales volume, P is the reasonable selling price, R is the total risk score, and C total To address the comprehensive costs, including transportation costs, warehousing costs, and risk losses, the model simultaneously sets three types of constraints: budget constraints, inventory constraints, and timeliness constraints. The range of values ​​for these constraints needs to be dynamically adjusted based on the characteristics of overseas operating scenarios. The risk value fitting model transforms the expected sales volume, reasonable selling price, expected decay period, freight risk coefficient, warehousing risk coefficient, and unsold inventory risk coefficient into standardized decision variables for a linear programming model. The objective function is then iteratively solved using the simplex method. By progressively optimizing the values ​​of the decision variables, the optimal solution satisfying all constraints is found. This optimal solution includes warehousing quantity, transportation mode weights, and replenishment cycle. Based on the optimal solution, initial supply decision recommendations are generated, including operational decisions and specific execution parameters.

6. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The supply decision correction step includes retrieving historical business behavior data consistent with the target product category and sales region from the overseas business behavior database, matching keywords including transportation mode and risk of unsold goods, storage volume and backlog costs, interaction heat and sales volume, and analyzing the correlation between behavior and result based on the matched historical behavior using a decision tree algorithm. If the impact of historical behavior on the result exceeds a preset threshold, the corresponding behavior in the initial decision is adjusted. When the regional interaction heat collected by the probe is greater than the preset threshold, the regional storage volume is dynamically adjusted based on the association rules retrieved from the overseas business behavior database. The deviation value of the corrected decision is calculated using a mean squared error algorithm based on the difference between the actual expected revenue and the corrected expected revenue. If it is less than the preset threshold, the final supply decision is output; otherwise, the supply decision correction continues.

7. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The overseas commodity attribute matching strategy includes setting multi-dimensional screening indicators, including transportation feasibility, warehousing adaptability, policy compliance, and iterative characteristic adaptability. Each screening indicator is assigned a corresponding weight, and a quantitative standard is set for each screening indicator. The core information of overseas operations is compared and scored item by item with the commodity attributes in the commodity database to calculate the comprehensive matching score of the commodity. Commodities with a comprehensive score greater than a preset threshold are selected as target commodities. If multiple commodities meet the conditions, they are sorted in descending order of comprehensive score, and the top 3 categories of commodities with the highest scores are selected to enter the subsequent evaluation stage.

8. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The aforementioned dynamic risk assessment strategy also includes a real-time update mechanism. By connecting with overseas logistics platforms, warehouse management systems, and customs policy release platforms, it obtains real-time information on transportation route congestion warnings, warehouse cost adjustment notices, and changes in import and export policies. When updated information is obtained, the risk coefficient is recalculated: the freight risk coefficient adjusts the delay probability based on the transportation route congestion level, the warehouse risk coefficient adjusts the backlog cost coefficient based on the latest daily warehouse rental rate, and the unsold inventory risk coefficient adjusts the supply-demand ratio based on the impact of policy changes on demand.

9. The commodity type supply decision-making method based on rapid iteration according to claim 1, characterized in that, The construction of the overseas business behavior database includes collecting historical data on the entire overseas business process, including transportation methods, warehousing volume, interaction intensity, sales volume, risk occurrence, and decision adjustment results. The data is then classified and archived according to behavior type, scenario characteristics, and result feedback. The correlation between each type of behavior and result is quantified, and the correlation weight is calculated using the Pearson correlation coefficient to obtain an overseas business behavior database that includes the correlation between several behaviors and results.

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