Cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system

Abstract

The invention discloses a cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, and relates to the technical field of data processing, and the system comprises a data processing module which obtains real-time data of purchase cost, exchange rate fluctuation, storage cost and logistics cost, cleans and standardizes multi-source heterogeneous data, obtains a cost data set of a unified format, and stores the cost data set of the unified format; the trend analysis module is used for calculating the change trend of the purchase cost, the exchange rate fluctuation, the storage cost and the logistics cost in the time dimension according to the cost data set in the unified format to obtain the dynamic weight of each cost factor, and the inventory pricing module is used for calculating the real-time comprehensive cost of each commodity by adopting a weighted average algorithm according to the dynamic weight of each cost factor and calculating the real-time comprehensive cost of each commodity. A dynamic inventory pricing result is obtained; according to the cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, the problems of pricing lag and profit loss caused by the fact that a traditional inventory pricing method cannot respond to cost fluctuation in real time are solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system. Background Technology

[0002] Inventory management and pricing strategies are core elements for cross-border e-commerce companies to achieve cost control and profit optimization, directly impacting their market competitiveness and financial performance. With the increasing complexity of global trade, companies need to accurately calculate the cost of their inventory and adjust pricing promptly to respond to market changes and consumer demands.

[0003] However, many current solutions have significant shortcomings in handling dynamic cost accounting and pricing adjustments. Some methods rely too heavily on manual adjustments, making it difficult to respond quickly to market fluctuations, resulting in lagging cost accounting and a disconnect between pricing strategies and actual costs. Other systems, while achieving partial automation, often overlook the combined impact of multiple cost factors, such as exchange rate fluctuations and dynamic changes in logistics costs, leading to inaccurate cost accounting and ultimately affecting a company's profitability and inventory turnover efficiency. In actual business operations, a key challenge for cross-border e-commerce companies is how to achieve real-time integration and dynamic reflection of multiple cost factors. The primary issue is the difficulty in uniformly quantifying the dynamic changes of factors such as procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs over time. Existing technologies often cannot efficiently handle the synchronous updates of such multi-dimensional dynamic data. How to quickly reflect these dynamic cost changes in the pricing strategies of various platforms is a critical challenge. For example, if a product's cost increases due to an appreciation in exchange rates, a delayed pricing adjustment may lead to a decrease in profits; conversely, adjusting too quickly may affect consumer trust due to frequent price fluctuations. This time lag and balance issue between cost accounting and pricing becomes a core obstacle for companies to optimize inventory turnover and gross profit margins. Summary of the Invention

[0004] The purpose of this invention is to provide a smart scheduling and inventory collaborative management system for orders across multiple cross-border e-commerce platforms. It constructs a dynamic inventory pricing system that can integrate multiple factors such as procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs in real time, and efficiently reflect cost changes in the pricing strategies of each platform. At the same time, it balances the relationship between inventory turnover rate and gross profit margin, thereby achieving precise cost control and profit analysis.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, comprising: a data processing module, which acquires real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs, cleans and standardizes multi-source heterogeneous data to obtain a unified format cost dataset; a trend analysis module, which calculates the changing trends of procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs over time for the unified format cost dataset, obtaining the dynamic weights of each cost factor; an inventory pricing module, which calculates the real-time comprehensive cost of each commodity using a weighted average algorithm based on the dynamic weights of each cost factor, obtaining a dynamic inventory pricing result; a threshold triggering module, which triggers a pricing strategy adjustment process and generates cost change signals for each commodity if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold; and a pricing prediction module. Based on cost change signals, a linear regression algorithm is used to predict the price adjustment range of each product on different platforms, resulting in a preliminary pricing plan for each platform. The sensitivity analysis module analyzes the price sensitivity and turnover efficiency of each product using historical sales data and inventory turnover rate, obtaining optimized pricing adjustment parameters. The pricing strategy generation module generates the final pricing strategy for each platform based on the optimized pricing adjustment parameters and synchronously updates the product price database of each sales platform. The performance monitoring module obtains real-time sales and inventory data from each sales platform, calculates the adjusted inventory turnover rate and gross profit margin, and obtains the performance indicators of the current pricing strategy. The strategy iteration module adjusts cost weights and pricing parameters based on the deviation value if the current pricing strategy performance indicators do not meet the preset inventory turnover rate and gross profit margin targets, generating a new dynamic inventory pricing and pricing strategy.

[0006] Preferably, the data processing module acquires real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs; cleans and standardizes the multi-source heterogeneous data to obtain a unified-format cost dataset. This includes acquiring real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs from the enterprise resource planning (ERP) system; extracting raw data streams from multiple data sources through a data interface to obtain an initial dataset containing multi-source heterogeneous data; if the initial dataset contains missing values ​​or inconsistent formats, data cleaning is performed using regular expression matching and null value imputation methods to handle abnormal data, resulting in a cleaned dataset; based on the cleaned dataset, data standardization methods are used to convert the multi-source heterogeneous data into a unified format using preset field mapping rules. A standardized cost dataset is obtained by first formatting the data. Then, a time series analysis algorithm is used to detect the changing trends of procurement costs and exchange rate fluctuations, resulting in a feature dataset of cost fluctuations. If the feature dataset shows abnormal fluctuations, K-means clustering is used to group warehousing costs and logistics costs to determine the source category of the cost anomalies, resulting in categorized cost distribution data. Based on the categorized cost distribution data, a weighted average algorithm is used to integrate procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs to generate a comprehensive cost dataset. Finally, data stream processing methods are applied to incrementally update the real-time data using the comprehensive cost dataset, resulting in a dynamically adjusted cost dataset.

[0007] Preferably, the trend analysis module, for a unified format cost dataset, calculates the changing trends of procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs over time, obtaining the dynamic weights of each cost factor, including the dynamic change sequences obtained through time series analysis algorithms. A sliding window is used to extract the fluctuation characteristics of each cost factor within a fixed time window, resulting in a fluctuation feature set of cost factors. Based on the fluctuation feature set, principal component analysis is used to identify the main contributing factors to the fluctuations of procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs, obtaining the weight distribution of cost factors. If the weight of a certain cost factor in the weight distribution exceeds a preset threshold, the cost factor is filtered using a mean filtering method. The dynamic change sequence of factors is smoothed to obtain a smoothed change sequence. Using this smoothed change sequence, a weighted summation method is employed to integrate the weight distribution and fluctuation characteristics of each cost factor, resulting in a comprehensive cost fluctuation index. Based on this comprehensive cost fluctuation index, a time series forecasting algorithm is used to calculate the cost change trend over a future time period, yielding a predicted cost trend sequence. Using this predicted cost trend sequence, a rule-matching method is used to compare it with historical cost data to determine if any abnormal fluctuation patterns exist, resulting in an abnormal fluctuation detection result. Based on the abnormal fluctuation detection result, a classification algorithm is used to perform attribution analysis on the abnormal fluctuations, identifying the cost factors that caused the abnormal fluctuations, and obtaining the attribution analysis result.

[0008] Preferably, the inventory pricing module calculates the real-time comprehensive cost of each commodity using a weighted average algorithm based on the dynamic weights of each cost factor, obtaining the dynamic inventory pricing result. This includes acquiring the basic cost data and corresponding dynamic weight allocation table for each commodity type; establishing a mapping relationship between commodities and weights through data matching methods to obtain a commodity weight mapping table; calculating the multi-dimensional cost data of each commodity in real time using a weighted average algorithm based on the commodity weight mapping table; adjusting the commodity weight allocation to obtain a comprehensive cost value; adding timestamp information to each pricing result using a time dimension marking method based on the comprehensive cost value to establish a dynamic inventory pricing data table; and then calculating the dynamic inventory pricing result based on the dynamic inventory pricing result. The data table uses a clustering algorithm to group goods with the same value assessment range, identifying sets of goods in different value intervals to obtain a tiered pricing structure. Using this tiered pricing structure, a threshold comparison method is used to detect fluctuations in the pricing results of goods at each level. If the fluctuation exceeds a preset threshold, a re-pricing process is triggered, resulting in an updated inventory pricing table. Based on the updated inventory pricing table, a difference calculation method is used to compare the changes in the two pricing results, identifying the list of goods requiring price adjustments and obtaining inventory value change records. Using these inventory value change records, statistical analysis methods are used to calculate the overall inventory value change trend and distribution characteristics, generating an inventory pricing analysis report.

[0009] Preferably, the threshold triggering module, if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold, triggers a pricing strategy adjustment process to generate cost change signals for each commodity. This includes: if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold, obtaining the cost change magnitude of each commodity through a difference calculation method to obtain a cost change magnitude set; standardizing the change magnitude using a normalization method based on the cost change magnitude set to obtain a standardized magnitude sequence; grouping commodities using a clustering analysis algorithm based on the standardized magnitude sequence to determine commodity clusters with similar cost changes; integrating the cost change weights of each commodity using a weighted summation method based on the commodity clusters to obtain a cluster comprehensive cost signal; if the cluster comprehensive cost signal exceeds a preset signal threshold, comparing historical signal data using a rule matching method to determine whether there is an abnormal signal pattern to obtain an abnormal signal detection result; classifying the abnormal signals using a decision tree algorithm based on the abnormal signal detection result to determine the source commodities of the abnormal signals to obtain a source commodity set; and adjusting the dynamic inventory pricing model using a data update method based on the source commodity set to obtain an updated pricing result.

[0010] Preferably, the pricing prediction module, based on cost change signals, uses a linear regression algorithm to predict the price adjustment range of each product on different platforms, obtaining a preliminary pricing scheme for each platform. This includes extracting price change records of each product on different sales platforms from a historical transaction database using a data filtering method, obtaining a historical price dataset for each platform; constructing a product pricing prediction model using a linear regression algorithm based on the historical price dataset, analyzing the correlation between cost changes and price adjustments, and determining the price sensitivity coefficient for each platform; if the price sensitivity coefficient exceeds a preset sensitivity threshold, then a weighting method is used to quantitatively assign pricing influencing factors to different platforms. The system is processed to obtain a platform weight matrix. Based on the platform weight matrix, a weighted calculation method is used in conjunction with the current cost change signal to calculate the theoretical adjustment range of each product on different platforms, obtaining a set of adjustment range values. Through the adjustment range value set, an interval mapping method is used to convert the values ​​into corresponding pricing adjustment levels, resulting in a tiered adjustment scheme. Based on the tiered adjustment scheme, a rule matching method is used to compare the pricing constraints of each platform, and adjustment schemes that conform to the platform rules are selected to determine the adjustment scheme set. If there are conflicting pricing strategies in the adjustment scheme set, the schemes of each platform are arranged by priority ranking method, and the optimal pricing combination is selected to generate the final multi-platform pricing strategy scheme.

[0011] Preferably, the sensitivity analysis module, for the initial pricing scheme, uses historical sales data and inventory turnover rate to analyze the price sensitivity and turnover efficiency of each product, obtaining optimized pricing adjustment parameters. This includes extracting the sales volume change trend and inventory turnover cycle of each product on different platforms using data filtering methods based on historical sales data and inventory turnover rate, constructing a product sales dynamic dataset; calculating the price sensitivity coefficient of each product on different platforms using regression analysis based on the product sales dynamic dataset, and determining the correlation matrix between the sensitivity coefficient and turnover efficiency; if the sensitivity coefficient in the correlation matrix exceeds a preset sensitivity threshold, then a weighted calculation method combined with the platform... Based on differences and product characteristics, a preliminary adjustment range set is generated for each product. Using this preliminary adjustment range set, an interval mapping method is employed to convert the adjustment range into corresponding pricing adjustment levels, obtaining a tiered pricing scheme. According to the tiered pricing scheme, a rule-matching method is used to compare the pricing constraints of each platform, filtering out a set of pricing adjustment schemes that conform to the platform rules. If conflicting adjustment strategies exist within the pricing adjustment scheme set, a priority ranking method is used to arrange the schemes based on product characteristics and platform differences, determining the optimal pricing adjustment combination. Using the optimal pricing adjustment combination, a data fusion method is employed to combine the adjustment parameters with historical sales data, generating the final multi-platform optimized pricing scheme.

[0012] Preferably, the pricing strategy generation module generates the final pricing strategy for each platform based on the optimized pricing adjustment parameters and synchronously updates it to the product price database of each sales platform. This includes extracting the pricing adjustment range of each product from the pricing adjustment parameters using a data filtering method to generate a preliminary pricing strategy set; adjusting the preliminary pricing strategy set to the specific data structure of each platform using a format conversion method to obtain a platform-compatible pricing strategy set; if the platform-compatible pricing strategy set is consistent with the format requirements of the sales platform interface, then checking the compliance of the pricing strategy set using an interface verification method to determine a compliant pricing strategy set; encrypting the pricing strategy set using a data encryption method based on the compliant pricing strategy set to obtain an encrypted pricing strategy set; pushing the encrypted pricing strategy set to the product price database of each sales platform using a data transmission method to complete price synchronization; comparing the update records in the product price database with the encrypted pricing strategy set using a data verification method to determine the integrity of price synchronization; if the integrity of price synchronization meets a preset threshold, then generating a log file for the synchronization operation using a log recording method to obtain the synchronization completion status.

[0013] Preferably, the performance monitoring module acquires real-time sales and inventory data from various sales platforms, calculates adjusted inventory turnover and gross profit margin, and obtains the performance indicators of the current pricing strategy. This includes extracting real-time sales and inventory data from various sales platforms using data acquisition methods to obtain an original sales and inventory dataset; processing the original sales and inventory dataset using data cleaning methods to remove outliers and missing values, generating a cleaned sales and inventory dataset; calculating the inventory turnover and gross profit margin of each product based on the cleaned sales and inventory dataset to obtain a product performance dataset; if the inventory turnover in the product performance dataset is lower than a preset threshold, extracting products with inventory turnover rates lower than the preset threshold using data filtering methods to generate a product list; predicting the sales trend of each product using regression analysis algorithms based on the product list to obtain a sales trend prediction set; comparing the sales trend prediction set with the current pricing strategy using data integration methods to determine the pricing strategy adjustment needs and generate an adjustment need list; and pushing the adjustment need list to the strategy update interface of each sales platform using data transmission methods to complete the pricing strategy optimization synchronization.

[0014] Preferably, the strategy iteration module, if the current pricing strategy performance indicators do not reach the preset inventory turnover rate and gross profit margin targets, adjusts cost weights and pricing parameters based on the deviation value to generate a new dynamic inventory pricing and pricing strategy. This includes: obtaining real-time sales data and inventory data from various sales platforms to generate an initial sales inventory dataset; processing the initial sales inventory dataset using a data cleaning method to remove outliers and missing values, resulting in a cleaned sales inventory dataset; calculating the deviation values ​​between the inventory turnover rate and gross profit margin of each product in the cleaned sales inventory dataset and the preset targets using a deviation analysis method, generating a deviation dataset; if the inventory turnover rate or gross profit margin in the deviation dataset is lower than the preset targets, predicting the cost weight adjustment trend of each product using a linear regression algorithm, resulting in a cost weight adjustment set; recalculating the pricing parameters of each product using a data integration method based on the cost weight adjustment set, generating an adjusted dynamic inventory pricing model; comparing the adjusted dynamic inventory pricing model with the current pricing strategy set using a data comparison method, generating an optimized pricing strategy set; and pushing the optimized pricing strategy set to the strategy update interface of each sales platform using a data transmission method to complete the synchronization of the dynamic pricing strategy.

[0015] As can be seen from the above technical solution, the present invention has the following beneficial effects:

[0016] This cross-border e-commerce multi-platform intelligent order scheduling and inventory collaborative management system uses data stream processing technology to clean and standardize multi-dimensional data from enterprise resource planning, including procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs. It dynamically calculates the weights of each cost factor using time series analysis algorithms and employs a weighted average algorithm to achieve real-time pricing of the overall cost of goods. When cost changes exceed a preset threshold, a pricing adjustment process is automatically triggered. A linear regression algorithm is used to predict the pricing adjustment magnitude for different platforms, and pricing parameters are optimized based on historical sales data and inventory turnover rate analysis. This invention establishes a closed-loop feedback mechanism from cost change perception to pricing strategy execution. By continuously monitoring performance indicators such as inventory turnover rate and gross profit margin, it dynamically adjusts cost weights and pricing parameters, achieving intelligent collaborative optimization of inventory pricing and pricing strategies. This significantly improves the enterprise's responsiveness to market changes and profitability, solving the problem of pricing lag and profit loss caused by the inability of traditional inventory pricing methods to respond to cost fluctuations in real time. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, this invention provides a technical solution: a cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, comprising:

[0020] The data processing module acquires real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs. It cleans and standardizes multi-source heterogeneous data to obtain a cost dataset in a unified format.

[0021] The trend analysis module calculates the changing trends of procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs over time for a unified format cost dataset, and obtains the dynamic weights of each cost factor.

[0022] The inventory valuation module calculates the real-time comprehensive cost of each product using a weighted average algorithm based on the dynamic weights of each cost factor, thus obtaining the dynamic inventory valuation result.

[0023] The threshold trigger module triggers the pricing strategy adjustment process and generates cost change signals for each commodity if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold.

[0024] The pricing prediction module uses a linear regression algorithm to predict the price adjustment range of each product on different platforms based on cost change signals, and obtains the preliminary pricing plan for each platform.

[0025] The sensitivity analysis module analyzes the price sensitivity and turnover efficiency of each product using historical sales data and inventory turnover rate for the initial pricing plan, and obtains the optimized pricing adjustment parameters.

[0026] The pricing strategy generation module generates the final pricing strategy for each platform based on the optimized pricing adjustment parameters and updates it synchronously to the product price database of each sales platform.

[0027] The performance monitoring module obtains real-time sales and inventory data from various sales platforms, calculates the adjusted inventory turnover rate and gross profit margin, and obtains the performance indicators of the current pricing strategy.

[0028] The strategy iteration module adjusts cost weights and pricing parameters based on deviation values ​​if the current pricing strategy performance indicators do not meet the preset inventory turnover rate and gross profit margin targets, thereby generating a new dynamic inventory pricing and pricing strategy.

[0029] This system is based on a modular architecture. The data processing module collects and standardizes multi-source data, including procurement costs, exchange rates, warehousing, and logistics expenses, ensuring that data enters subsequent calculation processes under a unified standard. The trend analysis module uses time series modeling to quantify historical trends in cost data and extract the weighting information of key cost factors. The inventory pricing module calculates the weighted average cost of goods based on dynamic weights, providing a dynamic pricing basis for inventory costs. The threshold trigger module monitors cost changes; once a threshold is exceeded, the pricing adjustment mechanism is immediately activated, ensuring the system's responsiveness to cost fluctuations. The pricing prediction module introduces a linear regression algorithm, combining platform characteristics to predict price adjustment trends. Subsequently, the sensitivity analysis module incorporates historical sales data and turnover efficiency parameters to optimize price adjustment strategies. The pricing strategy generation module converts the results into executable prices for the actual platforms and automatically synchronizes them to the databases of each e-commerce platform. The performance monitoring module monitors adjusted sales and inventory performance in real time, extracting key performance indicators for the strategy iteration module to optimize based on deviations, achieving closed-loop learning and adaptive adjustment of the system.

[0030] This system enables efficient collaborative management of orders and inventory across platforms, improving the real-time nature and accuracy of product pricing through a dynamic inventory pricing mechanism. The combination of trend analysis and sensitivity analysis makes pricing strategies more aligned with market realities, enhancing the scientific basis of sales strategies. The introduction of performance monitoring and strategy iteration mechanisms constructs an automated, data-driven pricing optimization loop, effectively improving inventory turnover and gross profit margin while reducing inventory backlog and pricing risks. Modular design enhances the system's maintainability and scalability, supporting intelligent scheduling and strategy optimization under multi-platform sales demands.

[0031] The data processing module acquires real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs. It cleans and standardizes this multi-source heterogeneous data to obtain a unified-format cost dataset. This includes acquiring real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs from enterprise resource planning (ERP) data. Raw data streams are extracted from multiple data sources via data interfaces to obtain an initial dataset containing multi-source heterogeneous data. If the initial dataset contains missing values ​​or inconsistent formats, data cleaning is performed using regular expression matching and null value imputation methods to handle abnormal data, resulting in a cleaned dataset. Based on the cleaned dataset, data standardization methods are used to convert the multi-source heterogeneous data into a unified format according to preset field mapping rules. A standardized cost dataset is obtained. For this standardized cost dataset, time series analysis algorithms are used to detect the changing trends of procurement costs and exchange rate fluctuations, resulting in a feature dataset of cost fluctuations. If the feature dataset of cost fluctuations shows abnormal fluctuations, K-means clustering is used to group warehousing costs and logistics costs to determine the source category of the cost anomalies, resulting in categorized cost distribution data. Based on the categorized cost distribution data, a weighted average algorithm is used to integrate procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs to generate a comprehensive cost dataset. Using this comprehensive cost dataset, data stream processing methods are applied to incrementally update the real-time data, resulting in a dynamically adjusted cost dataset.

[0032] This data processing module first configures the data interface during the system initialization phase, establishing a bidirectional data transmission connection with the Enterprise Resource Planning (ERP) system to acquire four types of data in real time: procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs. The interface periodically requests data via HTTPS, with a fixed interval of every 5 minutes. Each request returns data items added or updated within the past 5 minutes, ensuring data real-time performance and completeness. The acquired raw data includes fields such as procurement amounts denominated in RMB, warehousing records corresponding to commodity codes, the USD / RMB exchange rate, warehousing unit price per cubic meter, and logistics costs per order. Due to differences in field naming, data format, and timestamp formats between data sources, the data cannot be directly used for calculations.

[0033] After receiving the original data stream, the system first constructs an initial data set. The construction process includes classifying and labeling the original data records. For example, the "purchase price" field is uniformly labeled as "purchase cost", the "midpoint exchange rate" field is uniformly labeled as "exchange rate fluctuation", and so on. If there are null values in the initial data set, for example, a certain purchase record does not return the exchange rate field value, the system executes a null value filling strategy. The null value filling rule is as follows: if there are at least 3 valid values in this field within the past 7 days, the arithmetic mean of its valid values within 7 days is used for filling; if there are no valid values, the fixed default values set by the platform are adopted. The default value of the purchase cost is set to 10 yuan, the default value of the exchange rate fluctuation is 7.1, the default value of the warehousing cost is 2.5 yuan per cubic meter, and the default value of the logistics cost is 15 yuan per order. For the situation of inconsistent formats, such as the coexistence of "July 22, 2025" and "2025 / 07 / 22" in the date field, the system uses predefined regular expressions to match the Chinese character year-month-day format and the slash format respectively, and uniformly converts them into the standard format of "2025-07-22". This format processing action is executed one by one by the Java program in the data preprocessing thread through the regular processing function.

[0034] The Java program executes the regular expression processing function in the data preprocessing thread to standardize the date format and unit format. The specific implementation steps are as follows: The system first accesses the original data stream in the data receiving module, passes each piece of data as a string object into the preprocessing queue, and then the processing threads in the multi-thread task pool take out the data entries one by one. Before each piece of data is processed, the system calls the regular expression matching function to first match the date field, and preferentially recognize the "year-month-day" format. If the match fails, it will then try other common formats such as "year / month / day", "month-day-year", etc. After successful matching, the system calls the string replacement function to uniformly convert the matching result into the "year-month-day" standard format. For example, "July 22, 2025" is replaced by "2025-07-22", and the regular expression rule such as "\d{4}年\d{1,2}月\d{1,2}日" is used to match the original string. For the unit field, if the two fields "USD to RMB" or "RMB to USD" are recognized, the system first performs text matching to recognize them as exchange rate fields. If the field value is "RMB to USD", the numerical processing function is called to take its reciprocal and label it as "USD to RMB" to ensure the unity of the exchange rate direction. After all processing is completed, this piece of data is marked as the "standardized" state and written into the standardized data set for use by subsequent modules. The entire process is executed in parallel through the thread pool in the Java environment, ensuring that the number of data entries processed per second is not less than 500.

[0035] After data cleaning, the system enters the standardization phase. Standardization field mapping rules are loaded via a configuration file, specifying that fields such as "Purchase Amount" should be converted to "Purchase Cost," with all monetary units standardized to RMB, weight units to kilograms, volume units to cubic meters, and time units to hours. During standardization, the system renames and converts units for each field in each record according to the rules. For example, if "Logistics Cost" is "3 RMB per kilogram," while the standard format requires "Cost per Order," the system calculates the cost per order by multiplying the weight of the item by the unit price. After all data items have undergone unified conversion, a standardized cost dataset with a consistent structure and format is formed.

[0036] Subsequently, the system performs trend analysis on the procurement cost and exchange rate fluctuation data in the standardized dataset. Taking procurement cost as an example, the system first groups the products by product number and generates a daily procurement cost sequence for the past 30 days for each group. Next, it calculates the daily cost change rate, which is obtained by subtracting the previous day's cost from the current day's procurement cost and then dividing by the previous day's cost. If the average change rate over a certain three days exceeds twice the average change rate of that product over the past 30 days, the system marks that period as a period of abnormal procurement fluctuation. The handling logic for exchange rate fluctuations is the same as for procurement costs. This step is triggered daily by the trend detection submodule in the system, with the execution time set between 2:00 AM and 4:00 AM daily to avoid high-concurrency business periods.

[0037] The specific implementation steps for detecting the changing trends of procurement costs and exchange rate fluctuations using time series analysis algorithms on a standardized cost dataset are as follows: First, the system groups the standardized cost dataset by commodity code and time field, ensuring that each group corresponds to a specific commodity and its continuous time series. Within each group, the system extracts the procurement cost field and the exchange rate fluctuation field, and sorts them in ascending order by the time field to construct a daily data series with a period of 30 days, meaning each analysis period includes 30 consecutive days of data. Next, the system enters the trend analysis phase, first calculating the daily procurement cost change rate, which is calculated by subtracting the previous day's procurement cost from the current day's cost, then dividing by the previous day's cost, and expressing the result as a percentage, rounded to two decimal places. The same method is applied to the exchange rate fluctuation field to generate the corresponding change rate series. Then, the system uses a moving average method to calculate the 7-day moving average change rate, i.e., averaging the change rate series over a 7-day moving window, taking 7 values ​​forward each day as the trend baseline. The system continues to evaluate the volatility of this trend baseline, setting a threshold of twice the average of the past 30 days' trend baseline as the anomaly detection threshold. If the rate of change exceeds the threshold for three consecutive days, the system marks that time period as an "abnormal trend segment." The system further aggregates information such as product codes, start and end times, fluctuation ranges, and daily average rates of change for all abnormal trend segments to generate a feature dataset of cost fluctuations. Finally, this feature dataset is stored in a dedicated database table and made available for subsequent K-means clustering analysis. The entire process is automatically triggered by the time series processing engine at 00:00 daily, generating processing logs for system administrator review.

[0038] If the trend analysis results indicate abnormal fluctuations, the system further calls the clustering analysis module to classify the warehousing cost and logistics cost data. The clustering analysis uses the K-means algorithm, with an initial number of cluster centers set to 3, representing low-cost, medium-cost, and high-cost groups respectively. The system extracts the values ​​of warehousing cost and logistics cost from each record to form a two-dimensional vector, which is then input into the K-means clusterer for 100 iterations to obtain the cluster affiliation of each record. The system then calculates the sample mean and variance for each group. If the variance of a group exceeds 1.5 times the total variance of the sample set, the system records that group as an abnormal cost source group and outputs the average cost range and order-of-magnitude distribution of that category.

[0039] After classification, the system enters the comprehensive cost calculation stage. The weighted average of the comprehensive cost consists of four parts: procurement cost, exchange rate fluctuation, warehousing costs, and logistics costs, with weights of 40%, 20%, 20%, and 20%, respectively. These weights are determined by the system administrator based on historical cost structure data analysis, with a sample size of no less than 30,000 historical data entries and a statistical period of the most recent 90 days. The weighted calculation process is as follows: First, each cost value is divided by its maximum value within the current time period to obtain a standardized value. Then, each standardized value is multiplied by its corresponding weight and summed to form the comprehensive cost value. For example, if the procurement cost of a certain product is 50 yuan and the maximum procurement cost is 100 yuan, its standardized value is 0.5, which, after multiplying by the weight, yields 0.2. The other three items are calculated similarly, and the final sum yields a comprehensive cost index of 0.65.

[0040] First, the system extracts four key cost data points for each record from the categorized cost distribution dataset: procurement cost, exchange rate fluctuation, warehousing costs, and logistics costs. Then, the system loads statically set weight parameters: procurement cost 40%, exchange rate fluctuation 20%, warehousing costs 20%, and logistics costs 20%. These weights are stored in the system configuration file and calculated from historical average data during initialization. Before weighted calculation, the system standardizes each cost value to eliminate orders-of-magnitude differences. The standardization method is range normalization, which involves subtracting the minimum value of that field from each cost value in the current dataset, then dividing by the difference between the maximum and minimum values, with the result limited to between 0 and 1. After standardization, the system multiplies each standardized cost value by its corresponding weight. For example, if a product's standardized procurement cost is 0.6, multiplying it by 0.4 yields 0.24, and the other three costs are calculated sequentially. The sum of the four weighted values ​​yields the comprehensive cost index value for the product. The system performs the same operation on all products, ultimately forming a complete comprehensive cost dataset. The dataset is structured such that each record contains a product code, four original cost values, four standardized values, four weighted values, and a composite index. The calculation process is performed in batch mode by the cost integration module once daily at midnight, and the results are updated to the real-time cost database for use by the inventory pricing module. After each calculation, the system records a calculation log and generates an execution report for the management backend to view.

[0041] The specific implementation steps for generating a dynamically adjusted cost dataset by incrementally updating real-time data using a data stream processing method on a comprehensive cost dataset are as follows: First, a real-time listener is deployed in the data receiving module to continuously monitor changes in four types of data from the Enterprise Resource Planning (ERP) system: procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs. Whenever any cost item is updated, the listener immediately generates a change record and writes it to the data change queue. The data stream processing module has a dedicated scheduling thread that polls the data change queue every 10 seconds. Once a new change record is detected, the incremental processing flow is immediately initiated. The system first extracts the historical data of the corresponding product from the comprehensive cost dataset based on the product code in the change record, including the original cost item, standardized value, and weighted value. Then, the system re-acquires the latest original cost item value for the product and performs standardization processing, using the maximum and minimum values ​​in the current cost distribution dataset for range normalization to ensure consistency with the previous batch processing stage. Next, the system recalculates the weighted sum of the four standardized values ​​according to fixed weight parameters to generate the updated comprehensive cost index. The system compares the old and new comprehensive cost indices. If the change exceeds 5%, the product is marked as "adjusted," and the corresponding comprehensive cost value and change timestamp in the dynamic cost dataset are updated. To ensure data consistency, the system immediately triggers a data synchronization operation after the update operation, writing the changes to the data table used by the inventory pricing module and recording the change operation in the system log, including the product code, changed fields, values ​​before and after the change, trigger time, and processing time. The entire process is controlled by an asynchronous task mechanism implemented in Java, supporting high-concurrency data update processing. The system ensures that incremental update operations of no less than 3,000 records are completed within one minute. The final dynamically adjusted cost dataset is always up-to-date, providing accurate cost input for subsequent pricing forecasts.

[0042] The system stores the comprehensive cost of all goods in an in-memory database and updates it in real time using a data stream processing module. Data stream processing employs a record-level trigger mechanism; if a record changes, the system automatically identifies the scope of impact and recalculates only the comprehensive cost of the corresponding goods. The data change monitoring mechanism checks the update flag every 10 seconds, and the update flag is automatically written via a database trigger. When a flag change is detected, the system loads the historical standardized value for that goods, re-standardizes and recalculates the cost, and overwrites the old value with the new one. The system then pushes the updated results to the inventory pricing module for use.

[0043] The trend analysis module, targeting a standardized cost dataset, calculates the time-varying trends of procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs. It obtains the dynamic weights of each cost factor, including dynamic change sequences decomposed using time series analysis algorithms. A sliding window is used to extract the fluctuation characteristics of each cost factor within a fixed time window, resulting in a fluctuation feature set. Based on this feature set, principal component analysis is employed to identify the main contributing factors to the fluctuations of procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs, yielding the weight distribution of these cost factors. If the weight of a certain cost factor in the weight distribution exceeds a preset threshold, a mean filtering method is used to adjust the weight of that cost factor. The dynamic change sequence is smoothed to obtain a smoothed change sequence. Using this smoothed change sequence, a weighted summation method is employed to integrate the weight distribution and fluctuation characteristics of each cost factor, resulting in a comprehensive cost fluctuation index. Based on this comprehensive cost fluctuation index, a time series forecasting algorithm is used to calculate the cost change trend over a future period, yielding a predicted cost trend sequence. Using this predicted cost trend sequence, a rule-matching method is used to compare it with historical cost data to determine if any abnormal fluctuation patterns exist, resulting in an abnormal fluctuation detection result. Based on the abnormal fluctuation detection result, a classification algorithm is used to perform attribution analysis on the abnormal fluctuations, identifying the cost factors that caused the abnormal fluctuations, and obtaining the attribution analysis result.

[0044] During daily scheduling, the system extracts four categories of fields from a standardized cost dataset: procurement cost, exchange rate fluctuation, warehousing fees, and logistics costs. The system first groups and sorts all data by product code and date field to ensure that each product forms a complete, continuous cost time series. For each cost factor, the system constructs a daily cost value time series for each product and performs data integrity checks, requiring each series to be at least 90 consecutive days; otherwise, the product is not included in the current calculation. After construction, the system performs time series decomposition on each cost time series. The algorithm used is an additive model decomposition method, aiming to split the original series into three parts: trend component, periodic component, and residual component. Specifically, the system first calculates a moving average with a 7-day period to obtain a trend line series; then, it calculates the difference between the original value and the trend value to obtain the periodic component; finally, it subtracts the sum of the trend value and the periodic component from the original value to obtain the residual component. The sum of the three components constitutes a dynamically changing series, which fully describes the actual daily fluctuations of each cost factor. The system performs this decomposition operation on each of the aforementioned cost factors, resulting in four types of dynamic change sequences. Each cost sequence for each commodity is stored as a separate time series structure. Subsequently, the system extracts features from these dynamic change sequences using a sliding window of fixed length (30 days). Each sliding window moves forward one day per day. Within each window, the system calculates the following five fluctuation characteristics in sequence: maximum value, minimum value, mean, standard deviation, and average rate of change. The maximum value is the largest value in the sequence within that window, the minimum value is the smallest value within that window, and the mean is the arithmetic mean of all values ​​within that window. The standard deviation is calculated by first calculating the mean of all values ​​within the window, then subtracting the mean from each value, squaring the result, and finally taking the square root of the average of all squared values. The units remain consistent with the original sequence. The average rate of change is calculated by taking the rate of change for each two consecutive days within the window, i.e., subtracting the previous day's value from the current day's value, dividing by the previous day's value, obtaining the percentage change, and then taking the arithmetic mean of all percentage changes within the entire window. The result is expressed as a percentage and rounded to two decimal places. For example, if the dynamic change sequence of procurement costs for a certain commodity from June 1, 2025 to June 30, 2025 consists of 30 consecutive values, the system will calculate the maximum value (e.g., 83 yuan), minimum value (74 yuan), mean value (78.2 yuan), standard deviation (3.9 yuan), and average daily change rate (1.85%) within this sequence. These five values ​​constitute the fluctuation feature vector of procurement costs within this window. The system will apply similar calculations sequentially to the dynamic change sequences of exchange rate fluctuations, warehousing costs, and logistics costs, and repeat this process for all sliding windows. Finally, for each 30-day window, a set of fluctuation features containing five values ​​will be generated for each of the four cost factors, resulting in a complete set of 20 feature values. These feature values ​​are identified by the commodity code and the window start date as primary keys and stored in the feature database for subsequent principal component analysis.The system log records the entire calculation process and results for each product in each sliding window to ensure the traceability and transparency of the calculation process.

[0045] After constructing the volatility feature sets for all commodities, principal component analysis (PCA) is performed on these sets to identify the main contributors of four cost factors to overall volatility. PCA is performed on a time window basis, with the system processing the following workflow for each volatility feature set within a sliding window as a sample batch. First, the system extracts five volatility feature values ​​for each record from the volatility feature set: maximum value, minimum value, mean, standard deviation, and average rate of change, totaling 20 values. The system uses these 20 feature values ​​for each record as an eigenvector to form a sample matrix, where rows represent samples (i.e., a commodity corresponding to the sliding window) and columns represent feature terms. To eliminate the influence of different dimensions between feature terms, the system standardizes the sample matrix using Z-score standardization. This involves subtracting the mean of all samples in that column from each feature value and then dividing by the standard deviation, resulting in a mean of 0 and a standard deviation of 1 for each column. After standardization, the system calculates the covariance matrix of the standardized sample matrix. The covariance matrix is ​​a 20-row, 20-column symmetric matrix representing the covariance relationships among all eigenvalues. The system then performs eigenvalue decomposition on the covariance matrix, obtaining 20 eigenvalues ​​and their corresponding eigenvectors. The system sorts all eigenvalues ​​in descending order and calculates the proportion of each eigenvalue to the sum of all eigenvalues, called the variance contribution rate. The system sequentially accumulates the contribution rates of the first few eigenvalues ​​until the cumulative contribution rate exceeds 85%. The corresponding eigenvectors at this point are the principal components. Empirically, the cumulative contribution rates of the first 3 to 4 eigenvalues ​​usually meet this criterion. Assuming the contribution rates of the first 3 eigenvalues ​​are 0.42, 0.29, and 0.16, the total is 0.87, which meets the requirement. These three principal components are then extracted for subsequent analysis. Based on the eigenvectors of these three principal components, the system analyzes the weighted components of each original feature term within each principal component. Since each cost factor consists of five eigenvalues, the system calculates the weighted average of the absolute values ​​of these five eigenvalues ​​for each of the principal components (purchase cost, exchange rate fluctuation, warehousing costs, and logistics costs) to represent the overall contribution of each cost factor to the principal components. For example, in the first principal component, the weights of the five eigenvalues ​​for purchase cost are 0.33, 0.31, 0.29, 0.34, and 0.30, respectively. Therefore, the average contribution of purchase cost to the first principal component is the average of these five values, which is 0.314. The system performs the same calculation for each cost factor to obtain its contribution value to each principal component. Then, the system uses the variance contribution rate of each principal component as a weight to calculate a weighted average of the contribution values ​​of each cost factor across different principal components, ultimately obtaining the total contribution score for each cost factor.For example, if the contribution of procurement cost to the three principal components is 0.314, 0.270, and 0.255, and the principal component contribution rates are 0.42, 0.29, and 0.16 respectively, then the final total score for procurement cost is 0.314 multiplied by 0.42 plus 0.270 multiplied by 0.29 plus 0.255 multiplied by 0.16, resulting in approximately 0.296. The other three cost factors are calculated similarly to obtain their respective total scores. Finally, the system normalizes the four scores by dividing each score by the sum of the four scores, making the sum equal to 1. The result is the dynamic weight distribution of procurement cost, exchange rate fluctuations, warehousing costs, and logistics costs. Each weight value is rounded to three decimal places. The system records the four weights corresponding to the product within each time window and stores them in the weight database for subsequent smoothing and comprehensive index calculation. This entire process is automatically completed by the statistical analysis engine, with an average processing time of less than 5 seconds per batch, ensuring efficiency and stability under large-scale data volumes.

[0046] After principal component analysis, the weight distributions of four cost factors—procurement cost, exchange rate fluctuation, warehousing costs, and logistics costs—are obtained. Each cost factor corresponds to a specific weight value, a decimal between 0 and 1, accurate to three decimal places. For example, within a certain time window, the weight of procurement cost is 0.437, exchange rate fluctuation is 0.256, warehousing costs are 0.162, and logistics costs are 0.145. The system sets a preset threshold of 0.4, a fixed parameter set based on historical weight statistics. The principle is that the weight should be higher than the historical average plus one standard deviation, ensuring that the cost factors that truly dominate fluctuations are selected. The system checks each of the four weight values ​​to determine if any exceed 0.4. If so, the cost factor is marked as a "dominant factor," and the corresponding smoothing process is initiated. For all marked dominant factors, the system calls the mean filtering module to smooth their dynamic change sequence. The dynamic change sequence is the numerical sequence obtained after daily decomposition of the cost of this commodity, typically longer than 90 days. The mean filtering method uses a sliding window to process data. The fixed length of the sliding window is set to 3 days. Each time, it takes three consecutive data points—the current point and the three points preceding and following it—and calculates their arithmetic mean, replacing the original change value at the current time point. For example, if the dynamic change series of a commodity's procurement cost has values ​​of 2.4, 2.7, and 2.5 for July 10th, 2025, and July 12th, respectively, the system calculates the new value for July 11th as 2.4 plus 2.7 plus 2.5, divided by 3, resulting in 2.533, which replaces the original value for July 11th. The system performs this processing starting from the second day of the series and ending on the second-to-last day, ensuring that each central point has adjacent points before and after it for mean calculation. For the first and last days of the series, due to insufficient preceding and following points, the system uses edge compensation. The mean for the first day is set as the average of the first two days, and the mean for the last day is set as the average of the last two days. For example, if the first and second day's values ​​are 2.1 and 2.3, then the new value for the first day is 2.2. The processed sequence is the smoothed variation sequence of the cost factor, used for subsequent calculation of the comprehensive volatility index. After each smoothing operation, the system compares the original sequence with the smoothed new sequence, records the difference and maximum change at each point, and writes the processing results to the smoothing log. The smoothed sequence is stored in an in-memory database, with a structure including fields such as product code, time, original value, smoothed value, and sliding window length parameter. The processing time per sequence is less than 50 milliseconds on average, and the system supports parallel processing to ensure real-time performance and data consistency under high concurrency conditions.

[0047] After smoothing the dynamic change series, four types of cost factors—procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs—are obtained, each with its own smoothed change series. Each smoothed series is arranged chronologically, with a daily value representing the daily fluctuation trend of that cost factor for a particular commodity. Next, the system looks back 30 days from the current time point and extracts the fluctuation characteristic values ​​for each cost factor over those 30 days. The fluctuation characteristic value for each cost factor is calculated from the corresponding 30-day smoothed change series, specifically including the following four items: First, the maximum value, taken as the highest value in the 30-day series; second, the minimum value, taken as the lowest value in the 30-day series; third, the standard deviation, calculated by first calculating the average of the 30-day series, then subtracting the average from each daily value, squared, and taking the square root of the average of all squared values; fourth, the average rate of change, calculated by dividing the difference between two consecutive days by the value of the previous day, resulting in 29 rates of change, and then taking their arithmetic mean as a percentage, rounded to two decimal places. The system calculates the four volatility characteristics for each of the four cost categories, obtaining volatility characteristic vectors for procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs. Each cost vector contains four values. Subsequently, the system retrieves the weight distribution of the four cost categories corresponding to the time window from the principal component analysis module. The sum of the weight values ​​is 1, and each value is a decimal between 0 and 1, accurate to three decimal places. For example, the weight for procurement costs is 0.437, for exchange rate fluctuations it is 0.253, for warehousing costs it is 0.168, and for logistics costs it is 0.142. The system first standardizes the volatility characteristic vectors for each cost category by normalizing the maximum, minimum, standard deviation, and average rate of change according to the range of each item. That is, for each characteristic, subtract the minimum value among all goods for that item, and then divide by the difference between the maximum and minimum values. This process ensures the comparability of volatility characteristics for different goods and different dimensions. Finally, the system calculates the arithmetic mean of the four characteristic values ​​for each cost category to obtain the "average volatility score" for that cost category in the current time window, which serves as its representative value. Taking procurement costs as an example, if the maximum, minimum, standard deviation, and average rate of change after normalization are 0.71, 0.32, 0.64, and 0.45 respectively, then the average volatility score is the sum of these four values ​​divided by 4, which is 0.53. The other three costs are calculated similarly, assuming an exchange rate fluctuation of 0.38, warehousing costs of 0.26, and logistics costs of 0.30. The system performs a weighted summation of the above four "average volatility scores" with their corresponding weights. Specifically, it calculates the sum of the average volatility score of each cost item multiplied by its weight. In this example, the procurement cost is 0.53 multiplied by 0.437, which equals 0.2319; the exchange rate fluctuation is 0.38 multiplied by 0.253, which equals 0.0961; the warehousing cost is 0.26 multiplied by 0.168, which equals 0.0437; and the logistics cost is 0.30 multiplied by 0.142, which equals 0.0426. The weighted sum of these four items is 0.4143.The system records the weighted sum as the "Comprehensive Cost Fluctuation Index" of the current commodity in the current time window, retaining 3 decimal places, with a final value of 0.414.

[0048] This indicator serves as a unified measure reflecting the overall volatility and impact of the four cost factors within the current timeframe. The system records this data in a dedicated database, with fields including product code, start and end dates of the time window, weights of the four cost factors, average volatility scores for each factor, weighted values, and the final indicator value. All data is stored synchronously and made available to the prediction and anomaly analysis modules. The entire calculation process takes an average of no more than 50 milliseconds per record, ensuring stable system operation even when processing over 100,000 products per day.

[0049] After generating the "Comprehensive Cost Fluctuation Index" for each commodity, the index is stored chronologically to form a continuous time series. Each commodity corresponds to an independent fluctuation index time series, with the unit being "daily value." Each value is the comprehensive fluctuation index calculated for that commodity on that day based on the previous 30 days, typically ranging from 0 to 1. To ensure the prediction model has a sufficient training foundation, the system requires a minimum length of 60 days for each time series, meaning each commodity must have valid fluctuation index data for 60 consecutive days; otherwise, that commodity will not participate in the calculation for the current prediction period. Commodity data meeting the conditions enters the time series prediction module. The system defaults to using an autoregressive moving average model for short-term prediction, aiming to predict the trend value for the next 7 days based on the fluctuation index series of the most recent 60 days. First, the system performs a stationarity test on the fluctuation index data over the 60 days, determining whether differencing preprocessing is needed by calculating whether the mean and variance after differencing are stable. If the original series contains a trend component, a first-order differencing process is performed, subtracting the previous day's index from the current day's index to form a new series as the prediction input. If the sequence remains non-stationary after differencing, the system records its status as "unsuitable for linear prediction," marks it as an anomaly, and proceeds to rule-based matching. For sequences that meet the stationarity requirement, the system inputs volatility indices from the past 60 days as training samples into the model. Model parameters include the autoregression order, moving average order, and differencing order. The system automatically selects the optimal combination within the order range of 1 to 3 using the Akaike Information Criterion (AIC). For example, if the autoregression order is 2, the moving average order is 1, and the differencing order is 1, then an ARIMA(2,1,1) model is constructed. During model training, the system fits the 60-day data to obtain the regression coefficient and error term coefficient for each time lag term. After training, the system uses the model to recursively calculate the predicted volatility indices for the next 7 days, resulting in predicted values ​​for 7 consecutive days, with the same units as the original indices and three decimal places. Taking a specific product as an example, if its average volatility index over the past 60 days is 0.427 and its standard deviation is 0.052, the system generates a recursive formula after model training. Based on this formula, the system generates predicted values ​​for the next 7 days, for example, 0.432, 0.445, 0.461, 0.448, 0.436, 0.429, and 0.421. The predicted values ​​are stored in a prediction database, with corresponding fields including product code, prediction start date, prediction period, daily predicted value, model parameters, autoregressive coefficients, mean squared error, and prediction timestamp. The system records all model execution logs, including the time taken for each prediction, residual test results, and prediction confidence intervals. This time series prediction result serves as the basic input for cost trend analysis, providing prior information for subsequent abnormal fluctuation detection and pricing adjustments. The system ensures that the average execution time of each prediction task does not exceed 100 milliseconds and supports batch processing of over 100,000 product data entries per day, achieving high concurrency and high accuracy in real-time cost trend prediction.

[0050] After forecasting the comprehensive cost fluctuation index for each commodity for the next 7 days, the sequence of these 7 values ​​is called the "predicted cost trend sequence." Each sequence is arranged chronologically, with one value per day, representing the expected trend of the overall cost fluctuation of the commodity over the next 7 consecutive days. To determine whether there are any abnormal fluctuation patterns in this trend, the system calls the rule matching module to match the predicted sequence with the "abnormal fluctuation pattern templates" defined in the historical cost fluctuation database one by one. This template database consists of 7-day fluctuation patterns extracted from abnormal fluctuation events confirmed by the system in historical data. Each template contains three items: fluctuation direction sequence, fluctuation amplitude range, and continuous fluctuation characteristics.

[0051] The first step is for the system to extract the fluctuation direction sequence from the prediction sequence. The fluctuation direction refers to whether the difference between the daily and previous day's indicators is positive or negative. A positive difference is recorded as "rising," a negative difference as "falling," and a difference less than a set minimum change threshold is recorded as "unchanged." The system sets this minimum change threshold to 0.005, or 0.5%. For example, if the predicted values ​​are 0.432, 0.445, 0.461, 0.448, 0.436, 0.429, and 0.421, the system will calculate the daily changes as positive, positive, negative, negative, negative, negative, corresponding to the direction sequence "rising, rising, falling, falling, falling, falling." The second step is for the system to calculate the percentage change in the daily fluctuation amplitude by subtracting the previous day's predicted value from the current day's predicted value and then dividing by the previous day's predicted value. For example, the change from day 1 to day 2 is (0.445 - 0.432) divided by 0.432, resulting in 3.01%; the change from day 2 to day 3 is (0.461 - 0.445) divided by 0.445, resulting in 3.6%, and so on. The system compares the daily change range of these 7 days with the minimum and maximum change ranges in each historical template. If the change ranges of all 7 days are within the range defined by a certain template, the range is considered to be successfully matched. The third step is for the system to analyze continuous fluctuation characteristics, that is, to determine whether the upward or downward direction has existed continuously for more than 3 days. If there are 4 consecutive days of "decline" in the predicted sequence, the system marks it as a "continuous downward abnormal trend". The system will match the three extracted feature items with all historical abnormal templates in turn. If the three feature items of a template are completely consistent with the predicted sequence, the system will mark the current predicted segment of the product as "abnormal fluctuation" and record the corresponding matching template number, matching type, start and end dates, and matching confidence level. Match confidence is calculated based on the consistency of the three features. A 100% match across all features results in a confidence score of 1.000; a match only between the direction sequence and amplitude range results in a confidence score of 0.667. If a match fails, the system marks the product as "normal fluctuation" and records a no-abnormality status. All abnormal match records are stored in the anomaly detection database, with fields including product code, prediction start date, prediction trend value, matching template number, matching item, confidence score, detection time, and flag. The system is set to automatically execute this detection process every morning, supporting the completion of prediction sequence matching calculations for over 100,000 products within 5 minutes. The final abnormal fluctuation detection results will be used as input for the next step, the attribution analysis module, to determine the specific cost factors leading to the anomaly.

[0052] After detecting a successful match between the predicted cost trend sequence of a product and a historical abnormal fluctuation pattern template, the current period of that product is marked as "abnormal fluctuation state." At this point, the system automatically invokes the attribution analysis module to conduct an in-depth analysis of the causes of the abnormal fluctuation, determining which factor(s)—procurement cost, exchange rate fluctuation, warehousing costs, or logistics costs—is the primary driver of the anomaly. The attribution analysis employs a classification algorithm; the system uses a supervised classification model based on a decision tree structure. The training dataset consists of confirmed abnormal fluctuation samples from history, and each training sample is accompanied by a clear attribution label, such as "procurement cost dominant" or "exchange rate fluctuation dominant." Each sample input includes the following 20 feature values: five fluctuation characteristics for four cost factors—procurement cost, exchange rate fluctuation, warehousing costs, and logistics costs—specifically, the maximum value, minimum value, mean, standard deviation, and average rate of change. All feature values ​​are standardized to between 0 and 1 using range normalization to eliminate the influence of dimensions. The anomaly sample being analyzed is also constructed as an input vector in the same way. The system extracts the aforementioned 20 feature values ​​from its smoothed change sequence over the past 30 days and processes them using the same normalization parameters as the training samples. These normalization parameters are stored in the pre-trained model to ensure consistency between the input data and the model. After input, the system inputs the anomaly sample vector into the decision tree model for inference. The decision tree starts from the root node and makes conditional judgments based on the magnitude of the feature values ​​until it reaches a leaf node. This leaf node records the attribution label corresponding to the optimal path. For example, the path conditions might be "standard deviation of procurement cost greater than 0.65," "average rate of change of procurement cost greater than 0.05," and "standard deviation of exchange rate fluctuation less than 0.2," ultimately determining "procurement cost as the primary factor." To improve the accuracy and interpretability of attribution, the system records the entire path process of the decision tree during each prediction, including the feature terms used in each judgment, the condition values, whether the conditions are met, the path direction, and the final attribution label. If multiple leaf nodes exist in the decision path, the system selects the node with the most covered samples as the attribution result and outputs the attribution confidence score. This confidence score is calculated by dividing the number of times the leaf node was correctly classified in the training set by the total number of samples in that node. For example, if a node has 100 samples in the training set, and 87 of them are correctly classified, the confidence score is 0.87. The system ultimately writes the attribution results into an attribution result database, including fields such as: product code, anomaly period start and end time, anomaly type, attribution cost item, attribution confidence score, decision path text description, and analysis timestamp. Simultaneously, the system sets signal markers for the attribution results. If the confidence score exceeds 0.8 and the same cost item is attributed as the primary cause of the anomaly for three consecutive periods, the system will give this factor higher priority in the next cost weight allocation or pricing strategy generation. The entire attribution analysis process is fully automated, with an average inference time of less than 50 milliseconds per sample. It supports parallel multi-threaded processing, ensuring real-time anomaly analysis capabilities for large-scale products.

[0053] The inventory pricing module calculates the real-time comprehensive cost of each product using a weighted average algorithm based on the dynamic weights of various cost factors. The dynamic inventory pricing results include: acquiring basic cost data and corresponding dynamic weight allocation tables for each product type; establishing a mapping relationship between products and weights through data matching methods to obtain a product weight mapping table; calculating the multi-dimensional cost data of each product in real time using a weighted average algorithm based on the product weight mapping table; adjusting the product weight allocation to obtain a comprehensive cost value; adding timestamp information to each pricing result using a time dimension marking method based on the comprehensive cost value to establish a dynamic inventory pricing data table; and then... The system employs clustering algorithms to group goods within the same value assessment range, identifying sets of goods in different value intervals and obtaining a tiered pricing structure. Using this tiered pricing structure, a threshold comparison method is used to detect fluctuations in the pricing results of goods at each level. If the fluctuation exceeds a preset threshold, a re-pricing process is triggered, resulting in an updated inventory pricing table. Based on the updated inventory pricing table, a difference calculation method is used to compare the changes in the two pricing results, identifying the list of goods requiring price adjustments and obtaining inventory value change records. Using these inventory value change records, statistical analysis methods are employed to calculate the overall inventory value change trend and distribution characteristics, generating an inventory pricing analysis report.

[0054] When the inventory pricing task starts, the system first extracts the basic cost data for each product from a unified format cost dataset, including four dimensions: procurement cost, exchange rate fluctuation, warehousing fees, and logistics costs. Each product record must contain all four cost data items completely, with the unit uniformly set to RMB. If any item is empty, the system automatically skips the product and records a missing item in the log. Simultaneously, the system retrieves a dynamically weighted allocation table generated from the trend analysis module. This table records the dynamic cost weight structure for all products within the current time window, also containing four weight values ​​corresponding to procurement cost weight, exchange rate weight, warehousing fee weight, and logistics cost weight. Each value is a decimal between 0 and 1, and the sum is strictly equal to 1, rounded to three decimal places. For example, a product might have weights of 0.432, 0.267, 0.153, and 0.148.

[0055] The system internally joins two types of data sources using the product code field as the key, employing a hash table matching method. Specifically, the system creates a product code index table in memory. Each time it receives a piece of cost or weight data, it performs a mapping lookup and merging based on the product code. If a product's basic cost data and weight data successfully match, the system marks it as "valuable" and merges the cost and weight data to form a product weight mapping table. This table has nine columns: Product Code, Procurement Cost, Exchange Rate Fluctuation, Warehousing Fees, Logistics Cost, Procurement Cost Weight, Exchange Rate Weight, Warehousing Weight, and Logistics Weight. Each column is a numeric field, with units in yuan or dimensionless coefficients, and retains a precision of three decimal places.

[0056] To ensure data consistency, the system performs a validation check after merging, verifying that the sum of the four weight values ​​for each product record is equal to 1. If the absolute value of the error is greater than 0.001, the record is considered invalid, and the system excludes the product and logs the anomaly. After validation, the system writes the valid product records into the official product weight mapping table, which is used for subsequent weighted average pricing calculations. The entire data matching and validation process is handled by a parallel computing engine, with a processing time of less than 30 seconds when there are 100,000 products, ensuring high accuracy and efficiency under batch processing conditions. The system regenerates this mapping table daily to ensure that each inventory pricing uses the latest cost and weight data as the basis.

[0057] The inventory pricing module executes daily at midnight. Its complete process includes nine steps: data extraction, standardization, weighted average calculation, timestamp marking, hierarchical clustering, fluctuation detection, revaluation, difference analysis, and statistical summarization. The parameters used in each step are clearly defined and traceable. The first step is data extraction. The system extracts four types of cost data for each product from a standardized cost dataset: current procurement cost, exchange rate fluctuation, warehousing fees, and logistics costs. These data are uniformly expressed in RMB and are obtained through structured retrieval using product codes, ensuring complete and continuous records for each cost category. The system simultaneously calls the dynamic weight allocation table output by the trend analysis module. This table assigns weight values ​​for each of the four cost categories to each product: procurement cost weight, exchange rate weight, warehousing weight, and logistics weight. The weights are decimals between 0 and 1, summing to 1. The weights are determined by standardizing the contribution rate vector calculated by the principal component analysis algorithm within a 30-day sliding window. The system merges the cost data table and the weight allocation table one-to-one based on the product code field, and establishes a mapping structure of "product code-cost group-weight group" to form a product weight mapping table.

[0058] The second step is the standardization process. To eliminate dimensional differences between different cost items, the system performs range normalization on each type of cost data according to the product category. The method is as follows: using the minimum value of the current product's cost group as the lower bound and the maximum value as the upper bound, subtract the minimum value from the cost value of each product and divide by the difference between the upper and lower bounds. The result is limited to between 0 and 1, accurate to 3 decimal places. Each product then obtains 4 normalized cost values, corresponding to procurement, exchange rate, warehousing, and logistics, respectively.

[0059] The third step is the weighted average calculation. The system multiplies each standardized cost value by its corresponding dynamic weight, and then adds the four results to obtain the comprehensive cost value of the product. For example, if the standardized procurement cost is 0.65 and the corresponding weight is 0.43, the calculated partial weighted value is 0.2795. After the other three items are processed in the same way, the results are added to obtain the current real-time comprehensive cost of the product, in the form of a "relative cost index", with a value between 0 and 1. The system then performs a timestamp operation, attaching a current system timestamp to each comprehensive cost value in the format of "year-month-day hour:minute:second" to ensure that each pricing record is uniquely traceable in time. All data is written to the dynamic inventory pricing data table, which includes fields such as product code, four cost values, four weight values, normalization result, comprehensive cost value, and timestamp.

[0060] The fourth step is the stratification of commodity value. Based on the comprehensive cost value of all commodities, the system calls the K-means clustering algorithm, setting the cluster number parameter K to 5, corresponding to five strata: "extremely low," "relatively low," "medium," "relatively high," and "extremely high." Initial centroids are constructed by uniformly selecting points between 0 and 1, for example, 0.1, 0.3, 0.5, 0.7, and 0.9. The system performs a maximum of 100 iterations, converging according to the least squares difference principle. After each commodity is assigned to a stratum, its "value stratum number" is written into the original pricing data table, forming a complete stratified structure.

[0061] The fifth step is fluctuation detection and processing. The system performs a 7-day average comparison for all products within each tier. The method is as follows: take the comprehensive cost value of each product over the past 7 days, calculate the arithmetic mean, then subtract this mean from the current daily comprehensive cost value, and divide the result by the mean to obtain the relative volatility, expressed as a percentage. If more than 30% of products in a certain tier have an absolute volatility exceeding 5%, the system determines that abnormal fluctuation has occurred in that tier and immediately triggers a repricing process. The 5% threshold is derived from the historical statistical average upper limit of the normal daily fluctuation range of product costs, while the 30% percentage is set according to system strategy, indicating that "collective fluctuations within this tier" are considered abnormal.

[0062] The sixth step is the revaluation operation. The system retrieves the latest original cost and dynamic weight of the goods in this level, performs standardization and weighted average calculation again, regenerates the comprehensive cost value and overwrites the original record, and updates the database.

[0063] The seventh step is difference analysis. The system performs a one-to-one comparison of the cost values ​​before and after revaluation. The difference is calculated as "new value minus old value". If the absolute value of this difference is greater than 2% of the original value, the product is marked as an "adjusted product". This threshold is set from the "visual adjustment" identification threshold in the pricing strategy. All difference records are written to the inventory value change record table, which includes fields such as product code, value before and after the change, difference, percentage change, and marking time.

[0064] The eighth step is the inventory statistical analysis phase. Based on product codes, the system performs trend backtracking and distribution calculations on inventory valuation records from the past 30 days. The system outputs, but is not limited to, the following indicators: a daily average total valuation value change curve for all products, the distribution percentage of product quantity across five value levels, a trend chart of average cost fluctuations between levels, and records of the level with the largest changes. All reports are generated in table, bar, and line chart formats, and PDF and Excel export functions are provided for operational management. The entire process, from data extraction to report output, is independent of external modules and is completed entirely by the inventory valuation module. The daily processing time is controlled within 30 minutes, supporting concurrent processing of over 100,000 products per transaction, ensuring accuracy, timeliness, and system stability.

[0065] The threshold trigger module, if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold, triggers the pricing strategy adjustment process, generating cost change signals for each commodity. This includes: if the difference exceeds the preset threshold, obtaining the cost change magnitude for each commodity through a difference calculation method, resulting in a cost change magnitude set; standardizing the change magnitude using a normalization method based on the cost change magnitude set, resulting in a standardized magnitude sequence; grouping commodities using a clustering analysis algorithm based on the standardized magnitude sequence to identify commodity clusters with similar cost changes; integrating the cost change weights of each commodity using a weighted summation method based on the commodity clusters, resulting in a cluster comprehensive cost signal; if the cluster comprehensive cost signal exceeds a preset signal threshold, comparing historical signal data using a rule matching method to determine if there are any abnormal signal patterns, resulting in an abnormal signal detection result; classifying the abnormal signals using a decision tree algorithm based on the abnormal signal detection result, identifying the source commodities of the abnormal signals, resulting in a source commodity set; and adjusting the dynamic inventory pricing model using a data update method based on the source commodity set, resulting in an updated pricing result.

[0066] The threshold trigger module starts immediately after the system's daily inventory pricing process is completed. Its core objective is to identify whether changes in commodity costs exceed a set warning range and, if necessary, trigger subsequent pricing strategy update logic. The entire process consists of eight steps, involving multiple explicit algorithms and parameter settings, as follows: The first step is difference calculation. The system extracts the comprehensive cost value of each commodity record from both the "Current Inventory Pricing Data Table" and the "Previous Period Inventory Pricing Data Table." For each commodity, the current value is subtracted from the previous value, and the result is divided by the previous value to obtain the relative change rate. The result is saved as a percentage, rounded to three decimal places. For example, if the current cost is 0.438 yuan and the previous value was 0.414 yuan, the difference is 0.058, and the change rate is 5.80%. The system performs this calculation process on all commodities one by one, obtaining a complete "Commodity Cost Change Range Set," in the format of a key-value pair structure of commodity code and corresponding change rate.

[0067] The second step is to filter the magnitude of change. The system sets a fixed difference trigger threshold of 2%. This threshold is derived from the statistical results of the daily standard deviation of the change rate of all products processed by the system over the past 90 days. The maximum value for volatile products is approximately 1.4%. To improve the sensitivity of identification and prevent false alarms, the system uses 1.4% plus a safety factor of 0.6% to arrive at 2%. The system compares the absolute value of the change rate of each product with 2%. If the difference is greater than or equal to 0.02, it is marked as an "abnormally fluctuating product" and added to the "abnormally fluctuating product list".

[0068] The third step is normalization. To eliminate the influence of numerical range, the system performs range normalization on the change range of abnormally fluctuating products. The normalization method is to first calculate the maximum and minimum values ​​of the change rate of all abnormal products, then perform "subtract the minimum value from the value" and divide by the range for each record, finally obtaining standardized values ​​between 0 and 1, forming a "standardized range sequence", which is used by the clustering algorithm.

[0069] The fourth step is cluster analysis. The system calls the K-means algorithm to cluster the standardized amplitude sequence, setting the number of clusters to 3, indicating that it is divided into three groups: small amplitude, medium amplitude, and large amplitude. During the algorithm initialization phase, 0.15, 0.5, and 0.85 are used as initial centroids. The system then iterates, calculating the Euclidean distance between each sample and each centroid, assigning it to the nearest centroid cluster, and then calculating the mean of the samples within each cluster as the new centroid. The process terminates if the centroids no longer change or if the maximum number of iterations (default 100 rounds) is reached. Finally, each product is assigned to a specific cluster category, forming a "product clustering mapping table."

[0070] The fifth step is weighted summation. For each item in a cluster, the system calculates its inventory value by multiplying its current inventory quantity by its previous comprehensive cost value. For example, if the inventory is 120 units and the cost per unit is 0.44 yuan, the inventory value is 52.8 yuan. The total inventory value is the sum of all item inventory values. Dividing the inventory value of each item by the total inventory value yields its inventory percentage. This percentage is then multiplied by the standardized change rate of the item to obtain a weighted change value. The sum of all weighted values ​​is the "cluster comprehensive cost signal" for that cluster. For example, if a group contains 5 items with weighted values ​​of 0.0021, 0.0019, 0.0024, 0.0018, and 0.0023, the cluster signal for that group is 0.0105.

[0071] The sixth step is signal judgment. The system sets the cluster signal judgment threshold to 0.007. This value is derived from the average of the signal clusters identified as requiring price adjustments within the past 30 days, plus one standard deviation, which is statistically calculated to be 0.007. If the comprehensive cost signal value of any current cluster exceeds this threshold, then that group of products enters the abnormal signal analysis process.

[0072] The seventh step is rule matching analysis. The system has 100 pre-set historical abnormal signal templates. Each template contains three elements: the 7-day signal trend direction, the daily signal amplitude, and the overall average value. The system compares the current clustered signal sequence with each historical template. First, it determines whether the direction is consistent, meaning the upward or downward direction is completely consistent for 7 consecutive days. Second, it determines that the daily amplitude difference should not exceed 10%. Third, it determines that the average value is within ±10% of the historical template. If two of the three conditions are met, the group is considered a successful match, and an "abnormal signal detection result" of "yes" is generated.

[0073] The eighth step is decision tree classification. The system calls the trained CART decision tree model to classify the products currently matching the anomalous signals as the primary cause. The input fields include four items: historical average rate of change, signal occurrence frequency, inventory value percentage, and last month's sales growth rate. Each node in the model contains conditional judgments, such as "average rate of change greater than 0.06 and sales growth less than -0.1". The final leaf node is marked as the dominant anomalous product. The system forms a "source product set" for records marked as "dominant source products", and records the decision path, conditional expression, and confidence level (prediction accuracy). Records with a confidence level less than 0.8 are not confirmed as the final source.

[0074] Finally, the system uses the source product set as the target object, re-accesses its latest procurement, exchange rate, warehousing, and logistics cost data, along with dynamic weights, and re-executes the standardization and weighted averaging process to generate a new comprehensive inventory cost. This cost is then written to the official inventory data table, and its change records and modification logs are updated, marking it as "re-priced due to cost anomalies." The entire process, from difference calculation to result update, is completed in batches on the same thread, supporting parallel tasks. When processing 100,000 products, the average processing time is less than 25 minutes. All relevant threshold parameters are explicitly recorded in the configuration file and support manual adjustment or automatic correction, ensuring traceability and compliance during audits.

[0075] The pricing prediction module, based on cost change signals, uses a linear regression algorithm to predict the price adjustment range of each product on different platforms, obtaining preliminary pricing schemes for each platform. This includes extracting price change records of each product from a historical transaction database on different sales platforms using data filtering methods, thus obtaining a historical price dataset for each platform. Using this historical price dataset, a product pricing prediction model is constructed using a linear regression algorithm to analyze the correlation between cost changes and price adjustments, determining the price sensitivity coefficient for each platform. If the price sensitivity coefficient exceeds a preset sensitivity threshold, a weighting method is used to quantify the pricing influencing factors for different platforms. The platform weight matrix is ​​obtained. Based on the platform weight matrix, a weighted calculation method is used in conjunction with the current cost change signal to calculate the theoretical adjustment range of each product on different platforms, and obtain the adjustment range value set. Through the adjustment range value set, the values ​​are converted into corresponding pricing adjustment levels using an interval mapping method to obtain a tiered adjustment scheme. Based on the tiered adjustment scheme, a rule matching method is used to compare the pricing constraints of each platform, and adjustment schemes that conform to the platform rules are selected to determine the adjustment scheme set. If there are conflicting pricing strategies in the adjustment scheme set, the schemes of each platform are arranged using a priority ranking method, and the optimal pricing combination is selected to generate the final multi-platform pricing strategy scheme.

[0076] The entire pricing forecasting process uses received cost change signals as input. First, the system's historical data processing module matches product codes and calls SQL query scripts to extract historical pricing records for the product from the transaction database across all registered sales platforms. The filtering time range is the most recent 180 days, with a minimum of 30 records required for each platform. Fields include record time, platform number, product price, current purchase cost, corresponding sales volume, and inventory status. All amounts are in yuan, and times are in days. The system sorts these data by time to form a historical price dataset for each platform. Subsequently, the system uses a least squares linear regression algorithm to model the relationship between historical cost changes and price changes for products on each platform. Specifically, the relative rate of change in purchase cost is used as the independent variable, and the relative rate of change in product price at the same point in time is used as the dependent variable. The system first calculates the average of the cost change rate and price change rate in the historical data for each platform, then calculates the sum of the products of deviations for each record point as the covariance term; simultaneously, it calculates the sum of the squares of the deviations for each variable as the variance term. The regression slope, or price sensitivity coefficient, is obtained by dividing the covariance by the cost variance, rounded to three decimal places. This coefficient serves as a key parameter for assessing the platform's pricing responsiveness. The system simultaneously calculates the coefficient of determination for the regression model to evaluate its goodness of fit. If this coefficient is below 0.65, the platform is considered to have insufficient model stability and is excluded from subsequent sensitivity calculations. The system stores the sensitivity coefficients for each product on each platform in a unified sensitivity coefficient table.

[0077] The system sets a price sensitivity threshold of 0.75. This threshold is obtained by statistically analyzing the mean and standard deviation of the historical regression slope distribution of platform products over the past year. The system calculates the average regression slope for all products to be 0.60, with a standard deviation of 0.15. Therefore, the threshold is set as the average plus one standard deviation, i.e., 0.75. Any product-platform combination with a price sensitivity greater than this threshold will be included in the next step of pricing influencing factor weighting. Influencing factors include four items: platform sales growth rate, platform transaction fee ratio, whether the platform offers price subsidies, and the platform's price constraint level. Based on historical platform operating data, the system sets initial weights of 0.4, 0.3, 0.2, and 0.1 respectively. If a platform does not have a logistics subsidy policy, the system automatically sets that weight to 0 and proportionally redistributes the original 0.2 to the remaining three items, ensuring a total weight of 1.

[0078] After assigning platform impact factor weights, the system performs weighted pricing prediction based on the current cost change signal value of each product, the corresponding platform sensitivity coefficient, and the platform impact factor weights. The system multiplies the current cost change value by the sensitivity coefficient, and then by the sum of the weighted coefficients of all factors to obtain the predicted price adjustment range. For example, if the current cost of a product increases by 0.06 yuan, the platform sensitivity coefficient is 0.85, and the total platform weighted factors are 0.7, then the predicted price adjustment value is 0.0357 yuan, rounded to three decimal places. All platform results are stored in the predicted price adjustment data table as product-platform key-value pairs.

[0079] The system then performs a price adjustment range mapping, setting five adjustment ranges: less than or equal to -0.05 for a significant price reduction; greater than -0.05 and less than or equal to -0.02 for a moderate price reduction; greater than -0.02 and less than 0.02 for no change; greater than or equal to 0.02 and less than 0.05 for a moderate price increase; and greater than or equal to 0.05 for a significant price increase. The system determines the predicted price adjustment level based on these ranges and writes this level as a price adjustment scheme label into the product price adjustment suggestion table.

[0080] Next, the system performs price adjustment compliance checks based on the actual operating rules of each sales platform. The checks include: the price adjustment frequency stipulated by each platform (e.g., whether daily updates are allowed), the maximum price adjustment range (e.g., no more than 0.03 yuan per adjustment), and the platform's minimum price constraint. The system compares each current price adjustment suggestion with the rules by calling the platform's rule dictionary. If a suggestion does not comply with the rules, it is marked as "needs to be pruned" or "not executable." Simultaneously, the system adjusts the non-compliant suggestions; for example, if the price adjustment range exceeds the platform's maximum, the system automatically reduces it to the maximum value. All suggestions that have undergone verification and processing form a preliminary set of adjustment plans.

[0081] If there are conflicting price adjustment suggestions for a product across different platforms (e.g., Platform A suggests a price increase of 0.04 yuan while Platform B suggests a price decrease of 0.02 yuan), the system will prioritize the suggestions. The ranking parameters include: the platform's contribution to the product's overall profit, the platform's sales share over the past 7 days, and the platform's customer activity. The system assigns fixed weights of 0.5, 0.3, and 0.2 to these three indicators. All parameters are dynamically retrieved from the system's business analysis module. After standardizing each parameter value, the system calculates a comprehensive score for each platform based on its weight. The platform with the highest score has its price adjustment suggestion set as the dominant strategy, and other platforms are adjusted synchronously in order of their scores. Finally, the system generates a consistent multi-platform price adjustment strategy for all products and stores it in a unified strategy push buffer for subsequent updates to platform product prices.

[0082] The entire process is executed once daily or triggered by events within an automated batch processing framework. The system ensures that every parameter and calculation logic is traceable and logged in the database configuration, meeting the platform's requirements for differentiated management, precise pricing control, and rule compliance. Each algorithm step, such as linear regression, weighted calculation, standardization, interval mapping, and priority sorting, is executed by a pre-configured Java class module, working in conjunction with database stored procedures to achieve a closed-loop calculation process.

[0083] The sensitivity analysis module, based on the initial pricing plan, analyzes the price sensitivity and turnover efficiency of each product using historical sales data and inventory turnover rate to obtain optimized pricing adjustment parameters. This includes extracting sales volume trends and inventory turnover cycles for each product across different platforms using data filtering methods, constructing a dynamic product sales dataset. Based on this dataset, regression analysis is used to calculate the price sensitivity coefficient of each product across different platforms, determining the correlation matrix between the sensitivity coefficient and turnover efficiency. If the sensitivity coefficient in the correlation matrix exceeds a preset sensitivity threshold, a weighted calculation method is applied, taking into account platform differences. Based on product characteristics, a preliminary adjustment range set is generated for each product. Using this preliminary adjustment range set, an interval mapping method is employed to convert the adjustment range into corresponding pricing adjustment levels, obtaining a tiered pricing scheme. Based on the tiered pricing scheme, a rule-matching method is used to compare the pricing constraints of each platform, filtering out a set of pricing adjustment schemes that conform to the platform rules. If conflicting adjustment strategies exist within the pricing adjustment scheme set, a priority ranking method is used to arrange the schemes based on product characteristics and platform differences, determining the optimal pricing adjustment combination. Using the optimal pricing adjustment combination, a data fusion method is employed to combine the adjustment parameters with historical sales data, generating the final multi-platform optimized pricing scheme.

[0084] In the sensitivity analysis module, the system first extracts daily sales and inventory data for each product on each platform over the past 180 days from the sales database, using the product code and platform identifier as a combined primary key. Extracted fields include daily selling price, daily sales volume, daily inventory level, and daily sales revenue. The system performs integrity checks on this data, removing samples containing records missing for 10 consecutive days or more, and sorts the remaining data chronologically to generate a dynamic sales dataset at the product-platform level.

[0085] Subsequently, the system performs sales price sensitivity modeling on each dataset. This is achieved by performing regression analysis on the daily price change ratio and the daily sales change ratio, using the least squares method to fit a linear relationship, and obtaining the price sensitivity coefficient for the product on the platform. This coefficient represents the proportion of sales volume affected by a 1% daily price change. For example, if a 1% increase in price leads to a 1.2% decrease in sales volume, the sensitivity coefficient for that product is -1.2. During the fitting process, the system calculates the coefficient of determination R-squared. If R-squared is less than 0.60, the regression model is considered unreliable, and the sample is removed. The sensitivity coefficient must be retained to three decimal places and recorded in the sensitivity calculation table. The minimum significance threshold used for sensitivity analysis is 0.30, which is derived by system operators based on the mean plus standard deviation of historical samples on the platform.

[0086] For all product combinations that have obtained effective sensitivity coefficients, the system calculates their inventory turnover efficiency, specifically by dividing the cumulative sales volume over the past 180 days by the average inventory level during the same period. Then, the system constructs a binary array of sensitivity coefficients and inventory turnover efficiency, categorized by product, and uses the Pearson correlation coefficient formula to calculate the linear correlation between the two. If the correlation coefficient is higher than 0.50, it indicates a strong positive correlation between price changes and inventory turnover efficiency, and the product combination is prioritized for adjustment. The correlation coefficient threshold is also set based on the historical sample average correlation coefficient plus one standard deviation, ensuring statistical reasonableness.

[0087] Provided that the sensitivity coefficient is greater than 0.30 and the inventory turnover efficiency correlation is greater than 0.50, the system continues to calculate the preliminary price adjustment range. In this step, the system multiplies the sensitivity coefficient by the platform weighting coefficient and then by the current cost change value to obtain the theoretical price adjustment range that the product should have on the platform. The platform weighting coefficient is a correction coefficient set based on the platform's promotion capabilities; for example, it is set to 1.0 for standard e-commerce platforms, 1.2 for promotional platforms with high traffic, and 0.8 for specific high-end platforms. The cost change value is the difference between the latest cost in the current inventory pricing model and the historical reference cost. If this value is positive, it indicates that the cost has increased, and a price increase is necessary. The price adjustment range result is a decimal value in yuan, retained to three decimal places.

[0088] After the initial price adjustment range is generated, the system converts it into adjustment levels using a range mapping method. For example, adjustments less than -0.02 yuan are marked as "moderate reduction," those between -0.02 yuan and 0.02 yuan are marked as "no adjustment," and those greater than 0.02 yuan are marked as "moderate increase." After mapping, the system verifies the legality of each price adjustment suggestion against the price adjustment constraint rule table of each platform. For example, if a platform stipulates that price adjustments cannot exceed 0.05 yuan and cannot be reduced, the system automatically truncates suggestions exceeding the limit to the maximum and attaches an adjustment explanation. After passing the legality check, the suggestion record is retained, generating an executable tiered pricing adjustment set at the platform-product level.

[0089] If a product experiences price adjustments in opposite directions or with significantly different magnitudes across multiple platforms simultaneously, the system enters the conflict resolution phase. The system reads three historical metrics for the product from each platform: sales revenue share, sales frequency, and customer activity index. These metrics are standardized and assigned weights of 0.5, 0.3, and 0.2 respectively. The weighted results are then aggregated into a platform priority score. The platform with the highest priority provides the primary adjustment suggestion, while other platforms, ranked accordingly, serve as supplementary suggestions, forming the final multi-platform pricing combination.

[0090] Finally, the system outputs structured records for each product on each platform, including sensitivity coefficient, inventory turnover efficiency, regression correlation, theoretical price adjustment range, adjustment level, platform rule adaptation results, and final priority ranking results. These records are then written into an optimized pricing scheme table, which serves as the data source for subsequent pricing strategy generation and push modules. The entire process is executed daily during the price adjustment cycle. All calculations are performed concurrently by a Java batch processing service, with logging, error tracking, and result verification mechanisms. Each parameter, such as sensitivity coefficient threshold, correlation coefficient threshold, interval mapping critical point, and platform weight coefficient, is maintained by the system administrator through configuration files and periodically corrected and updated based on actual operational results to ensure the accuracy and compliance of the system's price adjustment recommendations.

[0091] The pricing strategy generation module generates the final pricing strategy for each platform based on the optimized pricing adjustment parameters and synchronously updates it to the product price database of each sales platform. This includes extracting the pricing adjustment range for each product from the pricing adjustment parameters using data filtering methods to generate a preliminary pricing strategy set; adjusting the preliminary pricing strategy set to the specific data structure of each platform using format conversion methods to obtain a platform-compatible pricing strategy set; if the platform-compatible pricing strategy set is consistent with the format requirements of the sales platform interface, then checking the compliance of the pricing strategy set using interface verification methods to determine a compliant pricing strategy set; encrypting the pricing strategy set using data encryption methods to obtain an encrypted pricing strategy set; pushing the encrypted pricing strategy set to the product price database of each sales platform using data transmission methods to complete price synchronization; comparing the update records in the product price database with the encrypted pricing strategy set using data verification methods to determine the completeness of price synchronization; if the completeness of price synchronization meets a preset threshold, generating a synchronization operation log file using log recording methods to obtain the synchronization completion status.

[0092] In one possible implementation, the pricing strategy generation module, based on optimized pricing adjustment parameters, progressively generates the final pricing strategies for each sales platform and synchronously updates the product price database. First, the system parses the optimized pricing adjustment parameters, extracting the price change value for each product on each platform using the fields "Product ID," "Platform ID," and "Adjustment Range." This adjustment range is a floating-point number in yuan, rounded to three decimal places. The system determines whether to generate a pricing strategy based on set conditions. If the adjustment range for a product is zero, the process is skipped; otherwise, a data table named the preliminary pricing strategy set is created with the product ID and platform ID as a composite primary key.

[0093] Subsequently, the system enters the format conversion stage. Based on the interface specifications of each sales platform, the system extracts the platform's field requirements, such as field naming, type, order, and time format. Taking platform A as an example, the field requirements are product ID, adjusted price, currency code, and effective date. The system programmatically maps the "product ID" to "skuId", adds the "adjustment amount" to the "current price" to obtain "targetPrice", uses the preset currency in the product settings as "currencyCode", and generates "effectiveDate" using the current system date. The generated data must ensure that the price is a floating-point number, retains two decimal places, and the time format is "YYYY-MM-DD", ultimately constructing a platform-compatible pricing strategy set.

[0094] Next, the system performs compliance checks. The system performs four checks on each record: First, it checks whether "targetPrice" is higher than the minimum price provided by the platform. The minimum price is obtained from the platform rules interface or platform settings table; for example, platform A stipulates that the minimum price for a product is 10 yuan. Second, it determines whether the difference between "targetPrice" and the current price exceeds the maximum allowed increase. The increase is provided by the configuration file, with a default value of 5 yuan. Third, it checks whether the frequency of price adjustments exceeds the limit, such as whether the price has been adjusted repeatedly within 24 hours. Fourth, it checks whether the field format conforms to the platform interface documentation definition, such as whether the field order is correct and whether the types match. If a record does not conform to any of the above rules, the system will automatically correct it according to the rules: for example, if the price is too low, it will automatically be raised to the minimum price; if the increase is too large, it will automatically be pruned to the maximum value. Records that fail to be corrected or whose format does not match will be removed and marked as non-compliant.

[0095] After compliance, the policy set enters the data encryption stage. The system calls the encryption module, employing the AES symmetric encryption algorithm with a 256-bit key. The key is provided by the platform's security interface or obtained from the key management system. The encrypted content includes all fields in each record, encoded as hexadecimal strings or Base64 to generate encrypted data packets.

[0096] The system then initiates the data transmission process. Each batch sends a maximum of 1000 encrypted policy records; if the total exceeds this limit, it will be processed in multiple batches. The system initiates POST requests to the designated interface addresses of each sales platform via HTTPS, carrying encrypted data packets and platform authentication information (such as API keys or tokens) in the request body. Upon receiving the data, the platform returns a response. The system records whether each record was successfully transmitted and the platform's return code. A status of "success" indicates success, while "error" records the error information and reason.

[0097] After data transmission is complete, the system performs a synchronization integrity check. The system calculates the synchronization success rate as a percentage by dividing the number of successful platform responses by the total number of transmitted records. The system's preset integrity threshold is 98%, derived from statistics on the platform's interface stability over the past three months. If the current synchronization success rate is greater than or equal to 98%, synchronization is considered complete; if it is lower than 98%, an automatic retransmission process is initiated, marking failed records and retrying after a 5-minute delay, with a maximum of 3 retries.

[0098] Finally, the system logs the entire pricing strategy synchronization process. Log fields include product ID, platform ID, original price, adjusted price, adjustment amount, effective time, verification status, encryption status, transmission status, transmission batch number, compliance status, reason for failure, timestamp, and operator identity. The system writes all logs to the database and log file system, and generates operation report files for management review.

[0099] The key parameters involved in this process are as follows: adjustment range (output from the optimization module, in yuan, with three decimal places); current price (from the commodity price database); minimum price limit (a fixed value provided by the platform or obtained through a dynamic interface); maximum price increase (defined by the configuration file, generally a fixed value, such as 5 yuan); price adjustment frequency (defined by platform rules, generally once every 24 hours); batch limit (default no more than 1000 items per batch); integrity threshold (default 98%, can be configured according to the platform's historical stability); encryption algorithm (fixed to AES-256); encryption key (platform configuration); date format (uniformly YYYY-MM-DD). All parameter sources are recorded in the system parameter configuration table and can be adjusted according to the configuration strategy.

[0100] The performance monitoring module acquires real-time sales and inventory data from various sales platforms, calculates adjusted inventory turnover and gross profit margin, and obtains the performance indicators of the current pricing strategy. This includes extracting real-time sales and inventory data from each sales platform using data acquisition methods to obtain the raw sales and inventory dataset; processing the raw sales and inventory dataset using data cleaning methods to remove outliers and missing values, generating a cleaned sales and inventory dataset; calculating the inventory turnover and gross profit margin of each product based on the cleaned sales and inventory dataset to obtain a product performance dataset; if the inventory turnover in the product performance dataset is lower than a preset threshold, extracting products with inventory turnover rates below the preset threshold using data filtering methods to generate a product list; predicting the sales trend of each product using regression analysis algorithms based on the product list to obtain a sales trend prediction set; comparing the sales trend prediction set with the current pricing strategy using data integration methods to determine the need for pricing strategy adjustments and generating an adjustment need list; and pushing the adjustment need list to the strategy update interface of each sales platform using data transmission methods to complete the synchronization of pricing strategy optimization.

[0101] In one possible implementation, the performance monitoring module first obtains real-time sales and inventory data from various sales platforms. Specifically, the system extracts daily data on product sales quantity, sales amount, remaining inventory quantity, and unit cost from each platform by calling the platform's open API or establishing a data subscription channel, forming the original sales and inventory dataset. Data fields include product code, platform identifier, sales time, sales quantity, sales amount, current inventory quantity, inventory unit cost, and update time. To ensure data accuracy, the system cleans the original data, performing the following steps: First, removing records with negative sales quantities or sales quantities greater than the inventory quantity; second, marking and removing records with zero sales amount but a sales quantity greater than zero; third, filling null values ​​in the inventory quantity field with the most recent valid inventory value; fourth, deleting product records where the inventory quantity has not changed for seven consecutive days and where sales records are missing. The cleaned data forms the cleaned sales and inventory dataset. Data quality is evaluated by two indicators: record completeness rate and field validity rate, with a target standard greater than 99%.

[0102] After acquiring high-quality cleaned data, the system calculates inventory turnover rate and gross profit margin by product and platform dimensions. The inventory turnover rate is calculated as follows: the system calculates the total sales quantity within a selected period, with a fixed period length of 30 days; simultaneously, the system records the daily inventory quantity and takes the 30-day average as the average inventory level. Inventory turnover rate is the total sales quantity divided by the average inventory level. If a product's total sales volume over 30 days is 600 units and the average inventory is 300 units, then the inventory turnover rate is 2,000 times. The gross profit margin is calculated as: total sales revenue minus the total cost of goods sold, then divided by the total sales revenue; for example, if the 30-day sales revenue is 12,000 yuan and the cost of goods sold is 9,000 yuan, then the gross profit margin is 25%. The system stores the inventory turnover rate and gross profit margin results for each product in the product performance dataset.

[0103] Next, the system sets the inventory turnover rate threshold to 0.7 times. This value is calculated from the historical average inventory turnover rate of similar products over the years, specifically the industry average of 0.9 times minus a safety margin of 0.2 times to prevent policy misjudgment. The system iterates through the product performance dataset, filters out product records with turnover rates below this threshold, generates a product list, and marks them as performance anomalies.

[0104] For each product in the product list, the system predicts sales trends using a regression analysis algorithm. The specific steps are as follows: The system extracts the daily sales quantity of the product over the past 30 days as the dependent variable, and the independent variables are consecutive integers from 1 to 30 for the corresponding dates. Linear fitting is performed using the least squares method to obtain the slope parameter of the linear regression equation. A slope greater than 0 indicates an increase in sales, while a slope less than 0 indicates a decrease in sales. The effectiveness of the regression model is evaluated by the coefficient of determination R-squared, and only predictions with R-squared not less than 0.6 are retained. The prediction results generate a sales trend prediction set. If the slope of a product is negative and the absolute value is greater than 1, it indicates that the daily sales volume has decreased by more than one unit, and its pricing strategy should be adjusted.

[0105] The system compares the sales trend forecast set with the current pricing strategy table item by item. The comparison logic is as follows: if the current pricing strategy is to increase prices, but the forecast result is a decrease in sales volume, it is judged that the pricing strategy may be too aggressive and needs to be adjusted downward; if the pricing strategy is to decrease prices, but sales volume increases, it indicates that the pricing may be too conservative and can be adjusted upward to compensate. The system summarizes such strategy deviation items into an adjustment requirement list.

[0106] Finally, the system synchronizes the adjusted requirement list through the strategy interfaces of each platform, submitting JSON data packets via POST. Fields include product code, platform identifier, suggested operation type, suggested adjustment range, and a rationale. The system checks the interface return status code; a 200 return code indicates success, and the synchronization time is recorded and written to the log; failure is recorded, and the system retryes in the next cycle. All synchronization actions are written to the operation log, with fields including synchronization time, status, number of data entries, and reason for failure. The system generates a synchronization report daily for strategy management personnel to review, ensuring the performance strategy closure loop is complete. The entire process encompasses five stages: data collection, cleaning, performance analysis, strategy matching, and automatic feedback updates. Each stage is controlled by specific numerical standards and algorithmic thresholds to ensure the system's logical integrity and operational verifiability.

[0107] The strategy iteration module, if the current pricing strategy's performance indicators fail to meet preset inventory turnover and gross profit margin targets, adjusts cost weights and pricing parameters based on deviation values ​​to generate new dynamic inventory pricing and pricing strategies. This includes acquiring real-time sales and inventory data from various sales platforms to generate an initial sales inventory dataset; processing the initial sales inventory dataset using data cleaning methods to remove outliers and missing values, resulting in a cleaned sales inventory dataset; calculating the deviation values ​​of inventory turnover and gross profit margin for each product in the cleaned sales inventory dataset from preset targets using deviation analysis methods, generating a deviation dataset; if the inventory turnover or gross profit margin in the deviation dataset is lower than the preset target, predicting the cost weight adjustment trend for each product using a linear regression algorithm, obtaining a cost weight adjustment set; recalculating the pricing parameters for each product based on the cost weight adjustment set using data integration methods, generating an adjusted dynamic inventory pricing model; comparing the adjusted dynamic inventory pricing model with the current pricing strategy set using data comparison methods, generating an optimized pricing strategy set; and pushing the optimized pricing strategy set to the strategy update interfaces of each sales platform using data transmission methods, completing the dynamic pricing strategy synchronization.

[0108] In one possible implementation, the execution flow of the strategy iteration module is as follows: First, the system extracts the sales data and inventory records for each product over the past 30 days through the data interfaces provided by each sales platform, generating an initial sales inventory dataset. Data items include product code, daily sales quantity, sales amount, daily inventory quantity, and unit purchase cost. Next, the system performs data cleaning on this dataset. The cleaning methods include three steps: First, removing abnormal records where the sales quantity is less than 0 or higher than the maximum daily inventory; second, filling in data entries with empty sales amounts using the average sales amount of the product over the past 7 days; and third, completing missing inventory records using the most recent valid inventory record. This results in a cleaned sales inventory dataset.

[0109] After data cleaning, the system calculates the inventory turnover rate and gross profit margin for each product using the following steps. Inventory turnover rate equals the cumulative sales quantity of the product during the evaluation period divided by the average inventory during that period. The average inventory is the sum of beginning and ending inventory divided by 2. Gross profit margin is calculated as the difference between sales revenue and cost of goods sold, divided by the sales revenue. Cost of goods sold equals the sales quantity multiplied by the unit purchase cost. The system creates a performance record for each product, storing its inventory turnover rate and gross profit margin.

[0110] The system then compares the performance results with preset targets item by item. The target threshold for inventory turnover rate is 1, and the target threshold for gross profit margin is 20%. The inventory turnover rate threshold is determined based on industry logistics efficiency standards and cash flow requirements, while the gross profit margin threshold is set according to the platform's break-even point and target profit. For example, if a product sells 600 units within 30 days, the average inventory is 600 units, and the turnover rate is 1; if the sales amount is 12,000 yuan and the cost of sales is 9,000 yuan, the gross profit margin is 25%. If any performance indicator falls below its threshold, the product will be marked as underperforming. The system calculates the difference between its actual performance and the target value. For example, if the turnover rate is 0.8, which is lower than the target value of 1, the deviation is -0.2; if the gross profit margin is 15%, which is lower than 20%, the deviation is -5. All deviation values ​​generate a deviation dataset for subsequent weight adjustment analysis.

[0111] When analyzing substandard products, the system uses a linear regression algorithm to establish a regression model between cost factors and performance. Specifically, it uses procurement cost, logistics costs, warehousing costs, and exchange rate adjustment factors as independent variables, and inventory turnover rate or gross profit margin as the dependent variable. A training set is formed by extracting daily factor values ​​and corresponding performance values ​​from 30 days of historical data. The regression coefficients are calculated using the least squares method to assess the sensitivity of each factor to performance. For example, if the procurement cost coefficient is 0.65, it indicates that procurement cost has the greatest impact on gross profit margin. Based on the regression results, the system adjusts the weights of these factors. If the original weight of procurement cost was 0.3, its weight is increased to 0.4 due to its high regression coefficient, while the weights of other factors are reduced accordingly, maintaining a total weight of 1.

[0112] Subsequently, the system uses the adjusted weights to perform dynamic inventory pricing for the goods, which involves multiplying each cost factor value by its weight and summing the results to obtain the new comprehensive cost of the goods. Based on the new comprehensive cost, the system recalculates the theoretical selling price of the goods by considering their price sensitivity coefficients on various platforms, and generates new pricing parameters. The system compares the new pricing parameters with the current pricing item by item. If the price difference exceeds 5%, the goods are considered to require adjustment and enter the pricing strategy update process. This threshold is set by operations personnel through A / B testing and profit simulation analysis, and is adjusted periodically.

[0113] Once the optimized pricing strategy set is formed, the system calls the data transmission interfaces of each platform to upload price data through authentication and data encryption. The price data structure includes fields such as product code, new price, adjustment time, and platform identifier. After successful upload, the system automatically compares the uploaded data with the product price status returned by the platform to ensure consistency. If they match, an upload log is recorded, including the price before and after each adjustment, timestamp, reason for adjustment, and synchronization status. The entire strategy iteration process is a closed-loop design, executed once a day, to ensure that products maintain the optimal price and inventory combination on the sales platform, thereby improving inventory turnover and profitability.

[0114] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, characterized in that: include: The data processing module acquires real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs. It cleans and standardizes multi-source heterogeneous data to obtain a cost dataset in a unified format. The trend analysis module calculates the changing trends of procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs over time for a unified format cost dataset, and obtains the dynamic weights of each cost factor. The inventory valuation module calculates the real-time comprehensive cost of each product using a weighted average algorithm based on the dynamic weights of each cost factor, thus obtaining the dynamic inventory valuation result. The threshold trigger module triggers the pricing strategy adjustment process and generates cost change signals for each commodity if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold. The pricing prediction module uses a linear regression algorithm to predict the price adjustment range of each product on different platforms based on cost change signals, and obtains the preliminary pricing plan for each platform. The sensitivity analysis module analyzes the price sensitivity and turnover efficiency of each product using historical sales data and inventory turnover rate for the initial pricing plan, and obtains the optimized pricing adjustment parameters. The pricing strategy generation module generates the final pricing strategy for each platform based on the optimized pricing adjustment parameters and updates it synchronously to the product price database of each sales platform. The performance monitoring module obtains real-time sales and inventory data from various sales platforms, calculates the adjusted inventory turnover rate and gross profit margin, and obtains the performance indicators of the current pricing strategy. The strategy iteration module adjusts cost weights and pricing parameters based on deviation values ​​if the current pricing strategy performance indicators do not meet the preset inventory turnover rate and gross profit margin targets, thereby generating a new dynamic inventory pricing and pricing strategy.

2. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The data processing module acquires real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs. It cleans and standardizes multi-source heterogeneous data to obtain a unified format cost dataset. This includes acquiring real-time data on procurement costs, exchange rate fluctuations, warehousing fees, and logistics costs from the enterprise resource plan, and extracting raw data streams from multiple data sources through data interfaces to obtain an initial dataset containing multi-source heterogeneous data. If the initial dataset contains missing values ​​or inconsistent formats, data cleaning is employed, using regular expression matching and null value imputation to handle outliers and obtain a cleaned dataset. Based on the cleaned dataset, data standardization is used to convert heterogeneous data from multiple sources into a unified format using predefined field mapping rules, resulting in a standardized cost dataset. For the standardized cost dataset, time series analysis algorithms are used to detect trends in procurement costs and exchange rate fluctuations, yielding a feature dataset of cost fluctuations. If the feature dataset of cost fluctuations shows abnormal fluctuations, K-means clustering is used to group warehousing costs and logistics costs, determining the source category of cost anomalies and obtaining categorized cost distribution data. Based on the categorized cost distribution data, a weighted average algorithm is used to integrate procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs, generating a comprehensive cost dataset. Using the comprehensive cost dataset, data stream processing methods are applied to incrementally update the real-time data, resulting in a dynamically adjusted cost dataset.

3. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The trend analysis module calculates the time-varying trends of procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs for a unified format cost dataset. It obtains the dynamic weights of each cost factor, including dynamic change sequences decomposed using time series analysis algorithms. A sliding window is used to extract the fluctuation characteristics of each cost factor within a fixed time window, resulting in a fluctuation feature set for the cost factors. Based on this fluctuation feature set, principal component analysis is used to identify the main contributing factors to the fluctuations of procurement costs, exchange rate fluctuations, warehousing costs, and logistics costs, yielding the weight distribution of the cost factors. If the weight of a certain cost factor in the weight distribution exceeds a preset threshold, a mean filtering method is applied to that cost factor. The dynamic change sequence is smoothed to obtain a smoothed change sequence. Using this smoothed change sequence, a weighted summation method is employed to integrate the weight distribution and fluctuation characteristics of each cost factor, resulting in a comprehensive cost fluctuation index. Based on this comprehensive cost fluctuation index, a time series forecasting algorithm is used to calculate the cost change trend over future periods, yielding a predicted cost trend sequence. Using this predicted cost trend sequence, a rule-matching method is used to compare it with historical cost data to determine if any abnormal fluctuation patterns exist, resulting in an abnormal fluctuation detection result. Based on the abnormal fluctuation detection result, a classification algorithm is used to perform attribution analysis on the abnormal fluctuations, identifying the cost factors that caused the abnormal fluctuations, and obtaining the attribution analysis result.

4. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The inventory pricing module calculates the real-time comprehensive cost of each commodity using a weighted average algorithm based on the dynamic weights of each cost factor. The dynamic inventory pricing result includes acquiring the basic cost data and corresponding dynamic weight allocation table for each commodity type, establishing a mapping relationship between commodities and weights through data matching methods to obtain a commodity weight mapping table, and using a weighted average algorithm to calculate the multi-dimensional cost data of each commodity in real time based on the commodity weight mapping table, adjusting the commodity weight allocation, and obtaining the comprehensive cost value. By combining cost values ​​and using a time-dimensional marking method to add timestamp information to each pricing result, a dynamic inventory pricing data table is established. Based on the dynamic inventory pricing data table, a clustering algorithm is used to group goods with the same value assessment range, identify sets of goods with different value ranges, and obtain a hierarchical pricing structure. By using a tiered pricing structure, a threshold comparison method is employed to detect the fluctuation range of pricing results for each level of goods. If the fluctuation range exceeds a preset threshold, a re-pricing process is triggered to obtain an updated inventory pricing table. Based on the updated inventory valuation table, the difference calculation method is used to compare the changes in the valuation results before and after the two valuations, identify the list of goods subject to valuation adjustments, and obtain the inventory value change record; By recording changes in inventory value, statistical analysis methods are used to calculate the overall inventory value change trend and distribution characteristics, and an inventory valuation analysis report is generated.

5. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The threshold triggering module, if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold, triggers the pricing strategy adjustment process, generating cost change signals for each commodity. This includes: if the difference between the dynamic inventory pricing result and the previous pricing result exceeds a preset threshold, obtaining the cost change magnitude of each commodity through a difference calculation method to obtain a cost change magnitude set; based on the cost change magnitude set, standardizing the change magnitude using a normalization processing method to obtain a standardized magnitude sequence; using the standardized magnitude sequence, grouping the commodities using a clustering analysis algorithm to determine commodity clusters with similar cost changes; and based on the commodity clusters, integrating the cost change weights of each commodity using a weighted summation method to obtain a cluster comprehensive cost signal. If the cluster's overall cost signal exceeds a preset signal threshold, the system compares historical signal data using a rule-matching method to determine if an abnormal signal pattern exists, thus obtaining an abnormal signal detection result. Based on the abnormal signal detection result, a decision tree algorithm is used to classify the abnormal signals, identify the source products of the abnormal signals, and obtain a source product set. According to the source product set, a data update method is used to adjust the dynamic inventory pricing model, resulting in an updated pricing result.

6. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The pricing prediction module, based on cost change signals, uses a linear regression algorithm to predict the price adjustment range of each product on different platforms, obtaining preliminary pricing schemes for each platform. This includes extracting price change records of each product from a historical transaction database on different sales platforms using a data filtering method, thus obtaining a historical price dataset for each platform. Using this historical price dataset, a product pricing prediction model is constructed using a linear regression algorithm to analyze the correlation between cost changes and price adjustments, determining the price sensitivity coefficient for each platform. If the price sensitivity coefficient exceeds a preset sensitivity threshold, a weighting method is used to quantify the pricing influencing factors of different platforms. The process involves several steps: First, a platform weight matrix is ​​obtained. Based on this matrix, a weighted calculation method combined with current cost change signals is used to calculate the theoretical adjustment range for each product across different platforms, resulting in a set of adjustment range values. This set is then used to convert the values ​​into corresponding pricing adjustment levels using an interval mapping method, yielding a tiered adjustment scheme. Based on this scheme, a rule-matching method is used to compare the pricing constraints of each platform, selecting adjustment schemes that conform to the platform rules and determining the adjustment scheme set. If conflicting pricing strategies exist within the adjustment scheme set, a priority ranking method is used to arrange the platform schemes, selecting the optimal pricing combination to generate the final multi-platform pricing strategy scheme.

7. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The sensitivity analysis module, for the initial pricing scheme, uses historical sales data and inventory turnover rate to analyze the price sensitivity and turnover efficiency of each product, and obtains optimized pricing adjustment parameters. This includes extracting the sales volume change trend and inventory turnover cycle of each product on different platforms through historical sales data and inventory turnover rate, and constructing a dynamic dataset of product sales. Based on the product sales dynamic dataset, regression analysis is used to calculate the price sensitivity coefficient of each product on different platforms, determining the correlation matrix between the sensitivity coefficient and turnover efficiency. If the sensitivity coefficient in the correlation matrix exceeds a preset sensitivity threshold, a weighted calculation method is used to combine platform differences and product characteristics to generate a preliminary adjustment range set for each product. Using this preliminary adjustment range set, an interval mapping method is used to convert the adjustment range into corresponding pricing adjustment levels, obtaining a tiered pricing scheme. Based on the tiered pricing scheme, a rule matching method is used to compare the pricing constraints of each platform, filtering out a set of pricing adjustment schemes that conform to the platform rules. If there are conflicting adjustment strategies in the pricing adjustment scheme set, a priority ranking method is used to arrange the schemes based on product characteristics and platform differences, determining the optimal pricing adjustment combination. Using the optimal pricing adjustment combination, a data fusion method is used to combine the adjustment parameters with historical sales data to generate the final multi-platform optimized pricing scheme.

8. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The pricing strategy generation module generates the final pricing strategy for each platform based on the optimized pricing adjustment parameters and synchronously updates it to the product price database of each sales platform. This includes extracting the pricing adjustment range for each product from the pricing adjustment parameters using a data filtering method to generate a preliminary pricing strategy set; adjusting the preliminary pricing strategy set to the specific data structure of each platform using a format conversion method to obtain a platform-compatible pricing strategy set; if the platform-compatible pricing strategy set is consistent with the format requirements of the sales platform interface, then checking the compliance of the pricing strategy set using an interface verification method to determine a compliant pricing strategy set; encrypting the pricing strategy set using a data encryption method to obtain an encrypted pricing strategy set; pushing the encrypted pricing strategy set to the product price database of each sales platform using a data transmission method to complete price synchronization; comparing the update records in the product price database with the encrypted pricing strategy set using a data verification method to determine the completeness of price synchronization; if the completeness of price synchronization meets a preset threshold, then generating a log file for the synchronization operation using a log recording method to obtain the synchronization completion status.

9. The intelligent scheduling and inventory collaborative management system for cross-border e-commerce multi-platform orders according to claim 1, characterized in that: The performance monitoring module obtains real-time sales data and inventory data from various sales platforms, calculates the adjusted inventory turnover rate and gross profit margin, and obtains the performance indicators of the current pricing strategy, including extracting real-time sales data and inventory data from various sales platforms through data acquisition methods to obtain the original sales and inventory dataset. The original sales inventory dataset was processed using data cleaning methods to remove outliers and missing values, resulting in a cleaned sales inventory dataset. Based on the cleaned sales inventory dataset, the inventory turnover rate and gross profit margin of each product are calculated to obtain the product performance dataset. If the inventory turnover rate in the product performance dataset is lower than the preset threshold, then the products with the inventory turnover rate lower than the preset threshold in the product performance dataset are extracted by the data filtering method, and a product list is generated. Based on the product list, a regression analysis algorithm is used to predict the sales trend of each product, resulting in a sales trend prediction set. By comparing the sales trend forecast set with the current pricing strategy through data integration, the need for pricing strategy adjustment is determined, and an adjustment need list is generated. The adjustment need list is then pushed to the strategy update interface of each sales platform through data transmission, thereby completing the synchronization of pricing strategy optimization.

10. The cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system according to claim 1, characterized in that: If the current pricing strategy performance indicators do not reach the preset inventory turnover rate and gross profit margin targets, the strategy iteration module adjusts the cost weight and pricing parameters based on the deviation value to generate a new dynamic inventory pricing and pricing strategy. This includes obtaining real-time sales data and inventory data from various sales platforms to generate an initial sales inventory dataset; and using data cleaning methods to process the initial sales inventory dataset, removing outliers and missing values ​​to obtain a cleaned sales inventory dataset. The deviation dataset is generated by calculating the deviation values ​​between the inventory turnover rate and gross profit margin of each product in the cleaned sales inventory dataset and the preset target using the deviation analysis method. If the centralized inventory turnover rate or gross profit margin in the deviation dataset is lower than the preset target, the cost weight adjustment trend of each product is predicted by a linear regression algorithm to obtain a cost weight adjustment set. Based on the cost weight adjustment set, the pricing parameters of each product are recalculated using a data integration method to generate an adjusted dynamic inventory pricing model. The adjusted dynamic inventory pricing model is compared with the current pricing strategy set using a data comparison method to generate an optimized pricing strategy set. The optimized pricing strategy set is pushed to the strategy update interface of each sales platform using a data transmission method to complete the synchronization of dynamic pricing strategies.

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