Multi-platform e-commerce inventory intelligent prediction and collaborative management system

Abstract

The invention discloses a multi-platform e-commerce inventory intelligent prediction and collaborative management system, which relates to the technical field of e-commerce inventory management, adopts a deviation heterogeneous inventory adaptation value feedback closed-loop self-circulation architecture, and comprises a heterogeneous rule decoding module, a deviation potential energy conversion module, a coupling matching engine module and a self-circulation gain module. In the invention, a deviation isomerism inventory is utilized to adapt a value feedback closed-loop self-circulation architecture, and a category deviation life cycle coupling sub-mechanism, a cost hedging threshold mechanism and a dynamic parameter self-optimization strategy are combined; the problems that prediction deviation under a multi-platform heterogeneous rule cannot be eliminated, an existing system lacks deviation and cost binding and collaborative hedging mechanisms and allocation decisions have no cost consideration, so that the full-link cost is high are solved, collaborative optimization of multi-platform multi-category inventory is achieved, the full-link cost is remarkably reduced, the inventory turnover efficiency is improved, and the economic benefit is improved. The supply chain response speed is accelerated, and a virtuous circle of nourishing new products by old products is formed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of e-commerce inventory management, in particular to a multi-platform e-commerce inventory intelligent prediction and collaborative management system. BACKGROUND

[0002] With the intensification of competition in the e-commerce industry, multi-platform layout has become the mainstream choice for enterprises to expand the market, and the importance of multi-platform e-commerce inventory intelligent prediction and collaborative management system is increasingly prominent. At present, the core research and development direction of the existing technology in this field is focused on improving the accuracy of inventory prediction, by optimizing machine learning algorithms, integrating multi-source demand data, and other ways, continuously correcting the sales prediction deviation under multi-platform, trying to narrow the gap between the predicted value and the actual order quantity.

[0003] However, the complexity of the multi-platform scenario determines that the prediction deviation is objectively unavoidable: the traffic distribution rules, promotion cycle, and refund policy of different platforms are significantly different, combined with the randomness of user consumption behavior, the volatility of product life cycle and other factors, making it difficult for any algorithm to achieve absolutely accurate prediction. More importantly, the existing technology ignores the essential demand of e-commerce enterprise inventory management, which is the optimization of the whole link cost: excessive pursuit of accuracy may lead to excessive replenishment, thereby increasing warehousing costs and near-expiration losses; and the out-of-stock problem caused by prediction deviation will also add out-of-stock losses and emergency allocation costs. At the same time, the existing system lacks a mechanism to bind prediction deviation and whole link cost, the deviation processing of each platform is independent of each other, without forming a collaborative hedging logic, and only through simple cross-platform allocation to achieve collaboration, not only difficult to cope with the cost fluctuations caused by deviation, but also may further increase the operating burden due to the lack of cost consideration in allocation decision-making.

[0004] Therefore, a multi-platform e-commerce inventory intelligent prediction and collaborative management system is provided to overcome the above problems. SUMMARY

[0005] The purpose of the present application is to provide a multi-platform e-commerce inventory intelligent prediction and collaborative management system to solve the problems raised in the background art.

[0006] To solve the above technical problems, the multi-platform e-commerce inventory intelligent prediction and collaborative management system provided by the present application adopts a deviation heterogeneity inventory adaptive value feedback closed-loop self-circulation architecture, including a heterogeneity rule decoding module, a deviation potential conversion module, a coupling matching engine module, and a self-circulation gain module. The coupling matching engine module is internally provided with a category deviation life cycle coupling sub-mechanism. The data flow direction of each module is as follows: the heterogeneous rule decoding module outputs heterogeneous coupling coefficients to the deviation potential conversion module and the coupling matching engine module, the deviation potential conversion module synchronizes category life cycle data and outputs deviation potential to the coupling matching engine module, the coupling matching engine module outputs inventory configuration instructions to the physical inventory management system and synchronizes matching data to the self-loop gain module, and the self-loop gain module outputs optimization parameters to the previous three modules to form a closed loop.

[0007] Further, the heterogeneous rule decoding module converts the multi-platform heterogeneous rules into heterogeneous coupling coefficients through a three-dimensional decoding matrix. The heterogeneous coupling coefficient calculation formula is: ; Wherein, is the heterogeneous coupling coefficient of platform , is the traffic elasticity coefficient of platform , is the promotion overlap degree of platform , is the refund influence coefficient of platform . The traffic elasticity coefficient is the ratio of the peak traffic of the platform to the average traffic multiplied by (1 minus the platform traffic allocation transparency). The promotion overlap degree is the ratio of the annual promotion days of the platform to the industry average promotion days multiplied by the platform promotion early warning period coefficient. The refund influence coefficient is the platform refund rate multiplied by (1 plus the ratio of the platform refund processing period to the industry average refund processing period). The module extracts parameter data from the platform order management system, the platform operation calendar, the platform after-sales management system and the industry report database at 3 a.m. every day, stores the calculated data in the system core database, and updates the data once a day.

[0008] Further, the deviation potential conversion module converts the predicted deviation into deviation potential. The deviation potential calculation formula is: ; Wherein: is the deviation potential of platform category , is the direction sign function of the predicted deviation of platform category , is the predicted deviation of platform category ; is the absolute value of the predicted deviation of platform category ; is the absolute value of the predicted deviation of platform category The unit cost across the entire supply chain; For category The industry average unit cost across the entire supply chain; For the platform The heterogeneous coupling coefficient; the module has a preset category lifecycle data interface, which synchronizes the category's sales growth rate and inventory turnover rate data for the past 30 days once an hour through the enterprise's product management system; it extracts parameter data from multiple systems such as the enterprise forecasting system, platform order management system and industry report database every hour, calculates and associates it with the category lifecycle data storage, and updates it once an hour.

[0009] Furthermore, the category deviation lifecycle coupling sub-mechanism of the coupled matching engine module includes a category lifecycle quantization encoding unit. This unit transforms the category lifecycle into a category deviation complementarity coefficient through a dual-index quantization model, with the following formula: ; in For category The category deviation complementarity coefficient, These are the weighting coefficients. For category Sales growth rate over the past 30 days For category The industry average sales growth rate For category Inventory turnover rate For category The industry average inventory turnover rate; the corresponding relationship between the category life cycle stage and the category deviation complementarity coefficient is as follows: introduction stage > 1.5, growth stage 1.0 to 1.5, maturity stage 0.5 to 1.0, decline stage < 0.5; this unit receives category life cycle data every hour, calculates it, associates it with the category identifier and stores it, and updates it once per hour.

[0010] Furthermore, the category deviation lifecycle coupling sub-mechanism of the coupling matching engine module includes a dynamic complementary priority rule unit. This unit uses a two-dimensional priority screening of cross-category matching combinations based on lifecycle cost. The first priority is combinations with reverse complementary relationships at different lifecycle stages and an absolute value of the difference in the category deviation complementary coefficient ≥ 0.8. The second priority is combinations with a unit end-to-end cost difference ≥ 30% within the same lifecycle stage. Matching combinations of category absorption potential during the decline phase with any category spillover potential, and category spillover potential during the introduction phase with non-introduction phase categories, is prohibited. This unit uses the formula: ; Calculate the priority dynamic adjustment coefficient; In the formula: For category With category Priority adjustment coefficient; For category The category deviation complementarity coefficient; For category The category deviation complementarity coefficient; For category With category The absolute value of the difference in the category deviation complementarity coefficient; Through the formula: ; Correct the cross-category matching gain value; In the formula: For the platform Category With the platform Category The corrected cross-category matching gain value; For the platform Category With the platform Category The original cross-category matching gain value; For category With category The priority coefficient is dynamically adjusted; relevant data is extracted every hour, candidate combinations are filtered and sorted, and the corrected gain value is calculated and stored in the system matching database.

[0011] Furthermore, the category deviation lifecycle coupling sub-mechanism of the coupled matching engine module includes a cost hedging threshold mechanism unit, which is defined by the formula: ; Calculate the cross-category matching cost hedging threshold, where For category With category Cross-category matching cost hedging threshold; For category The unit hidden cost; To be from the platform Category Transferred to the platform Category The quantity of goods; For the platform With the platform The unit logistics loss coefficient; the cross-category matching execution condition is (the platform category unit overestimation cost minus the platform category unit out-of-stock cost) multiplied by the proposed transfer quantity ≥ cost hedging threshold; this unit extracts relevant cost data every hour, calculates the threshold and judges the execution condition, and updates it to the system matching database.

[0012] Furthermore, the detailed execution process of the coupling matching engine module is as follows: priority is given to matching within the same category, and inventory configuration instructions are generated by sorting according to the original cross-category matching gain value; when there is no matching object, cross-category matching is started, and the category life cycle quantification coding unit is called in sequence, combinations are selected according to the dynamic complementary priority rule, and the cost hedging threshold mechanism is called to select executable combinations. The three-dimensional coupling matching instructions containing the transfer source, target, category, quantity and time limit are generated according to the corrected cross-category matching gain value from high to low; after the matching is completed, the life cycle related data is synchronized to the self-circulating gain module, and an inventory configuration instruction is sent to the physical inventory management system once per hour.

[0013] Furthermore, the self-circulating gain module is defined by the formula: ; Adjust the category weighting coefficients in the maturity stage using the formula: ; Adjust the category weighting coefficient during the introduction phase, among which These are the adjusted weighting coefficients. The weighting coefficients before adjustment. This represents the actual gain value for cross-category matching. The actual gain value is the historical maximum. Based on the ratio of the actual gain value to the historical maximum actual gain value, this module adjusts the deviation potential energy decay index between 0.7, 0.8, and 0.9. Relevant data is extracted at 4:00 AM every day, the actual gain value is calculated and the parameters are optimized, and the data is synchronized to the three preceding modules. The update frequency is once a day.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. By leveraging the category deviation lifecycle coupling mechanism, the prediction deviations of categories at different lifecycle stages are transformed into complementary deviation potential energy, achieving inventory coordination without pursuing absolute prediction accuracy. Categories in the introduction phase absorb the deviation inventory of mature phase categories, mature phase categories overflow with highly stable inventory, and categories in the decline phase only complement categories in the introduction phase. This completely escapes the algorithm optimization trap, allowing for efficient utilization of deviation resources across heterogeneous platforms.

[0015] 2. The cost hedging threshold mechanism covers both explicit and implicit allocation costs. It strictly judges cost feasibility before cross-category matching to ensure that collaborative behavior is always centered on cost hedging, thus solving the problem of increased operational burden caused by the lack of cost consideration in allocation decisions due to existing technologies.

[0016] 3. Mature product categories improve inventory turnover efficiency and shorten turnover days. Their deviation inventory provides low-cost trial sales resources for introductory product categories, reducing new product preparation costs and promotion risks. Declining product categories avoid the price loss of traditional clearance through targeted complementarity, while helping new products complete market validation, forming an ecological effect where old products nourish new products and new products take over the old product inventory.

[0017] 4. The self-circulating gain module dynamically adjusts the weight coefficient and deviation potential energy decay index according to the actual matching gain, and achieves seamless switching of deviation complementary strategy during the transition of product category life cycle stage, without the need for manual intervention throughout the process, which greatly reduces operation and management costs.

[0018] 5. Three-dimensional coupling matching replaces simple cross-platform allocation, realizing multi-dimensional complementarity in space, time, and deviation. The greater the differences in platform heterogeneity, the higher the synergistic gain. In the face of sudden changes in the product life cycle, the system can quickly adjust the product deviation complementarity coefficient, and the supply chain response speed is significantly improved compared with existing technologies, effectively responding to sudden changes in the market and life cycle. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of a multi-platform e-commerce inventory intelligent prediction and collaborative management system according to the present invention. Detailed Implementation

[0020] 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.

[0021] Please see Figure 1 The present invention provides a technical solution: See Figure 1 As shown, an embodiment of a multi-platform e-commerce inventory intelligent forecasting and collaborative management system is provided: I. System Overall Architecture: This system adopts a closed-loop self-circulating architecture for heterogeneous inventory adaptation and value feedback, comprising four core modules: a heterogeneous rule decoding module, a deviation potential energy conversion module, a coupled matching engine module, and a self-circulating gain module. The coupled matching engine module incorporates a category deviation lifecycle coupling sub-mechanism, serving as the core execution logic for cross-category coupled matching, supporting the original triangular coupled matching mechanism, and enabling dimensional upgrades.

[0022] The data flow of each module is as follows: the heterogeneous rule decoding module outputs the heterogeneous coupling coefficient to the deviation potential energy conversion module and the coupling matching engine module; the deviation potential energy conversion module synchronizes the category lifecycle data and outputs the deviation potential energy to the coupling matching engine module; the coupling matching engine module outputs the inventory configuration instruction to the physical inventory management system, and at the same time synchronizes the matching data to the self-circulating gain module; the self-circulating gain module outputs the optimization parameters to the three preceding modules, forming a closed loop.

[0023] II. Core Modules: 1. Heterogeneous rule decoding module: This module transforms multi-platform heterogeneous rules into heterogeneous coupling coefficients using a three-dimensional decoding matrix, as detailed below: The formula for calculating the heterogeneous coupling coefficient is: ; In the formula: :platform Heterogeneous coupling coefficients are used for quantization platforms. The extent to which heterogeneous rules affect inventory coordination; :platform The traffic elasticity coefficient ranges from 1.0 to 3.0, and is calculated as follows: Platform Peak traffic and platform The ratio of the average traffic to the platform traffic, multiplied by (1 minus the platform's average traffic). Traffic allocation transparency); among which the platform Peak traffic is obtained by extracting the highest daily order-related traffic data from the platform's order management system over the past 90 days; platform The average traffic is calculated using the platform's order management system, based on the average daily order-related traffic data over the past 90 days. Traffic allocation transparency was calculated by analyzing the traffic distribution rules document officially released by the platform, having three people skilled in the art independently score the rules for clarity (out of 100), taking the average score, and then calculating the result as "score / 100". :platform The promotional overlap, ranging from 0.5 to 2.0, is calculated as follows: Platform The ratio of the annual number of promotional days to the industry average number of promotional days, multiplied by the platform's... Promotional advance warning cycle coefficient; among which the platform The annual number of promotional days is obtained by counting the number of days marked as promotions within the past 365 days using the platform's operational calendar; the industry average number of promotional days is obtained from the annual e-commerce operation report published by the industry association; the platform The promotion early warning cycle coefficient is determined based on the number of days the platform releases promotion information in advance: 1.2 for 7 days or more in advance, 1.0 for 3 to 6 days in advance, and 0.8 for 2 days or less in advance. :platform The refund impact coefficient ranges from 0.3 to 1.8, and is calculated as follows: Platform Refund rate multiplied by (1 plus platform) (The ratio of the refund processing time to the industry average refund processing time); where the platform The refund rate is calculated using the platform's after-sales management system, which shows the ratio of refunded orders to total orders over the past 30 days. The refund processing time is calculated using the platform's after-sales management system, taking the average number of days from refund application to refund completion over the past 30 days. The industry average refund processing time is obtained from e-commerce after-sales reports published by industry associations.

[0024] The execution steps of this module are as follows: extract the above parameter data from the platform order management system, platform operation calendar, platform after-sales management system and industry report database at 3:00 AM every day; calculate the heterogeneous coupling coefficient of each platform by substituting it into the formula using the system's built-in matrix calculation algorithm; and store the calculation results in the system's core database, with an update frequency of once a day.

[0025] 2. Deviation potential energy conversion module: This module converts prediction bias into bias potential energy, as follows: The formula for calculating the deviation potential energy is: ; In the formula: :platform Category The deviation potential energy is used for quantization platform Category The forecast bias has a driving effect on inventory flow. A positive value indicates the potential for inventory overflow (inventory needs to be allocated to other areas), while a negative value indicates the potential for inventory absorption (inventory needs to be received from external sources). :platform Category The direction sign function of the prediction deviation, when the platform Category The prediction bias is set to 1 when it is positive and -1 when it is negative. :platform Category The prediction bias is calculated as follows: platform Category Forecast sales minus platform Category Actual sales volume; where predicted sales volume is output through the enterprise's forecasting system platform. Category The number of goods corresponding to the daily predicted order volume is obtained; actual sales are obtained through the platform. Order management system statistics platform Category The actual number of goods sold on a single day was obtained; :platform Category The absolute value of the prediction bias; :platform Category The total unit cost across the entire supply chain is calculated as follows: Platform Category The sum of unit warehousing fees, unit stockout losses, unit allocation costs, and unit capital occupation fees; where unit warehousing fees are calculated through the enterprise's warehousing management system platform. Category Daily warehousing cost divided by inventory quantity; unit stockout loss is calculated through the enterprise customer service system platform. Category Losses from stockouts and customer complaint compensation are averaged per item; unit allocation costs are calculated through inter-platform allocation platforms as tracked by the enterprise's logistics management system. Category The cost per logistics transaction is obtained by dividing the quantity allocated; the unit cost of capital is calculated by multiplying the company's annual cost of capital rate by the platform's cost of capital. Category The unit procurement cost is obtained; Category The industry average unit cost across the entire supply chain is obtained through category cost reports published by industry associations. :platform The heterogeneous coupling coefficient is obtained in the same way as the heterogeneous rule decoding module.

[0026] This module has a preset category lifecycle data interface. The interface synchronizes the lifecycle-related data of each category in real time through the enterprise product management system, including the sales growth rate and inventory turnover rate in the past 30 days. The synchronization frequency is once per hour. The synchronized data is used for the calculation of the subsequent category deviation lifecycle coupling sub-mechanism.

[0027] The execution steps of this module are as follows: every hour, extract the above parameter data from the enterprise forecasting system, platform order management system, enterprise warehouse management system, enterprise customer service system, enterprise logistics management system and industry report database; call the system's built-in potential energy calculation algorithm and substitute it into the formula to calculate the deviation potential energy of each product category on each platform; and associate the calculation results with the synchronized product lifecycle data and store them in the system's core database, with an update frequency of once per hour.

[0028] 3. Coupled Matching Engine Module: Based on triangular coupling matching, this module achieves three-dimensional dynamic complementarity of the product deviation lifecycle through a built-in category deviation lifecycle coupling sub-mechanism, as detailed below: (1) Category lifecycle quantification coding: This unit uses a dual-index quantitative model to transform the category lifecycle into a category deviation complementarity coefficient, the formula of which is: ; In the formula: Category The category deviation complementarity coefficient is used to quantify the category. The degree of influence of the life cycle stage on bias complementarity; Weighting coefficients are determined based on product category attributes, with 0.6 for sales-driven categories and 0.4 for inventory-driven categories. Sales-driven categories refer to those whose sales fluctuations are greater than inventory fluctuations over the past 30 days. Sales fluctuations are calculated as "(maximum daily sales - minimum daily sales) / average daily sales," and inventory fluctuations are calculated in the same way. Inventory-driven categories refer to those whose inventory fluctuations are greater than sales fluctuations over the past 30 days. Category The sales growth rate over the past 30 days is calculated as follows: (Category) Total sales in the last 30 days - by category Total sales in the first 31 to 60 days / Product category "Total sales volume for the first 31 to 60 days," data obtained through the enterprise's sales management system; Category The industry average sales growth rate is obtained from category sales reports published by industry associations. Category Inventory turnover rate is calculated as "category". Total sales volume / category in the past 30 days "Average inventory level over the past 30 days," data obtained through the enterprise inventory management system; Category The industry average inventory turnover rate is obtained through category inventory reports published by industry associations.

[0029] The correspondence between category life cycle stages and category deviation complementarity coefficients is as follows: Introduction phase: The category deviation complementarity coefficient is greater than 1.5. During this phase, the category can absorb low-risk deviation inventory, such as a slight overestimation of deviation inventory in the mature stage category. Growth stage: The category deviation complementarity coefficient ranges from 1.0 to 1.5. During this stage, the categories can complement each other in both directions, that is, they can absorb or overflow inventory with moderate deviation. Maturity stage: The category deviation complementarity coefficient ranges from 0.5 to 1.0. At this stage, the category needs to overflow high stability inventory, that is, the overestimated deviation inventory should be transferred first. Decline phase: The category deviation complementarity coefficient is less than 0.5. During this phase, the category can only overflow clearance inventory and can only complement the deviation of the category in the introduction phase.

[0030] The execution steps of this unit are as follows: receive the category lifecycle data synchronized by the deviation potential energy conversion module; call the system's built-in quantization coding algorithm and substitute it into the formula to calculate the category deviation complementarity coefficient of each category; and store the calculation results with the category identifier in the system's core database, with an update frequency of once per hour.

[0031] (2) Dynamic complementary priority rule: This unit uses a two-dimensional priority selection method based on lifecycle cost to filter cross-category matching combinations. The specific rules are as follows: First priority: Reverse complementary combinations of product lifecycle stages, namely, the combination of category absorption potential in the introduction phase and category spillover potential in the maturity phase, and the combination of category absorption potential in the growth phase and category spillover potential in the decline phase. With category The absolute value of the difference in the product category deviation complementarity coefficient is greater than or equal to 0.8, and the greater the difference in life cycle, the higher the priority. Second priority: Cost complementary combinations within the same life cycle stage, that is, combinations where the unit cost difference between product categories within the same life cycle stage is greater than or equal to 30%, such as the combination of the spillover potential of product categories with higher unit costs and the absorption potential of product categories with lower unit costs in the growth stage. Forbidden Matches: Combinations of category absorption potential during the decline phase with any category spillover potential, and combinations of category spillover potential during the introduction phase with non-introduction phase categories. Forbidden matches are directly excluded from the candidate list.

[0032] This unit optimizes matching accuracy by dynamically adjusting coefficients based on priority. The calculation formula is as follows: ; In the formula: Category With category The priority adjustment coefficient is used to optimize the gain calculation accuracy of cross-category matching; Category The category deviation complementarity coefficient is obtained in the same way as the category lifecycle quantitative coding. Category The category deviation complementarity coefficient is obtained in the same way as the category lifecycle quantitative coding. Category With category The absolute value of the difference in the category deviation complementarity coefficient.

[0033] The final cross-category matching gain value is corrected using the following formula: ; In the formula: :platform Category With the platform Category The corrected cross-category matching gain value; :platform Category With the platform Category The original cross-category matching gain value is calculated as follows: (absolute value of deviation potential energy of category j2 on platform A + absolute value of deviation potential energy of category j1 on platform B) multiplied by (heterogeneous coupling coefficient of platform A multiplied by heterogeneous coupling coefficient of platform B) divided by (absolute value of difference between heterogeneous coupling coefficients of platform A and platform B plus 1), and then the unit logistics loss coefficient of platform A and platform B is subtracted. Category With category The priority adjustment coefficient is obtained in the same way as above.

[0034] The execution steps of this unit are as follows: extract the deviation potential energy, heterogeneous coupling coefficient and category deviation complementarity coefficient of each platform and each category from the system core database; call the system's built-in priority sorting algorithm to filter and sort candidate category combinations according to the above rules; calculate the corrected cross-category matching gain value through the gain correction formula; and store the sorting results and the corrected gain value in the system matching database, with an update frequency of once per hour.

[0035] (3) Cost hedging threshold mechanism: This unit determines the cost feasibility of cross-category matching by setting a cost hedging threshold. The calculation formula is as follows: ; In the formula: Category With category Cross-category matching cost hedging threshold; Category The unit hidden cost is determined according to the life cycle stage: 5 yuan / unit in the introduction stage, 2 yuan / unit in the growth stage, 0.5 yuan / unit in the maturity stage, and 1 yuan / unit in the decline stage. The life cycle stage is determined by the range of values ​​of the category deviation complementarity coefficient. : Intending to use the platform Category Transferred to the platform Category The quantity of goods, initially set as the platform's... Category The number of underestimated deviations, i.e., the platform Category Actual sales volume minus forecasted sales volume; :platform With the platform The unit logistics loss coefficient is calculated based on statistics from the enterprise logistics management system over the past 30 days on the platform. With the platform The average logistics loss cost (including transportation loss and warehousing and transit loss) of goods transferred between different locations is obtained.

[0036] The execution condition for cross-category matching is: when (platform) Category Unit overestimation cost minus platform Category When the unit out-of-stock cost multiplied by the proposed transfer quantity is greater than or equal to the cost hedging threshold, cross-category matching is performed. Among these, the platform... Category The unit overestimation of costs for the platform Category The sum of unit storage fees and unit near-expiry loss fees, platform Category The unit out-of-stock cost for the platform Category The data for the sum of unit order loss and unit customer complaint compensation is obtained through the corresponding enterprise management system.

[0037] The execution steps of this unit are as follows: extract data on unit overestimation cost, unit stockout cost, unit logistics loss coefficient, and unit hidden cost from the enterprise warehouse management system, enterprise customer service system, and enterprise logistics management system; calculate the cost hedging threshold by substituting the proposed transfer quantity into the formula; determine whether the execution conditions are met, mark cross-category matching combinations that meet the conditions as "executable", and mark those that do not meet the conditions as "to be optimized"; update the judgment results to the system matching database, with an update frequency of once per hour.

[0038] (4) Refined execution flow of coupling matching: The complete execution flow of this module is as follows: Prioritize same-category matching: Invoke the system's built-in same-category matching algorithm to filter platform combinations with complementary deviation characteristics within the same category (i.e., one platform is overflow potential energy and the other is absorption potential energy), sort them according to the original cross-category matching gain value, and generate same-category inventory configuration instructions. If no matching object exists within the same product category, initiate cross-category matching: a. Call the category lifecycle quantification coding unit to obtain the category deviation complementarity coefficient for each category; b. Filter and sort candidate category combinations according to the dynamic complementary priority rule, and calculate the corrected cross-category matching gain value; c. Invoke the cost hedging threshold mechanism, calculate the cost hedging threshold and determine the execution conditions, and filter out executable cross-category matching combinations; d. Generate three-dimensional coupling matching instructions from high to low according to the corrected cross-category matching gain value. The instructions include the source platform, target platform, category, quantity, and time limit of the transfer. After matching is complete, lifecycle-related data (including category deviation complementarity coefficient and priority adjustment coefficient) will be synchronized to the self-circulating gain module. The generated inventory configuration instructions are sent to the enterprise's physical inventory management system to trigger inventory transfer operations. The instructions are sent once per hour.

[0039] 4. Self-circulating gain module: This module feeds back the actual gains generated by the inventory configuration to optimize the parameters of the preceding module, as detailed below: For categories at different lifecycle stages in cross-category matching, the weighting coefficient adjustment formulas are as follows: Mature product categories (by category) (For example) Weighting coefficient adjustment formula: ; Product categories in the introduction phase (by category) (For example) Weighting coefficient adjustment formula: ; The parameters in the formula are explained below: Adjusted weighting coefficients; : Weighting coefficients before adjustment; The actual gain value for cross-category matching is calculated as follows: (Platform) Category Warehousing cost savings + platform Category (Reduction in stockout losses) minus (cross-category transfer costs + category) (Total amount of hidden costs). Among these, the savings in warehousing costs are attributed to the platform. Category The unit overestimation cost is multiplied by the actual allocation quantity; the reduction in stockout losses is the platform's... Category The unit out-of-stock cost multiplied by the actual transfer volume; the cross-category transfer cost is the platform's cost. With the platform The unit logistics loss coefficient multiplied by the actual allocation quantity; the total amount of implicit costs is calculated by category. The unit hidden cost multiplied by the actual allocation amount; The historical maximum actual gain value, in yuan, is obtained by extracting the maximum value from all cross-category matching actual gain values ​​stored in the system database over the past 90 days.

[0040] In addition, this module also optimizes the deviation potential energy attenuation index (originally 0.8) of the deviation potential energy conversion module. When the actual gain value is greater than 80% of the historical maximum actual gain value, the attenuation index is adjusted to 0.7; when the actual gain value is less than 20% of the historical maximum actual gain value, the attenuation index is adjusted to 0.9; and in other cases, it remains unchanged at 0.8.

[0041] The execution steps of this module are as follows: extract actual transfer volume, warehousing cost savings, stockout loss reduction, cross-category transfer costs, and total hidden costs from the enterprise warehouse management system, enterprise customer service system, and enterprise logistics management system; calculate the actual gain value and compare it with the historical maximum actual gain value; substitute it into the formula to adjust the weight coefficient and deviation potential energy decay index of the corresponding category; update the optimized parameters to the system core database and synchronize them to the heterogeneous rule decoding module, deviation potential energy conversion module, and coupled matching engine module, with an update frequency of once a day.

[0042] III. System Workflow: The complete workflow of this system is as follows: Heterogeneous rule decoding: Every day at 3:00 AM, the heterogeneous rule decoding module extracts parameters from the platform system and industry reports, calculates the heterogeneous coupling coefficient of each platform, and stores it in the system's core database; Deviation potential energy conversion: Every hour, the deviation potential energy conversion module extracts parameters from various management systems of the enterprise, synchronizes product lifecycle data, calculates the deviation potential energy of each product category on each platform, and stores it in the system's core database. Coupled matching execution: Every hour, the coupled matching engine module performs same-category matching. When there is no matching object, cross-category matching is started. Executable combinations are filtered through the category deviation lifecycle coupling sub-mechanism. Inventory configuration instructions are generated and sent to the physical inventory management system. Lifecycle data is synchronized to the self-circulating gain module. Physical inventory adaptation: After receiving the inventory configuration instruction, the enterprise physical inventory management system executes the inventory transfer operation according to the instruction requirements and provides real-time feedback on the transfer progress to the system's core database. Self-looping gain: Every day at 4:00 AM, the self-looping gain module calculates the actual gain value, optimizes the weighting coefficient and the deviation potential energy decay index, updates it to the preceding module, and completes the closed loop.

[0043] Summarize: Through the category deviation lifecycle coupling sub-mechanism, the deviations of categories at different stages can find complementary objects. For example, the deviation inventory of categories in the introduction stage can be absorbed by categories in the mature stage. The optimal allocation of resources can be achieved without eliminating the deviation, and the trap of algorithm optimization can be completely avoided. The cost hedging threshold mechanism covers both explicit and implicit costs. Before cross-category matching is executed, the cost feasibility is strictly judged to ensure that collaborative behavior truly achieves the optimal cost across the entire chain and avoids the waste of implicit costs of the original technology. Three-dimensional coupling matching replaces simple allocation, upgrading collaboration from spatial complementarity to multi-dimensional complementarity with spatial and temporal deviations. The greater the heterogeneity of the platform, the higher the collaboration gain, completely changing the inefficient mode of passive allocation.

[0044] and: By absorbing the deviation inventory of mature product categories, the inventory of new products in the introduction phase is significantly reduced, and the inventory cost is greatly reduced. At the same time, the inventory turnover efficiency of mature product categories is improved and the inventory turnover days are shortened, realizing the ecological effect of old products nourishing new products and forming a virtuous cycle of synergistic growth among product categories. The system automatically adjusts the weight coefficients through self-circulating gain. During the growth process of a product category, it can automatically optimize the deviation complementarity strategy. For example, when a product category transitions from the growth stage to the maturity stage, the weight coefficients are dynamically adjusted according to the product category's life cycle stage, achieving a seamless switch from absorbing potential energy to overflowing potential energy. No manual intervention is required throughout the process, reducing operation and management costs. The deviation inventory of the declining product category is only used to complement the deviation of the product category in the introduction period, which effectively avoids the discount losses in the traditional clearance model and significantly reduces clearance-related costs; at the same time, it provides low-cost trial inventory for the product category in the introduction period, helping new products to complete market validation at a lower cost and reducing the risk of new product promotion. When faced with sudden changes in the product category life cycle (such as a product category in the growth stage suddenly entering the decline stage), the system can quickly switch the deviation complementarity strategy by dynamically adjusting the product category deviation complementarity coefficient. The supply chain response speed is significantly improved compared with existing technologies, effectively responding to sudden changes in market demand and product category life cycle.

Claims

1. A multi-platform e-commerce inventory intelligent forecasting and collaborative management system, characterized in that, It adopts a closed-loop self-circulating architecture for adapting value feedback to heterogeneous inventory, including a heterogeneous rule decoding module, a deviation potential energy conversion module, a coupled matching engine module, and a self-circulating gain module; The coupling matching engine module has a built-in category deviation lifecycle coupling sub-mechanism; the data flow of each module is as follows: the heterogeneous rule decoding module outputs the heterogeneous coupling coefficient to the deviation potential energy conversion module and the coupling matching engine module; the deviation potential energy conversion module synchronizes the category lifecycle data and outputs the deviation potential energy to the coupling matching engine module; the coupling matching engine module outputs the inventory configuration instruction to the physical inventory management system and synchronizes the matching data to the self-circulating gain module; the self-circulating gain module outputs the optimization parameters to the preceding three modules to form a closed loop.

2. The multi-platform e-commerce inventory intelligent forecasting and collaborative management system as described in claim 1, characterized in that: The heterogeneous rule decoding module converts multi-platform heterogeneous rules into heterogeneous coupling coefficients using a three-dimensional decoding matrix. The formula for calculating the heterogeneous coupling coefficients is as follows: ； in For the platform heterogeneous coupling coefficient, For the platform The flow elasticity coefficient, For the platform Promotional overlap For the platform The refund impact coefficient is calculated as follows: the traffic elasticity coefficient is the ratio of the platform's peak traffic to its average traffic multiplied by (1 minus the platform's traffic allocation transparency); the promotion overlap coefficient is the ratio of the platform's annual promotion days to the industry average promotion days multiplied by the platform's promotion early warning cycle coefficient; and the refund impact coefficient is the platform's refund rate multiplied by (1 plus the ratio of the platform's refund processing cycle to the industry average refund processing cycle). This module extracts parameter data from the platform's order management system, platform operation calendar, platform after-sales management system, and industry report database at 3:00 AM every day, calculates the data, and stores it in the system's core database. The update frequency is once a day.

3. The multi-platform e-commerce inventory intelligent forecasting and collaborative management system as described in claim 1, characterized in that: The deviation potential energy conversion module converts the prediction deviation into deviation potential energy. The formula for calculating deviation potential energy is as follows: ； in: For the platform Category The deviation potential energy, For the platform Category The direction sign function of the prediction bias For the platform Category Prediction bias; For the platform Category The absolute value of the prediction bias; For the platform Category The unit cost across the entire supply chain; For category The industry average unit cost across the entire supply chain; For the platform The heterogeneous coupling coefficient; the module has a preset category lifecycle data interface, which synchronizes the category's sales growth rate and inventory turnover rate data for the past 30 days once an hour through the enterprise's product management system; it extracts parameter data from multiple systems such as the enterprise forecasting system, platform order management system and industry report database every hour, calculates and associates it with the category lifecycle data storage, and updates it once an hour.

4. The multi-platform e-commerce inventory intelligent forecasting and collaborative management system as described in claim 1, characterized in that: The category deviation lifecycle coupling sub-mechanism of the coupled matching engine module includes a category lifecycle quantization encoding unit. This unit transforms the category lifecycle into a category deviation complementarity coefficient through a dual-index quantization model, with the following formula: ； in For category The category deviation complementarity coefficient, These are the weighting coefficients. For category Sales growth rate over the past 30 days For category The industry average sales growth rate For category Inventory turnover rate For category The industry average inventory turnover rate; the corresponding relationship between the category life cycle stage and the category deviation complementarity coefficient is as follows: introduction stage > 1.5, growth stage 1.0 to 1.5, maturity stage 0.5 to 1.0, decline stage < 0.5; this unit receives category life cycle data every hour, calculates it, associates it with the category identifier and stores it, and updates it once per hour.

5. The multi-platform e-commerce inventory intelligent forecasting and collaborative management system as described in claim 1, characterized in that: The category deviation lifecycle coupling sub-mechanism of the coupling matching engine module includes a dynamic complementary priority rule unit. This unit uses a two-dimensional priority screening method based on lifecycle cost to select cross-category matching combinations. The first priority is for combinations with reverse complementary relationships at different lifecycle stages and an absolute value of the difference in the category deviation complementary coefficient ≥ 0.

8. The second priority is for combinations with a unit end-to-end cost difference ≥ 30% within the same lifecycle stage. Matching combinations of category absorption potential during the decline phase with any category spillover potential, and category spillover potential during the introduction phase with non-introduction phase categories, is prohibited. This unit uses the formula: ； Calculate the priority dynamic adjustment coefficient; In the formula: For category With category Priority adjustment coefficient; For category The category deviation complementarity coefficient; For category The category deviation complementarity coefficient; For category With category The absolute value of the difference in the category deviation complementarity coefficient; Through the formula: ； Correct the cross-category matching gain value; In the formula: For the platform Category With the platform Category The corrected cross-category matching gain value; For the platform Category With the platform Category The original cross-category matching gain value; For category With category The priority coefficient is dynamically adjusted; relevant data is extracted every hour, candidate combinations are filtered and sorted, and the corrected gain value is calculated and stored in the system matching database.

6. The multi-platform e-commerce inventory intelligent forecasting and collaborative management system as described in claim 1, characterized in that: The category deviation lifecycle coupling sub-mechanism of the coupled matching engine module includes a cost hedging threshold mechanism unit, which is defined by the formula: ； Calculate the cross-category matching cost hedging threshold, where For category With category Cross-category matching cost hedging threshold; For category The unit hidden cost; To be from the platform Category Transferred to the platform Category The quantity of goods; For the platform With the platform The unit logistics loss coefficient; the cross-category matching execution condition is (the platform category unit overestimation cost minus the platform category unit out-of-stock cost) multiplied by the proposed transfer quantity ≥ cost hedging threshold; this unit extracts relevant cost data every hour, calculates the threshold and judges the execution condition, and updates it to the system matching database.

7. The multi-platform e-commerce inventory intelligent forecasting and collaborative management system as described in claim 1, characterized in that: The detailed execution flow of the coupling matching engine module is as follows: priority is given to matching within the same category, and inventory configuration instructions are generated by sorting according to the original cross-category matching gain value; if there is no matching object, cross-category matching is started, and the category life cycle quantitative coding unit is called in sequence, the combination is selected according to the dynamic complementary priority rule, and the cost hedging threshold mechanism is called to select executable combinations. The three-dimensional coupling matching instructions containing the transfer source, target, category, quantity and time limit are generated according to the corrected cross-category matching gain value from high to low; after the matching is completed, the life cycle related data is synchronized to the self-circulating gain module, and an inventory configuration instruction is sent to the physical inventory management system once per hour.

8. The multi-platform e-commerce inventory intelligent forecasting and collaborative management system as described in claim 1, characterized in that: The self-circulating gain module is obtained through the formula: ； Adjust the category weighting coefficients in the maturity stage using the formula: ； Adjust the category weighting coefficient during the introduction phase, among which These are the adjusted weighting coefficients. These are the weighting coefficients before adjustment. This represents the actual gain value for cross-category matching. The actual gain value is the historical maximum. Based on the ratio of the actual gain value to the historical maximum actual gain value, this module adjusts the deviation potential energy decay index between 0.7, 0.8, and 0.

9. Relevant data is extracted at 4:00 AM every day, the actual gain value is calculated and the parameters are optimized, and the data is synchronized to the three preceding modules. The update frequency is once a day.

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