Cross-border e-commerce logistics and inventory intelligent collaborative scheduling method based on strategy optimization

Abstract

The application provides a kind of cross-border e-commerce logistics and inventory intelligent collaborative scheduling method based on policy optimization, belongs to inventory collaborative scheduling technical field.Method includes collecting cross-border e-commerce operation full-link data, generates structured collaborative scheduling basic data.Collect historical operation data, calculate theoretical scheduling reference benchmark according to inventory turnover rate and logistics time limit curve.Generate initial collaborative scheduling graph according to the order of fulfillment process.Build adaptive scheduling correction matrix, obtain dynamic collaborative scheduling graph.Compare the actual scheduling efficiency average of each fulfillment stage, optimize and adjust the cross-border e-commerce logistics and inventory collaborative scheduling state in real time.The application realizes the intelligent and collaborative scheduling of cross-border e-commerce logistics and inventory through full-link data acquisition and structured processing, historical data mining and theoretical benchmark calculation, dynamic tolerance range configuration and initial graph construction, real-time environment parameter sensing and dynamic graph correction, matching degree analysis and closed-loop optimization adjustment.

Description

Technical Field

[0001] This invention belongs to the field of inventory collaborative scheduling technology, specifically a strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory. Background Technology

[0002] With the deepening of globalization, the cross-border e-commerce industry has experienced explosive growth, but the bottleneck in logistics and inventory management has become increasingly prominent, becoming a core pain point restricting the industry's development.

[0003] Currently, most cross-border e-commerce companies still rely on traditional scheduling management models, resulting in prominent problems such as weak data integration capabilities, low response efficiency, and insufficient scientific decision-making. At the data level, information across the entire supply chain, including orders, inventory, and logistics, is scattered across different systems, making it difficult to form an integrated analytical foundation and leading to a lack of comprehensive data support for scheduling decisions. At the dynamic response level, cross-border logistics networks involve multiple nodes and transportation modes, and face uncertainties such as fluctuating customs clearance policies and logistics node congestion. Traditional manual scheduling models struggle to adapt quickly to changes, often resulting in scheduling delays. At the inventory management level, static threshold setting models cannot accurately match market demand, easily leading to the dual dilemma of stockouts for popular items and stockpiles of slow-moving goods, severely impacting capital turnover efficiency. Furthermore, scheduling decisions rely heavily on manual experience, lacking scientific analytical benchmarks and dynamic adjustment mechanisms, resulting in low efficiency and insufficient customer satisfaction. Summary of the Invention

[0004] This invention provides a strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory to address the shortcomings of existing technologies.

[0005] This invention provides a strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory, comprising: Collect data from the entire cross-border e-commerce operation chain, identify the fulfillment requirements of different product categories by parsing order content, and extract the scheduling characteristics of each fulfillment link by combining logistics network parameters to generate structured collaborative scheduling basic data.

[0006] Collect historical operational data, extract inventory turnover rate, logistics timeliness curve and order sorting order from the historical data, and calculate theoretical scheduling reference benchmark based on inventory turnover rate and logistics timeliness curve.

[0007] Configure dynamic scheduling tolerance range and generate an initial collaborative scheduling map according to the order of the performance process by combining theoretical scheduling reference benchmarks.

[0008] By collecting data on logistics node congestion index, cross-border customs clearance timeliness fluctuations, and warehousing operation efficiency parameters, an adaptive scheduling correction matrix is ​​constructed. This adaptive scheduling correction matrix is ​​then injected into the initial collaborative scheduling graph to obtain a dynamic collaborative scheduling graph.

[0009] Collect real-time order flow data and inventory dynamic data, compare the average actual scheduling efficiency of each fulfillment stage, and calculate the matching degree between the actual scheduling curve and the dynamic collaborative scheduling map within the time window of each fulfillment link, so as to optimize and adjust the status of cross-border e-commerce logistics and inventory collaborative scheduling in real time.

[0010] According to the strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory provided by this invention, the full-chain data of cross-border e-commerce operations includes order information files, inventory instruction files issued by the WMS system, and operational parameter configuration files generated by the ERP software. Logistics network parameters include the rated capacity of cross-border transportation routes, the processing efficiency of warehousing and sorting equipment, customs clearance channel thresholds, logistics node level parameters, and a cross-border logistics service provider resource library. Scheduling features include transportation mode codes, inventory allocation ratios, order processing priorities, inventory turnover calculations, and warehousing and sorting batch capacities.

[0011] The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization provided by this invention includes the following process for identifying the fulfillment requirements of different product categories through order content parsing: Semantic segmentation is performed on the order information file to identify core requirement instructions.

[0012] Based on the type of core requirement instructions, continuous fulfillment segments are divided to generate an initial scheduling sequence.

[0013] Extract the core scheduling parameter set for each fulfillment stage in the initial scheduling sequence. The core scheduling parameter set includes the order delivery range and product category code.

[0014] Non-core auxiliary fulfillment links in the initial scheduling sequence are filtered out based on the core scheduling parameter set.

[0015] The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization provided by this invention, which extracts scheduling features of each fulfillment link by combining logistics network parameters to generate structured collaborative scheduling basic data, includes the following steps: Based on the order delivery range and product category code, the system links to the cross-border logistics service provider resource database to obtain the service provider's transportation capacity parameters.

[0016] By combining the order delivery range with the service provider's transportation capacity parameters, the logistics demand for the fulfillment process is calculated.

[0017] By associating the rated capacity of cross-border transportation routes, the maximum allowable dispatch capacity at the current contract fulfillment stage is derived.

[0018] By combining logistics node level parameters, a congestion sensitivity coefficient is calibrated.

[0019] The unique identifier of the fulfillment process, the set of scheduling parameters, and the logistics demand are encapsulated into structured data objects to obtain the basic data for structured collaborative scheduling.

[0020] According to the strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory provided by the present invention, the process of extracting inventory turnover rate, logistics timeliness curve and order sorting sequence from historical data includes: Filter historical operating records in the same target market.

[0021] Analyze inventory turnover-related data in historical structured data objects and statistically analyze the distribution of inventory turnover rates.

[0022] Analyze historical logistics timeliness monitoring records and construct a mapping model between logistics timeliness and order volume.

[0023] Extract the historical performance chain sequence and timestamp dependencies.

[0024] The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization provided by this invention includes the following process for calculating the theoretical scheduling reference benchmark based on inventory turnover rate and logistics timeliness curve: Based on the inventory turnover rate, a logistics timeliness model is mapped to generate basic scheduling reference values.

[0025] The baseline value of cross-border logistics costs is superimposed.

[0026] A scheduling safety redundancy coefficient is introduced to generate a theoretical scheduling reference benchmark.

[0027] The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization provided by the present invention includes the following process for generating an initial collaborative scheduling map according to the order of the fulfillment process, combined with a theoretical scheduling reference benchmark: Create a two-dimensional coordinate system for timing and scheduling efficiency.

[0028] A reference curve is formed by connecting theoretical scheduling reference benchmarks according to the performance process sequence.

[0029] Differentiated tolerance ranges are set based on the characteristics of each stage of the contract performance.

[0030] Visualize and label the tolerance range boundaries to form an initial collaborative scheduling map.

[0031] According to the strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory provided by this invention, the process of constructing an adaptive scheduling correction matrix includes: The congestion impact entropy value is calculated based on the congestion index of logistics nodes, and the elasticity coefficient of the logistics network is generated.

[0032] Based on the data on fluctuations in cross-border customs clearance time, calculate the customs clearance time compensation coefficient.

[0033] Analyze warehousing operation efficiency parameters and calibrate inventory dynamic adjustment characteristic parameters.

[0034] By integrating the logistics network elasticity coefficient, customs clearance timeliness compensation coefficient, and inventory dynamic adjustment characteristic parameters, an adaptive scheduling correction matrix is ​​obtained.

[0035] According to the strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory provided by the present invention, the process of injecting an adaptive scheduling correction matrix into the initial collaborative scheduling graph to obtain a dynamic collaborative scheduling graph includes: Based on the adaptive scheduling correction matrix, the initial map scheduling baseline value is adjusted for each fulfillment stage.

[0036] The characteristic parameters are dynamically adjusted based on inventory levels, and the tolerance range is dynamically expanded or contracted.

[0037] A layer of correction factor numerical labels is superimposed on the initial cooperative scheduling graph.

[0038] Establish a real-time data interface with the cross-border e-commerce operation monitoring system and update the adaptive scheduling correction matrix according to a preset cycle.

[0039] According to the strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory provided by this invention, the process of calculating the matching degree between the actual scheduling curve and the dynamic collaborative scheduling map within the time window of each fulfillment stage includes: An improved cosine similarity algorithm is used to calculate the matching degree.

[0040] When the matching degree is lower than the preset threshold or the average actual scheduling efficiency exceeds the preset range, a scheduling optimization instruction is triggered to adjust the inventory allocation ratio and logistics transportation plan.

[0041] This invention provides a strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory. By integrating data from the entire cross-border e-commerce chain, it breaks down the fragmented barriers of order, inventory, and logistics data. Combined with professional analysis techniques, it uncovers core order fulfillment needs, providing comprehensive and accurate foundational support for scheduling decisions and effectively addressing the problem of insufficient data support in traditional scheduling. By mining historical operational data to construct a scientific theoretical scheduling benchmark, it replaces decision-making models relying on human experience. Introducing risk control coefficients to calibrate scheduling targets improves the scientific rigor and reliability of scheduling decisions, avoiding the subjective limitations of experience-based decisions. By constructing an adaptive scheduling correction mechanism, it integrates dynamic information such as logistics node congestion and customs clearance time fluctuations in real time, dynamically adjusting the scheduling benchmark and tolerance range, significantly enhancing the ability to cope with uncertainties in cross-border logistics and reducing scheduling lag. Through professional algorithms, it monitors the matching degree between actual scheduling and expected goals in real time, triggering closed-loop optimization of inventory allocation and transportation plans, alleviating the contradiction between stockouts of popular goods and stockpiles of slow-moving goods, and improving inventory turnover efficiency. Through the synergistic application of the above-mentioned technologies, the intelligent, collaborative, and dynamic scheduling of logistics and inventory can be achieved, significantly improving fulfillment efficiency and customer satisfaction. It is suitable for enterprises of different sizes and various target markets, and has outstanding practicality and broad promotional value. Attached Figure Description

[0042] The invention will now be further described with reference to the accompanying drawings.

[0043] Figure 1 This is a flowchart illustrating the intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization in this invention. Figure 2 This is a schematic diagram of the process for generating structured collaborative scheduling basic data in this invention; Figure 3 This is a schematic diagram of the process for constructing the adaptive scheduling correction matrix in this invention. Detailed Implementation

[0044] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0045] like Figures 1 to 3 As shown in the embodiment of the present invention, the intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization includes: Collect data from the entire cross-border e-commerce operation chain, identify the fulfillment requirements of different product categories by parsing order content, and extract the scheduling characteristics of each fulfillment link by combining logistics network parameters to generate structured collaborative scheduling basic data; The full-chain data for cross-border e-commerce operations includes order information files, inventory instruction files issued by the WMS system, and operational parameter configuration files generated by the ERP software; the logistics network parameters include the rated capacity of cross-border transportation routes, the processing efficiency of warehousing and sorting equipment, the passage threshold of customs clearance channels, logistics node level parameters, and a resource library of cross-border logistics service providers; the scheduling features include transportation mode codes, inventory allocation ratios, order processing priorities, inventory turnover calculations, and warehousing and sorting batch capacities.

[0046] The order information file includes detailed fields such as product name, specifications, quantity, customer's shipping address, contact information, and expected delivery time; the inventory instruction file issued by the WMS system covers information such as inventory location, inventory quantity, inventory status, and transfer direction; the operation parameter configuration file generated by the ERP software includes cost control indicators, service quality standards, and supplier cooperation terms. The rated capacity of cross-border transportation routes is specified in "tons / day" or "cubic meters / day," the processing efficiency of warehousing and sorting equipment is specified in "pieces / hour," the customs clearance channel threshold is clearly defined as the maximum number of declarations per day, and the logistics node hierarchy parameters are divided into three levels: "core node, backbone node, and terminal node," each assigned a corresponding weight (core node weight 1.0, backbone node 0.8, terminal node 0.5). The cross-border logistics service provider resource database is updated in real time with data such as the service scope, timeliness commitment, pricing system, and service rating of service providers. The transportation mode codes adopt the industry-standard coding (such as sea freight HY, air freight KY, land freight LY, international express KD), the inventory allocation ratio is accurate to two decimal places, the order processing priority is divided into four levels: P0 (urgent), P1 (high), P2 (medium), and P3 (low), the inventory turnover calculation value is retained to the integer place, and the warehouse sorting batch capacity is set to a fixed range value according to the equipment model and site size.

[0047] The process of identifying fulfillment requirements for different product categories through order content analysis includes: Perform semantic word segmentation on the order information file to identify core requirement instructions; The order information file is segmented using the jieba word segmentation algorithm combined with an industry dictionary. The industry dictionary contains professional terms in the cross-border e-commerce field (such as "bonded warehouse delivery", "cross-border direct mail", "customs clearance agency" etc.). After word segmentation, the core demand instructions are identified through keyword matching and semantic understanding models. The core demand instructions include delivery time requirements (such as "72-hour delivery" "next-day delivery"), special handling requirements for goods (such as "cold chain transportation" "fragile item protection"), and special requirements for delivery address (such as "delivery to remote areas" "self-pickup point" etc.).

[0048] Based on the type of core requirement instructions, continuous fulfillment segments are divided to generate an initial scheduling sequence; Core demand instructions are categorized into three types: "delivery type, processing priority, and special requirements." Delivery types include standard delivery, expedited delivery, and special delivery. Processing priorities are graded from P0 to P3 as described above. Special requirements include cold chain, fragile, and oversized items. Based on the categorization results, the fulfillment process is divided into continuous fulfillment segments such as "order review - inventory retrieval - warehousing and sorting - cross-border transportation - customs declaration - local delivery - receipt confirmation." Each fulfillment segment is arranged in chronological order to generate an initial scheduling sequence, which clearly indicates the estimated time and dependencies of each step.

[0049] Extract the core scheduling parameter set for each fulfillment stage in the initial scheduling sequence. The core scheduling parameter set includes the order delivery range and the product category code. The order delivery range is accurate to the region + city + district / county level, and is marked with international standard administrative division codes. The product category code adopts a combination of administrative division code and industry category code to ensure the uniqueness and universality of the code. The core scheduling parameter set can also be supplemented with parameters such as order amount and customer level according to actual business needs.

[0050] Non-core auxiliary fulfillment links in the initial scheduling sequence are filtered out based on the core scheduling parameter set.

[0051] Non-core auxiliary fulfillment processes include order consultation, after-sales follow-up, invoice issuance, and other processes that do not directly participate in logistics and inventory scheduling. Through a parameter matching and screening mechanism, only the core fulfillment processes that are directly related to the order delivery range and product category code are retained. After screening, the initial scheduling sequence is optimized and sorted to ensure smooth connection between each core process.

[0052] The process of extracting scheduling features for each fulfillment stage by combining logistics network parameters to generate structured collaborative scheduling foundation data includes: Based on the order delivery range and product category code, link the cross-border logistics service provider resource database to obtain the service provider's transportation capacity parameters; By using database association query technology, the system uses order delivery range and product category code as search keywords to match qualified service providers from the cross-border logistics service provider resource library. Service provider capacity parameters include maximum transportation volume, transportation time, transportation cost, service coverage, capacity redundancy rate, etc. During the association process, a matching threshold is set (such as service coverage matching degree ≥90%) to ensure that the obtained service provider capacity parameters are highly consistent with the fulfillment requirements.

[0053] Based on the order delivery range and the service provider's transportation capacity parameters, the logistics demand for the fulfillment process is calculated using the following formula: ; in, This represents the logistics demand during the fulfillment process. For the order quantity of the first type of goods, For the unit transportation distance of the first category of goods, The logistics loss coefficient (range 1.02-1.05) is given by n, where n is the number of product categories. The actual quantity of the order will be used as the basis. If there are split or merged orders, the actual quantity after splitting or merging will be used for calculation. The shortest transportation distance from the goods outbound storage node to the customer's delivery address is calculated using a geographic information system, in kilometers. If multiple transportation routes exist, the distance of the optimal route is selected. The value is determined based on the mode of transportation: 1.05 for sea freight, 1.02 for air freight, 1.03 for land freight, and 1.04 for international express. This range is based on statistical analysis of a large number of actual cross-border logistics cases and can accurately reflect the level of logistics loss under different modes of transportation; n is the total number of different categories of goods included in the same order. If there are multiple specifications of the same category of goods, they are counted as the same category.

[0054] Based on the rated capacity of the relevant cross-border transportation routes, the maximum allowable scheduling capacity at the current fulfillment stage is derived using the following formula: ; in, To the maximum allowed scheduling capacity, Rated capacity for the transportation route, The overall efficiency of logistics equipment (value range 0.8-0.95). Capacity reservation factor (value range: 0.05-0.1); This parameter is determined by official data provided by the transportation route operator and is updated in real time if there are any adjustments to the route capacity. The calculation is based on the operating status of logistics equipment, maintenance records, and the skill level of operators. The upper limit is taken when the equipment is operating stably, maintenance is timely, and operators are skilled, and the lower limit is taken otherwise. The value is determined based on the busyness of the transportation route: 0.1 for busy routes, 0.07 for ordinary routes, and 0.05 for idle routes. Reserved capacity is used to cope with emergencies such as sudden orders or route failures, ensuring the flexibility and stability of scheduling.

[0055] By combining logistics node level parameters, the congestion sensitivity coefficient is calibrated; The congestion sensitivity coefficient ranges from 0.5 to 1.0, with 1.0 for core nodes, 0.8 for backbone nodes, and 0.5 for terminal nodes. The higher the node level, the higher the sensitivity to congestion. This coefficient is determined by analyzing the scope and degree of congestion impact on logistics nodes at different levels, and is used for assessing and responding to congestion risks in subsequent dynamic scheduling.

[0056] The unique identifier of the fulfillment process, the set of scheduling parameters, and the logistics demand are encapsulated into structured data objects to obtain the basic data for structured collaborative scheduling.

[0057] The unique identifier for each performance stage is generated in the form of "date + stage code + random number" to ensure its uniqueness; the scheduling parameter set includes the aforementioned core scheduling parameters as well as derived parameters such as service provider capacity parameters and congestion sensitivity coefficients; the structured data objects are encapsulated in JSON format for easy data storage, transmission and parsing. After encapsulation, the data objects are verified to ensure data integrity and accuracy. Data objects that fail verification are returned for reprocessing.

[0058] Collect historical operational data, extract inventory turnover rate, logistics timeliness curve and order sorting order from the historical data, and calculate the theoretical scheduling reference benchmark based on inventory turnover rate and logistics timeliness curve; The process of extracting inventory turnover rate, logistics timeliness curves, and order sorting sequence from historical data includes: Filter historical operating records in the same target market; Parse the inventory turnover data in the historical structured data object, statistically analyze the distribution of inventory turnover rate, and the formula for calculating inventory turnover rate is: ; in, For inventory turnover rate, This represents the average inventory value within the statistical period. The statistical period can be set to monthly, quarterly, or annually; the default is quarterly statistics. Enterprises can adjust this according to their business characteristics. The net value after deducting return and discount amounts from the actual transaction amount of goods within the statistical period is expressed in RMB and rounded to two decimal places. The calculation formula is (beginning inventory amount + ending inventory amount) / 2. The beginning inventory amount is the book value of inventory at the beginning of the statistical period, and the ending inventory amount is the book value of inventory at the end of the statistical period. Both are calculated based on the actual purchase cost.

[0059] This study analyzes historical logistics timeliness monitoring records to construct a mapping model between logistics timeliness and order volume. The logistics timeliness monitoring records include data such as start time, end time, duration, and order volume for each fulfillment stage. A linear regression analysis method is used to construct the mapping model, with the expression y = kx + b (where y is logistics timeliness, x is order volume, k is the timeliness sensitivity coefficient, and b is the base timeliness). The values ​​of k and b are obtained by fitting historical data, and the goodness of fit R0 is calculated. 2 A value ≥0.8 is required to ensure the model's predictive accuracy. After the model is built, it should be updated and optimized regularly (quarterly) to adapt to market changes.

[0060] Extract the historical fulfillment process chain sequence and timestamp dependencies. The fulfillment process chain sequence is extracted according to the actual fulfillment process of historical orders, clarifying the sequence of each step. The timestamp dependencies mark the earliest start time, latest start time, earliest end time, and latest end time of each step, providing a basis for scheduling and timing planning. Time series analysis methods are used in the extraction process to identify the time correlation patterns of the fulfillment steps.

[0061] The process of calculating the theoretical scheduling reference baseline based on inventory turnover rate and logistics timeliness curve includes: Based on the inventory turnover rate mapping logistics timeliness model, a basic scheduling reference value is generated; the calculated inventory turnover rate is then used to generate a basic scheduling reference value. Substitute the logistics timeliness into the mapping model of order volume to obtain the corresponding logistics timeliness prediction value. This prediction value is the basic scheduling reference value. During the mapping process, the inventory turnover rate is standardized to ensure that it is consistent with the format of the model input parameters. If the inventory turnover rate exceeds the training data range of the model, the extrapolation method is used for prediction, and the prediction risk level (low, medium, high) is marked.

[0062] The calculation formula is as follows: (This is based on the benchmark value of cross-border logistics costs.) ; in, This is the base reference value after adding costs. Based on the basic scheduling reference value, For inventory turnover rate, the baseline value of cross-border logistics basic costs is determined based on the target market, transportation mode, and commodity category. The latest cost accounting data is provided by the company's finance department and updated regularly (monthly). This represents the reduction in unit logistics cost corresponding to each increase in inventory turnover rate, expressed in "yuan / time". The correlation between historical inventory turnover rate and logistics cost is analyzed to determine the differences for different product categories and transportation methods. Different values ​​need to be set separately.

[0063] Introducing a scheduling safety redundancy coefficient, a theoretical scheduling reference benchmark is generated, calculated using the following formula: ; in, As a theoretical scheduling reference benchmark, The value is determined based on the risk level of the target market. The risk level is divided into three levels: low, medium, and high, based on indicators such as policy stability, the completeness of logistics infrastructure, and the leniency of customs clearance policies. The value is 0.08 for low-risk markets, 0.12 for medium-risk markets, and 0.15 for high-risk markets.

[0064] Configure dynamic scheduling tolerance range, and generate an initial collaborative scheduling map according to the order of the fulfillment process by combining theoretical scheduling reference benchmarks. The process includes: Create a two-dimensional coordinate system for time-series scheduling efficiency; the horizontal axis represents the time sequence of the fulfillment process, in hours, and is marked sequentially according to the order of the fulfillment steps, with a time scale accuracy of 1 hour; the vertical axis represents scheduling efficiency, in "orders / hour", and the scale range is determined based on historical scheduling efficiency data.

[0065] The theoretical scheduling reference benchmarks are connected according to the time sequence of the performance process to form a reference curve; each performance stage corresponds to a theoretical scheduling reference benchmark value. The benchmark values ​​are connected by a smooth curve according to the time sequence of the performance process. The curve is fitted with a Bézier curve to ensure the continuity and smoothness of the curve. The color and line type of the reference curve are set differently to facilitate the distinction from the subsequent tolerance range boundary.

[0066] Differentiated tolerance ranges are set based on the characteristics of each performance stage, and the calculation formula is as follows: ; in, For tolerance range, The characteristic coefficients for the fulfillment stage are: 0.2 for the inventory storage stage, 0.15 for the logistics and transportation stage, and 0.25 for the customs clearance stage. This is the market demand fluctuation coefficient (range: 0.9-1.1). The value is determined based on the complexity and uncertainty of the process at each stage of the contract fulfillment. The process at the inventory storage stage is relatively fixed and the uncertainty is low, so a smaller value is taken. The customs clearance stage is greatly affected by factors such as policies and inspections and has high uncertainty, so a larger value is taken. The coefficient is calculated based on historical demand fluctuation data of the target market. For markets with large demand fluctuations, the coefficient is 1.1; for markets with small fluctuations, the coefficient is 0.9; and for markets with medium fluctuations, the coefficient is 1.0. This coefficient can reflect the impact of changes in market demand on scheduling tolerance, making the tolerance range setting more in line with actual needs.

[0067] Visualize and annotate the tolerance range boundaries to form an initial collaborative scheduling graph. The tolerance range boundaries include the upper boundary ( ) and lower boundary ( The initial collaborative scheduling map is marked with dashed lines and distinguished by color from the reference curve. The upper boundary is marked with a red dashed line, the lower boundary with a blue dashed line, and the reference curve with a black solid line. The map also marks key information such as the name of the performance link, the theoretical scheduling reference value, and the tolerance range. The generated initial collaborative scheduling map can be exported to PNG, PDF and other formats.

[0068] Collect logistics node congestion index, cross-border customs clearance timeliness fluctuation data and warehousing operation efficiency parameters, construct an adaptive scheduling correction matrix, and inject the adaptive scheduling correction matrix into the initial collaborative scheduling graph to obtain a dynamic collaborative scheduling graph. The process of constructing the adaptive scheduling correction matrix includes: The congestion impact entropy value is calculated based on the congestion index of logistics nodes, and the logistics network elasticity coefficient is generated. The calculation formula is as follows: ; in, This is the elasticity coefficient of the logistics network. The actual impact of congestion on entropy value, The maximum permissible entropy value for congestion impact; the entropy value for congestion impact. The information entropy formula is used to calculate and reflect the degree of disorder in the impact of congestion. (in Let i be the weight of the i-th congestion influencing factor. (This refers to the number of factors affecting congestion), which include the amount of goods piled up, waiting time, and operation delay rate, etc. The capacity and processing power of the nodes are determined based on the statistical analysis of historical maximum congestion event data. The value ranges from 0 to 1. The closer it is to 1, the stronger the logistics network's ability to cope with congestion. The closer it is to 0, the more fragile the network is.

[0069] Based on the data on fluctuations in cross-border customs clearance time, the customs clearance time compensation coefficient is calculated using the following formula: ; in, This is a customs clearance time compensation coefficient. The standard clearance time stipulated by the customs authorities of the target country / region is determined by combining historical average clearance time. If the clearance policy is adjusted, this parameter will be updated in a timely manner. The actual customs clearance time for the current order is the total time from submitting the customs declaration to completing customs clearance and release. This indicates that the actual customs clearance time is faster than the standard, and the scheduling time for subsequent steps can be appropriately reduced. This indicates that the actual customs clearance time is slower than the standard, requiring extended scheduling time for subsequent steps or adjustments to the planning of earlier steps. This indicates that the actual situation is consistent with the standard and no compensation is required.

[0070] Analyze warehousing operation efficiency parameters, calibrate inventory dynamic adjustment characteristic parameters, and calculate the formula as follows: ; in, To dynamically adjust characteristic parameters for inventory, For actual warehousing operation efficiency, To standardize warehousing operation efficiency, This is the utilization coefficient of warehousing resources (range: 0.85-0.98). It represents the actual workload of a warehouse node within a unit of time, including the number of sorting orders and the number of outbound goods, which is statistically analyzed in real time through the warehouse management system; The design efficiency of a warehousing node is determined based on equipment performance, personnel configuration, and work processes. The value is determined based on the actual utilization of warehousing resources: 0.98 for resource utilization ≥ 90%, 0.92 for 70%-90%, and 0.85 for ≤ 70%. This coefficient can correct for operational efficiency deviations caused by underutilization of resources. More accurately reflects the actual level of warehousing operations. This indicates that actual operational efficiency is higher than the standard, allowing for an increase in inventory allocation. This indicates that actual efficiency is below standard, and inventory allocation needs to be reduced.

[0071] By integrating the logistics network elasticity coefficient, customs clearance timeliness compensation coefficient, and inventory dynamic adjustment characteristic parameters, an adaptive scheduling correction matrix is ​​obtained.

[0072] The adaptive scheduling correction matrix is ​​a 3×3 diagonal matrix, and the matrix elements are as follows: , , The off-diagonal elements are 0, and the matrix is ​​in the following form: The matrix is ​​constructed and stored using a matrix operation library, which supports operations such as matrix multiplication and inverse matrix solving, facilitating subsequent adjustments to the scheduling baseline value.

[0073] The process of injecting the adaptive scheduling correction matrix into the initial cooperative scheduling graph to obtain the dynamic cooperative scheduling graph includes: Based on the adaptive scheduling correction matrix, the initial map scheduling baseline value is adjusted for each fulfillment stage. The adjustment formula is as follows: ; in, This is the adjusted scheduling baseline value; Each node in the initial collaborative scheduling graph is traversed for each fulfillment stage to obtain the corresponding theoretical scheduling reference value. By multiplying the corresponding coefficients in the adaptive scheduling correction matrix, the adjusted scheduling baseline value is obtained. During the adjustment process, the calculation results are verified. If the value exceeds the reasonable range (the upper and lower limits determined based on historical scheduling data), the boundary value of the reasonable range will be taken as the final adjustment value to ensure the effectiveness of the scheduling baseline value.

[0074] Based on inventory dynamics, characteristic parameters are adjusted, and the tolerance range is dynamically expanded or contracted. when When warehousing efficiency exceeds the standard, the tolerance range can be appropriately reduced, and the adjustment formula is as follows: ;when When warehousing efficiency falls below the standard, the tolerance range needs to be expanded, and the adjustment formula is as follows: ;when At the same time, the tolerance range remains unchanged. The upper and lower boundaries of the dynamically expanded tolerance range are also marked, and the colors are different from the initial tolerance range boundaries for easy comparison.

[0075] A layer of correction factor numerical markers is superimposed on the initial cooperative scheduling graph; Establish a real-time data interface with the cross-border e-commerce operation monitoring system and update the adaptive scheduling correction matrix according to a preset cycle (1-2 hours).

[0076] Collect real-time order flow data and inventory dynamic data, compare the average actual scheduling efficiency of each fulfillment stage, and calculate the matching degree between the actual scheduling curve and the dynamic collaborative scheduling map within the time window of each fulfillment link, so as to optimize and adjust the status of cross-border e-commerce logistics and inventory collaborative scheduling in real time.

[0077] The process of calculating the matching degree between the actual scheduling curve and the dynamic collaborative scheduling map within the time window of each fulfillment stage includes: The matching degree is calculated using an improved cosine similarity algorithm. The calculation formula is as follows: ; Where M is the matching degree (value range 0-1). For the k-th data point of the actual scheduling curve, Let k be the k-th data point in the dynamic collaborative scheduling graph, and let be the number of data points. , where m is the time alignment coefficient (range 0.95-1.0), and m represents the number of data points between the actual scheduling curve and the dynamic collaborative scheduling map; When the matching degree is lower than the preset threshold or the average actual scheduling efficiency exceeds ±20% of the theoretical scheduling reference benchmark, a scheduling optimization instruction is triggered to adjust the inventory allocation ratio and logistics transportation plan.

[0078] The preset threshold is the optimal threshold derived from the analysis of numerous cross-border e-commerce scheduling cases. It can effectively distinguish between normal scheduling deviations and abnormal situations requiring optimization. Enterprises can adjust it according to their own service quality requirements. The average actual scheduling efficiency is calculated as the arithmetic mean of the actual scheduling efficiency data within the time window. The judgment standard exceeding ±20% is determined through statistical significance testing to ensure the reliability of the judgment results. Scheduling optimization instructions include adjustments to inventory allocation ratios (such as increasing / decreasing the inventory allocation of a certain warehouse node, adjusting the inventory allocation ratio of different product categories) and adjustments to logistics and transportation plans (such as switching transportation modes, changing logistics service providers, and adjusting transportation routes). During the adjustment process, real-time data is used for dynamic optimization to ensure the feasibility and effectiveness of the adjustment plan. After the adjustment, the matching degree and the average actual scheduling efficiency are continuously monitored until the preset standard is reached.

[0079] In summary, this embodiment provides a strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory. By integrating data from the entire cross-border e-commerce chain, it breaks down the fragmented barriers of order, inventory, and logistics data. Combined with professional analysis techniques, it uncovers core order fulfillment needs, providing comprehensive and accurate foundational support for scheduling decisions and effectively addressing the problem of insufficient data support in traditional scheduling. By mining historical operational data to construct a scientific theoretical scheduling benchmark, it replaces decision-making models relying on human experience. Introducing risk control coefficients to calibrate scheduling targets improves the scientific rigor and reliability of scheduling decisions, avoiding the subjective limitations of experience-based decisions. By constructing an adaptive scheduling correction mechanism, it integrates dynamic information such as logistics node congestion and customs clearance time fluctuations in real time, dynamically adjusting the scheduling benchmark and tolerance range, significantly enhancing the ability to cope with uncertainties in cross-border logistics and reducing scheduling lag. Through professional algorithms, it monitors the matching degree between actual scheduling and expected goals in real time, triggering closed-loop optimization of inventory allocation and transportation plans, alleviating the contradiction between stockouts of popular goods and stockpiles of slow-moving goods, and improving inventory turnover efficiency. Through the synergistic application of the above-mentioned technologies, the intelligent, collaborative, and dynamic scheduling of logistics and inventory can be achieved, significantly improving fulfillment efficiency and customer satisfaction. It is suitable for enterprises of different sizes and various target markets, and has outstanding practicality and broad promotional value.

[0080] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A strategy-optimized intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory, characterized in that: include: Collect data from the entire cross-border e-commerce operation chain, identify the fulfillment requirements of different product categories by parsing order content, and extract the scheduling characteristics of each fulfillment link by combining logistics network parameters to generate structured collaborative scheduling basic data; Collect historical operational data, extract inventory turnover rate, logistics timeliness curve and order sorting order from the historical data, and calculate the theoretical scheduling reference benchmark based on inventory turnover rate and logistics timeliness curve; Configure dynamic scheduling tolerance range and generate an initial collaborative scheduling map according to the order of the performance process by combining theoretical scheduling reference benchmarks; Collect logistics node congestion index, cross-border customs clearance timeliness fluctuation data and warehousing operation efficiency parameters, construct an adaptive scheduling correction matrix, and inject the adaptive scheduling correction matrix into the initial collaborative scheduling graph to obtain a dynamic collaborative scheduling graph. Collect real-time order flow data and inventory dynamic data, compare the average actual scheduling efficiency of each fulfillment stage, and calculate the matching degree between the actual scheduling curve and the dynamic collaborative scheduling map within the time window of each fulfillment link, so as to optimize and adjust the status of cross-border e-commerce logistics and inventory collaborative scheduling in real time. The process of generating an initial collaborative scheduling map based on the theoretical scheduling reference benchmark and in the order of the performance process includes: Create a two-dimensional coordinate system for timing and scheduling efficiency; A reference curve is formed by connecting theoretical scheduling reference benchmarks according to the performance process sequence; Differentiated tolerance ranges are set based on the characteristics of each stage of the contract performance; Visualize and annotate the tolerance range boundaries to form an initial collaborative scheduling map; The process of constructing the adaptive scheduling correction matrix includes: Calculate the congestion impact entropy value based on the congestion index of logistics nodes, and generate the logistics network elasticity coefficient; Calculate the clearance time compensation coefficient based on cross-border customs clearance time fluctuation data; Analyze warehouse operation efficiency parameters and calibrate inventory dynamic adjustment characteristic parameters; By integrating the logistics network elasticity coefficient, customs clearance timeliness compensation coefficient, and inventory dynamic adjustment characteristic parameters, an adaptive scheduling correction matrix is ​​obtained. The process of injecting the adaptive scheduling correction matrix into the initial cooperative scheduling graph to obtain the dynamic cooperative scheduling graph includes: Based on the adaptive scheduling correction matrix, the initial map scheduling baseline value is adjusted for each fulfillment stage; Based on inventory dynamics, characteristic parameters are adjusted, and the tolerance range is dynamically expanded or contracted. A layer of correction factor numerical markers is superimposed on the initial cooperative scheduling graph; Establish a real-time data interface with the cross-border e-commerce operation monitoring system and update the adaptive scheduling correction matrix according to a preset cycle.

2. The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization according to claim 1, characterized in that, The full-chain data for cross-border e-commerce operations includes order information files, inventory instruction files issued by the WMS system, and operational parameter configuration files generated by the ERP software; the logistics network parameters include the rated capacity of cross-border transportation routes, the processing efficiency of warehousing and sorting equipment, the passage threshold of customs clearance channels, logistics node level parameters, and a resource library of cross-border logistics service providers; the scheduling features include transportation mode codes, inventory allocation ratios, order processing priorities, inventory turnover calculations, and warehousing and sorting batch capacities.

3. The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization according to claim 1, characterized in that, The process of identifying fulfillment requirements for different product categories through order content analysis includes: Perform semantic word segmentation on the order information file to identify core requirement instructions; Based on the type of core requirement instructions, continuous fulfillment segments are divided to generate an initial scheduling sequence; Extract the core scheduling parameter set for each fulfillment stage in the initial scheduling sequence. The core scheduling parameter set includes the order delivery range and the product category code. Non-core auxiliary fulfillment links in the initial scheduling sequence are filtered out based on the core scheduling parameter set.

4. The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization according to claim 1, characterized in that, The process of extracting scheduling features for each fulfillment stage by combining logistics network parameters to generate structured collaborative scheduling foundation data includes: Based on the order delivery range and product category code, link the cross-border logistics service provider resource database to obtain the service provider's transportation capacity parameters; Calculate the logistics requirements for the fulfillment process by combining the order delivery range with the service provider's transportation capacity parameters; Based on the rated capacity of cross-border transportation routes, the maximum allowable dispatch capacity at the current fulfillment stage is derived; By combining logistics node level parameters, the congestion sensitivity coefficient is calibrated; The unique identifier of the fulfillment process, the set of scheduling parameters, and the logistics demand are encapsulated into structured data objects to obtain the basic data for structured collaborative scheduling.

5. The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization according to claim 1, characterized in that, The process of extracting inventory turnover rate, logistics timeliness curves, and order sorting sequence from historical data includes: Filter historical operating records in the same target market; Parse inventory turnover-related data in historical structured data objects and statistically analyze the distribution of inventory turnover rates; Analyze historical logistics timeliness monitoring records and construct a mapping model between logistics timeliness and order volume; Extract the historical performance chain sequence and timestamp dependencies.

6. The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization according to claim 1, characterized in that, The process of calculating the theoretical scheduling reference baseline based on inventory turnover rate and logistics timeliness curve includes: Based on the inventory turnover rate, a logistics timeliness model is mapped to generate basic scheduling reference values; Overlaying the benchmark value of basic cross-border logistics costs; A scheduling safety redundancy coefficient is introduced to generate a theoretical scheduling reference benchmark.

7. The intelligent collaborative scheduling method for cross-border e-commerce logistics and inventory based on strategy optimization according to claim 1, characterized in that, The process of calculating the matching degree between the actual scheduling curve and the dynamic collaborative scheduling map within the time window of each fulfillment stage includes: An improved cosine similarity algorithm is used to calculate the matching degree; When the matching degree is lower than the preset threshold or the average actual scheduling efficiency exceeds the preset range, a scheduling optimization instruction is triggered to adjust the inventory allocation ratio and logistics transportation plan.

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