A method and system for optimizing intelligent consolidation in cross-border e-commerce

Abstract

This invention discloses a method and system for optimizing intelligent consolidation in cross-border e-commerce. It relates to the field of container consolidation transportation technology. The method includes: Step 1: acquiring basic data on the cargo and container environment; Step 2: establishing an adaptive weight model based on the basic data on the cargo and container environment, determining the weight dimensions, and quantitatively scoring the cargo to generate a priority list; Step 3: generating an optimal stacking scheme based on the priority list, designing storage units based on the optimal stacking scheme, numbering the storage units, and binding cargo information; Step 4: calculating the container's center of gravity coordinates in real time based on the cargo information associated with the storage unit number; a preset center of gravity offset threshold is set, and when the offset exceeds the threshold, the stacking method of high-priority cargo is automatically adjusted and the storage units are optimized. This invention can significantly improve the intelligence, efficiency, and safety of cross-border e-commerce consolidation transportation.

Description

Technical Field

[0001] This invention relates to the field of container consolidation transportation technology, and more specifically to a smart consolidation optimization method and system for cross-border e-commerce. Background Technology

[0002] With the rapid development of the cross-border e-commerce industry, LCL (Less than Container Load) shipping has become the mainstream transportation method for small and medium-sized cross-border goods due to its ability to reduce logistics costs per shipment. However, existing cross-border LCL technologies still have many shortcomings in adaptability: 1. Fragmented data collection: Most methods only collect basic data such as cargo volume and weight, ignoring key information such as customs clearance categories, prohibited and restricted items, and destination concentration in cross-border scenarios, resulting in a disconnect between subsequent solutions and cross-border needs.

[0003] 2. Poor adaptability of the weight model: It lacks adaptive weight design for cross-border scenarios and mostly adopts fixed-dimensional scoring. It cannot dynamically adjust the weight according to different categories such as fresh food and fragile goods, making it difficult to balance safety, timeliness and customs clearance efficiency.

[0004] 3. Disconnect between storage units and stacking schemes: Storage units are mostly designed with fixed specifications, which cannot be dynamically adapted to the optimal stacking scheme. In addition, the binding of numbers and cargo information is not standardized, which is not conducive to cross-border traceability and unpacking and distribution.

[0005] 4. Delayed center of gravity adjustment: Center of gravity verification is mostly static inspection after packing. The adjustment is not linked to high-priority cargo protection and storage unit optimization, which can easily affect the stability of cross-border long-distance transportation and increase the risk of cargo damage.

[0006] Therefore, there is an urgent need for intelligent consolidation optimization methods and systems for cross-border e-commerce to solve the problem of insufficient coordination among data, models, solutions, and verification. Summary of the Invention

[0007] In view of this, the present invention provides a method and system for optimizing intelligent consolidation in cross-border e-commerce, which can realize automated control of the entire consolidation process, reduce human intervention errors, adapt to the dynamic changes of complex cross-border scenarios, and take into account space utilization, transportation safety and operational efficiency, thereby significantly reducing the overall cost of cross-border logistics.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A method for optimizing intelligent consolidation in cross-border e-commerce includes: Step 1: Obtain basic environmental data for cargo and containers; Step 2: Establish an adaptive weight model based on the basic environmental data of goods and containers, determine the weight dimensions, quantify and score the goods, and generate a priority list; Step 3: Generate the optimal stacking scheme based on the priority list, design storage units based on the optimal stacking scheme, number the storage units and bind them to cargo information; Step 4: Calculate the container's center of gravity coordinates in real time based on the cargo information associated with the storage unit number; preset a center of gravity offset threshold, and automatically adjust the stacking method of high-priority cargo and optimize the storage unit when the offset exceeds the threshold.

[0009] Preferably, step 1 involves scanning the goods using a depth camera array and a high-definition industrial camera to collect goods information, including: external dimensions, actual volume, appearance, and packaging integrity data; obtaining the goods weight using a piezoelectric weight sensor integrated into the conveyor line; reading the goods order information using a QR code scanner; scanning the inside of the container to generate a three-dimensional usable space model, clarifying the usable dimensions and maximum load-bearing weight of the container, and eliminating unusable space in the container; and performing outlier verification on all collected data using the 3σ principle, storing the data in a dedicated cross-border LCL database after successful verification.

[0010] Preferably, the weighting dimensions of step 2 include fragility, transportation timeliness, destination customs clearance difficulty, order priority, and cargo volume and weight ratio; the weighting coefficients of each dimension can be dynamically adjusted according to the cross-border scenario; and a standardized quantitative score is applied to each dimension, using the following formula: Overall score = Σ quantified value of each dimension × corresponding weight coefficient; A priority list is generated based on scores from highest to lowest, with high-priority goods receiving priority in terms of storage safety and customs clearance convenience.

[0011] Preferably, step 3 adopts an improved three-dimensional packing algorithm and a genetic algorithm fusion model. The model adopts a global optimization and local adaptation collaborative mechanism, and automatically iteratively calculates the optimal stacking scheme based on the priority list, cargo information and container three-dimensional available space model. Among them, the genetic algorithm is used for global optimization, aiming to maximize space utilization and optimize the convenience of cross-border unpacking, and iteratively optimizes the stacking order and layering logic; the improved 3D packing algorithm is used for local adaptation, accurately calculating the cargo placement posture and storage unit adaptation size to generate the optimal stacking scheme.

[0012] Preferably, the optimal stacking scheme includes stacking order, placement posture, and layering method, specifically including: Stacking order: Strictly follow the priority list, with high-priority goods stacked first; goods of the same priority are clustered according to destination distance and sorted according to customs clearance category relevance; Placement orientation: The cargo is iteratively screened for horizontal and vertical orientations to select the optimal space utilization method to maximize container space utilization; fragile and moisture-proof special properties are placed vertically with buffer space reserved. Stacking method: High-priority goods and heavy goods are placed in the stable area at the bottom of the container, with heavy goods close to the bottom center to ensure overall stability; fragile items and moisture-proof special goods are separated into separate layers and isolated from ordinary goods; goods with the same destination and customs clearance category are grouped together in adjacent areas to form dedicated storage units, which can meet the needs of cross-border customs clearance and distribution.

[0013] Preferably, the storage unit is individually numbered according to the container number-layer number-location number rule, and the storage unit number is bound to the corresponding cargo information and updated synchronously to the cross-border LCL dedicated database.

[0014] Preferably, step 4, which involves calculating the container's center of gravity coordinates in real time, includes: Based on a three-dimensional coordinate system established with the bottom left corner of the container's interior as the origin, the core logic is to superimpose all cargo and container accessories to obtain the overall three-dimensional coordinates of the center of gravity (X0, Y0, Z0). X0=(Σm i X i +ΣM j X j ) / (gross weight); Y0=(Σm i Y i +ΣM j Y j ) / (gross weight); Z0=(Σm i Z i +ΣM j Z j ) / (gross weight); Where m i For the weight of a single item, (X) i Y i Z i M represents the coordinates of the center of gravity of a single cargo. j Weight of auxiliary components (X) j Y j Z j ( ) represents the coordinates of the center of gravity of the auxiliary component; A preset center of gravity offset threshold is set, which is adjusted according to container specifications and transportation methods; at the same time, an early warning threshold is set to trigger a center of gravity monitoring warning in advance. When the detected center of gravity offset exceeds the preset threshold, a dynamic adjustment mechanism is activated to prioritize the protection needs of high-priority goods.

[0015] Preferably, a cross-border e-commerce intelligent consolidation optimization system includes: Data acquisition module: used to acquire basic environmental data of goods and containers; Priority module: Based on the basic environmental data of goods and containers, an adaptive weight model is established to determine the weight dimensions, and the goods are quantitatively scored to generate a priority list; Cargo stacking module: Generates the optimal stacking plan based on the priority list, designs storage units based on the optimal stacking plan, and assigns numbers to the storage units and binds them to cargo information; Cargo optimization module: Based on the cargo information associated with the storage unit number, calculate the center of gravity coordinates of the container in real time; preset center of gravity offset threshold, when the offset exceeds the threshold, automatically adjust the stacking method of high-priority cargo and optimize the storage unit.

[0016] As can be seen from the above technical solutions, compared with existing technologies, this invention discloses a method and system for optimizing intelligent consolidation in cross-border e-commerce. Through multi-device collaborative data collection and anomaly verification, it ensures data accuracy, laying the foundation for subsequent optimization. The adaptive weight model and algorithm fusion mechanism effectively improves container space utilization while prioritizing the safety and timeliness of high-priority goods. Real-time monitoring and dynamic adjustment of the center of gravity effectively avoids the risk of center of gravity shift during transportation, reducing cargo damage rates. Standardized numbering and information binding of storage units simplify customs clearance and distribution processes, shortening turnaround time. This invention enables fully automated control of the consolidation process, reducing human intervention errors, adapting to the dynamic changes of complex cross-border scenarios, balancing space utilization, transportation safety, and operational efficiency, and significantly reducing the overall cost of cross-border logistics. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 The method flowchart provided by the present invention. Detailed Implementation

[0019] 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.

[0020] like Figure 1 As shown in the figure, an embodiment of the present invention discloses a smart consolidation optimization method for cross-border e-commerce, including: Step 1: Obtain basic environmental data for cargo and containers; Step 2: Establish an adaptive weight model based on the basic environmental data of goods and containers, determine the weight dimensions, quantify and score the goods, and generate a priority list; Step 3: Generate the optimal stacking scheme based on the priority list, design storage units based on the optimal stacking scheme, number the storage units and bind them to cargo information; Step 4: Calculate the container's center of gravity coordinates in real time based on the cargo information associated with the storage unit number; preset a center of gravity offset threshold, and automatically adjust the stacking method of high-priority cargo and optimize the storage unit when the offset exceeds the threshold.

[0021] Specifically, step 1 involves scanning the goods using a depth camera array and a high-definition industrial camera to collect information, including: external dimensions, actual volume, appearance, and packaging integrity data; obtaining the weight of the goods using a piezoelectric weight sensor integrated into the conveyor line; reading the order information of the goods using a QR code scanner; scanning the interior of the container to generate a three-dimensional usable space model, clarifying the usable dimensions and maximum load-bearing weight of the container, and eliminating unusable space in the container; and performing outlier verification on all collected data using the 3σ principle, storing the data in a dedicated cross-border LCL database after the verification is passed.

[0022] Specifically, the weighting dimensions of step 2 include fragility, transportation timeliness, destination customs clearance difficulty, order priority, and cargo volume and weight ratio; the weight coefficients of each dimension can be dynamically adjusted according to the cross-border scenario; and a standardized quantitative score is applied to each dimension, using the following formula: Overall score = Σ quantified value of each dimension × corresponding weight coefficient; A priority list is generated based on scores from highest to lowest, with high-priority goods receiving priority in terms of storage safety and customs clearance convenience.

[0023] In a specific embodiment of this invention, core weight dimensions are determined, including fragility, transportation timeliness, destination customs clearance difficulty, order priority, and cargo volume-weight ratio. The weight coefficients for each dimension can be dynamically adjusted according to the cross-border scenario (e.g., increasing the weight of timeliness in fresh produce cross-border scenarios, and increasing the weight of fragility in fragile goods cross-border scenarios). Each dimension is standardized and quantitatively scored: fragility (1 point for level 1, 5 points for level 5), timeliness (the shorter the remaining delivery period, the higher the score, maximum 5 points), customs clearance difficulty (1 point for general goods, 5 points for specially regulated goods), order priority (5 points for expedited orders, 3 points for general orders), and volume-weight ratio (the higher the reasonable ratio for container space, the higher the score, maximum 5 points). The comprehensive score for each shipment is calculated using the formula "Comprehensive Score = Σ Quantitative Value of Each Dimension × Corresponding Weight Coefficient". A cargo priority list is generated based on the scores from highest to lowest, with high-priority cargo receiving priority in terms of storage safety and customs clearance convenience.

[0024] Furthermore, three typical shipments were selected: fresh fruit, glassware, and general daily necessities. Weighting coefficients were set for fresh fruit transportation scenarios: fragility 15%, timeliness 40%, customs clearance difficulty 20%, order priority 15%, and volumetric weight 10%. The quantitative scores and overall scores for each shipment are calculated as follows: 1. Fresh fruit (strawberries): Fragility level 2 (2 points), remaining delivery time 2 days (timeliness 5 points), general edible agricultural products (customs clearance difficulty 2 points), expedited order (order priority 5 points), volume and weight ratio appropriate (3 points); Overall score = 2×15%+5×40%+2×20%+5×15%+3×10%=0.3+2.0+0.4+0.75+0.3=3.75 points.

[0025] 2. Glassware (wine glasses): Fragility level 5 (5 points), remaining delivery time 7 days (timeliness 3 points), general industrial products (customs clearance difficulty 1 point), expedited orders (order priority 5 points), volume and weight ratio appropriate (4 points); Overall score = 5×15%+3×40%+1×20%+5×15%+4×10%=0.75+1.2+0.2+0.75+0.4=3.3 points.

[0026] 3. General daily necessities (towels): Fragility level 1 (1 point), remaining delivery period 15 days (timeliness 1 point), general industrial products (customs clearance difficulty 1 point), general orders (order priority 3 points), volume and weight ratio appropriate (3 points); Overall score = 1×15%+1×40%+1×20%+3×15%+3×10%=0.15+0.4+0.2+0.45+0.3=1.5 points.

[0027] A priority list is generated based on the overall score from highest to lowest: fresh fruit > glassware > general daily necessities. High-priority goods (fresh fruit and glassware) will be given priority in terms of storage safety and customs clearance convenience.

[0028] Specifically, step 3 adopts an improved three-dimensional packing algorithm and a genetic algorithm fusion model. The model adopts a global optimization and local adaptation collaborative mechanism. Based on the priority list, cargo information and container three-dimensional available space model, it automatically iteratively calculates the optimal stacking scheme. Among them, the genetic algorithm is used for global optimization, aiming to maximize space utilization and optimize the convenience of cross-border unpacking, and iteratively optimizes the stacking order and layering logic; the improved 3D packing algorithm is used for local adaptation, accurately calculating the cargo placement posture and storage unit adaptation size to generate the optimal stacking scheme.

[0029] In a specific embodiment of the present invention, the optimal stacking scheme is generated in stages through a fusion model of an improved 3D container packing algorithm and a genetic algorithm. Using a priority list as the core constraint, and combining collected cargo information (size, volume, physical attributes) with a 3D usable space model of the container, the scheme balances cross-border LCL security, space utilization, and customs clearance and distribution convenience. The specific generation process is as follows: Global stacking planning (genetic algorithm-driven): With "maximizing space utilization and maximizing the concentration of goods with the same destination / clearance category" as the dual objective functions, a priority list is invoked to iteratively optimize the stacking order. The core logic strictly adheres to the principle of "prioritizing high-priority goods." For goods of the same priority, they are clustered by destination distance and sorted by the correlation of customs clearance category, reducing the workload of subsequent cross-border unpacking and sorting. Simultaneously, the approximate stacking area for each type of goods is initially delineated to avoid conflicts with fixed components and obstacles inside the container.

[0030] Further optimize the vertical layout: high-priority goods and heavy goods are placed in the stable area at the bottom of the container, with heavy goods close to the bottom center; fragile and moisture-proof goods are separated into separate layers, with a 5-10cm buffer space reserved and isolated from ordinary goods; goods with the same destination and customs clearance category are grouped together in adjacent areas to form a dedicated stacking area to meet the needs of cross-border customs clearance and inspection.

[0031] Finally, the solution is verified and fine-tuned: the fusion model integrates the global planning and local adaptation results, calculates the space utilization rate, cargo protection compliance rate and customs clearance and distribution convenience of the preliminary solution. If there are problems such as excessive space waste, insufficient protection of special goods or dispersion of the same category, the stacking order, posture and stacking position are iteratively adjusted until the preset optimization goal is met, and the final optimal stacking solution is formed.

[0032] Based on the optimal stacking scheme generated above, the storage unit specifications are dynamically adjusted through multi-directional retractable mechanical partitions in length, width, and height within the container. Each storage unit precisely matches the space requirements of the corresponding goods (groups), with dimensional errors controlled within ±5mm to prevent goods from shaking during transportation. The storage units adopt a three-level coding rule of "container number-layer number-position number" (e.g., "JC001-L02-W03" represents position 3 on layer 2 of container number 1). After being numbered, the unit is immediately bound to the full information of the corresponding goods, including order number, customs clearance category code, weight and dimensions, priority, stacking posture, and protection requirements. This information is simultaneously updated to the cross-border LCL dedicated database, enabling one-to-one traceability of cargo location and cargo information, and adapting to the needs of cross-border full-process supervision and problem tracing.

[0033] Furthermore, the specific process of the improved 3D bin packing algorithm and genetic algorithm fusion model includes: 1. Initialize basic data and algorithm parameters: Retrieve preprocessed cargo basic data (external dimensions, weight, fragility / timeliness scores, etc.), container 3D usable space model (precise dimensions after removing unusable space), and generated cargo priority list from the cross-border LCL dedicated database; For the genetic algorithm, initialize core parameters: set the population size to 60-80 individuals (each individual corresponds to a set of stacking scheme codes to be optimized), crossover probability 0.75, mutation probability 0.08, maximum number of iterations 80 generations, and the coding rule adopts a four-segment structure of priority segment - cargo ID - initial posture - layer coding to ensure that high-priority cargo occupies priority in coding and avoids subsequent iterations from destroying the core priority rules; For the improved 3D packing algorithm, preset a local adaptation constraint library, including rules such as placing fragile items upright, attaching heavy cargo to the bottom center of the container, and grouping cargo with the same destination, while setting buffer space thresholds (5cm for fragile items and 2cm for general cargo).

[0034] 2. Global optimization phase of genetic algorithm: This stage focuses on the iterative optimization of the "macro-level stacking logic," with the core being the selection, crossover, and mutation operations of a genetic algorithm to screen out a stacking scheme framework that meets multiple objectives. Fitness Function Design: A multi-dimensional weighted fitness function is constructed, with the formula: Fitness Value = 0.4 × Space Utilization Rate + 0.35 × Unpacking Convenience - 0.25 × Center of Gravity Shift Risk Value. Where, Space Utilization Rate = Σ Actual Cargo Volume / Usable Container Volume; Unpacking Convenience is quantified based on the concentration of goods of the same destination / customs clearance category and the ease of handling high-priority goods (1-10 points); the Center of Gravity Shift Risk Value is calculated by converting the pre-calculated center of gravity coordinates with the container center offset distance, ensuring that the overall solution balances space and stability.

[0035] Selection operation: A combination of roulette wheel selection and elite retention strategy is adopted to retain the top 10% of "elite individuals" in each generation to avoid losing the optimal solution. The remaining individuals are randomly selected according to their fitness proportion to ensure population diversity.

[0036] Crossover operation: Perform single-point crossover on non-elite individuals. The crossover point is strictly selected at the "boundary of the same priority cargo group". For example, retain the "priority 1 cargo segment" of individual A and the same segment code of individual B, and only exchange priority 2 and lower code segments to ensure that the stacking priority of high priority cargo is not destroyed.

[0037] Mutation operation: Perform a small probability mutation (0.08) on the "cargo placement posture" and "layer area allocation" in the code. After mutation, the constraint library needs to be verified (e.g., fragile items will still be in an upright posture after mutation) to avoid violating the core protection rules and prevent the algorithm from getting stuck in local optima.

[0038] 3. Local adaptation stage of the improved 3D bin packing algorithm

[0039] For each generation of optimal individuals (the stacking scheme framework) output by the genetic algorithm, the improved 3D bin packing algorithm is responsible for "microscopic precise adaptation," translating the macroscopic framework into executable stacking details: Cargo posture iterative selection: For each cargo, simulate the space occupation of both horizontal and vertical postures, calculate the space fit (the smaller the space occupied and the more reasonable the gap with surrounding cargo, the higher the fit), and select the optimal posture; fragile and moisture-proof cargo directly locks the vertical posture and automatically reserves a preset buffer space.

[0040] Precise stacking planning: Based on cargo weight and priority, stacking areas are allocated: high priority / heavy cargo is allocated to the 0-1.2m stable area on the lower layer of the container, and the X and Y coordinates of heavy cargo are forced to be close to the bottom center; fragile items and moisture-proof goods are separated into independent layers, with an 8cm protective isolation zone set up between them and ordinary goods; goods with the same destination and the same customs clearance category are grouped into adjacent areas to form dedicated storage units.

[0041] Storage unit size adaptation: Calculate the adaptation dimensions (length × width × height) of each storage unit to ensure that the unit fits seamlessly with the container space, while reserving 10cm of sorting and handling space to meet cross-border customs clearance and unpacking requirements.

[0042] 4. Collaborative Iteration and Termination Correction

[0043] The model employs a "global-local" closed-loop iteration: after each selection-crossover-mutation cycle of the genetic algorithm, an improved 3D packing algorithm is immediately invoked to complete local adaptation, updating the individual's actual space utilization, unpacking convenience, and other indicators, and recalculating the fitness. The iteration terminates when the fitness improvement is less than 0.5% for 10 consecutive iterations, or when the maximum number of iterations (80) is reached. The final optimal individual is then constrained (total weight ≤ maximum container load, center of gravity offset ≤ warning threshold). If these constraints are not met, only the position / orientation of low-priority goods is adjusted, ensuring that the constraints of high-priority goods remain unchanged. This process ultimately generates an optimal stacking scheme that includes stacking order, placement orientation, and layering method.

[0044] Specifically, the optimal stacking scheme includes stacking order, placement posture, and layering method, specifically including: Stacking order: Strictly follow the priority list, with high-priority goods stacked first; goods of the same priority are clustered according to destination distance and sorted according to customs clearance category relevance; Placement orientation: The cargo is iteratively screened for horizontal and vertical orientations to select the optimal space utilization method to maximize container space utilization; fragile and moisture-proof special properties are placed vertically with buffer space reserved. Stacking method: High-priority goods and heavy goods are placed in the stable area at the bottom of the container, with heavy goods close to the bottom center to ensure overall stability; fragile items and moisture-proof special goods are separated into separate layers and isolated from ordinary goods; goods with the same destination and customs clearance category are grouped together in adjacent areas to form dedicated storage units, which can meet the needs of cross-border customs clearance and distribution.

[0045] Specifically, the storage units are individually numbered according to the container number-layer number-location number rule, and the storage unit number is bound to the corresponding cargo information and updated synchronously to the cross-border LCL dedicated database.

[0046] Specifically, step 4, which involves calculating the container's center of gravity coordinates in real time, includes: Based on a three-dimensional coordinate system established with the bottom left corner of the container's interior as the origin, the core logic is to superimpose all cargo and container accessories to obtain the overall three-dimensional coordinates of the center of gravity (X0, Y0, Z0). X0=(Σm i X i +ΣM j X j ) / (gross weight); Y0=(Σm i Y i +ΣM j Y j ) / (gross weight); Z0=(Σm i Z i +ΣM j Z j ) / (gross weight); Where m i For the weight of a single item, (X) i Y i Z i M represents the coordinates of the center of gravity of a single cargo. j Weight of auxiliary components (X) j Y j Z j ( ) represents the coordinates of the center of gravity of the auxiliary component; A preset center of gravity offset threshold is set, which is adjusted according to container specifications and transportation methods; at the same time, an early warning threshold is set to trigger a center of gravity monitoring warning in advance. When the detected center of gravity offset exceeds the preset threshold, a dynamic adjustment mechanism is activated to prioritize the protection needs of high-priority goods.

[0047] In one specific embodiment of the present invention, the center of gravity coordinates are calculated in real time every 0.5 seconds (the response time of the matching center of gravity algorithm). The discrete coordinate data points with timestamps are continuously recorded in a time sequence. When changes in weight or coordinates occur due to adjustments in cargo placement or optimization of storage units, the corresponding time node data is immediately updated. The discrete points are then fitted into a smooth curve using an algorithm, visually presenting the trend of center of gravity changes over time and in the stacking state, providing support for early warning and adjustment. A preset center of gravity offset threshold is used to adapt to cross-border long-distance transportation. The default threshold is ±5cm, which can be flexibly adjusted according to container specifications (20 feet / 40 feet) and transportation methods (sea / land). Simultaneously, an early warning threshold is set (when the offset reaches 80% of the threshold), triggering an early warning for center of gravity monitoring. When a center of gravity offset exceeds a preset threshold, a dynamic adjustment mechanism is activated to prioritize the protection of high-priority cargo. The adjustment strategies include: first, fine-tuning the stacking position of high-priority cargo by moving excessively heavy cargo towards the center of the container or supplementing adjacent storage units with lighter cargo to balance its weight; second, optimizing the stacking order by moving excessively heavy high-priority cargo from the upper layer to a lower, vacant, stable area, and simultaneously replacing lighter cargo from the lower layer with cargo on the upper layer; and third, optimizing the distribution of storage units by adjusting partition positions, merging or splitting adjacent storage units, and replanning the cargo placement area to balance the overall weight distribution. After each adjustment, the center of gravity coordinates are immediately recalculated using the above algorithm until the offset is below the threshold. Simultaneously, the binding information between the storage unit number and the cargo is updated. If storage unit splitting / merging is involved, the coding and database records are updated synchronously to ensure consistency between storage unit information, cargo stacking status, and center of gravity data, thus ensuring the structural stability of cross-border long-distance transportation and reducing the risk of cargo damage.

[0048] Furthermore, in a certain cross-border transportation scenario, goods include expedited fragile items (glassware, ordinary customs clearance, fragile level 5), general daily necessities (ordinary customs clearance, priority level 3), and fresh produce (high timeliness requirements, special customs clearance). After collecting all data in step 1, step 2 adjusts the weights (timeliness 30%, fragileness 25%) to generate a priority: fresh produce > glassware > daily necessities; step 3 generates a plan where fresh produce and glassware are stored in separate units, and goods destined for the same destination are stacked together; the center of gravity is checked in real time, and overweight glassware is adjusted towards the center, the partition position is optimized to ensure compliance with the center of gravity, and the cargo location information is linked throughout the process for easy cross-border traceability.

[0049] Specifically, a cross-border e-commerce intelligent consolidation optimization system includes: Data acquisition module: used to acquire basic environmental data of goods and containers; Priority module: Based on the basic environmental data of goods and containers, an adaptive weight model is established to determine the weight dimensions, and the goods are quantitatively scored to generate a priority list; Cargo stacking module: Generates the optimal stacking plan based on the priority list, designs storage units based on the optimal stacking plan, and assigns numbers to the storage units and binds them to cargo information; Cargo optimization module: Based on the cargo information associated with the storage unit number, calculate the center of gravity coordinates of the container in real time; preset center of gravity offset threshold, when the offset exceeds the threshold, automatically adjust the stacking method of high-priority cargo and optimize the storage unit.

[0050] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0051] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing intelligent consolidation in cross-border e-commerce, characterized in that, include: Step 1: Obtain basic environmental data for cargo and containers; Step 2: Establish an adaptive weight model based on the basic environmental data of goods and containers, determine the weight dimensions, quantify and score the goods, and generate a priority list; Step 3: Generate the optimal stacking scheme based on the priority list, design storage units based on the optimal stacking scheme, number the storage units and bind them to cargo information; Step 4: Calculate the container's center of gravity coordinates in real time based on the cargo information associated with the storage unit number; preset a center of gravity offset threshold, and automatically adjust the stacking method of high-priority cargo and optimize the storage unit when the offset exceeds the threshold.

2. The intelligent consolidation optimization method for cross-border e-commerce according to claim 1, characterized in that, Step 1 involves scanning the goods using a depth camera array and a high-definition industrial camera to collect information, including external dimensions, actual volume, appearance, and packaging integrity data; obtaining the weight of the goods using a piezoelectric weight sensor integrated into the conveyor line; reading the order information of the goods using a QR code scanner; scanning the interior of the container to generate a three-dimensional usable space model, clarifying the usable dimensions and maximum load capacity of the container, and eliminating unusable space in the container; and performing outlier verification on all collected data using the 3σ principle, storing the data in a dedicated cross-border LCL database after successful verification.

3. The intelligent consolidation optimization method for cross-border e-commerce according to claim 2, characterized in that, The weighting dimensions for step 2 include fragility, transportation timeliness, destination customs clearance difficulty, order priority, and cargo volume and weight ratio; the weight coefficients for each dimension can be dynamically adjusted according to the cross-border scenario; each dimension is standardized and quantitatively scored using the following formula: Overall score = Σ quantified value of each dimension × corresponding weight coefficient; A priority list is generated based on scores from highest to lowest, with high-priority goods receiving priority in terms of storage safety and customs clearance convenience.

4. The intelligent consolidation optimization method for cross-border e-commerce according to claim 3, characterized in that, Step 3 adopts an improved three-dimensional packing algorithm and a genetic algorithm fusion model. The model adopts a global optimization and local adaptation collaborative mechanism. Based on the priority list, cargo information and container three-dimensional available space model, it automatically iteratively calculates the optimal stacking scheme. Among them, the genetic algorithm is used for global optimization, aiming to maximize space utilization and optimize the convenience of cross-border unpacking, and iteratively optimizes the stacking order and layering logic; the improved 3D packing algorithm is used for local adaptation, accurately calculating the cargo placement posture and storage unit adaptation size to generate the optimal stacking scheme.

5. The intelligent consolidation optimization method for cross-border e-commerce according to claim 4, characterized in that, The optimal stacking scheme includes stacking order, placement posture, and layering method, specifically including: Stacking order: Strictly follow the priority list, with high-priority goods stacked first; goods of the same priority are clustered according to destination distance and sorted according to customs clearance category relevance; Placement orientation: The cargo is iteratively screened for horizontal and vertical orientations to select the optimal space utilization method to maximize container space utilization; fragile and moisture-proof special properties are placed vertically with buffer space reserved. Stacking method: High-priority goods and heavy goods are placed in the stable area at the bottom of the container, with heavy goods close to the bottom center to ensure overall stability; fragile items and moisture-proof special goods are separated into separate layers and isolated from ordinary goods; goods with the same destination and customs clearance category are grouped together in adjacent areas to form dedicated storage units, which can meet the needs of cross-border customs clearance and distribution.

6. The intelligent consolidation optimization method for cross-border e-commerce according to claim 5, characterized in that, The storage units are individually numbered according to the container number-layer number-location number rule, and the storage unit number is also bound to the corresponding cargo information and updated synchronously to the cross-border LCL dedicated database.

7. The intelligent consolidation optimization method for cross-border e-commerce according to claim 1, characterized in that, Step 4, which involves real-time calculation of the container's center of gravity coordinates, includes: Based on a three-dimensional coordinate system established with the bottom left corner of the container's interior as the origin, the core logic is to superimpose all cargo and container accessories to obtain the overall three-dimensional coordinates of the center of gravity (X0, Y0, Z0). X0=(Σm i X i +ΣM j X j ) / (gross weight); Y0=(Σm i Y i +ΣM j Y j ) / (gross weight); Z0=(Σm i Z i +ΣM j Z j ) / (gross weight); Where m i For the weight of a single item, (X) i Y i Z i M represents the coordinates of the center of gravity of a single cargo. j Weight of auxiliary components (X) j Y j Z j ( ) represents the coordinates of the center of gravity of the auxiliary component; A preset center of gravity offset threshold is set, which is adjusted according to container specifications and transportation methods; at the same time, an early warning threshold is set to trigger a center of gravity monitoring warning in advance. When the detected center of gravity offset exceeds the preset threshold, a dynamic adjustment mechanism is activated to prioritize the protection needs of high-priority goods.

8. A cross-border e-commerce intelligent consolidation optimization system, characterized in that, include: Data acquisition module: used to acquire basic environmental data of goods and containers; Priority module: Based on the basic environmental data of goods and containers, an adaptive weight model is established to determine the weight dimensions, and the goods are quantitatively scored to generate a priority list; Cargo stacking module: Generates the optimal stacking plan based on the priority list, designs storage units based on the optimal stacking plan, and assigns numbers to the storage units and binds them to cargo information; Cargo optimization module: Based on the cargo information associated with the storage unit number, calculate the center of gravity coordinates of the container in real time; preset center of gravity offset threshold, when the offset exceeds the threshold, automatically adjust the stacking method of high-priority cargo and optimize the storage unit.

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