Multi-vehicle cooperation-oriented loading scheme generation method
By using an automated and intelligent method to generate loading plans, the problem of low efficiency in multi-vehicle collaborative loading plans has been solved, achieving efficient and safe multi-objective optimization and improving space utilization and transportation efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-04
- Publication Date
- 2026-03-10
AI Technical Summary
In existing technologies, multi-vehicle collaborative loading solutions rely on human experience, resulting in low planning efficiency and difficulty in achieving the minimum number of vehicles, the highest space utilization, the optimal transportation route, and the most stable center of gravity. In particular, it is difficult to accurately calculate the spatial combination for irregularly shaped goods, which poses safety hazards.
An automated and intelligent loading scheme generation method is adopted. By generating multiple loading sequences and candidate schemes, and combining the spatial integration and center of gravity adjustment of virtual cargo and carriages, the loading scheme is optimized, and the optimal scheme is selected by algorithm scoring.
It enables the automated generation of loading plans, improves planning speed and decision-making quality, reduces vehicle frequency, increases space utilization, reduces transportation costs, and ensures transportation safety.
Smart Images
Figure CN121639059A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of cargo loading, and in particular to a loading scheme generation method for multi-vehicle cooperation. BACKGROUND
[0002] In the current logistics transportation field, especially in the transportation of large cargo and multi-batch cargo, the planning of the loading scheme is mostly dependent on manual experience. Although the operator needs to consider multiple factors such as cargo weight, volume, destination priority, grouping constraints, etc., to coordinate multi-vehicle cooperative loading, this manual planning method has inherent drawbacks: it is highly dependent on individual experience, the decision-making quality is unstable, and it is difficult to quickly achieve overall balanced optimization of multiple objectives such as the least number of vehicles, the highest space utilization, the optimal transportation path, and the most stable center of gravity, resulting in low planning efficiency and high transportation costs. In addition, for irregularly shaped cargo, manual planning cannot accurately calculate the spatial combination and arrangement, which not only makes it difficult to further improve the loading efficiency, but also may cause stability and safety hazards during transportation due to unstable center of gravity.
[0003] Therefore, in view of the defects of low efficiency, difficulty in handling complex multi-objective optimization and irregular cargo in the prior art, there is an urgent need in the field for a method that can automatically and intelligently generate an overall optimal multi-vehicle cooperative loading scheme that can dynamically adapt to changes. SUMMARY
[0004] In view of the shortcomings of the prior art, the purpose of the present application is to provide a loading scheme generation method for multi-vehicle cooperation to solve one or more problems in the prior art.
[0005] To achieve the above-mentioned purpose, the technical solution of the present application is as follows: A loading scheme generation method for multi-vehicle cooperation, comprising the following steps: Obtaining cargo information of all cargo to be transported and vehicle information of available vehicles, the cargo information including cargo type, cargo size, and cargo weight; Generating a plurality of different loading sequences of the cargo to be transported according to the cargo information; Generating a corresponding candidate loading scheme for each loading sequence according to the cargo information and the vehicle information; Scoring all candidate loading schemes and selecting the highest scoring candidate loading scheme as the output scheme; Wherein, generating a corresponding candidate loading scheme comprises the following steps: Selecting a vehicle from the available vehicles and establishing a corresponding virtual vehicle compartment according to the corresponding vehicle information; Generating a virtual cargo according to the cargo information of the cargo to be transported; placing the corresponding virtual goods into the virtual vehicle in sequence according to the loading sequence, After placing each virtual good in the virtual vehicle, it is determined whether the virtual good exceeds the boundary of the virtual vehicle. If so, the posture of the virtual good is adjusted, and it is again determined whether the virtual good exceeds the boundary of the virtual vehicle. If still exceeds, the next virtual good is placed according to the loading sequence, and the virtual good is skipped. If the virtual good does not exceed the virtual vehicle in any judgment, the virtual good and the adjacent virtual good are spatially integrated, and the placement of the next virtual good is completed after spatial integration. If there are still virtual goods that have not been placed in the current virtual vehicle after traversing the entire loading sequence, another vehicle is selected from the available vehicles, a corresponding virtual vehicle is established, and the remaining virtual goods are placed in the newly established virtual vehicle in sequence. Until all virtual goods are allocated to the corresponding virtual vehicle.
[0006] Further, the generating of the plurality of different loading sequences of the to-be-transported goods according to the goods information comprises the following steps: Classifying the to-be-transported goods according to the goods type to form a plurality of goods clusters; and sorting the plurality of goods clusters according to a preset order; The same to-be-transported goods in each goods cluster are sorted according to a plurality of different preset sorting methods to form a plurality of basic sorting; Each basic sorting is disturbed according to a preset disturbance condition to form a disturbed sorting; All basic sortings and disturbed sortings are used as loading sequences.
[0007] Further, the spatial integration of the virtual good and the adjacent virtual good comprises the following steps: It is determined whether the goods type of the adjacent virtual good is the same as the goods type of the current virtual good. If so, it is further determined whether the goods size of the adjacent virtual good is lower than a preset threshold. If so, the virtual good and the adjacent virtual good are merged into a new virtual good, and new goods information is generated according to the corresponding goods information.
[0008] Further, after all virtual goods are allocated to the corresponding virtual vehicle, the following steps are further included: The center of gravity position of each virtual vehicle is determined; The offset between the center of gravity position and the center position of the virtual vehicle is calculated; It is determined whether the offset exceeds a threshold, If so, the center of gravity of the virtual good in the corresponding virtual vehicle is adjusted.
[0009] Further, the center of gravity adjustment of the virtual good in the virtual vehicle comprises the following steps: The virtual cargo corresponding to the virtual carriages is sorted in descending order of weight to obtain a weight sequence; Starting from the center of the virtual carriage, the corresponding virtual goods are placed alternately on both sides of the starting point according to their weight sequence.
[0010] Furthermore, the scoring of all candidate loading schemes includes the following steps: For each candidate loading scheme, the corresponding center of gravity offset, number of vehicles, and volume ratio are determined, where the volume ratio is the ratio of the total volume of the virtual cargo to the total volume of the virtual carriage. A comprehensive score is calculated based on the center of gravity offset, the number of vehicles, and the floor area ratio.
[0011] Furthermore, the process of generating virtual cargo based on the cargo information of the goods to be transported includes the following steps: Determine whether the goods are classified as regular goods. If so, then the dimensions of the virtual goods will be based on the corresponding dimensions of the goods. If not, the height in the corresponding cargo size is used as the height of the virtual cargo, and the smallest rectangle containing the maximum cross-section in the cargo size is generated, and the smallest rectangle is used as the cross-section of the virtual cargo.
[0012] Compared with the prior art, the beneficial technical effects of the present invention are as follows: The system achieves automated and intelligent generation of loading plans, resulting in an exponential increase in planning speed compared to tedious and time-consuming manual CAD drawing and experience-based estimation. Furthermore, through multi-rule exploration and algorithm optimization, the decision-making quality is stable and significantly higher than that relying on personal experience.
[0013] By optimizing multiple objectives, the frequency of vehicles required to complete transportation tasks is directly reduced, and the transportation cost per unit of goods is reduced through efficient use of space.
[0014] By employing a rational and specialized layout method for handling irregularly shaped goods, the space utilization of the vehicle is maximized, allowing it to carry more goods at the same cost. Attached Figure Description
[0015] Figure 1 The flowchart shown is a method for generating vehicle loading schemes for multi-vehicle collaboration provided by the present invention. Detailed Implementation
[0016] A method for generating vehicle loading schemes for multi-vehicle collaboration, see [link / reference]. Figure 1 This includes the following steps: S100: Obtain cargo information for all goods to be transported and vehicle information for available vehicles.
[0017] Cargo information includes cargo type, dimensions, weight, and corresponding business constraints. Business constraints include loading / unloading priority and product grouping requirements. Product grouping requirements stipulate that certain goods must be grouped together in the same truck and cannot be scattered. If the customer does not specify additional conditions such as loading / unloading priority or product grouping requirements, the business constraints information will be empty, but other cargo information must still be obtained.
[0018] Vehicle information includes load capacity, volume, and floor dimensions.
[0019] In this embodiment, the cargo types include packaged iron cores, individual iron cores, iron core pillars, and yellow plates.
[0020] S200: Generate multiple different loading sequences for goods to be transported based on the cargo information.
[0021] The loading sequence refers to the order in which all goods to be transported are arranged according to preset rules. In subsequent steps, the goods to be transported will be loaded onto the vehicle in the order of the loading sequence.
[0022] There are multiple sorting methods in the preset rules, which can generate multiple different loading sequences.
[0023] S300 generates corresponding candidate loading schemes for each loading sequence based on cargo information and vehicle information.
[0024] Due to the limitations of vehicle volume and size, it is difficult to load the transported goods exactly according to the loading sequence. Therefore, candidate loading schemes will be generated based on the cargo information and vehicle information, while adhering to the loading sequence as much as possible.
[0025] The candidate loading scheme includes the number of vehicles to be called, the vehicle information for each vehicle, the cargo information for each vehicle, and the placement and orientation of the cargo in the corresponding vehicle.
[0026] S400: Score all candidate loading schemes and select the candidate loading scheme with the highest score as the output scheme.
[0027] Different loading sequences often correspond to different candidate loading schemes. Each candidate loading scheme is scored using a preset scoring method, and the candidate loading scheme with the highest score is selected as the optimal scheme. When outputting the optimal scheme, a graphics engine is invoked to generate a 3D loading layout diagram that can be used to guide on-site operations based on information such as the number of vehicles, the placement and orientation of goods in the optimal scheme, and a detailed loading plan is also output at the same time.
[0028] In one embodiment, generating multiple different loading sequences of goods to be transported based on cargo information includes the following steps: S210. Classify the goods to be transported according to their type to form multiple goods clusters, and sort the multiple goods clusters in a preset order.
[0029] In this embodiment, four cargo clusters can be generated according to the cargo type: packaged iron core cluster, individual iron core cluster, iron core column cluster, and yellow plate cluster. The four cargo clusters are also arranged in the order of packaged iron core cluster, individual iron core cluster, iron core column cluster, and yellow plate cluster.
[0030] S220. In each cargo cluster, the same type of cargo to be transported is sorted according to multiple different preset sorting methods to form multiple basic sortings.
[0031] The preset sorting methods are formulated based on various rules applied in practice. Common sorting methods are as follows: Sorting 1: Prioritize sorting by loading and unloading priority from high to low, and then group goods with the same priority according to different production lines; Sorting 2: After sorting by cargo size from large to small, select alternating large and small items from both ends of the sequence for sorting; Sorting 3: After sorting by weight in descending order, start weighing from the head of the sequence, and when the sum of the weights of several consecutive cargoes approaches the preset vehicle load limit, extract several lightweight cargoes from the tail of the sequence and add them in.
[0032] S230. Perturb each basic sort according to the preset perturbation conditions to form a perturbed sort.
[0033] There are multiple preset perturbation conditions, including randomly swapping the order of adjacent goods in the same goods cluster and randomly swapping the order of two goods in different goods clusters. For the same basic sort, one or more perturbations can be performed, and each perturbation can be different, thus deriving a large number of perturbed sorts based on a few basic sorts.
[0034] S240, use all basic sorts and perturbation sorts as loading sequences.
[0035] By using both the basic sorting of the normal sorting and the perturbed sorting after perturbation as loading sequences, the number of available solutions is increased, thereby increasing the probability of the theoretically optimal solution appearing.
[0036] In one embodiment, generating a corresponding candidate loading scheme includes the following steps: S310. Select a vehicle from the available vehicles and create a corresponding virtual carriage based on the vehicle information.
[0037] The selection of vehicles should be based on meeting the cargo loading requirements. For example, overweight or oversized cargo requires specific vehicles, and then the vehicles should be selected from the lowest to the highest transportation cost.
[0038] The virtual carriage is a virtual 3D space, built one-to-one with the vehicle dimensions in the vehicle information.
[0039] S320. Generate virtual cargo based on the cargo information of the goods to be transported.
[0040] Virtual cargo is placed in virtual carriages to simulate the placement of goods to be transported in actual vehicle carriages, thereby increasing the generation speed while ensuring the feasibility of the solution.
[0041] S330. Place the corresponding virtual goods into the virtual carriages in sequence according to the loading sequence.
[0042] When placing virtual goods in the loading sequence, the lower left corner of the virtual carriage is used as the loading reference point, and each virtual goods is placed in the most feasible position in the available space of the virtual carriage that is closest to the reference point.
[0043] To make the explanation clearer, a three-dimensional coordinate system is established for the carriage, with the length of the carriage as the X-axis, the width as the Y-axis, and the height as the Z-axis. The lower left corner of the virtual carriage is the origin. Assume that the first virtual cargo is also a cuboid, with one corner located at the lower left corner of the carriage, and its long side placed against the wide side of the carriage. Due to the space occupied by this virtual cargo, the remaining available space in the virtual carriage can be divided into the first space from the first virtual cargo to the long side of the carriage, the second space from the first virtual cargo to the wide side of the carriage, and the third space from the first virtual cargo to the top of the carriage. Since the length of the first virtual cargo is greater than its height, and the height is greater than its width, the lower left corner of the second space is closest to the reference point, while the lower left corner of the first space is farthest from the reference point. Therefore, the next virtual cargo is preferentially placed in the second space.
[0044] S340. Each time a virtual cargo is placed in a virtual carriage, determine whether the virtual cargo exceeds the boundary of the virtual carriage. If so, adjust the posture of the virtual cargo and determine again whether the virtual cargo exceeds the boundary of the virtual carriage. If it still exceeds the boundary, skip the virtual cargo and place the next virtual cargo according to the loading sequence.
[0045] Since the cargo to be loaded has a similar structure, being either cuboid or approximately cuboid, and has relatively low degrees of freedom, the virtual cargo is placed in two fixed positions within the virtual carriage. If the first placement position exceeds the carriage boundary, the system switches to the second placement position and tries again. If placement still fails, it indicates that the corresponding cargo cannot be loaded into the current carriage, and the cargo is temporarily shelved while subsequent cargo is scheduled for loading into the current carriage.
[0046] S350 If the virtual cargo does not exceed the virtual carriage in any judgment, then the virtual cargo and the adjacent virtual cargo are spatially integrated, and the next virtual cargo is placed after the spatial integration is completed.
[0047] After the first virtual cargo is successfully placed into the virtual carriage, all subsequent virtual cargo placed into the virtual carriage needs to be spatially integrated with adjacent virtual cargo in order to reduce the space occupied in the carriage and provide conditions for the carriage to accommodate larger or more cargo.
[0048] S360. If there are still virtual goods that have not been placed in the current virtual car after traversing the entire loading sequence, select another vehicle from the available vehicles, create the corresponding virtual vehicle, and repeat the process of placing the remaining virtual goods into the newly created virtual car in sequence until all virtual goods have been allocated to the corresponding virtual car.
[0049] The loading method between the remaining virtual cargo and the new virtual carriage in the loading sequence is still carried out in the order of steps S330 to S350.
[0050] In one embodiment, generating virtual cargo based on cargo information of the goods to be transported includes the following steps: S321. Determine whether the goods are regular goods.
[0051] S322. If so, then the size of the virtual goods shall be based on the corresponding goods size.
[0052] S323. If not, then the height in the corresponding cargo size is used as the height of the virtual cargo, and the smallest rectangle containing the maximum cross-section in the cargo size is generated, and the smallest rectangle is used as the cross-section of the virtual cargo.
[0053] Regular goods refer to goods whose cross-sectional dimensions are similar or identical at all points along the height direction, and whose top view is rectangular. In this embodiment, since the cross-sectional dimensions of various types of goods are similar at all points along the height direction, it is only necessary to determine whether they are regular goods based on their top views. Packaging cores and yellow plates are regular goods, while individual cores and core columns are not. Specifically, the top view of an individual core is "I"-shaped, and the top view of a core column is an isosceles trapezoid.
[0054] In one embodiment, spatial integration of the virtual cargo and adjacent virtual cargo includes the following steps: S351. Determine whether the cargo type of the adjacent virtual cargo is the same as the cargo type of the current virtual cargo.
[0055] S352. If they are the same, then further determine whether the size of the adjacent virtual goods is lower than the preset threshold.
[0056] S353. If the size of an adjacent virtual cargo is lower than a preset threshold, then the virtual cargo and the adjacent virtual cargo are merged into a new virtual cargo, and new cargo information is generated based on the corresponding cargo information.
[0057] Combining two virtual goods into a new virtual goods involves packing the two virtual goods together in their original, unpackaged state to form a larger package. This merging not only reduces the overall space required but also decreases the use of auxiliary packaging materials and strengthens the loading structure.
[0058] The merged new cargo information also needs to meet cargo constraints, such as size and weight constraints. If these constraints cannot be met, the merge will be rolled back.
[0059] Combining similar types of goods is only one way to integrate space. In addition to combining similar goods, space occupancy can also be reduced by adjusting the spatial structure. Spatial structure adjustment, under the premise of meeting spatial constraints, optimizes the relative positions of irregular goods such as sliding or aligning yellow plates or individual iron cores, so as to reduce ineffective space and enhance the neatness and stability of the layout.
[0060] In one embodiment, after all virtual goods have been allocated to their respective virtual carriages, the following steps are also included: S370. Determine the center of gravity position of each virtual carriage and calculate the offset between the center of gravity position and the center position of the virtual carriage.
[0061] S380. Determine if the offset exceeds the threshold.
[0062] S390. If the load exceeds the limit, adjust the center of gravity of the virtual cargo in the corresponding virtual carriage.
[0063] The threshold is preset by staff. When the offset exceeds the threshold, the vehicle is prone to tipping over during transportation due to instability of the center of gravity. Therefore, it is necessary to adjust the center of gravity to eliminate the potential risk.
[0064] In one embodiment, adjusting the center of gravity of virtual cargo in a virtual carriage includes the following steps: S391. Sort the virtual cargo corresponding to the virtual carriages in descending order of weight to obtain a weight sequence.
[0065] S392. Starting from the center of the virtual carriage, place the corresponding virtual goods alternately on both sides of the starting point according to the weight sequence.
[0066] By using the above methods, the total weight of the goods located on both sides of the center of the carriage is made as close as possible to meet the center of gravity requirements.
[0067] If, after step S392, the center of gravity of the virtual carriage still deviates from the center position by more than a threshold, then a virtual carriage that fails to meet the center of gravity requirement is selected. The goods in that virtual carriage are then selected sequentially, and each item is attempted to swap positions with all the goods in the remaining virtual carriages that fail to meet the center of gravity requirement. If a position swap is achieved while meeting the size requirements, the center of gravity of each virtual carriage is checked. Once a virtual carriage meets the center of gravity requirement, it is removed from the process, and the remaining virtual carriages that fail to meet the center of gravity requirement are processed repeatedly. If, after traversing all virtual carriages, there are still virtual carriages that fail to meet the center of gravity requirement, the current loading plan is retained, and the process proceeds to the next step.
[0068] In one embodiment, scoring all candidate loading schemes includes the following steps: S410. Determine the corresponding center of gravity offset, number of vehicles, and volume ratio for each candidate loading scheme.
[0069] S420. A comprehensive score is calculated based on the center of gravity offset, number of vehicles, and floor area ratio.
[0070] The floor area ratio is the ratio of the total volume of virtual cargo to the total volume of virtual carriages.
[0071] The vehicle utilization rate is calculated based on the number of vehicles. The vehicle utilization rate is the quotient of the actual number of vehicles in use divided by the total number of available vehicles.
[0072] The deviation rate is calculated based on the center of gravity offset. The deviation rate is the difference between (1 - center of gravity offset / threshold).
[0073] The overall score is the sum of (L*vehicle utilization rate + M*floor ratio + N*deviation rate).
[0074] L, M, and N are all weights, and the sum of L, M, and N is 1, with L greater than M and M greater than N. This is because increasing the number of vehicles incurs the greatest cost, and underutilization of vehicle space also leads to wasted capacity. Therefore, the weight corresponding to vehicle utilization rate is the largest, and the weight corresponding to deviation rate is the smallest. In this embodiment, L is 0.7, M is 0.2, and N is 0.1.
[0075] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0076] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.
Claims
1. A method for generating a loading scheme for multi-vehicle cooperation, characterized in that, The method comprises the following steps: Obtaining cargo information of all goods to be transported and vehicle information of available vehicles, the cargo information comprising cargo type, cargo size, and cargo weight; Generating a plurality of different loading sequences of the goods to be transported according to the cargo information; Generating a corresponding candidate loading scheme for each loading sequence according to the cargo information and the vehicle information; Scoring all candidate loading schemes and selecting the candidate loading scheme with the highest score as the output scheme; The generation of the corresponding candidate loading scheme comprises the following steps: Selecting a vehicle from the available vehicles and establishing a corresponding virtual vehicle compartment according to the corresponding vehicle information; Generating virtual goods according to the cargo information of the goods to be transported; Placing the corresponding virtual goods into the virtual vehicle compartment in sequence according to the loading sequence, Judging whether the virtual goods exceed the boundary of the virtual vehicle compartment after placing each virtual good in the virtual vehicle compartment, if so, adjusting the posture of the virtual goods, and judging again whether the virtual goods exceed the boundary of the virtual vehicle compartment, if still exceeding, skipping the virtual goods and placing the next virtual goods according to the loading sequence; If the virtual goods do not exceed the virtual vehicle compartment in any judgment, integrating the virtual goods and adjacent virtual goods in space, and placing the next virtual goods after completing the space integration; If there are still virtual goods not placed in the current virtual vehicle compartment after traversing the entire loading sequence, selecting another vehicle from the available vehicles, establishing a corresponding virtual vehicle, and repeatedly placing the remaining virtual goods into the newly established virtual vehicle compartment in sequence until all virtual goods are allocated to the corresponding virtual vehicle compartment.
2. The method of claim 1, wherein: The generation of a plurality of different loading sequences of the goods to be transported according to the cargo information comprises the following steps: Classifying the goods to be transported according to the cargo type to form a plurality of cargo clusters; and sorting the plurality of cargo clusters in a preset order; Sorting the same type of goods to be transported in each cargo cluster according to a plurality of different preset sorting methods to form a plurality of basic sorting; Disturbing each basic sorting according to a preset disturbance condition to form a disturbed sorting; Taking all basic sorting and disturbed sorting as loading sequences.
3. The method of claim 1, wherein, The space integration of the virtual goods and adjacent virtual goods comprises the following steps: Judging whether the cargo type of the adjacent virtual goods is the same as that of the current virtual goods, if so, further judging whether the cargo size of the adjacent virtual goods is lower than a preset threshold, if so, merging the virtual goods and adjacent virtual goods into new virtual goods, and generating new cargo information according to the corresponding cargo information.
4. The method of claim 1, wherein, After all virtual goods are allocated to the corresponding virtual vehicle compartment, the method further comprises the following steps: Determining the center of gravity position of each virtual vehicle compartment; Calculating the offset between the center of gravity position and the center position of the virtual vehicle compartment; Judging whether the offset exceeds a threshold, If so, adjusting the center of gravity of the virtual goods in the corresponding virtual vehicle compartment.
5. The method of claim 4, wherein, The center of gravity adjustment of the virtual goods in the virtual vehicle compartment comprises the following steps: Sorting the virtual goods corresponding to the virtual vehicle compartment in descending order of weight to obtain a weight sequence; The center of the virtual vehicle cabin is taken as a starting point, and corresponding virtual goods are alternately placed on both sides of the starting point in sequence according to the weight sequence.
6. The method of claim 1, wherein, The scoring of all candidate loading schemes comprises the following steps: The center of gravity offset, the number of vehicles and the volume rate are determined according to each candidate loading scheme, wherein the volume rate is the ratio of the total volume of the virtual goods to the total volume of the virtual vehicle cabin; The comprehensive score is calculated according to the center of gravity offset, the number of vehicles and the volume rate.
7. The method of claim 1, wherein, The virtual goods are generated according to the goods information of the goods to be transported, comprising the following steps: It is judged whether the goods type is a regular goods type, If yes, the corresponding goods size is taken as the size of the virtual goods, If not, the height in the corresponding goods size is taken as the height of the virtual goods, and the minimum rectangle containing the maximum cross section in the goods size is generated according to the maximum cross section, and the minimum rectangle is taken as the cross section of the virtual goods.