Container model construction method based on three-dimensional software

Abstract

The invention relates to the technical field of container models, in particular to a container model construction method based on three-dimensional software. The method comprises the following steps: obtaining point cloud data of a container through three-dimensional laser scanning, carrying out feature subdivision to generate a container feature set, carrying out space positioning on features to obtain a vertex coordinate set, carrying out geometric constraint modeling based on the vertex coordinate set, forming a container skeleton model, obtaining a target cargo image, extracting a basic contour, and carrying out feature extraction; the method comprises the following steps: carrying out three-dimensional reconstruction to generate a cargo model, carrying out space planning on a container skeleton based on the model, calculating gravity center distribution to generate a cargo balance parameter, then carrying out interference simulation and conflict detection to obtain interference conflict data, positioning a conflict focus area, and finally carrying out variant design on the container skeleton according to the conflict area. And generating an adjusted container model. According to the invention, a more comprehensive and more accurate container model construction method is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of container models, and in particular to a container model construction method based on three-dimensional software. Background Art

[0002] Traditional container loading methods often rely on manual experience and lack scientific planning and design, resulting in low space utilization and increased risk of cargo damage during loading. In container loading, due to the diversity of cargo shapes, sizes and weights, how to effectively plan space to ensure the stability and safety of cargo has become an urgent problem to be solved. The main problem in container loading design is the lack of efficient three-dimensional modeling and simulation tools, which makes it impossible to evaluate the rationality of loading plans in real time. Many existing methods rely on two-dimensional drawings or simple three-dimensional models, which cannot fully consider the actual form of the cargo and the complexity of the loading environment. In addition, traditional methods often rely on manual inspection or simple collision detection algorithms when performing interference detection and conflict analysis, which makes it difficult to fully and accurately identify potential interference areas, which further increases the risk in the cargo loading process. Summary of the invention

[0003] Based on this, it is necessary to provide a container model construction method based on three-dimensional software to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a container model construction method based on three-dimensional software includes the following steps:

[0005] Step S1: perform three-dimensional laser scanning on the container to obtain container point cloud data; perform feature segmentation on the container point cloud data to generate a container feature collection;

[0006] Step S2: spatially locate the feature points of the container feature set to obtain a vertex coordinate set; perform geometric constraint modeling based on the vertex coordinate set to generate a container skeleton model;

[0007] Step S3: acquiring a target cargo image; extracting a basic contour of the target cargo image to generate a cargo basic contour image; and performing three-dimensional reconstruction of the cargo basic contour image to obtain a reconstructed cargo model;

[0008] Step S4: performing space planning processing on the container skeleton model based on the reconstructed cargo model to obtain an initial cargo planning model; performing gravity center distribution calculation on the initial cargo planning model to generate cargo balance parameters;

[0009] Step S5: performing interference simulation on the initial cargo planning model based on the cargo balance parameters to obtain simulated cargo interference data; performing conflict detection on the simulated cargo interference data to generate interference conflict data;

[0010] Step S6: locate the conflict focus of the interference conflict data to obtain the conflict focus area; perform variant design on the container skeleton model according to the conflict focus area to generate an adjusted container model.

[0011] The present invention ensures the acquisition of high-precision point cloud data by implementing 3D laser scanning on the container. Through the feature segmentation of the point cloud data, the generated container feature collection can fully reflect the geometric shape and structural features of the container. The implementation of spatial positioning of feature points on the container feature collection ensures the accurate positioning of each feature point. The generated vertex coordinate set provides necessary data support for 3D modeling. The operation of geometric constraint modeling based on the vertex coordinate set can generate a container skeleton model. The implementation of acquiring the target cargo image and performing basic contour extraction ensures that the shape features of the cargo can be accurately captured. The generated cargo basic contour image provides necessary visual information for subsequent 3D reconstruction. Through the 3D reconstruction of the contour image, the reconstructed cargo model obtained can truly reflect the spatial occupancy of the cargo and provide accurate data support for container loading. The implementation of spatial planning processing of the container skeleton model based on the reconstructed cargo model ensures that it can efficiently Arrange the layout of the cargo in the container. The generated initial cargo planning model provides a basis for the subsequent balance analysis. The results of the center of gravity distribution calculation can quantify the balance parameters of the cargo. The implementation of interference simulation based on the cargo balance parameters can identify potential conflicts between cargoes and between cargoes and containers. The generated simulated cargo interference data provides detailed information support for subsequent conflict detection. The interference conflict data generated by conflict detection can efficiently identify problems in the loading process, ensure that the plan can be adjusted in time in actual operation, optimize the loading layout of cargo, and implement conflict focus positioning of the interference conflict data to ensure that the specific conflict area can be identified, which provides an important basis for the subsequent container skeleton model variant design. The model adjustment based on the conflict focus area can effectively avoid interference problems during loading. The generated adjusted container model can better adapt to actual loading needs, and ultimately improve the utilization efficiency of containers and the safety of cargo transportation.

[0012] Preferably, step S1 comprises the following steps:

[0013] Step S11: scanning the container at multiple angles to obtain original container point cloud data; performing noise filtering on the original container point cloud data to generate filtered point cloud data;

[0014] Step S12: performing coordinate conversion processing on the filtered point cloud data to generate container point cloud data;

[0015] Step S13: performing geometric feature point recognition on the container point cloud data to obtain a container feature point set; performing topological relationship analysis on the container feature point set to obtain a topological relationship of the feature points;

[0016] Step S14: Connecting the feature points of the container feature point set based on the topological relationship of the feature points to obtain a feature point connection graph; performing geometric feature analysis on the feature point connection graph to generate a container feature collection.

[0017] The present invention can fully capture the three-dimensional shape of the container by scanning it from multiple angles, remove invalid point data by noise filtering, and improve the accuracy and quality of point cloud data. Through coordinate conversion processing, the container point cloud data can unify the coordinate system and eliminate the coordinate deviation caused by different scanning devices or environmental changes. The geometric feature point recognition can accurately extract the key geometric features of the container, and the topological relationship analysis reveals the spatial association between the feature points. Through the connection of feature points and the analysis of geometric features, a detailed feature map of the container can be formed, which can accurately describe the geometric structure of the container, ensure the accuracy and operability of the three-dimensional model, and improve the efficiency and quality of model construction.

[0018] Preferably, step S2 comprises the following steps:

[0019] Step S21: performing spatial coordinate mapping on the container feature set to obtain a three-dimensional coordinate point set; performing vertex clustering analysis on the three-dimensional coordinate point set to generate a candidate vertex set;

[0020] Step S22: performing precise positioning calculation on the candidate vertex set according to the three-dimensional coordinate point set to obtain a vertex coordinate set;

[0021] Step S23: geometrically extend the vertex coordinate set to generate surface area relationship data; perform geometric constraint analysis on the surface area relationship data to generate geometric constraint data;

[0022] Step S24: Establish a framework structure based on the geometric constraint data to obtain an initial skeleton model; fill in nodes of the initial skeleton model according to the face region relationship data to generate a container skeleton model.

[0023] The present invention ensures that the feature points are effectively converted into three-dimensional coordinate point sets by implementing spatial coordinate mapping on the container feature set. The generated three-dimensional coordinate point set provides basic data for subsequent analysis and modeling. By performing vertex clustering analysis on the three-dimensional coordinate point set, the obtained candidate vertex set can efficiently identify important geometric features. The implementation of precise positioning calculation of the candidate vertex set according to the three-dimensional coordinate point set can ensure the accurate coordinates of each candidate vertex. The obtained vertex coordinate set provides accurate data support for geometric modeling. The result of precise positioning can reduce the error in the subsequent modeling process and improve the overall accuracy of the model. The implementation of geometric relationship extension of the vertex coordinate set ensures that the spatial relationship between the vertices can be fully analyzed. The generated surface domain relationship data provides necessary information support for subsequent geometric constraint analysis. By performing geometric constraint analysis on the surface domain relationship data, the generated geometric constraint data can clarify the constraint relationship between each feature point. The implementation of establishing a framework structure based on the geometric constraint data can effectively construct an initial skeleton model of the container, ensuring that the model can truly reflect the geometric features of the container. By performing node connection and filling operations on the initial skeleton model according to the surface domain relationship data, the generated container skeleton model is more complete and has practical application value, and the structural strength and stability of the model are improved.

[0024] Preferably, step S23 includes the following steps:

[0025] Performing adjacency analysis on the vertex coordinate point set to obtain a point pair relationship set; performing edge connection processing on the point pair relationship set to generate edge data;

[0026] Perform parallelism detection on edge data to obtain parallel edge data; perform verticality detection on parallel edge data to generate orthogonal edge data set;

[0027] Construct face element relationships based on orthogonal edge data sets to generate face domain relationship data;

[0028] Extract dimension parameters from the surface domain relationship data to obtain dimension constraint data;

[0029] Performing angle relationship analysis on the size constraint data to generate angle constraint data; performing symmetry calculation on the angle constraint data to obtain symmetric constraint data;

[0030] The size constraint data, angle constraint data and symmetry constraint data are integrated to generate geometric constraint data.

[0031] The present invention ensures that the correlation between each vertex can be identified by implementing adjacency relationship analysis on the vertex coordinate point set, and the generated point pair relationship set provides a basis for subsequent edge generation. The implementation of edge connection processing can efficiently construct edge data connecting each vertex, ensuring the geometric structure integrity of the model. By performing parallelism detection on the edge data, the obtained parallel edge data provides an important basis for subsequent geometric analysis. The orthogonal edge data set generated by the implementation of verticality detection lays a foundation for the construction of face element relationship. The implementation of face element relationship construction based on the orthogonal edge data set can accurately form face domain relationship data, ensuring that the geometric features of the model are clearly reflected. The implementation of dimensional parameter extraction ensures that the key dimensional information of the face domain can be obtained. The generated dimensional constraint data provides important support for subsequent geometric constraint analysis. The angle constraint data generated by the implementation of angle relationship analysis can quantify the model. The implementation of constraint integration of dimensional constraint data, angle constraint data and symmetric constraint data can integrate multiple constraint conditions. The generated geometric constraint data provides a comprehensive constraint basis for the construction of the container model, ensuring the accuracy and rationality of the model in the subsequent modeling process, and improving the structural quality and application effect of the container model.

[0032] Preferably, step S3 comprises the following steps:

[0033] Step S31: photographing the target goods at multiple angles to obtain images of the target goods; performing goods edge detection on the target goods images to obtain edge data sets;

[0034] Step S32: performing contour segmentation processing on the edge data set to obtain a line segment data set; performing basic contour cutting on the target cargo image based on the line segment data set to generate a cargo basic contour image;

[0035] Step S33: performing stereo matching on the cargo basic outline image to obtain a cargo disparity atlas; performing depth calculation on the cargo disparity atlas to generate a depth data set;

[0036] Step S34: performing point cloud conversion on the depth data set to generate a space point set; performing surface surface fitting on the space point set to generate a complete surface set;

[0037] Step S35: reconstructing the three-dimensional model of the complete surface set to obtain a reconstructed cargo model.

[0038] The present invention ensures that multi-dimensional information of the goods can be captured by implementing multi-angle shooting of the target goods, and the generated target goods image provides rich visual data for subsequent processing. By performing goods edge detection on the target goods image, the edge data set obtained can clearly reflect the shape contour of the goods. The implementation of contour segmentation processing on the edge data set ensures that complex edge information can be simplified into a manageable segment data set. The generated segment data set provides a clear reference for basic contour cutting. The basic contour image of the goods generated by implementing basic contour cutting of the target goods image based on the segment data set can effectively extract the shape features of the goods. The implementation of stereo matching on the basic contour image of the goods can obtain the disparity information of the goods. The generated cargo disparity atlas is used for subsequent depth calculation. It provides necessary data support. By performing depth calculation on the cargo disparity atlas, the obtained depth data set can reflect the actual position of the cargo in space. The implementation of point cloud conversion on the depth data set ensures that the depth information can be converted into a spatial point set. The generated spatial point set provides basic data for subsequent surface fitting. The complete surface set generated by performing surface surface fitting on the spatial point set can effectively describe the three-dimensional form of the cargo and improve the smoothness and realism of the model. The implementation of three-dimensional model reconstruction on the complete surface set can integrate all processing results into a complete three-dimensional cargo model. The generated reconstructed cargo model truly reproduces the appearance and structure of the cargo, provides an accurate three-dimensional view for subsequent container loading planning, and ensures the reasonable layout and safety of the cargo in the container.

[0039] Preferably, step S4 comprises the following steps:

[0040] Step S41: voxelizing the container skeleton model to obtain a spatial grid set; marking the available space of the spatial grid set to generate loading partition data;

[0041] Step S42: Calculate the size of the reconstructed cargo model to generate cargo parameters; sort the loading order based on the cargo parameters to generate a cargo loading sequence;

[0042] Step S43: Perform spatial matching planning on the cargo loading sequence based on the loading partition data to generate an initial cargo planning model;

[0043] Step S44: locating the centroid of the initial cargo planning model to obtain cargo centroid data; performing moment calculation on the cargo centroid data to generate a moment data set;

[0044] Step S45: Perform stability analysis on the moment data set to generate cargo balance parameters.

[0045] The present invention ensures that the model can be converted into a spatial grid set by voxelizing the container skeleton model. The generated spatial grid set provides a basic unit for subsequent loading analysis. By marking the available space of the spatial grid set, the generated loading partition data can clearly divide the available space in the container and optimize the loading layout of the cargo. The implementation of size calculation of the reconstructed cargo model ensures that the detailed parameters of the cargo can be obtained. The generated cargo parameters provide necessary information for the subsequent loading sequence sorting. The implementation of loading sequence sorting based on cargo parameters can effectively optimize the loading sequence of the cargo and make the loading sequence reasonable. The implementation of spatial matching planning of the cargo loading sequence based on the loading partition data is The implementation ensures that the position of the goods in the container can be reasonably arranged. The generated initial cargo planning model reflects the best fit between the goods and the available space. The implementation of centroid positioning of the initial cargo planning model can accurately identify the center of gravity of each piece of cargo. The obtained cargo centroid data provides a basis for subsequent moment calculation. The implementation of moment calculation can quantify the force conditions of the cargo in the container. The implementation of stability analysis of the moment data set can comprehensively evaluate the balance state of the cargo in the container. The generated cargo balance parameters provide a scientific basis for optimizing the loading plan. Through stability analysis, the safety and stability of the cargo during transportation are ensured, the risk of cargo damage is reduced, and the safety and reliability of the entire transportation process are improved.

[0046] Preferably, step S43 includes the following steps:

[0047] Calculate the available volume of the loaded partition data to generate partition available volume data; perform boundary restriction mapping based on the partition available volume data to generate partition boundary restriction data;

[0048] Performing connectivity analysis on the loaded partition data to obtain partition connectivity data; performing partition carrying capacity evaluation on the loaded partition data according to the partition connectivity data to obtain the partition carrying capacity;

[0049] Based on the partition carrying capacity and partition boundary restriction data, the adaptation capacity is determined to generate a regional adaptation capacity value;

[0050] Carry out load demand calculation for cargo loading sequence to obtain cargo load demand data;

[0051] Spatial matching planning is carried out according to the regional adaptation capacity value and cargo carrying demand data to generate an initial cargo planning model.

[0052] The present invention ensures that the available space in each partition can be accurately identified by implementing the available volume calculation for the loading partition data. The generated partition available volume data provides key data for subsequent space planning. By performing boundary restriction mapping based on the partition available volume data, the generated partition boundary restriction data can clearly define the boundary of the loading area. The implementation of connectivity analysis for the loading partition data ensures that the connection status between each partition can be evaluated. The obtained partition connectivity data provides an important basis for the subsequent carrying capacity evaluation. By implementing the partition carrying capacity evaluation for the loading partition data, the amount of cargo that each partition can carry can be quantified to ensure that it does not exceed the load capacity during the actual loading process. The implementation of adaptation capacity judgment based on partition carrying capacity and partition boundary restriction data can evaluate the adaptability of each partition to different goods. The generated regional adaptation capacity value provides a scientific basis for subsequent loading decisions. By implementing the carrying demand calculation of the cargo loading sequence, the cargo carrying demand data obtained can clarify the space required for each piece of cargo. The implementation of space matching planning based on the regional adaptation capacity value and cargo carrying demand data ensures that the position of the cargo in the container can be reasonably arranged. The generated initial cargo planning model reflects the best adaptation of the cargo to the available space, improves loading efficiency, reduces potential loading problems, and provides a scientific and reasonable basis for subsequent transportation.

[0053] Preferably, step S5 comprises the following steps:

[0054] Step S51: Calculate the distance between adjacent goods in the initial goods planning model to obtain adjacent goods distance parameters; perform translation contact simulation according to the adjacent goods distance parameters to generate a simulated contact area;

[0055] Step S52: performing contact pressure distribution analysis based on cargo balance parameters to generate pressure distribution data;

[0056] Step S53: Cargo interference simulation is performed on the simulated contact area according to the pressure distribution data to obtain simulated cargo interference data;

[0057] Step S54: performing pressure threshold mapping based on the simulated contact area and pressure distribution data to generate a cargo interference pressure threshold;

[0058] Step S55: Detect conflict points on the simulated cargo interference data according to the cargo interference pressure threshold to generate interference conflict data.

[0059] The present invention ensures that the relative positions between goods can be quantified by implementing the adjacent goods distance calculation on the initial goods planning model, and the obtained adjacent goods distance parameters provide necessary data support for subsequent contact simulation. The simulated contact area generated by implementing the translation contact simulation according to the adjacent goods distance parameters can reflect the contact situation between goods. The implementation of the contact pressure distribution analysis based on the cargo balance parameters can deeply evaluate the pressure transmission situation between goods. The generated pressure distribution data provides a basis for the identification of contact defects. Through the detailed analysis of the pressure distribution data, it is ensured that the potential compression area can be accurately identified, and the effectiveness of the model is enhanced. The implementation of the cargo interference simulation on the simulated contact area according to the pressure distribution data can simulate the interaction between goods. The obtained simulated cargo interference data reflects the interference situation of goods in actual loading. The implementation of the pressure threshold mapping based on the simulated contact area and the pressure distribution data can quantify the pressure limit of the contact area. The generated cargo interference pressure threshold provides an important basis for subsequent conflict detection. The implementation of the conflict point detection on the simulated cargo interference data according to the cargo interference pressure threshold can accurately identify the interference point between goods. The generated interference conflict data provides detailed information for subsequent loading adjustment, ensuring that the cargo arrangement can be optimized in time, preventing potential loading problems, and improving the overall transportation efficiency and safety.

[0060] Preferably, step S53 includes the following steps:

[0061] According to the pressure distribution data, the pressure concentration area of ​​the simulated contact area is located to obtain the key contact point;

[0062] Identify the direction of force on key contact points and generate pressure transmission direction data; detect the pressure value of key contact points and obtain contact point pressure data;

[0063] Cargo contact simulation is performed on key contact points based on pressure transmission direction data and contact point pressure data to generate simulated cargo contact data;

[0064] Perform local deformation analysis on the simulated cargo contact data to obtain a local deformation data set; define the deformation propagation range of the local deformation data set to generate a deformation propagation range;

[0065] Perform relative displacement analysis based on the local deformation data set and deformation propagation range to generate cargo displacement;

[0066] Cargo interference mapping is performed based on the cargo displacement to obtain simulated cargo interference data.

[0067] The present invention ensures that key contact points can be identified by locating the pressure concentration area of ​​the simulated contact area according to the pressure distribution data. The obtained key contact points provide a basis for the subsequent pressure transmission analysis. The pressure transmission direction data generated by the implementation of force direction identification on the key contact points can clearly reflect the pressure transmission path. The implementation of pressure value detection on the key contact points can quantify the actual pressure of each contact point. The obtained contact point pressure data provides necessary information for the subsequent contact simulation. The simulated cargo contact data generated by the implementation of cargo contact simulation on the key contact points based on the pressure transmission direction data and the contact point pressure data can reflect the actual contact situation. The implementation of local deformation analysis ensures that the deformation of the contact area can be identified. The obtained local deformation data set provides a necessary basis for the subsequent deformation propagation analysis. The deformation propagation range generated by defining the deformation propagation range of the local deformation data set can accurately reflect the impact area of ​​the deformation. The implementation of relative displacement analysis based on the local deformation data set and the deformation propagation range can quantify the relative displacement between goods. The generated cargo displacement provides an important basis for the subsequent interference mapping. The simulated cargo interference data obtained by implementing cargo interference mapping based on the cargo displacement can reflect the interference between goods in detail, provide scientific guidance for actual loading optimization, and improve the safety and efficiency of the transportation process.

[0068] Preferably, step S6 comprises the following steps:

[0069] Step S61: locating the conflict points of the initial cargo planning model according to the interference conflict data to generate the conflict point coordinates; calculating the point cluster concentration of the conflict point coordinates to generate the conflict point concentration degree;

[0070] Step S62: locating the conflict focus based on the concentration of conflict points to obtain the conflict focus area;

[0071] Step S63: performing region mapping on the container skeleton model according to the conflict focus area to generate a container model conflict area;

[0072] Step S64: performing container restriction analysis on the conflict area of ​​the container model based on the interference conflict data to obtain container restriction factors;

[0073] Step S65: performing variant optimization on the container skeleton model based on the container restriction factors to generate an adjusted container model.

[0074] The present invention implements the location of conflict points of the initial cargo planning model through interference conflict data to ensure that all conflict points can be accurately identified, and the generated conflict point coordinates provide an important basis for subsequent analysis. The concentration of conflict points generated by implementing point cluster concentration calculation on the conflict point coordinates can quantify the severity of the conflict. The implementation of conflict focus location based on the concentration of conflict points can effectively focus on the most critical conflict area, and the obtained conflict focus area provides a clear goal for subsequent optimization design. The implementation of regional mapping of the container skeleton model according to the conflict focus area can directly reflect the identified conflict area in the container model, and the generated container model conflict area provides intuitive data support for subsequent analysis. The implementation of container restriction analysis of the container model conflict area based on the interference conflict data can comprehensively evaluate various restriction factors affecting loading, and the obtained container restriction factors provide an important basis for subsequent optimization. The implementation of variant optimization of the container skeleton model based on the container restriction factors ensures that effective adjustments can be made to the identified restrictions, and the generated adjusted container model can better adapt to actual loading requirements, improve the overall design efficiency of the container, reduce potential loading problems, and ensure the safety and efficiency of the transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of the steps of a container model construction method based on three-dimensional software;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0077] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0078] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0079] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3 , a container model construction method based on three-dimensional software, comprising the following steps:

[0083] Step S1: perform three-dimensional laser scanning on the container to obtain container point cloud data; perform feature segmentation on the container point cloud data to generate a container feature collection;

[0084] Step S2: spatially locate the feature points of the container feature set to obtain a vertex coordinate set; perform geometric constraint modeling based on the vertex coordinate set to generate a container skeleton model;

[0085] Step S3: acquiring a target cargo image; extracting a basic contour of the target cargo image to generate a cargo basic contour image; and performing three-dimensional reconstruction of the cargo basic contour image to obtain a reconstructed cargo model;

[0086] Step S4: performing space planning processing on the container skeleton model based on the reconstructed cargo model to obtain an initial cargo planning model; performing gravity center distribution calculation on the initial cargo planning model to generate cargo balance parameters;

[0087] Step S5: performing interference simulation on the initial cargo planning model based on the cargo balance parameters to obtain simulated cargo interference data; performing conflict detection on the simulated cargo interference data to generate interference conflict data;

[0088] Step S6: locate the conflict focus of the interference conflict data to obtain the conflict focus area; perform variant design on the container skeleton model according to the conflict focus area to generate an adjusted container model.

[0089] The present invention ensures the acquisition of high-precision point cloud data by implementing 3D laser scanning on the container. Through the feature segmentation of the point cloud data, the generated container feature collection can fully reflect the geometric shape and structural features of the container. The implementation of spatial positioning of feature points on the container feature collection ensures the accurate positioning of each feature point. The generated vertex coordinate set provides necessary data support for 3D modeling. The operation of geometric constraint modeling based on the vertex coordinate set can generate a container skeleton model. The implementation of acquiring the target cargo image and performing basic contour extraction ensures that the shape features of the cargo can be accurately captured. The generated cargo basic contour image provides necessary visual information for subsequent 3D reconstruction. Through the 3D reconstruction of the contour image, the reconstructed cargo model obtained can truly reflect the spatial occupancy of the cargo and provide accurate data support for container loading. The implementation of spatial planning processing of the container skeleton model based on the reconstructed cargo model ensures that it can efficiently Arrange the layout of the cargo in the container. The generated initial cargo planning model provides a basis for the subsequent balance analysis. The results of the center of gravity distribution calculation can quantify the balance parameters of the cargo. The implementation of interference simulation based on the cargo balance parameters can identify potential conflicts between cargoes and between cargoes and containers. The generated simulated cargo interference data provides detailed information support for subsequent conflict detection. The interference conflict data generated by conflict detection can efficiently identify problems in the loading process, ensure that the plan can be adjusted in time in actual operation, optimize the loading layout of cargo, and implement conflict focus positioning of the interference conflict data to ensure that the specific conflict area can be identified, which provides an important basis for the subsequent container skeleton model variant design. The model adjustment based on the conflict focus area can effectively avoid interference problems during loading. The generated adjusted container model can better adapt to actual loading needs, and ultimately improve the utilization efficiency of containers and the safety of cargo transportation.

[0090] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of a container model construction method based on three-dimensional software of the present invention. In this example, the container model construction method based on three-dimensional software includes the following steps:

[0091] Step S1: perform three-dimensional laser scanning on the container to obtain container point cloud data; perform feature segmentation on the container point cloud data to generate a container feature collection;

[0092] In this embodiment, a 3D laser scanner (such as Leica RTC360) is used to perform an all-round scan on the outside of the container. The scanning angle covers all surfaces of the container to ensure the integrity of the scanned data. The scanning result generates a high-density point cloud data. Each point in the point cloud data contains the coordinate information of the container surface. The point cloud data is then preprocessed, including removing noise points, filling blank areas, and smoothing. A point cloud filtering algorithm (such as a Voxel Grid filter) is used to reduce redundant points to obtain more accurate container point cloud data. Subsequently, a feature segmentation algorithm (such as a plane fitting method based on a RANSAC algorithm) is applied to feature segment the container point cloud data, identify geometric features related to the container shape, such as faces, edges, corners, etc., and form a feature collection of the container. These feature collections contain geometric information of the container shape and provide basic data for subsequent model construction.

[0093] Step S2: spatially locate the feature points of the container feature set to obtain a vertex coordinate set; perform geometric constraint modeling based on the vertex coordinate set to generate a container skeleton model;

[0094] In this embodiment, the feature collection of the container is input into a geometric modeling tool (such as Autodesk Revit). By spatially locating the feature points, the key vertices in the container structure are identified to obtain a vertex coordinate set. On this basis, geometric constraint modeling technology is applied to the vertex coordinate set, such as by defining boundary conditions and constraint relationships and using the least squares method to optimize the vertex coordinates to eliminate errors and ensure the accuracy of the coordinate data, thereby generating a container skeleton model. The skeleton model provides an accurate geometric morphology basis for subsequent cargo layout and interference simulation, and ensures that there is no deformation and geometric error during the modeling process.

[0095] Step S3: acquiring a target cargo image; extracting a basic contour of the target cargo image to generate a cargo basic contour image; and performing three-dimensional reconstruction of the cargo basic contour image to obtain a reconstructed cargo model;

[0096] In this embodiment, a high-resolution digital camera (such as Canon EOS 5D Mark IV) is used to capture multi-angle images of the target cargo. The images should include the front, side and top of the cargo to ensure that the image data is sufficient. Then, an edge detection algorithm (such as Canny edge detection) is used to extract the basic contour of the target cargo image to obtain a basic contour image of the cargo. The contour image includes the appearance information of the cargo. Subsequently, the basic contour image of the cargo is three-dimensionally reconstructed. Structured light scanning technology or stereo vision technology (such as by using an Intel RealSense depth camera) is used to convert the two-dimensional image data into a three-dimensional model. After data fusion and optimization, an accurate reconstructed cargo model is obtained.

[0097] Step S4: performing space planning processing on the container skeleton model based on the reconstructed cargo model to obtain an initial cargo planning model; performing gravity center distribution calculation on the initial cargo planning model to generate cargo balance parameters;

[0098] In this embodiment, based on the reconstructed cargo model, a space planning algorithm (such as a space search method based on an A* algorithm) is used to perform space planning processing on the container skeleton model to ensure the matching of the cargo with the internal structure of the container, and to generate an initial cargo planning model. Next, a mass distribution calculation method (such as a central force method) is used to calculate the center of gravity distribution of the cargo planning model to obtain the center of gravity position of the cargo, and further calculate the balance parameters of the cargo, including the center of gravity offset and stability index of the cargo. These balance parameters provide the necessary basis for subsequent cargo interference simulation and adjustment.

[0099] Step S5: performing interference simulation on the initial cargo planning model based on the cargo balance parameters to obtain simulated cargo interference data; performing conflict detection on the simulated cargo interference data to generate interference conflict data;

[0100] In this embodiment, based on the cargo balance parameters, the initial cargo planning model is simulated for interference, and the interference detection module in the computer-aided design (CAD) software (such as SolidWorks) is used to simulate the interference between the cargo and the inner wall of the container and other cargoes to obtain simulated cargo interference data. The interference data records the spatial relationship and interference area between the cargo and the container structure. Then, the simulated cargo interference data is analyzed through a conflict detection algorithm (such as a collision detection algorithm based on volume calculation) to identify and generate interference conflict data. These conflict data provide specific optimization directions for the next step of adjusting the container model.

[0101] Step S6: locate the conflict focus of the interference conflict data to obtain the conflict focus area; perform variant design on the container skeleton model according to the conflict focus area to generate an adjusted container model.

[0102] In this embodiment, according to the interference conflict data, by analyzing the spatial distribution of the conflict focus area, a conflict minimization optimization algorithm (such as an optimization method based on simulated annealing) is used to locate the focus, and the specific area inside the container where the interference exists is accurately identified. Then, a variant design of the container skeleton model is performed based on the conflict focus area. The parametric modeling method is used in the design (such as parametric design through a Grasshopper plug-in), the skeleton structure of the container is adjusted, an adjusted container model is generated, and the size or shape of the container is adjusted to ensure that the goods can be reasonably placed in the container to avoid interference or damage, and finally an optimized container model that meets the requirements of cargo balance and space utilization is obtained.

[0103] Preferably, step S1 comprises the following steps:

[0104] Step S11: scanning the container at multiple angles to obtain original container point cloud data; performing noise filtering on the original container point cloud data to generate filtered point cloud data;

[0105] Step S12: performing coordinate conversion processing on the filtered point cloud data to generate container point cloud data;

[0106] Step S13: performing geometric feature point recognition on the container point cloud data to obtain a container feature point set; performing topological relationship analysis on the container feature point set to obtain a topological relationship of the feature points;

[0107] Step S14: Connecting the feature points of the container feature point set based on the topological relationship of the feature points to obtain a feature point connection graph; performing geometric feature analysis on the feature point connection graph to generate a container feature collection.

[0108] In this embodiment, the container is scanned at multiple angles using a 3D laser scanner (such as Leica RTC360). During the scanning process, data is collected from different angles of the container. The scanned data includes all point cloud data on the surface of the container. The point cloud density is set to about 2,000 points per square meter to ensure the meticulousness of the data. After the raw scan data is generated, point cloud processing software (such as CloudCompare) is used to filter noise. The Statistical Outlier Removal Algorithm (Statistical Outlier Removal) is used to remove the outliers. The point cloud data is denoised by SOR (Sor Removal) to remove abnormal points far away from the main point cloud cluster. The filtered point cloud data will contain the main geometric information of the container surface. The number of points in the generated filtered point cloud data is reduced by about 10% to 15%. The filtered point cloud data is input into a coordinate conversion tool (such as AutoCAD or PDMS) for coordinate system conversion. The original point cloud data of the container is converted from the local coordinate system of the scanning device to the world coordinate system. The conversion uses a coordinate matching algorithm based on the least squares method. By comparing the known geographic coordinate points, the registration is performed to ensure that the point cloud data after coordinate conversion is consistent with the actual coordinates. After the coordinate system of the container is converted, the container point cloud data is generated with a conversion accuracy of 0.5 mm. The container point cloud data is used to identify geometric feature points using a geometric feature recognition algorithm (such as the RANSAC algorithm). First, the plane, straight line and arc features of the container surface are extracted by setting parameters, with special attention to the edges, corners and face points of the container. By setting the point cloud density threshold, the feature point set of the container surface is identified, such as the four corner points on the top and each side. The midpoint, bottom intersection and other key points of the container are identified. Then, the topological relationship analysis is performed on the identified feature point set. The connectivity analysis method (such as the topological analysis algorithm based on the neighborhood relationship) is used to analyze the relative position and connection relationship between points to obtain the topological relationship of the feature points. On the basis of the container feature point set and the topological relationship, the feature points are connected using a graphical modeling tool (such as the Grasshopper plug-in of Rhino). Based on the topological relationship, the feature points are connected in sequence according to the spatial position and geometric relationship to generate a feature point connection graph of the container. Each node in the graph represents a feature point, and each edge represents the connection relationship between adjacent feature points. After generation, the feature point connection graph is further geometrically analyzed using a geometric analysis method (such as geometric analysis based on least squares fitting). The geometric shape and symmetry in the feature point connection graph are analyzed to finally form a container feature collection. The container feature collection contains all the key geometric features of the container, covering the outline, surface, edge and spatial topological structure of the container, providing a detailed geometric description for the modeling and subsequent analysis of the container.

[0109] Preferably, step S2 comprises the following steps:

[0110] Step S21: performing spatial coordinate mapping on the container feature set to obtain a three-dimensional coordinate point set; performing vertex clustering analysis on the three-dimensional coordinate point set to generate a candidate vertex set;

[0111] Step S22: performing precise positioning calculation on the candidate vertex set according to the three-dimensional coordinate point set to obtain a vertex coordinate set;

[0112] Step S23: geometrically extend the vertex coordinate set to generate surface area relationship data; perform geometric constraint analysis on the surface area relationship data to generate geometric constraint data;

[0113] Step S24: Establish a framework structure based on the geometric constraint data to obtain an initial skeleton model; fill in nodes of the initial skeleton model according to the face region relationship data to generate a container skeleton model.

[0114] In this embodiment, a 3D laser scanning device (such as Leica RTC360) is used to scan the container at multiple angles to obtain point cloud data of the container. The point cloud data contains all geometric feature information of the exterior of the container. Then, point cloud processing software (such as CloudCompare) is used to pre-process the original point cloud data, remove noise points, and filter algorithms (such as Voxel Grid filtering) is applied to the data to reduce redundant points and enhance the clarity of the data. The processed point cloud data is used as input for spatial coordinate mapping. The coordinate transformation method is used to convert the point cloud data into a unified three-dimensional spatial coordinate system to generate a three-dimensional coordinate point set. Then, the three-dimensional coordinate point set is clustered and analyzed based on the K-means clustering algorithm to identify the main feature points related to the geometric shape of the container and obtain the candidate vertex set. The candidate vertex set is positioned and calculated using the precise positioning algorithm based on the least squares method (LeastSquares) to ensure the accuracy of the vertex position. First, the relative position and distance in the three-dimensional coordinate point set are calculated to optimize the preliminary positioning of the candidate vertex set, reduce the positioning error to a minimum, and ensure that the position of each vertex is highly matched with the actual shape of the container. The spatial position of the vertex is further corrected using the geometric constraint method (such as the rigid body transformation method) to obtain an accurate vertex coordinate set. For the vertex coordinate set after precise positioning, the topological method is used to expand the geometric relationship. Based on the relative position between the vertices, the various face domains that constitute the surface and skeleton of the container are identified. Connectivity analysis and topological structure construction technology (such as using Graph Theory method) establishes the relationship between face domains and generates face domain relationship data. Then, geometric constraint analysis is applied to the face domain relationship data. By analyzing the constraint relationships such as angles and distances between different face domains, the geometric constraint conditions that affect the stability and variability of the container skeleton structure are identified and geometric constraint data are generated. Based on the geometric constraint data, a parametric modeling method (such as the parametric modeling tool in Grasshopper) is used to establish the container frame structure. The frame structure first generates an initial skeleton model based on the geometric constraint data. The initial skeleton model includes the basic structural skeleton of the container, the nodes and boundaries of the connecting frame, and the relative positions and shape constraints between the nodes are calculated to ensure that the geometric characteristics of the container skeleton meet the container design requirements. Then, the nodes of the initial skeleton model are connected and filled according to the face domain relationship data. The meshing technology is used during filling to ensure that the connection between each node is accurate. The generated container skeleton model has a complete geometric structure and is suitable for the subsequent cargo layout and space optimization design.

[0115] Preferably, step S23 includes the following steps:

[0116] Performing adjacency analysis on the vertex coordinate point set to obtain a point pair relationship set; performing edge connection processing on the point pair relationship set to generate edge data;

[0117] Perform parallelism detection on edge data to obtain parallel edge data; perform verticality detection on parallel edge data to generate orthogonal edge data set;

[0118] Construct face element relationships based on orthogonal edge data sets to generate face domain relationship data;

[0119] Extract dimension parameters from the surface domain relationship data to obtain dimension constraint data;

[0120] Performing angle relationship analysis on the size constraint data to generate angle constraint data; performing symmetry calculation on the angle constraint data to obtain symmetric constraint data;

[0121] The size constraint data, angle constraint data and symmetry constraint data are integrated to generate geometric constraint data.

[0122] In this embodiment, the vertex coordinate set after precise positioning is input, and the Euclidean distance of each pair of vertices is calculated. The adjacency matrix method is used to analyze which vertices have a direct geometric relationship. Whether the point pair is adjacent is determined based on the distance threshold. The threshold is set to 5 mm. If the distance between the vertices is less than the threshold, they are considered to be adjacent in space. A point pair relationship set is generated, which contains information about all adjacent vertex pairs and represents the connection relationship between the container surface and the skeleton structure. By traversing the point pair relationship set, a straight line fitting algorithm (such as the least squares method) is used to connect adjacent vertex pairs to generate edge data. Each edge is defined by two vertices. The edge data contains the boundary information of the container skeleton. The length of each edge and its direction in space are further analyzed to construct the spatial coordinates of the edge, and these edge lines are compared with the vertex data. According to the combination, to ensure the rationality of each edge in the physical space, by calculating the angle between the edge lines, the vector operation method is used to calculate the angle value of each pair of edge lines. If the angle is less than 5°, the two edge lines are considered to be parallel edges. The parallelism detection algorithm is used to compare all edge lines, and the edges that meet the parallelism conditions are extracted to generate parallel edge data. The data represents the spatial relationship of parallel edges in the container structure. By calculating the angle of each pair of edge lines in the parallel edge data set, if the angle is 90°, the two edge lines are considered to be vertical. The orthogonal detection algorithm is used to calculate the verticality of all edge pairs, extract all vertical edge pairs, generate an orthogonal edge data set, and determine which orthogonal edge pairs are by analyzing the orthogonal edge data set. The surface elements (surface areas) of the container surface are constructed based on the connection relationship of the edge lines and a polygon construction algorithm (such as a quadrilateral or triangle mesh generation algorithm) is used. In the process of constructing the surface elements, the edge lines are used as boundary conditions to ensure the planarity and angle accuracy of the surface elements in space. The generated surface area relationship data records the geometric relationship between the faces in the container structure. The size parameters (such as length, width, height, diagonal length, etc.) of each surface area are extracted from the surface area relationship data. The boundary of each surface area is measured using a geometric size calculation method to obtain accurate size data. The size parameters include the side length and angle of each surface area. The size constraint data is generated by calculating the side length and angle extracted from the size constraint data. The angle between each two face domains is further analyzed, and the angle between adjacent face domains is calculated. The angle constraint data is analyzed by applying geometric calculation methods (such as the cosine theorem) to obtain the angle constraint data. The angle constraint data ensures that the angles between the faces in the container model remain consistent. The angle constraint data is further analyzed, and a symmetry analysis algorithm (such as the reflection transformation detection method) is used to identify the symmetric structure in the model. It is calculated whether there is symmetry between the faces in the angle constraint data. If there is a symmetric relationship, symmetric constraint data is generated. By integrating the size constraint data, angle constraint data and symmetric constraint data, a constraint optimization algorithm (such as the linear programming method) is used to uniformly process all constraint conditions.Generate complete geometric constraint data, which ensures that the container model meets all geometric constraint conditions in space and guarantees the accuracy and stability of the model.

[0123] Preferably, step S3 comprises the following steps:

[0124] Step S31: photographing the target goods at multiple angles to obtain images of the target goods; performing goods edge detection on the target goods images to obtain edge data sets;

[0125] Step S32: performing contour segmentation processing on the edge data set to obtain a line segment data set; performing basic contour cutting on the target cargo image based on the line segment data set to generate a cargo basic contour image;

[0126] Step S33: performing stereo matching on the cargo basic outline image to obtain a cargo disparity atlas; performing depth calculation on the cargo disparity atlas to generate a depth data set;

[0127] Step S34: performing point cloud conversion on the depth data set to generate a space point set; performing surface surface fitting on the space point set to generate a complete surface set;

[0128] Step S35: reconstructing the three-dimensional model of the complete surface set to obtain a reconstructed cargo model.

[0129] In this embodiment, a high-resolution camera is used to shoot the target goods at multiple angles. During the shooting process, the camera automatic calibration method is used to ensure the accuracy of the shooting angle. The angle between each shooting angle is set to 15°, and 3 pictures are taken at each angle. During shooting, the camera focal length is set to 50 mm, and the aperture is adjusted to F8 to ensure that a clear image of the goods is captured. Images at multiple angles can provide sufficient viewing angle differences. The Canny edge detection algorithm is used to process each target goods image. During the edge detection process, the high threshold is set to 200, the low threshold is set to 100, and the image size is set to 1024×1024 pixels. After edge detection, the edge data set of the target goods is obtained, which contains the surface contour information of the goods. The edge data set is processed into contour segments, and the Hough transform (Hough transform) is used to extract the contour of the target goods. Transform) algorithm is used to detect straight lines on edge data, and the threshold is set to 50 to detect all significant straight lines in the image. Then, the detected straight lines are fitted by a line segmentation algorithm (such as the RANSAC algorithm) to generate a line segment dataset containing all line segments. The line segment dataset records the linear features of the outer contour of the target goods. Basic contour cutting is performed based on the line segment dataset, and the target goods image is cut using image segmentation technology (such as the threshold-based region growing method) to obtain an accurate outer contour of the goods. During the cutting process, the threshold is set to 0.3, and all pixel points in the image area are classified according to whether they belong to the boundary of the goods. Finally, a clear basic contour image of the goods is generated, and an appropriate stereo matching algorithm (such as Block Matching or Semi-GlobalMatching) is used to perform stereo matching on the basic outline image of the cargo. The left and right image pairs are input, the image window size is set to 9×9, and the disparity range is selected to be -64 to 64. The algorithm generates a disparity map by calculating the pixel difference between the left and right images. The disparity atlas contains the depth information of different areas on the surface of the cargo. The surface structure of the cargo under different viewing angles can be obtained through the disparity map. The depth calculation formula is used: Depth = Baseline × Focal Length / Disparity ("Depth" = ("Baseline" × "Focal Length") / "Disparity"), and the baseline is set to 0 as the camera spacing.1 meter, the focal length is set to 50mm, and the calculated depth data set provides accurate depth information of each area of ​​the cargo. According to the depth data set, the depth value of each pixel is converted into a spatial point through a 3D reconstruction algorithm. The position of the spatial point is calculated by the formula: X = (x×"Depth") / "Focal Length", Y = (y×"Depth") / "Focal Length", Z = "Depth". During the conversion process, x and y are pixel coordinates, "Depth" is the depth value corresponding to the pixel, and "Focal Length" is the camera focal length. The final generated spatial point set records the 3D point cloud data of the cargo surface. The spatial point set is processed using the least squares method (Least Squares Method) is used to fit the surface of the cargo. The cargo surface is fitted by selecting an appropriate fitting model (such as B-spline surface or NURBS surface). During the fitting process, the number of control points is set to 100 and the fitting accuracy is set to 0.5 mm. The generated surface set contains a smooth three-dimensional surface model of the cargo surface. The surface set is used to reconstruct the three-dimensional model. The meshing algorithm (such as Delaunay triangulation algorithm) is used to convert the surface into a three-dimensional mesh model. During the meshing process, the mesh density is set to 5 mm. After the three-dimensional mesh model is generated, the mesh is smoothed by a surface refinement algorithm (such as Laplacian smoothing). The reconstructed cargo model has a high-precision three-dimensional geometric shape.

[0130] Preferably, step S4 comprises the following steps:

[0131] Step S41: voxelizing the container skeleton model to obtain a spatial grid set; marking the available space of the spatial grid set to generate loading partition data;

[0132] Step S42: Calculate the size of the reconstructed cargo model to generate cargo parameters; sort the loading order based on the cargo parameters to generate a cargo loading sequence;

[0133] Step S43: Perform spatial matching planning on the cargo loading sequence based on the loading partition data to generate an initial cargo planning model;

[0134] Step S44: locating the centroid of the initial cargo planning model to obtain cargo centroid data; performing moment calculation on the cargo centroid data to generate a moment data set;

[0135] Step S45: Perform stability analysis on the moment data set to generate cargo balance parameters.

[0136] In this embodiment, the container skeleton model is converted into a three-dimensional grid format, and voxel processing is performed based on the grid. During the voxelization process, the space of the container is first divided into multiple small cubic units, each unit represents a voxel, and the size of the voxel is set according to the actual size of the container. For example, the length, width, and height of the container are divided into equally spaced units. These voxels will represent the space in the container in a discrete manner. Through voxelization, the geometric position of each voxel can be clearly defined, and the number and shape of the voxels can accurately represent all the spaces in the container. The result of voxelization generates a spatial grid set. Next, the spatial grid is marked with available space, and the state of each voxel is marked as available or unavailable. The specific marking method is to detect the existing objects in the skeleton model, identify the part that occupies the space, mark the corresponding voxel as unavailable, and mark the remaining voxels as available, and finally obtain a spatial grid set containing all available voxels, generate loading partition data, and convert the three-dimensional reconstruction model of the cargo into a standard geometric format, such as STL or OBJ file. By parsing the geometric data in the file, the basic dimensions of the cargo such as length, width, and height are obtained, and the volume and surface area of ​​the cargo are calculated. When calculating the size, the geometric analysis method is used, and the bounding box method is used to calculate the maximum circumscribed cuboid size of the goods in three-dimensional space. The minimum length, width and height of the goods are determined by scanning the surface of the goods point by point and calculating the minimum and maximum values ​​in each coordinate axis direction. Then, based on these size data, the parameters of the goods are further generated. These parameters include the volume, mass, center of gravity position and appearance characteristics of the goods. The center of gravity position is calculated using the centroid formula, which is solved according to the geometric shape and mass distribution of the goods. All cargo parameters provide data support for the subsequent loading sequence sorting and generate the basis for the cargo loading sequence. According to the aforementioned generated cargo loading sequence, combined with the loading partition data, each cargo is spatially adapted through the three-dimensional space matching algorithm. During the matching process, the loading partition is first spatially divided and optimized to ensure that the cargo can be reasonably loaded into the appropriate space area. When matching in space, the size, shape, weight and center of gravity of the cargo are considered. At the same time, according to the available space and carrying capacity of each partition, all cargoes are allocated using a heuristic search algorithm (such as the A* algorithm) to ensure the rationality of loading. Each time a match is made, the loading position that best suits the current cargo is selected to ensure that the cargo layout in the container is optimized and the space utilization is maximized. Through this matching plan, a preliminary cargo planning model is generated, which includes the specific location, direction and loading order of each cargo. Based on the location and size of each cargo in the initial cargo planning model, the center of mass of each cargo is calculated. The center of mass is calculated based on the geometric shape and mass distribution of the cargo, and the center of mass formula is used to calculate the center of gravity coordinates of the cargo through the integration method.In actual operation, by scanning the three-dimensional model of the cargo, the geometric volume of the cargo is divided into multiple small volume units, and the mass density of each unit is combined to find the centroid coordinates of each volume unit, and then the overall centroid of the cargo is found according to the weighted average method. Then, using these centroid data, adjust the position of the cargo in the container to ensure that the centroid of the cargo can be evenly distributed and avoid excessive center of gravity offset. This can provide accurate data support for subsequent moment calculations, obtain the cargo centroid data, record the centroid coordinates of each cargo, and calculate the moment distribution of the cargo in the container according to the centroid data of each cargo and its position in the container through the moment calculation formula. The calculation of the moment is based on the centroid position of each cargo and the length of the lever arm relative to the container, and the traditional moment formula is used to solve it. The specific method is to calculate the relative position according to the centroid coordinates and the container coordinate system to obtain the moment value of each cargo. During the calculation process, the torque of the cargo is mapped to the coordinate system of the container through a three-dimensional coordinate system. Combined with the location and mass information of the cargo, a torque data set is generated. The torque data set records the torque values ​​of all cargo and the impact of these torque values ​​on the overall stability of the container. Specialized mechanical analysis software (such as ANSYS or SolidWorks Simulation) is used to simulate the generated torque data to analyze the equilibrium state of the cargo under different loading conditions. Multiple scenarios are set during the simulation process, including different cargo arrangements and container tilt angles, to generate cargo balance parameters. The analysis software determines the stability of each arrangement by calculating the center of gravity and force conditions of the cargo.

[0137] Preferably, step S43 includes the following steps:

[0138] Calculate the available volume of the loaded partition data to generate partition available volume data; perform boundary restriction mapping based on the partition available volume data to generate partition boundary restriction data;

[0139] Performing connectivity analysis on the loaded partition data to obtain partition connectivity data; performing partition carrying capacity evaluation on the loaded partition data according to the partition connectivity data to obtain the partition carrying capacity;

[0140] Based on the partition carrying capacity and partition boundary restriction data, the adaptation capacity is determined to generate a regional adaptation capacity value;

[0141] Carry out load demand calculation for cargo loading sequence to obtain cargo load demand data;

[0142] Spatial matching planning is carried out according to the regional adaptation capacity value and cargo carrying demand data to generate an initial cargo planning model.

[0143] In this embodiment, based on the loading partition data, the spatial size of each partition is determined, and the volume of the loading partition is solved using a three-dimensional geometric calculation method. In the specific operation, each partition is divided into multiple small volume units by gridding the partition boundary, and the volume of these small units is weighted and summed using a volume integration algorithm to obtain the available volume of each partition. By accurately calculating the available volume through gridding partitions, the size of the free space of each partition can be accurately obtained, and this data becomes the basis for subsequent loading planning. The partition available volume data finally generated includes the volume size of each partition, as well as the specific location of the available space area and the occupied area in the partition. Based on the partition available volume data, the spatial boundary restriction mapping is performed. The specific steps are to perform geometric analysis on the spatial range of each loading partition, determine the boundary conditions and spatial constraints of each partition, and use the constraint conditions to perform boundary restriction mapping. The boundary restriction mapping process clarifies the shape and size of each partition by setting the maximum and minimum size limits of the partition. The method for generating restriction data is based on the available volume data of the partition, combining the volume with the boundary constraints, and accurately describing the boundary of each partition. Through boundary restriction mapping, the spatial scope of each partition can be clearly defined, and the connectivity analysis of the loading partition data can be performed to obtain the partition connectivity data. First, the connectivity analysis of the loading partition is performed to analyze the connection relationship between each partition and its adjacent partitions. In the specific operation, by constructing the adjacency matrix between partitions, it is indicated whether there is a direct path between partitions. Based on the geometric calculation method, the spatial distance and relative position between each partition are calculated to determine whether the partitions are connected. The connectivity analysis uses the depth-first search algorithm (DFS) or the breadth-first search algorithm (BFS) to traverse all partitions and generate connectivity data between partitions. The data records whether each partition is connected to other partitions, as well as the spatial scope and location of the connected area. According to the partition connectivity data, the partition carrying capacity of the loading partition data is evaluated to obtain the partition carrying capacity. Based on the partition connectivity data, the carrying capacity of each partition is analyzed. The specific operation is to evaluate it based on the available volume data of each partition and its connection relationship with other partitions, combined with the physical bearing model. The physical parameters such as volume ratio and gravity distribution are used in the evaluation to calculate the carrying capacity of each partition under a given weight. The mechanical analysis tools are used to simulate the carrying capacity of each partition and calculate the maximum load and carrying capacity of the partition.By considering the connectivity between partitions, the carrying capacity data of each partition is obtained, and the carrying performance of each partition is further evaluated. According to the partition carrying capacity data and the partition boundary restriction data, the adaptability of each loading partition is determined. In the specific implementation, the adaptability evaluation is carried out according to the partition carrying capacity and the boundary space restriction conditions to determine whether each partition can meet the cargo carrying demand and space requirements. In the evaluation process, the matching algorithm is used to combine the loading requirements and space restrictions to determine the adaptability value of each area. The adaptability value reflects the degree of adaptability of each partition under the requirements of weight, volume, shape, etc. Finally, the adaptability data of each partition is obtained, and the carrying demand of the cargo loading sequence is calculated to obtain the cargo carrying demand data. First, based on the cargo loading sequence data, the carrying demand of each cargo is calculated. In the specific operation, the load distribution analysis is first performed based on the geometric size, weight and shape of the cargo using a physical model (such as a physical mechanics model) to obtain the carrying demand of each cargo for the partition. When calculating the load demand, a mechanical model (such as the center of mass moment balance) is used to analyze the weight distribution and moment distribution of the cargo during loading, ensuring that the center of gravity of each cargo matches the loading area, and obtaining the cargo load demand data, which includes the cargo weight demand, space demand and its distribution. According to the regional adaptation capacity value and the cargo load demand data, spatial matching planning is performed to generate an initial cargo planning model. Based on the regional adaptation capacity value and the cargo load demand data, spatial matching planning is performed. First, the load demand data of each cargo is matched with the adaptation capacity value of each partition. The space is allocated to each cargo through a spatial optimization algorithm (such as the minimum distance method or the greedy algorithm), and the optimal position and loading order of each cargo in the loading partition are determined. When planning, the weight, volume, shape and space demand of the cargo are comprehensively considered, and the adaptation capacity of the partition is combined to finally generate an initial cargo planning model, which includes the position, direction and loading order of each cargo in the container.

[0144] Preferably, step S5 comprises the following steps:

[0145] Step S51: Calculate the distance between adjacent goods in the initial goods planning model to obtain adjacent goods distance parameters; perform translation contact simulation according to the adjacent goods distance parameters to generate a simulated contact area;

[0146] Step S52: performing contact pressure distribution analysis based on cargo balance parameters to generate pressure distribution data;

[0147] Step S53: Cargo interference simulation is performed on the simulated contact area according to the pressure distribution data to obtain simulated cargo interference data;

[0148] Step S54: performing pressure threshold mapping based on the simulated contact area and pressure distribution data to generate a cargo interference pressure threshold;

[0149] Step S55: Detect conflict points on the simulated cargo interference data according to the cargo interference pressure threshold to generate interference conflict data.

[0150] In this embodiment, based on the cargo position data in the initial cargo planning model, the distance between each pair of adjacent cargoes is calculated to obtain the distance parameters of the adjacent cargoes. This calculation is performed by traversing the centroid position of each pair of cargoes and using the Euclidean distance formula to calculate the straight-line distance between each pair of cargoes. Through these adjacent cargo distance parameters, the spatial relationship between the cargoes is evaluated, and further translation contact simulation is performed. In the translation contact simulation process, the cargo is translated according to the adjacent cargo distance parameters to simulate the contact area of ​​the cargo in the contact state. The translation simulation is calculated by setting the bounding box of the cargo, gradually simulating the translation process of the cargo until the cargo comes into contact, and then determining the contact area. The simulated contact area is the geometric area of ​​the intersection of the two cargo surfaces on the contact surface, forming simulated contact data, and according to the cargo balance parameters, the mass distribution and torque data of the cargo in the contact area are determined, and the distribution analysis of the contact pressure is performed in combination with the surface characteristics and material properties of the cargo. The contact pressure distribution analysis calculates the magnitude and distribution of the force on the cargo surface to obtain the distribution of the pressure on the contact area. The specific method is to combine the finite element analysis (FEA) model to perform pressure simulation and evaluate the pressure size at the contact point. According to the cargo balance parameters, the pressure value at each contact point is determined, and the pressure distribution data is generated based on the pressure distribution data. The cargo interference simulation is completed by performing shape overlap detection on the cargo in the simulated contact area to evaluate whether the cargo will cause interference in the contact area. Using the generated contact pressure distribution and the geometric shape of the cargo, it is simulated whether the contact point between the two cargoes causes physical overlap, and whether interference will occur based on the pressure value. When the pressure value exceeds the set threshold, it is considered that interference occurs. Through this method, simulated cargo interference data is obtained, which cargoes have interfered and their corresponding interference areas are recorded, and pressure threshold mapping is performed based on the simulated contact area and pressure distribution data to generate cargo interference pressure threshold. According to the simulated contact area and pressure distribution data, threshold mapping is performed on the pressure of the contact area. A certain pressure threshold is set to determine which areas have pressure values ​​exceeding the threshold, and it is considered that the contact of these areas will cause interference. Through this process, a cargo interference pressure threshold is generated for subsequent interference conflict detection. This mapping process determines a reasonable pressure threshold through statistical analysis of pressure data, and detects conflict points on simulated cargo interference data in combination with cargo interference pressure threshold. The detection method checks point by point in the simulated contact area to see if the contact pressure value exceeds the set threshold. If it exceeds the threshold, it is considered that cargo interference has occurred at that location, and the interference point and its interference intensity are recorded to generate interference conflict data. This data will be used in subsequent optimized loading plans to ensure that interference problems between cargoes can be discovered and adjusted in a timely manner.

[0151] Preferably, step S53 includes the following steps:

[0152] According to the pressure distribution data, the pressure concentration area of ​​the simulated contact area is located to obtain the key contact point;

[0153] Identify the direction of force on key contact points and generate pressure transmission direction data; detect the pressure value of key contact points and obtain contact point pressure data;

[0154] Cargo contact simulation is performed on key contact points based on pressure transmission direction data and contact point pressure data to generate simulated cargo contact data;

[0155] Perform local deformation analysis on the simulated cargo contact data to obtain a local deformation data set; define the deformation propagation range of the local deformation data set to generate a deformation propagation range;

[0156] Perform relative displacement analysis based on the local deformation data set and deformation propagation range to generate cargo displacement;

[0157] Cargo interference mapping is performed based on the cargo displacement to obtain simulated cargo interference data.

[0158] In this embodiment, based on the pressure distribution data in the simulated contact area, the area with high pressure is located, and the area with concentrated pressure in the contact surface is determined by mapping the pressure data. These areas are regarded as key contact points. The positioning process adopts a grid division method to divide the contact area into a number of grid units, calculate the pressure value of each unit and compare it with the global pressure data, find out the area with high pressure and exceeding the preset threshold, and then determine it as a key contact point. Through the analysis of the pressure distribution map, the position coordinates of these key contact points and their corresponding pressure values ​​are obtained. By analyzing the pressure distribution data of the key contact points, the direction of pressure transmission is identified and the propagation path of the force is determined. The direction of the force of each key contact point is calculated according to the pressure size and direction of its adjacent points, and the force transmission direction is determined by the vector analysis method, and a data model of the force transmission direction is established. Through the propagation path of the force, the pressure transmission direction data is generated to represent the stress direction transmitted on the contact surface, and a detailed pressure value detection is performed on each key contact point to obtain the actual force condition of the point during the contact process. Through further refined grid analysis, combined with the finite element analysis (FEA) model, the pressure value of the key contact point is determined, and these pressure data are recorded. The pressure value of each contact point is compared with other areas in the simulated contact area to confirm the pressure change trend between it and the adjacent areas, generate contact point pressure data, and simulate the cargo contact at the key contact points based on the pressure transmission direction data and the contact point pressure data to generate simulated cargo contact data. Cargo contact simulation is performed based on the pressure transmission direction data and contact point pressure data generated above. This simulation models the pressure change of the contact point and uses contact mechanics theory (such as Hertz contact theory) for numerical simulation to simulate the deformation of the cargo on the contact surface. During the simulation, the pressure transmission direction is combined with the pressure data to infer the deformation of the cargo surface during the contact process, and the relevant data of the simulated contact is recorded to finally obtain the simulated cargo contact data. Local deformation analysis is performed on the simulated cargo contact data to obtain a local deformation data set. Based on the simulated cargo contact data, the finite element analysis method (FEA) is used to perform local deformation analysis on the contact area to detect the deformation degree and distribution of the contact area. By calculating the displacement near the contact point, the local deformation of the cargo during contact is analyzed and a local deformation data set is obtained. This data set contains the displacement and deformation of each contact point. Combined with the pressure data, the geometric changes in the deformation area are calculated. The local deformation data set is further processed to analyze the propagation range of the deformation on the cargo surface. By performing a step-by-step propagation simulation on the displacement of each contact point, the influence range of the deformation on the entire cargo model is determined, and then the propagation range of the deformation is defined. Using the step-by-step recursive method and the spatial distribution law of the deformation, the influence range of each deformation point is combined with the surrounding contact points, and finally the complete deformation propagation range data is generated.Relative displacement analysis is performed by combining the local deformation data set and the deformation propagation range. This analysis determines the overall displacement of the cargo under pressure by calculating the displacement change of each contact point relative to other points. The relative displacement formula is used in combination with the simulated contact data to obtain the displacement of the cargo, generate cargo displacement data, and then analyze the interference that may occur with the cargo. Based on the cargo displacement generated above, cargo interference mapping is performed. By comparing with the initial loading model, the interference area between the cargoes is identified. The mapping process determines the location and size of the interference area by mapping the displacement, and uses 3D modeling tools (such as MeshLab) to accurately calculate the simulated cargo interference to obtain the simulated cargo interference data.

[0159] Preferably, step S6 comprises the following steps:

[0160] Step S61: locating the conflict points of the initial cargo planning model according to the interference conflict data to generate the conflict point coordinates; calculating the point cluster concentration of the conflict point coordinates to generate the conflict point concentration degree;

[0161] Step S62: locating the conflict focus based on the concentration of conflict points to obtain the conflict focus area;

[0162] Step S63: performing region mapping on the container skeleton model according to the conflict focus area to generate a container model conflict area;

[0163] Step S64: performing container restriction analysis on the conflict area of ​​the container model based on the interference conflict data to obtain container restriction factors;

[0164] Step S65: performing variant optimization on the container skeleton model based on the container restriction factors to generate an adjusted container model.

[0165] In this embodiment, based on the aforementioned simulated cargo interference data, the interference conflict points are located, the contact points or interference points between cargoes and between cargoes and container frames are identified, the entire container loading area is divided into a number of grid units by a three-dimensional gridding method, and each conflict point in the interference area is determined by geometric overlap calculation. The coordinates of the conflict points are obtained by solving the spatial relationship between the cargo model and the container frame. The specific coordinates of each conflict point are determined by comparing with the grid model and the collision detection algorithm (such as AABB collision detection). By analyzing the spatial distribution of the conflict points, the point cluster concentration is calculated. First, cluster analysis is performed on the conflict points, and the K-means clustering algorithm is used to group the conflict points according to the spatial distribution. The concentration of each group of conflict points is calculated, and the concentration reflects the degree of interference between the cargoes. For each cluster group, by calculating the density of the point group, the geometric center of the point group, the radius of the point group, etc., the concentrated area where each conflict point is located is determined, and the corresponding conflict point concentration data is generated. According to the previously generated conflict point concentration data, the clustering results are used to locate the area with higher concentration, and the center position of these conflict point groups is identified using the centroid positioning algorithm and determined as the conflict focus area. The size of the conflict focus area is directly related to the concentration of the conflict points. The higher the concentration, the larger the area of ​​the conflict focus. The determination process of the conflict focus area is realized by extending the calculation of the cluster center and its surrounding points, which specifically includes defining the local optimal area of ​​each cluster and obtaining the boundary of the conflict focus. According to the data of the conflict focus area, the areas corresponding to these conflict focuses are compared with the spatial positions of the container skeleton model using a mapping algorithm to determine the areas in the container where interference will occur. The conflict focus area is mapped to the coordinate system of the container skeleton model using a three-dimensional spatial mapping technology (for example, a spatial transformation matrix) to generate the conflict area of ​​the container model. The conflict area is delineated by comparing the geometric shapes of the focus area and the container skeleton, and then the interference area of ​​the container skeleton is obtained. According to the conflict area and interference conflict data of the container model, the restriction analysis of the container is performed. First, the restriction conditions such as the carrying capacity, size, and shape of the container are evaluated. The relative position relationship between the conflict area and the container model is analyzed using spatial analysis tools such as the Convex Hull algorithm to determine the physical restriction factors that the container needs to follow when dealing with conflicts.These limiting factors include the range of available space, maximum cargo load, geometric structure and rigidity limitations of containers. Through these data, the limiting factors of the container are generated, and the container model is optimized using the limiting factors. First, the shape, size and carrying capacity of the container skeleton model are compared with the limiting factors to determine the optimal adaptability of the container in the current form. The container skeleton is fine-tuned through a variant optimization algorithm (such as shape optimization based on a genetic algorithm) to adjust the size, structure or form of the container skeleton model to maximize adaptation to cargo loading requirements and eliminate interference. During the optimization process, the geometric shape of the container is modified according to the limiting factors to generate a new adjusted container model. Finally, a verification algorithm is used to check whether the new model meets all loading and carrying requirements.

[0166] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0167] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A container model construction method based on three-dimensional software, characterized in that: The following steps are involved: Step S1: perform three-dimensional laser scanning on the container to obtain container point cloud data; perform feature segmentation on the container point cloud data to generate a container feature collection; Step S2: spatially locate the feature points of the container feature set to obtain a vertex coordinate set; perform geometric constraint modeling based on the vertex coordinate set to generate a container skeleton model; Step S3: Acquire a target cargo image; extract a basic contour of the target cargo image to generate a cargo basic contour image; Perform three-dimensional reconstruction on the basic contour image of the cargo to obtain a reconstructed cargo model; Step S4: performing space planning processing on the container skeleton model based on the reconstructed cargo model to obtain an initial cargo planning model; performing gravity center distribution calculation on the initial cargo planning model to generate cargo balance parameters; Step S5: performing interference simulation on the initial cargo planning model based on the cargo balance parameters to obtain simulated cargo interference data; performing conflict detection on the simulated cargo interference data to generate interference conflict data; Step S6: locate the conflict focus of the interference conflict data to obtain the conflict focus area; perform variant design on the container skeleton model according to the conflict focus area to generate an adjusted container model.

2. The container model construction method based on three-dimensional software according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: scanning the container at multiple angles to obtain original container point cloud data; performing noise filtering on the original container point cloud data to generate filtered point cloud data; Step S12: performing coordinate conversion processing on the filtered point cloud data to generate container point cloud data; Step S13: performing geometric feature point recognition on the container point cloud data to obtain a container feature point set; performing topological relationship analysis on the container feature point set to obtain a topological relationship of the feature points; Step S14: Connecting the feature points of the container feature point set based on the topological relationship of the feature points to obtain a feature point connection graph; performing geometric feature analysis on the feature point connection graph to generate a container feature collection.

3. The container model construction method based on three-dimensional software according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing spatial coordinate mapping on the container feature set to obtain a three-dimensional coordinate point set; performing vertex clustering analysis on the three-dimensional coordinate point set to generate a candidate vertex set; Step S22: performing precise positioning calculation on the candidate vertex set according to the three-dimensional coordinate point set to obtain a vertex coordinate set; Step S23: geometrically extend the vertex coordinate set to generate surface area relationship data; perform geometric constraint analysis on the surface area relationship data to generate geometric constraint data; Step S24: Establish a framework structure based on the geometric constraint data to obtain an initial skeleton model; fill in nodes of the initial skeleton model according to the face region relationship data to generate a container skeleton model.

4. The container model construction method based on three-dimensional software according to claim 3 is characterized in that: Step S23 includes the following steps: Performing adjacency analysis on the vertex coordinate point set to obtain a point pair relationship set; performing edge connection processing on the point pair relationship set to generate edge data; Perform parallelism detection on edge data to obtain parallel edge data; perform verticality detection on parallel edge data to generate orthogonal edge data set; Construct face element relationships based on orthogonal edge data sets to generate face domain relationship data; Extract dimension parameters from the surface area relationship data to obtain dimension constraint data; Performing angle relationship analysis on the size constraint data to generate angle constraint data; performing symmetry calculation on the angle constraint data to obtain symmetric constraint data; The size constraint data, angle constraint data and symmetry constraint data are integrated to generate geometric constraint data.

5. The container model construction method based on three-dimensional software according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: photographing the target goods at multiple angles to obtain images of the target goods; performing goods edge detection on the target goods images to obtain edge data sets; Step S32: performing contour segmentation processing on the edge data set to obtain a line segment data set; performing basic contour cutting on the target cargo image based on the line segment data set to generate a cargo basic contour image; Step S33: performing stereo matching on the cargo basic outline image to obtain a cargo disparity atlas; performing depth calculation on the cargo disparity atlas to generate a depth data set; Step S34: performing point cloud conversion on the depth data set to generate a space point set; performing surface surface fitting on the space point set to generate a complete surface set; Step S35: reconstructing the three-dimensional model of the complete surface set to obtain a reconstructed cargo model.

6. The container model construction method based on three-dimensional software according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: voxelizing the container skeleton model to obtain a spatial grid set; marking the available space of the spatial grid set to generate loading partition data; Step S42: Calculate the size of the reconstructed cargo model to generate cargo parameters; sort the loading order based on the cargo parameters to generate a cargo loading sequence; Step S43: Perform spatial matching planning on the cargo loading sequence based on the loading partition data to generate an initial cargo planning model; Step S44: locating the centroid of the initial cargo planning model to obtain cargo centroid data; performing moment calculation on the cargo centroid data to generate a moment data set; Step S45: Perform stability analysis on the moment data set to generate cargo balance parameters.

7. The container model construction method based on three-dimensional software according to claim 6 is characterized in that: Step S43 includes the following steps: Calculate the available volume of the loaded partition data to generate partition available volume data; perform boundary restriction mapping based on the partition available volume data to generate partition boundary restriction data; Performing connectivity analysis on the loaded partition data to obtain partition connectivity data; performing partition carrying capacity evaluation on the loaded partition data according to the partition connectivity data to obtain the partition carrying capacity; Based on the partition carrying capacity and partition boundary restriction data, the adaptation capacity is determined to generate a regional adaptation capacity value; Carry out load demand calculation for cargo loading sequence to obtain cargo load demand data; Spatial matching planning is carried out according to the regional adaptation capacity value and cargo carrying demand data to generate an initial cargo planning model.

8. The container model construction method based on three-dimensional software according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Calculate the distance between adjacent goods in the initial goods planning model to obtain adjacent goods distance parameters; perform translation contact simulation according to the adjacent goods distance parameters to generate a simulated contact area; Step S52: performing contact pressure distribution analysis based on cargo balance parameters to generate pressure distribution data; Step S53: Cargo interference simulation is performed on the simulated contact area according to the pressure distribution data to obtain simulated cargo interference data; Step S54: performing pressure threshold mapping based on the simulated contact area and pressure distribution data to generate a cargo interference pressure threshold; Step S55: Detect conflict points on the simulated cargo interference data according to the cargo interference pressure threshold to generate interference conflict data.

9. The container model construction method based on three-dimensional software according to claim 8, characterized in that: Step S53 includes the following steps: According to the pressure distribution data, the pressure concentration area of ​​the simulated contact area is located to obtain the key contact point; Identify the direction of force on key contact points and generate pressure transmission direction data; detect the pressure value of key contact points and obtain contact point pressure data; Cargo contact simulation is performed on key contact points based on pressure transmission direction data and contact point pressure data to generate simulated cargo contact data; Perform local deformation analysis on the simulated cargo contact data to obtain a local deformation data set; define the deformation propagation range of the local deformation data set to generate a deformation propagation range; Perform relative displacement analysis based on the local deformation data set and deformation propagation range to generate cargo displacement; Cargo interference mapping is performed based on the cargo displacement to obtain simulated cargo interference data.

10. The container model construction method based on three-dimensional software according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: locating the conflict points of the initial cargo planning model according to the interference conflict data to generate the conflict point coordinates; calculating the point cluster concentration of the conflict point coordinates to generate the conflict point concentration degree; Step S62: locating the conflict focus based on the concentration of conflict points to obtain the conflict focus area; Step S63: performing region mapping on the container skeleton model according to the conflict focus area to generate a container model conflict area; Step S64: performing container restriction analysis on the conflict area of ​​the container model based on the interference conflict data to obtain container restriction factors; Step S65: performing variant optimization on the container skeleton model based on the container restriction factors to generate an adjusted container model.

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