Container loading capacity evaluation method and system based on residual space configuration

Abstract

The application discloses a container loading capacity evaluation method and system based on residual space configuration, and relates to the field of container loading. The method comprises the following steps: obtaining the corner point coordinates of the loading space in the container, constructing a full space voxel set covering the loading space, wherein the full space voxel set comprises regularly arranged voxels, dividing the full space voxel set into an occupied space voxel set and a residual space voxel set, determining K independent local cavities, screening G candidate cavities based on the voxel subsets of the local cavities, calculating the space compatibility degrees of the G candidate cavities, and selecting the candidate cavity corresponding to the maximum space compatibility degree as the recommended loading position of the to-be-loaded goods among the G candidate cavities. The application can select the loading position with a higher space compatibility degree under the premise of meeting the current goods size constraint, so as to reserve more continuous and concentrated available areas, and improve the overall space utilization efficiency and loading success rate of the container in the multi-round loading process.

Description

Technical Field

[0001] This invention relates to the field of container loading assessment, specifically to a method and system for assessing container loading capacity based on remaining space configuration. Background Technology

[0002] In the field of cargo transportation and warehousing, the space utilization rate of containers or loading containers directly affects logistics costs and operational efficiency. Existing loading methods typically select loading locations based on the geometric matching of the cargo to be loaded and the remaining space, that is, only verifying whether the candidate area can accommodate the current cargo's dimensions. While this method can ensure the feasibility of a single loading, in the continuous loading of multiple batches and types of cargo, it is easy to lead to a scattered distribution of remaining space and insufficient volume of individual usable areas, making it difficult to meet the loading needs of subsequent cargo. Especially in transportation scenarios with high requirements for space utilization efficiency (such as air freight), even if the overall remaining volume is sufficient, scattered local cavities often cannot be effectively reused, resulting in wasted carrying capacity. Therefore, there is an urgent need for an evaluation method that can meet the current loading constraints while taking into account the overall usability of the remaining space, in order to improve the comprehensive loading capacity of containers throughout the entire loading process. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method for evaluating container loading capacity based on residual space configuration, which solves the technical problems mentioned in the background by introducing a construction voxel set and spatial compatibility.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention discloses a method for evaluating the loading capacity of containers based on the remaining space configuration, the method comprising the following steps:

[0006] S1. Obtain the coordinates of the minimum and maximum corner points of the loading space inside the container;

[0007] S2. Obtain the predefined voxel side lengths, and construct a full space voxel set covering the loading space based on the minimum and maximum corner coordinates of the loading space; wherein, the full space voxel set includes regularly arranged voxels;

[0008] S3. Anchor the loaded goods in the loading space and divide the entire space voxel set into the occupied space voxel set and the remaining space voxel set based on the loaded goods;

[0009] S4. Perform connectivity analysis on the remaining space voxel set to determine K independent local cavities; wherein each local cavity is bound to its corresponding voxel subset;

[0010] S5. Based on the voxel subset of local cavities, select G candidate cavities from K local cavities that meet the loading conditions of the cargo to be loaded.

[0011] S6. Calculate the spatial compatibility of each of the G candidate cavities; wherein, the spatial compatibility is characterized as a comprehensive evaluation of the proportion of large cavities in the remaining space and the degree of spatial concentration when the cargo to be loaded is loaded into the target candidate cavity.

[0012] S7. Among the G candidate cavities, select the candidate cavity with the highest spatial compatibility as the recommended loading location for the cargo to be loaded.

[0013] In some embodiments, constructing a full space voxel set covering the loading space includes:

[0014] S2-1. Extract the maximum and minimum coordinate components of the loading space on each spatial axis from the minimum and maximum corner coordinates of the loading space; where each spatial axis is the mutually orthogonal X-axis, Y-axis and Z-axis.

[0015] S2-2. Based on the maximum and minimum coordinate components of each spatial axis, calculate the difference between the coordinate components of each spatial axis;

[0016] S2-3. Calculate the ratio between the coordinate component differences and the voxel side lengths to determine the number of voxels for each spatial axis.

[0017] S2-4. Based on the number of voxels in each spatial axis, determine the three-axis index range of the loading space respectively;

[0018] S2-5. Perform permutations and combinations on the respective indices within the three-axis index range, and traverse to generate all triplet indices. ;

[0019] in, , , These represent the indices for the X, Y, and Z axes, respectively.

[0020] S2-6. For each triplet index, calculate the coordinates of its corresponding minimum corner point;

[0021] S2-7. Using the smallest corner coordinates of each triplet index as a reference, extend the voxel side length along each spatial axis to generate a cube of voxels.

[0022] S2-8. For each generated voxel, determine whether the coordinate components of its smallest corner point coordinates on any axis are greater than or equal to the corresponding axis coordinate components of the largest corner point coordinates in the loading space.

[0023] S2-9. If the value is greater than or equal to the value, the generated voxel is discarded; otherwise, the generated voxel is retained.

[0024] S2-10. Bind each retained voxel to its triplet index and minimum corner coordinates to obtain the full space voxel set.

[0025] In some embodiments, the division of the entire space voxel set into an occupied space voxel set and a remaining space voxel set based on the loaded cargo includes:

[0026] S3-1. Obtain the minimum and maximum corner coordinates of each of the D loaded goods within the loading space;

[0027] S3-2. Based on the minimum and maximum corner coordinates of each of the D loaded goods, mark the occupied voxels of each loaded goods and the unoccupied voxels of the remaining space in the full space voxel set.

[0028] S3-3. Collect the occupied voxels of each loaded cargo and the unoccupied voxels of the remaining space to generate the occupied space voxel set and the remaining space voxel set.

[0029] In some embodiments, based on the minimum and maximum corner coordinates of each of the D loaded goods, the occupied voxels and unoccupied voxels of the remaining space for each loaded goods in the full-space voxel set are marked, including:

[0030] S3-2-1. For each loaded cargo, extract the minimum and maximum coordinate components of the loaded cargo on each spatial axis from its minimum and maximum corner coordinates.

[0031] S3-2-2. Based on the minimum and maximum coordinate components of the loaded goods on each spatial axis, the coordinates of the minimum corner point of the loading space, and the voxel side length, calculate the upper and lower bounds of the voxel index of the loaded goods on each spatial axis.

[0032] S3-2-3. Based on the upper and lower bounds of the voxel indexes of the loaded goods on each spatial axis, determine the index traversal range of the loaded goods on each spatial axis.

[0033] S3-2-4. Perform permutations and combinations on the index traversal range of each spatial axis to generate a candidate triplet index set corresponding to the loaded goods.

[0034] S3-2-5. Select a target triplet index from the set of candidate triplet indexes corresponding to the loaded goods;

[0035] S3-2-6, The target voxel corresponding to the anchored target triplet index in the full space voxel set;

[0036] S3-2-7. Extract the coordinates of the smallest corner point bound to the target voxel, and calculate the range of voxel coordinates of the target voxel based on the voxel side length.

[0037] S3-2-8. Determine whether the range of voxel coordinates of the target voxel overlaps with the range of cargo coordinates constructed by the minimum and maximum corner coordinates of the loaded cargo.

[0038] S3-2-9. If there is overlap, mark the target voxel as occupied voxel; otherwise, mark it as unoccupied voxel.

[0039] S3-2-10. Traverse the minimum and maximum corner coordinates of each of the D loaded goods until the occupied voxels of each loaded goods and the unoccupied voxels of the remaining space in the full space voxel set are marked.

[0040] In some embodiments, connectivity analysis is performed on the remaining space voxel set to identify K independent local cavities, including:

[0041] S4-1. Mark all voxels in the remaining space voxel set as unvisited, and initialize the connected component counter K=0;

[0042] S4-2, Process each unvisited voxel in the remaining space voxel set in turn:

[0043] Whenever an unvisited voxel is encountered, the connected component counter K is incremented by 1, the unvisited voxel is used as the current starting voxel, and a subset of voxels corresponding to the Kth local cavity is created.

[0044] S4-3. Add the current starting voxel to the Kth voxel subset and update the current starting voxel's label to "visited".

[0045] S4-4. Based on the visited voxels in the Kth voxel subset, find the neighboring voxels of the six-neighborhood in the remaining space voxel set.

[0046] S4-5. Add the adjacent voxels to the Kth voxel subset and update the adjacent voxel's label to "visited".

[0047] S4-6. Continue performing the search and addition operations for adjacent voxels until the Kth voxel subset no longer adds voxels.

[0048] S4-7. Traverse each unvisited voxel in the remaining space voxel set until all voxels are visited, and obtain K local cavities; each local cavity is bound to its corresponding voxel subset.

[0049] In some embodiments, selecting G candidate cavities from K local cavities that meet the loading conditions for the cargo to be loaded includes:

[0050] S5-1. Select a target local cavity from the K local cavities;

[0051] S5-2. Calculate the configurational accommodating size of the target local cavity based on the voxel subset bound to it.

[0052] Wherein, the configuration accommodating size represents the side length of the minimum circumscribed body of the target local cavity in the X, Y, and Z axis directions;

[0053] S5-3. Obtain the geometric dimensions of the cargo to be loaded; wherein, the geometric dimensions include the length, width, height, and side length of the cargo to be loaded in the X-axis, Y-axis, and Z-axis directions;

[0054] S5-4. Compare the geometric dimensions of the cargo to be loaded with the configuration dimensions of the target local cavity;

[0055] S5-5. If the geometric dimensions meet the loading constraints, then the target local cavity is determined to meet the loading requirements of the goods to be loaded.

[0056] The loading constraint is that the size of the cargo to be loaded on any spatial axis is not greater than the component of the configuration's accommodating size on the corresponding axis.

[0057] S5-6. Mark the target local cavity that meets the loading requirements as a candidate cavity; otherwise, mark it as a cavity that does not meet the requirements; wherein, the candidate cavity inherits the voxel subset of its local cavity;

[0058] S5-7. Traverse the K local cavities until all G candidate cavities that meet the loading requirements are selected.

[0059] In some embodiments, the configurational accommodating size of the target local cavity is calculated based on the subset of voxels bound to the target local cavity, including:

[0060] S5-2-1. Extract the minimum corner coordinates of all voxel bindings from the voxel subset of the target local cavity;

[0061] S5-2-2, Determine the minimum and maximum values ​​of the coordinates of the minimum corner point in the X-axis, Y-axis and Z-axis directions respectively;

[0062] S5-2-3. Based on the minimum and maximum values, and combined with the predefined voxel side lengths, calculate the side lengths of the minimum circumscribed body of the target local cavity in each spatial axis direction.

[0063] S5-2-4. Define the side length of the smallest circumscribed body in each spatial axis direction as the configuration accommodating size of the target local cavity.

[0064] In some embodiments, the spatial compatibility of each of the G candidate cavities is calculated, including:

[0065] S6-1. Select a target candidate cavity from the G candidate cavities;

[0066] The target candidate cavity is characterized as: a candidate cavity into which the goods to be loaded will be loaded;

[0067] S6-2. Exclude the local cavity corresponding to the target candidate cavity from the K local cavities to obtain the remaining space composed of the remaining K−1 local cavities;

[0068] S6-3. Extract the voxel subsets of each of the K-1 local cavities in the remaining space;

[0069] S6-4. Based on the coordinates of the smallest corner point in each set of elements, determine the smallest circumscribed body of each local cavity, and calculate the circumscribed volume and centroid coordinates of the smallest circumscribed body.

[0070] S6-5. Based on the centroid coordinates of K-1 smallest circumscribed bodies, calculate the global centroid coordinates of the remaining space;

[0071] S6-6. Calculate the spatial compatibility of the remaining space based on the circumscribed volume, centroid coordinates, and global centroid coordinates of the remaining space of the K-1 local cavities with the smallest circumscribed bodies.

[0072] S6-7. Traverse the G candidate cavities and calculate the spatial compatibility of each one until the spatial compatibility of the G candidate cavities is obtained.

[0073] This invention provides a method for evaluating container loading capacity based on remaining space configuration, which has the following beneficial effects:

[0074] This invention constructs a full set of voxels representing the container loading space and marks the occupancy status of each voxel based on the minimum and maximum corner coordinates of the loaded cargo, transforming a continuous spatial region into a set whose occupancy can be determined voxel by voxel. Furthermore, based on the six-neighbor connectivity of the remaining space voxels, multiple independent local cavities are identified. By comparing the geometric dimensions of the cargo to be loaded with the configuration and capacity of each local cavity, candidate cavities capable of accommodating the cargo in the X, Y, and Z axes are selected. Finally, by calculating the spatial compatibility of each candidate loading position, this index comprehensively reflects the proportion of large cavities and the degree of spatial concentration in the remaining space after loading. This allows for the priority selection of loading positions with higher spatial compatibility, while meeting the current cargo size constraints, to retain more continuous and concentrated usable areas, thereby improving the overall space utilization efficiency and loading success rate of the container during multiple loading cycles.

[0075] Secondly, the present invention discloses a container loading capacity assessment system based on residual space configuration, which performs the container loading capacity assessment method described in the first aspect. The assessment system includes the following units:

[0076] The dual-coordinate acquisition unit is used to acquire the minimum and maximum corner coordinates of the loading space inside the container;

[0077] The voxel set construction unit is used to obtain the predefined voxel side lengths and construct a full-space voxel set covering the loading space based on the minimum and maximum corner coordinates of the loading space.

[0078] The voxel set partitioning unit is used to anchor the loaded goods in the loading space and divide the entire space voxel set into the occupied space voxel set and the remaining space voxel set based on the loaded goods;

[0079] A local cavity determination unit is used to perform connectivity analysis on the remaining space voxel set to determine K independent local cavities; wherein each local cavity is bound to its corresponding voxel subset;

[0080] The candidate cavity screening unit is used to select G candidate cavities that meet the loading conditions of the cargo to be loaded from K local cavities based on a voxel subset of local cavities.

[0081] The compatibility calculation unit is used to calculate the spatial compatibility of each of the G candidate cavities; wherein, the spatial compatibility is characterized as a comprehensive evaluation of the proportion of large cavities in the remaining space and the degree of spatial concentration when the cargo to be loaded is loaded into the target candidate cavity;

[0082] The location recommendation unit is used to select the candidate cavity with the highest spatial compatibility from G candidate cavities as the recommended loading location for the goods to be loaded.

[0083] Compared with the prior art, the beneficial effects of the container loading capacity assessment system based on the remaining space configuration of the present invention are the same as those of the container loading capacity assessment method based on the remaining space configuration described above, so they will not be repeated here. Attached Figure Description

[0084] Figure 1 This is a flowchart illustrating the container loading capacity assessment method based on residual space configuration of the present invention.

[0085] Figure 2 This is a schematic diagram illustrating the process of obtaining the local cavity described in this invention;

[0086] Figure 3 This is a schematic diagram illustrating the definition process of the configuration accommodate size described in this invention;

[0087] Figure 4 This is a schematic diagram of the calculation process for spatial compatibility described in this invention;

[0088] Figure 5 This is a structural block diagram of the container loading capacity assessment system based on the remaining space configuration of the present invention. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] First, the prior art and related concepts involved in the embodiments of the present invention will be described:

[0091] Corner coordinates: These refer to the coordinates of a vertex in three-dimensional space, determined by the extreme coordinate components along three orthogonal axes. For any axis-aligned three-dimensional region, the minimum corner coordinates are the points formed by the minimum coordinate components along the X, Y, and Z axes, while the maximum corner coordinates are the points formed by the corresponding maximum coordinate components. This coordinate representation is used to uniquely define the spatial extent of a region.

[0092] Minimal bounding box: For any three-dimensional region composed of voxels, the minimum value of the minimum corner coordinates of all voxels on the X, Y, and Z axes is used as the lower boundary of the region on the corresponding axes, and the maximum value of the minimum corner coordinates on each axis plus the voxel side length is used as the upper boundary. The minimum axis-aligned cuboid that can completely cover all voxels in the region is constructed.

[0093] Example 1: Please refer to Figures 1 to 4 This invention provides a method for evaluating container loading capacity based on remaining space configuration, comprising the following steps:

[0094] S1. Obtain the coordinates of the minimum and maximum corner points of the loading space inside the container;

[0095] S2. Obtain the predefined voxel side lengths, and construct a full space voxel set covering the loading space based on the minimum and maximum corner coordinates of the loading space; wherein, the full space voxel set includes regularly arranged voxels;

[0096] S3. Anchor the loaded goods in the loading space and divide the entire space voxel set into the occupied space voxel set and the remaining space voxel set based on the loaded goods;

[0097] S4. Perform connectivity analysis on the remaining space voxel set to determine K independent local cavities; wherein each local cavity is bound to its corresponding voxel subset;

[0098] S5. Based on the voxel subset of local cavities, select G candidate cavities from K local cavities that meet the loading conditions of the cargo to be loaded.

[0099] S6. Calculate the spatial compatibility of each of the G candidate cavities; wherein, the spatial compatibility is characterized as a comprehensive evaluation of the proportion of large cavities in the remaining space and the degree of spatial concentration when the cargo to be loaded is loaded into the target candidate cavity.

[0100] S7. Among the G candidate cavities, select the candidate cavity with the highest spatial compatibility as the recommended loading location for the cargo to be loaded.

[0101] This embodiment discretizes the container loading space into a voxel grid, divides the occupied and remaining areas based on the space range of the loaded goods, identifies local cavities in the remaining space, filters out candidate cavities that can accommodate the goods to be loaded, and calculates the spatial compatibility of each candidate cavity based on the proportion of large cavities in the remaining space after loading and the degree of spatial concentration. Finally, the cavity corresponding to the maximum compatibility is used as the recommended loading location, thus realizing a quantitative assessment of the configuration of the remaining space of the container and a structured judgment of its loading capacity.

[0102] Specifically, in this embodiment, step S2 includes:

[0103] S2-1, Coordinates of the smallest corner point in the loading space Coordinates of the largest corner point In this process, the maximum and minimum coordinate components of the loading space on each spatial axis are extracted; where each spatial axis is a mutually orthogonal X-axis, Y-axis, and Z-axis.

[0104] S2-2. Based on the maximum and minimum coordinate components of each spatial axis, calculate the difference between the coordinate components of each spatial axis;

[0105] S2-3. Calculate the ratio between the coordinate component differences and the voxel side lengths to determine the number of voxels for each spatial axis.

[0106] Specifically, the formula for calculating the number of voxels is:

[0107] ;

[0108] in, , , These represent the number of voxels along the X, Y, and Z axes, respectively, and their values ​​are rounded down. This indicates the preset voxel side length.

[0109] S2-4. Based on the number of voxels in each spatial axis, determine the three-axis index range of the loading space respectively;

[0110] Specifically, the three-axis index ranges are represented as follows:

[0111] X-axis index range: ;

[0112] Y-axis index range: ;

[0113] Z-axis index range: ;

[0114] S2-5. Perform permutations and combinations on the respective indices within the three-axis index range, and traverse to generate all triplet indices. ;

[0115] in, , , These represent the indices for the X, Y, and Z axes, respectively.

[0116] S2-6. For each triplet index, calculate the coordinates of its corresponding minimum corner point;

[0117] The formula for calculating the coordinates of the minimum corner point is:

[0118] ;

[0119] in, , , These represent the coordinate components of the smallest corner point;

[0120] S2-7. Using the smallest corner coordinates of each triplet index as a reference, extend the voxel side length along each spatial axis to generate a cube of voxels.

[0121] S2-8. For each generated voxel, determine whether the coordinate components of its smallest corner point coordinates on any axis are greater than or equal to the corresponding axis coordinate components of the largest corner point coordinates in the loading space.

[0122] S2-9. If the value is greater than or equal to the value, the generated voxel is discarded; otherwise, the generated voxel is retained.

[0123] S2-10. Bind each retained voxel to its triplet index and minimum corner coordinates to obtain the full space voxel set.

[0124] This embodiment calculates the number of voxels axis by axis based on the minimum and maximum corner coordinates of the container loading space and the preset voxel side length, and generates a triplet index. Then, it accurately assigns the minimum corner coordinates to each voxel and removes voxels that exceed the loading space boundary after generation. Finally, it constructs a full space voxel set that is strictly aligned with the actual loading space and has no redundancy, so that the continuous geometric space of the container loading space is transformed into a voxel set with clear spatial coordinates and index identifiers.

[0125] Specifically, in this embodiment, step S3 includes:

[0126] S3-1. Obtain the minimum corner coordinates of each of the D loaded goods within the loading space. and the coordinates of the largest corner point ;

[0127] It should be noted that the smallest axis-aligned cuboid representing the package of the goods can be calculated using the minimum and maximum corner coordinates, representing the size range it occupies in the loading space.

[0128] S3-2. Based on the minimum and maximum corner coordinates of each of the D loaded goods, mark the occupied voxels of each loaded goods and the unoccupied voxels of the remaining space in the full space voxel set.

[0129] S3-3. Collect the occupied voxels of each loaded cargo and the unoccupied voxels of the remaining space to generate the occupied space voxel set and the remaining space voxel set.

[0130] This embodiment uses the space defined by the minimum and maximum corner coordinates of the loaded cargo as the basis to divide the entire space voxel set into two types of voxel sets: occupied and remaining, so that the used space and available space in the container can be clearly separated at the voxel level.

[0131] Step S3-2 further includes:

[0132] S3-2-1. For each loaded cargo, extract the minimum and maximum coordinate components of the loaded cargo on each spatial axis from its minimum and maximum corner coordinates.

[0133] S3-2-2. Based on the minimum and maximum coordinate components of the loaded goods on each spatial axis, the coordinates of the minimum corner point of the loading space, and the voxel side length, calculate the upper and lower bounds of the voxel index of the loaded goods on each spatial axis.

[0134] Specifically, the formulas for calculating the upper and lower bounds of the voxel index are as follows:

[0135] ;

[0136] in:

[0137] , These represent the lower bound and upper bound of the voxel index on the X-axis, respectively.

[0138] , These represent the lower bound and upper bound of the voxel index on the Y-axis, respectively.

[0139] , These represent the lower bound and upper bound of the voxel index for the Z-axis, respectively.

[0140] S3-2-3. Based on the upper and lower bounds of the voxel indexes of the loaded goods on each spatial axis, determine the index traversal range of the loaded goods on each spatial axis.

[0141] Specifically, the index traversal ranges on each spatial axis are represented as follows:

[0142] X-axis index range: ;

[0143] Y-axis index range: ;

[0144] Z-axis index range: ;

[0145] S3-2-4. Perform permutations and combinations on the index traversal range of each spatial axis to generate a candidate triplet index set corresponding to the loaded goods.

[0146] S3-2-5. Select a target triplet index from the set of candidate triplet indexes corresponding to the loaded goods;

[0147] S3-2-6, The target voxel corresponding to the anchored target triplet index in the full space voxel set;

[0148] S3-2-7. Extract the coordinates of the smallest corner point bound to the target voxel. And based on the voxel side length, calculate the voxel coordinate range of the target voxel;

[0149] Specifically, the range of voxel coordinates on the X-axis is as follows: The coordinate range on the Y-axis is The coordinate range on the Z-axis is ;

[0150] S3-2-8. Determine whether the range of voxel coordinates of the target voxel overlaps with the range of cargo coordinates constructed by the minimum and maximum corner coordinates of the loaded cargo.

[0151] The overlap is characterized by all spatial axes simultaneously satisfying:

[0152] Formula ① and ;

[0153] Formula ② and ;

[0154] Formula ③ and ;

[0155] Specifically, equations ①, ②, and ③ represent:

[0156] In the X-axis direction, the minimum corner coordinate of the target voxel is less than the X component of the maximum corner coordinate of the loaded cargo, and the minimum corner coordinate of the target voxel plus the voxel side length is greater than the X component of the minimum corner coordinate of the loaded cargo.

[0157] In the Y-axis direction, the minimum corner coordinate of the target voxel is less than the Y component of the maximum corner coordinate of the loaded cargo, and the minimum corner coordinate of the target voxel plus the voxel side length is greater than the Y component of the minimum corner coordinate of the loaded cargo.

[0158] In the Z-axis direction, the minimum corner coordinate of the target voxel is less than the Z component of the maximum corner coordinate of the loaded cargo, and the minimum corner coordinate of the target voxel plus the voxel side length is greater than the Z component of the minimum corner coordinate of the loaded cargo.

[0159] S3-2-9. If there is overlap, mark the target voxel as occupied voxel; otherwise, mark it as unoccupied voxel.

[0160] S3-2-10. Traverse the minimum and maximum corner coordinates of each of the D loaded goods until the occupied voxels of each loaded goods and the unoccupied voxels of the remaining space in the full space voxel set are marked.

[0161] This embodiment calculates the upper and lower bounds of the voxel index of the loaded cargo on each spatial axis, generates candidate triplet indexes, and marks the occupancy status of each voxel in the full space voxel set based on the overlap between the voxel coordinate range and the cargo coordinate range, thereby distinguishing between occupied and unoccupied voxels.

[0162] Specifically, in this embodiment, step S4 includes:

[0163] S4-1. Mark all voxels in the remaining space voxel set as unvisited, and initialize the connected component counter K=0;

[0164] S4-2, Process each unvisited voxel in the remaining space voxel set in turn:

[0165] Whenever an unvisited voxel is encountered, the connected component counter K is incremented by 1, the unvisited voxel is used as the current starting voxel, and a subset of voxels corresponding to the Kth local cavity is created.

[0166] S4-3. Add the current starting voxel to the Kth voxel subset and update the current starting voxel's label to "visited".

[0167] S4-4. Based on the visited voxels in the Kth voxel subset, find the neighboring voxels of the six-neighborhood in the remaining space voxel set.

[0168] The six neighboring regions refer to the six face directions determined by taking the visited voxel as the center and along the positive and negative directions of the X-axis, Y-axis, and Z-axis.

[0169] The adjacent voxels are: unvisited voxels whose triplet indices differ by 1 only on one spatial axis and whose indices on the other two spatial axes are the same.

[0170] S4-5. Add the adjacent voxels to the Kth voxel subset and update the adjacent voxel's label to "visited".

[0171] S4-6. Continue performing the search and addition operations for adjacent voxels until the Kth voxel subset no longer adds voxels.

[0172] S4-7. Traverse each unvisited voxel in the remaining space voxel set until all voxels are visited, and obtain K local cavities; each local cavity is bound to its corresponding voxel subset.

[0173] This embodiment identifies K disconnected local cavities by performing connectivity traversal based on six-neighbor relationships in the remaining space voxel set, clustering interconnected voxels into independent voxel subsets, thereby decomposing the remaining space into several structurally separated local cavities at the voxel level.

[0174] Specifically, in this embodiment, step S5 includes:

[0175] S5-1. Select a target local cavity from the K local cavities;

[0176] S5-2. Calculate the configurational accommodating size of the target local cavity based on the voxel subset bound to it.

[0177] Wherein, the configuration accommodating size represents the side length of the minimum circumscribed body of the target local cavity in the X, Y, and Z axis directions;

[0178] S5-3. Obtain the geometric dimensions of the cargo to be loaded; wherein, the geometric dimensions include the length, width, height, and side length of the cargo to be loaded in the X-axis, Y-axis, and Z-axis directions;

[0179] S5-4. Compare the geometric dimensions of the cargo to be loaded with the configuration dimensions of the target local cavity;

[0180] S5-5. If the geometric dimensions meet the loading constraints, then the target local cavity is determined to meet the loading requirements of the goods to be loaded.

[0181] The loading constraint is that the size of the cargo to be loaded on any spatial axis is not greater than the component of the configuration's accommodating size on the corresponding axis.

[0182] S5-6. Mark the target local cavity that meets the loading requirements as a candidate cavity; otherwise, mark it as a cavity that does not meet the requirements; wherein, the candidate cavity inherits the voxel subset of its local cavity;

[0183] S5-7. Traverse the K local cavities until all G candidate cavities that meet the loading requirements are selected.

[0184] This embodiment determines the axial matching between the geometric dimensions of the cargo to be loaded and the configuration and accommodating dimensions of each local cavity, and retains only the local cavities that meet the accommodating conditions in all three axial directions as candidate cavities, ensuring that the selected candidate cavities can be actually loaded in terms of spatial dimensions.

[0185] Step S5-2 further includes:

[0186] S5-2-1. Extract the minimum corner coordinates of all voxel bindings from the voxel subset of the target local cavity;

[0187] S5-2-2, Determine the minimum and maximum values ​​of the coordinates of the minimum corner point in the X-axis, Y-axis and Z-axis directions respectively;

[0188] S5-2-3. Based on the minimum and maximum values, and combined with the predefined voxel side lengths, calculate the side lengths of the minimum circumscribed body of the target local cavity in each spatial axis direction.

[0189] The formulas for calculating the side lengths along each spatial axis are as follows:

[0190]

[0191] in: , and These represent the side lengths of the smallest circumscribed body of the target local cavity along the X, Y, and Z axes, respectively.

[0192] S5-2-4. Define the side length of the smallest circumscribed body in each spatial axis direction as the configuration accommodating size of the target local cavity.

[0193] It should be noted that the configuration accommodation size refers to the side length of the smallest circumscribed body that the local cavity can accommodate in the X, Y, and Z axes. Since each voxel extends its side length in the positive direction based on its smallest corner coordinates, the physical extension length of the local cavity on any spatial axis is equal to the range of the smallest corner coordinates of all voxels on that axis plus the voxel side length.

[0194] Specifically, in this embodiment, step S6 includes:

[0195] S6-1. Select a target candidate cavity from the G candidate cavities;

[0196] The target candidate cavity is characterized as: a candidate cavity into which the goods to be loaded will be loaded;

[0197] S6-2. Exclude the local cavity corresponding to the target candidate cavity from the K local cavities to obtain the remaining space composed of the remaining K−1 local cavities;

[0198] S6-3. Extract the voxel subsets of each of the K-1 local cavities in the remaining space;

[0199] S6-4. Based on the coordinates of the smallest corner point in each set of elements, determine the smallest circumscribed body of each local cavity, and calculate the circumscribed volume and centroid coordinates of the smallest circumscribed body.

[0200] Specifically, for each local cavity, first find the minimum corner coordinates of all voxels bound to it, and determine the minimum and maximum values ​​of these coordinates in the X, Y, and Z axes; then, with the minimum value of each axis as the lower boundary and the maximum value plus the voxel side length as the upper boundary, construct a minimum axis-aligned cuboid that can completely enclose the physical space of all voxels in the local cavity, i.e., the minimum circumscribed body.

[0201] The volume of the minimum circumscribed body is obtained by calculating its side lengths in the X, Y, and Z directions and multiplying these three side lengths together.

[0202] The centroid coordinates are the average of the smallest and largest corner coordinates in the X, Y, and Z directions of the smallest circumscribed body, thus determining the geometric center of the cuboid.

[0203] S6-5. Based on the centroid coordinates of K-1 smallest circumscribed bodies, calculate the global centroid coordinates of the remaining space;

[0204] S6-6. Calculate the spatial compatibility of the remaining space based on the circumscribed volume, centroid coordinates, and global centroid coordinates of the remaining space of the K-1 local cavities with the smallest circumscribed bodies.

[0205] The formula for calculating the spatial compatibility is:

[0206] ;

[0207] in:

[0208] S represents the spatial compatibility of the remaining space, which is used to comprehensively evaluate the quality of the remaining space after loading.

[0209] This indicates the number of local cavities with an external volume not less than a preset volume threshold;

[0210] Represents the centroid coordinates of the minimum circumscribed body of the i-th local cavity;

[0211] It represents the overall centroid coordinates of the remaining space, that is, the arithmetic mean of the centroid coordinates of all local cavities;

[0212] This represents the Euclidean distance between the centroid coordinates of the i-th local cavity's smallest circumscribed body and the global centroid coordinates;

[0213] This represents the diagonal length of the container's loading space, calculated based on the coordinates of the minimum and maximum corner points of the loading space.

[0214] and These are the weighting coefficients for the proportion of large cavities and the degree of spatial concentration, respectively, and both have positive values.

[0215] Specifically, It reflects the proportion of large cavities in the remaining space. The larger the value, the more large continuous spaces are retained, which is more conducive to the efficient loading of goods.

[0216] This indicates the average offset of the centroid of each local cavity relative to the overall center. A smaller value indicates that the centroid of each cavity is closer to the overall center, the spatial distribution is more concentrated, and the loading flexibility is higher. Therefore... It indicates the degree of spatial concentration; the larger the value, the more concentrated the space and the lower the risk of fragmentation.

[0217] S6-7. Traverse the G candidate cavities and calculate the spatial compatibility of each one until the spatial compatibility of the G candidate cavities is obtained.

[0218] This embodiment calculates the spatial compatibility, which comprehensively reflects the proportion of large cavities and the degree of spatial concentration, based on the minimum circumscribed volume of the remaining local cavities and the average offset of their centroids relative to the overall centroid coordinates after excluding target candidate cavities. This allows for a quantifiable comparison of the structural impact of different candidate cavities on the remaining space, thus providing quantifiable and comparable options for recommending loading locations.

[0219] Example 2: This example differs from Example 1 in that it also discloses a container loading capacity assessment system based on remaining space configuration. This system is used to implement the above method examples, and details already described will not be repeated. The terms "module," "unit," and "subunit" used below refer to combinations of software and / or hardware that achieve a predetermined function. Although the system described in the following examples is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0220] like Figure 5 As shown, Figure 5This is a structural block diagram of the container loading capacity assessment system based on residual space configuration of the present invention. The system includes:

[0221] The dual-coordinate acquisition unit is used to acquire the minimum and maximum corner coordinates of the loading space inside the container;

[0222] The voxel set construction unit is used to obtain the predefined voxel side lengths and construct a full-space voxel set covering the loading space based on the minimum and maximum corner coordinates of the loading space.

[0223] The voxel set partitioning unit is used to anchor the loaded goods in the loading space and divide the entire space voxel set into the occupied space voxel set and the remaining space voxel set based on the loaded goods;

[0224] A local cavity determination unit is used to perform connectivity analysis on the remaining space voxel set to determine K independent local cavities; wherein each local cavity is bound to its corresponding voxel subset;

[0225] The candidate cavity screening unit is used to select G candidate cavities that meet the loading conditions of the cargo to be loaded from K local cavities based on a voxel subset of local cavities.

[0226] The compatibility calculation unit is used to calculate the spatial compatibility of each of the G candidate cavities; wherein, the spatial compatibility is characterized as a comprehensive evaluation of the proportion of large cavities in the remaining space and the degree of spatial concentration when the cargo to be loaded is loaded into the target candidate cavity;

[0227] The location recommendation unit is used to select the candidate cavity with the highest spatial compatibility from G candidate cavities as the recommended loading location for the goods to be loaded.

[0228] In the above system, the minimum and maximum corner coordinates are obtained through a dual-coordinate acquisition unit; a full-space voxel set is constructed through a voxel set construction unit; an occupied space voxel set and a remaining space voxel set are divided through a voxel set partitioning unit; K independent local cavities are determined through a local cavity determination unit, where each local cavity is bound to its corresponding voxel subset; G candidate cavities are selected through a candidate cavity screening unit; the spatial compatibility of each of the G candidate cavities is calculated through a compatibility calculation unit; and a recommended loading location for the cargo to be loaded is selected through a location recommendation unit, thus solving the problem of wasted carrying capacity.

[0229] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interface; the indirect coupling or communication connection of apparatuses or units may be electrical, mechanical, or other forms.

[0230] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for assessing container loading capacity based on residual space configuration, characterized in that, include: S1. Obtain the coordinates of the minimum and maximum corner points of the loading space inside the container; S2. Obtain the predefined voxel side lengths, and construct a full space voxel set covering the loading space based on the minimum and maximum corner coordinates of the loading space; wherein, the full space voxel set includes regularly arranged voxels; S3. Anchor the loaded goods in the loading space and divide the entire space voxel set into the occupied space voxel set and the remaining space voxel set based on the loaded goods; S4. Perform connectivity analysis on the remaining space voxel set to determine K independent local cavities; wherein each local cavity is bound to its corresponding voxel subset; S5. Based on the voxel subset of local cavities, select G candidate cavities from K local cavities that meet the loading conditions of the cargo to be loaded. S6. Calculate the spatial compatibility of each of the G candidate cavities; wherein, the spatial compatibility is characterized by: a comprehensive evaluation of the proportion of large cavities in the remaining space and the degree of spatial concentration when the cargo to be loaded is loaded into the target candidate cavity; The calculation of the spatial compatibility of each of the G candidate cavities includes: S6-1. Select a target candidate cavity from the G candidate cavities; The target candidate cavity is characterized as: a candidate cavity into which the goods to be loaded will be loaded; S6-2. Exclude the local cavity corresponding to the target candidate cavity from the K local cavities to obtain the remaining space composed of the remaining K−1 local cavities; S6-3. Extract the voxel subsets of each of the K-1 local cavities in the remaining space; S6-4. Based on the coordinates of the smallest corner point in each set of elements, determine the smallest circumscribed body of each local cavity, and calculate the circumscribed volume and centroid coordinates of the smallest circumscribed body. S6-5. Based on the centroid coordinates of K-1 smallest circumscribed bodies, calculate the global centroid coordinates of the remaining space; S6-6. Calculate the spatial compatibility of the remaining space based on the circumscribed volume, centroid coordinates, and global centroid coordinates of the remaining space of the K-1 local cavities with the smallest circumscribed bodies. The formula for calculating the spatial compatibility is: ； in: This indicates the spatial compatibility of the remaining space, used to comprehensively evaluate the quality of the remaining space after loading. This indicates the number of local cavities with an external volume not less than a preset volume threshold; Represents the centroid coordinates of the minimum circumscribed body of the i-th local cavity; It represents the overall centroid coordinates of the remaining space, that is, the arithmetic mean of the centroid coordinates of all local cavities; This represents the Euclidean distance between the centroid coordinates of the i-th local cavity's smallest circumscribed body and the global centroid coordinates; This represents the diagonal length of the container's loading space, calculated based on the coordinates of the minimum and maximum corner points of the loading space. and These are the weighting coefficients for the proportion of large cavities and the degree of spatial concentration, respectively, and both have positive values; It reflects the proportion of large cavities in the remaining space; the larger the value, the more large continuous spaces are retained. This indicates the average offset of the centroid of each local cavity relative to the overall center. The smaller the value, the closer the centroid of each cavity is to the overall center, and the more concentrated the spatial distribution. This indicates the degree of spatial concentration; the larger the value, the more concentrated the space and the lower the risk of fragmentation. S6-7. Traverse the G candidate cavities and calculate the spatial compatibility of each one until the spatial compatibility of the G candidate cavities is obtained. S7. Among the G candidate cavities, select the candidate cavity with the highest spatial compatibility as the recommended loading location for the cargo to be loaded.

2. The container loading capacity assessment method based on residual space configuration according to claim 1, characterized in that, The construction of the full space voxel set covering the loading space includes: S2-1. Extract the maximum and minimum coordinate components of the loading space on each spatial axis from the minimum and maximum corner coordinates of the loading space; where each spatial axis is the mutually orthogonal X-axis, Y-axis and Z-axis. S2-2. Based on the maximum and minimum coordinate components of each spatial axis, calculate the difference between the coordinate components of each spatial axis; S2-3. Calculate the ratio between the coordinate component differences and the voxel side lengths to determine the number of voxels for each spatial axis. S2-4. Based on the number of voxels in each spatial axis, determine the three-axis index range of the loading space respectively; S2-5. Perform permutations and combinations on the respective indices within the three-axis index range, and traverse to generate all triplet indices. ; in, , , These represent the indices for the X, Y, and Z axes, respectively. S2-6. For each triplet index, calculate the coordinates of its corresponding minimum corner point; S2-7. Using the smallest corner coordinates of each triplet index as a reference, extend the voxel side length along each spatial axis to generate a cube of voxels. S2-8. For each generated voxel, determine whether the coordinate components of its smallest corner point coordinates on any axis are greater than or equal to the corresponding axis coordinate components of the largest corner point coordinates in the loading space. S2-9. If the value is greater than or equal to the value, the generated voxel is discarded; otherwise, the generated voxel is retained. S2-10. Bind each retained voxel to its triplet index and minimum corner coordinates to obtain the full space voxel set.

3. The container loading capacity assessment method based on residual space configuration according to claim 1, characterized in that, The method of dividing the entire space voxel set into an occupied space voxel set and a remaining space voxel set based on the loaded cargo includes: S3-1. Obtain the minimum and maximum corner coordinates of each of the D loaded goods within the loading space; S3-2. Based on the minimum and maximum corner coordinates of each of the D loaded goods, mark the occupied voxels of each loaded goods and the unoccupied voxels of the remaining space in the full space voxel set. S3-3. Collect the occupied voxels of each loaded cargo and the unoccupied voxels of the remaining space to generate the occupied space voxel set and the remaining space voxel set.

4. The container loading capacity assessment method based on residual space configuration according to claim 3, characterized in that, Based on the minimum and maximum corner coordinates of each of the D loaded goods, mark the occupied voxels and unoccupied voxels of the remaining space for each loaded goods in the full space voxel set, including: S3-2-1. For each loaded cargo, extract the minimum and maximum coordinate components of the loaded cargo on each spatial axis from its minimum and maximum corner coordinates. S3-2-2. Based on the minimum and maximum coordinate components of the loaded goods on each spatial axis, the coordinates of the minimum corner point of the loading space, and the voxel side length, calculate the upper and lower bounds of the voxel index of the loaded goods on each spatial axis. S3-2-3. Based on the upper and lower bounds of the voxel indexes of the loaded goods on each spatial axis, determine the index traversal range of the loaded goods on each spatial axis. S3-2-4. Perform permutations and combinations on the index traversal range of each spatial axis to generate a candidate triplet index set corresponding to the loaded goods. S3-2-5. Select a target triplet index from the set of candidate triplet indexes corresponding to the loaded goods; S3-2-6, The target voxel corresponding to the anchored target triplet index in the full space voxel set; S3-2-7. Extract the coordinates of the smallest corner point bound to the target voxel, and calculate the range of voxel coordinates of the target voxel based on the voxel side length. S3-2-8. Determine whether the range of voxel coordinates of the target voxel overlaps with the range of cargo coordinates constructed by the minimum and maximum corner coordinates of the loaded cargo. S3-2-9. If there is overlap, mark the target voxel as occupied voxel; otherwise, mark it as unoccupied voxel. S3-2-10. Traverse the minimum and maximum corner coordinates of each of the D loaded goods until the occupied voxels of each loaded goods and the unoccupied voxels of the remaining space in the full space voxel set are marked.

5. The container loading capacity assessment method based on residual space configuration according to claim 1, characterized in that, Connectivity analysis was performed on the remaining space voxel set to identify K independent local cavities, including: S4-1. Mark each voxel in the remaining space voxel set as unvisited and initialize the connected component counter K=0. S4-2, Process each unvisited voxel in the remaining space voxel set in turn: Whenever an unvisited voxel is encountered, the connected component counter K is incremented by 1, the unvisited voxel is used as the current starting voxel, and a subset of voxels corresponding to the Kth local cavity is created. S4-3. Add the current starting voxel to the Kth voxel subset and update the current starting voxel's label to "visited". S4-4. Based on the visited voxels in the Kth voxel subset, find the neighboring voxels of the six-neighborhood in the remaining space voxel set. S4-5. Add the adjacent voxels to the Kth voxel subset and update the adjacent voxel's label to "visited". S4-6. Continue to perform the search and addition operations of adjacent voxels until the Kth voxel subset no longer adds voxels; S4-7. Traverse each unvisited voxel in the remaining space voxel set until all voxels are visited, and obtain K local cavities; each local cavity is bound to its corresponding voxel subset.

6. The container loading capacity assessment method based on residual space configuration according to claim 1, characterized in that, Based on a voxel subset of local cavities, G candidate cavities that meet the loading conditions of the cargo to be loaded are selected from K local cavities, including: S5-1. Select a target local cavity from the K local cavities; S5-2. Calculate the configurational accommodating size of the target local cavity based on the voxel subset bound to it. Wherein, the configuration accommodating size represents the side length of the minimum circumscribed body of the target local cavity in the X, Y, and Z axis directions; S5-3. Obtain the geometric dimensions of the cargo to be loaded; wherein, the geometric dimensions include the length, width, height, and side length of the cargo to be loaded in the X-axis, Y-axis, and Z-axis directions; S5-4. Compare the geometric dimensions of the cargo to be loaded with the configuration dimensions of the target local cavity; S5-5. If the geometric dimensions meet the loading constraints, then the target local cavity is determined to meet the loading requirements of the goods to be loaded. The loading constraint is that the size of the cargo to be loaded on any spatial axis is not greater than the component of the configuration's accommodating size on the corresponding axis. S5-6. Mark the target local cavity that meets the loading requirements as a candidate cavity; otherwise, mark it as a cavity that does not meet the requirements; wherein, the candidate cavity inherits the voxel subset of its local cavity; S5-7. Traverse the K local cavities until all G candidate cavities that meet the loading requirements are selected.

7. The container loading capacity assessment method based on residual space configuration according to claim 6, characterized in that, Based on the subset of voxels bound to the target local cavity, calculate the configurational accommodating size of the target local cavity, including: S5-2-1. Extract the minimum corner coordinates of all voxel bindings from the voxel subset of the target local cavity; S5-2-2, Determine the minimum and maximum values ​​of the coordinates of the minimum corner point in the X-axis, Y-axis and Z-axis directions respectively; S5-2-3. Based on the minimum and maximum values, and combined with the predefined voxel side lengths, calculate the side lengths of the minimum circumscribed body of the target local cavity in each spatial axis direction. S5-2-4. Define the side length of the smallest circumscribed body in each spatial axis direction as the configuration accommodating size of the target local cavity.

8. A container loading capacity assessment system based on residual space configuration, characterized in that, The container loading capacity assessment method according to any one of claims 1 to 7 includes: The dual-coordinate acquisition unit is used to acquire the minimum and maximum corner coordinates of the loading space inside the container; The voxel set construction unit is used to obtain the predefined voxel side lengths and construct a full-space voxel set covering the loading space based on the minimum and maximum corner coordinates of the loading space. The voxel set partitioning unit is used to anchor the loaded goods in the loading space and divide the entire space voxel set into the occupied space voxel set and the remaining space voxel set based on the loaded goods; A local cavity determination unit is used to perform connectivity analysis on the remaining space voxel set to determine K independent local cavities; wherein each local cavity is bound to its corresponding voxel subset; The candidate cavity screening unit is used to select G candidate cavities that meet the loading conditions of the cargo to be loaded from K local cavities based on a voxel subset of local cavities. The compatibility calculation unit is used to calculate the spatial compatibility of each of the G candidate cavities; wherein, the spatial compatibility is characterized as a comprehensive evaluation of the proportion of large cavities in the remaining space and the degree of spatial concentration when the cargo to be loaded is loaded into the target candidate cavity; The calculation of the spatial compatibility of each of the G candidate cavities includes: S6-1. Select a target candidate cavity from the G candidate cavities; The target candidate cavity is characterized as: a candidate cavity into which the goods to be loaded will be loaded; S6-2. Exclude the local cavity corresponding to the target candidate cavity from the K local cavities to obtain the remaining space composed of the remaining K−1 local cavities; S6-3. Extract the voxel subsets of each of the K-1 local cavities in the remaining space; S6-4. Based on the coordinates of the smallest corner point in each set of elements, determine the smallest circumscribed body of each local cavity, and calculate the circumscribed volume and centroid coordinates of the smallest circumscribed body. S6-5. Based on the centroid coordinates of K-1 smallest circumscribed bodies, calculate the global centroid coordinates of the remaining space; S6-6. Calculate the spatial compatibility of the remaining space based on the circumscribed volume, centroid coordinates, and global centroid coordinates of the remaining space of the K-1 local cavities with the smallest circumscribed bodies. The formula for calculating the spatial compatibility is: ； in: This indicates the spatial compatibility of the remaining space, used to comprehensively evaluate the quality of the remaining space after loading. This indicates the number of local cavities with an external volume not less than a preset volume threshold; Represents the centroid coordinates of the minimum circumscribed body of the i-th local cavity; It represents the overall centroid coordinates of the remaining space, that is, the arithmetic mean of the centroid coordinates of all local cavities; This represents the Euclidean distance between the centroid coordinates of the i-th local cavity's smallest circumscribed body and the global centroid coordinates; This represents the diagonal length of the container's loading space, calculated based on the coordinates of the minimum and maximum corner points of the loading space. and These are the weighting coefficients for the proportion of large cavities and the degree of spatial concentration, respectively, and both have positive values; It reflects the proportion of large cavities in the remaining space; the larger the value, the more large continuous spaces are retained. This indicates the average offset of the centroid of each local cavity relative to the overall center. The smaller the value, the closer the centroid of each cavity is to the overall center, and the more concentrated the spatial distribution. This indicates the degree of spatial concentration; the larger the value, the more concentrated the space and the lower the risk of fragmentation. S6-7. Traverse the G candidate cavities and calculate the spatial compatibility of each one until the spatial compatibility of the G candidate cavities is obtained. The location recommendation unit is used to select the candidate cavity with the highest spatial compatibility from G candidate cavities as the recommended loading location for the goods to be loaded.

Images