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

Abstract

The invention discloses a container loading capacity evaluation method and system based on a residual spatial configuration, and relates to the field of container loading, the method comprises the steps of obtaining corner coordinates of a loading space in a container, and constructing a full space voxel set covering the loading space, the full space voxel set comprising voxels arranged regularly, dividing the whole space voxel set into an occupied space voxel set and a residual space voxel set, determining K independent local cavities, screening out G candidate cavities based on voxel subsets of the local cavities, calculating respective spatial compatibility of the G candidate cavities, and calculating the spatial compatibility of the G candidate cavities in the G candidate cavities; according to the method, on the premise that the current cargo size constraint is met, the loading position with the higher spatial compatibility is preferentially selected, so that more continuous and concentrated available areas are reserved, and the loading efficiency is improved. And the overall space utilization efficiency and the loading success rate of the container in the multi-round loading process are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of container loading evaluation, in particular to a container loading capacity evaluation method and system based on residual space configuration. BACKGROUND

[0002] In the field of cargo transportation and storage, the space utilization rate of containers or loading containers directly affects the logistics cost and operation efficiency. The existing loading method usually selects the loading position based on the geometric size matching of the to-be-loaded cargo and the residual space, that is, only verifies whether the candidate area can accommodate the external size of the current cargo. Such a method can ensure the feasibility of single loading, but in the process of continuous loading of multiple batches and multiple types of goods, it is easy to cause the scattered distribution of residual space and the insufficient volume of single available area, which is difficult to meet the loading demand of subsequent goods. Especially in the transportation scene with high space utilization efficiency requirement (such as air freight), even if the total residual volume is sufficient, the scattered local cavities often cannot be effectively reused, resulting in waste of carrying capacity. Therefore, an evaluation method that can meet the current loading constraints while taking into account the overall availability of residual space is urgently needed to improve the overall loading capacity of containers in the entire loading process. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides a container loading capacity evaluation method based on residual space configuration, which solves the technical problems raised in the background art by introducing a voxel set and a space compatibility.

[0004] To achieve the above purpose, the present application realizes the following technical scheme:

[0005] In a first aspect, the present application discloses a container loading capacity evaluation method based on residual space configuration, which comprises the following steps:

[0006] S1, obtaining the minimum corner point coordinates and the maximum corner point coordinates of the loading space in the container;

[0007] S2, obtaining a predefined voxel edge length, and based on the minimum corner point coordinates and the maximum corner point coordinates of the loading space, constructing a full-space voxel set covering the loading space; wherein the full-space voxel set comprises regularly arranged voxels;

[0008] S3, anchoring the loaded goods in the loading space, and based on the loaded goods, dividing the full-space voxel set into an occupied space voxel set and a residual space voxel set;

[0009] S4, performing connectedness analysis on the residual space voxel set to determine K independent local cavities; wherein each local cavity is bound to its corresponding voxel subset;

[0010] S5, filtering G candidate cavities from the K local cavities based on the local cavity-based voxel subset, which satisfy the loading condition of the to-be-loaded cargo;

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

[0012] S7, selecting the candidate cavity corresponding to the maximum spatial compatibility as the recommended loading position of the to-be-loaded cargo from the G candidate cavities.

[0013] In some embodiments, the full-space voxel set covering the loading space is constructed, including:

[0014] S2-1, extracting the maximum coordinate component and the minimum coordinate component of the loading space on each spatial axis from the minimum corner point coordinates and the maximum corner point coordinates of the loading space; wherein each spatial axis is the X-axis, the Y-axis and the Z-axis which are mutually orthogonal;

[0015] S2-2, calculating the coordinate component difference of each spatial axis based on the maximum coordinate component and the minimum coordinate component of each spatial axis;

[0016] S2-3, performing ratio operation on the coordinate component difference and the voxel edge length to determine the voxel number of each spatial axis;

[0017] S2-4, determining the three-axis index range of the loading space based on the voxel number of each spatial axis, respectively;

[0018] S2-5, performing permutation and combination on each index in the three-axis index range to generate all triple indexes by traversal ;

[0019] wherein, , , Xi, Yi and Zi represent the index of the X-axis, the Y-axis and the Z-axis, respectively;

[0020] S2-6, calculating the minimum corner point coordinates corresponding to each triple index;

[0021] S2-7, generating a voxel of a cube by extending the voxel edge length along each spatial axis based on the minimum corner point coordinates of each triple index;

[0022] S2-8, for each generated voxel, judging whether the coordinate component of the minimum corner point coordinates on any axis is greater than or equal to the corresponding axis coordinate component of the maximum corner point coordinates of the loading space;

[0023] S2-9, if greater than or equal to, the generated voxel is removed; 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, judging whether the voxel coordinate range of the target voxel overlaps with the cargo coordinate range constructed by the minimum corner point coordinate and the maximum corner point coordinate of the loaded cargo;

[0038] S3-2-9, if there is an overlap, marking the target voxel as an occupied voxel, otherwise marking it as an unoccupied voxel;

[0039] S3-2-10, traversing the minimum corner point coordinate and the maximum corner point coordinate of each of the D loaded cargos until the occupied voxels of each loaded cargo and the unoccupied voxels of the remaining space in the full-space voxel set are marked.

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

[0041] S4-1, marking each voxel in the remaining space voxel set as unvisited and initializing a connectivity component counter K = 0;

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

[0043] Each time an unvisited voxel is encountered, the connectivity component counter K is incremented by 1, the unvisited voxel is taken as the current starting voxel, and a voxel subset corresponding to the Kth local cavity is created

[0044] S4-3, adding the current starting voxel to the Kth voxel subset and updating the label of the current starting voxel to visited;

[0045] S4-4, based on the visited voxels in the Kth voxel subset, searching for six-neighbor voxels in the remaining space voxel set;

[0046] S4-5, adding the adjacent voxels to the Kth voxel subset and updating the labels of the adjacent voxels to visited;

[0047] S4-6, continuously performing the searching and adding operations on adjacent voxels until the Kth voxel subset no longer adds voxels

[0048] S4-7, traversing each unvisited voxel in the remaining space voxel set until each voxel is visited, obtaining K local cavities; wherein each local cavity is bound to its corresponding voxel subset.

[0049] In some embodiments, G candidate cavities that meet the loading conditions of the to-be-loaded cargo are selected from the K local cavities, including:

[0050] S5-1, selecting a target local cavity from the K local cavities;

[0051] S5-2, calculate a configuration containing size of the target local cavity according to the voxel subset bound by the target local cavity;

[0052] The configuration containing size represents the side length of the minimum circumscribed body of the target local cavity in the X-axis, Y-axis and Z-axis directions.

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

[0054] S5-4, compare the geometric size of the to-be-loaded cargo with the configuration containing size of the target local cavity;

[0055] S5-5, if the geometric size meets the loading constraint condition, it is determined that the target local cavity meets the loading requirement of the to-be-loaded cargo;

[0056] The loading constraint condition is that the size of the to-be-loaded cargo in any spatial axis is not greater than the component of the configuration containing size in the corresponding axis.

[0057] S5-6, mark the target local cavity meeting the loading requirement as a candidate cavity; otherwise, mark it as a cavity that does not meet the requirement; wherein the candidate cavity inherits the voxel subset of the local cavity to which it belongs.

[0058] S5-7, traverse the K local cavities until G candidate cavities that meet the loading requirement are screened out.

[0059] In some embodiments, the configuration containing size of the target local cavity is calculated according to the voxel subset bound by the target local cavity, including:

[0060] S5-2-1, extract all the minimum corner point coordinates bound by the voxels from the voxel subset of the target local cavity;

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

[0062] S5-2-3, based on the minimum value and the maximum value, and in combination with a predefined voxel side length, calculate the side length 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 minimum circumscribed body in each spatial axis direction as the configuration containing 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, selecting a target candidate cavity from the G candidate cavities;

[0066] The target candidate cavity is characterized by: a candidate cavity in which the to-be-loaded cargo is to be loaded into.

[0067] S6-2, excluding the local cavity corresponding to the target candidate cavity from the K local cavities to obtain a remaining space composed of the remaining K-1 local cavities;

[0068] S6-3, extracting a voxel subset of each of the K-1 local cavities in the remaining space;

[0069] S6-4, determining the minimum circumscribed body of each local cavity based on the minimum corner point coordinates in each voxel subset, and calculating the circumscribed volume and centroid coordinates of the minimum circumscribed body;

[0070] S6-5, calculating the overall centroid coordinates of the remaining space based on the centroid coordinates of the K-1 minimum circumscribed bodies;

[0071] S6-6, calculating the space compatibility of the remaining space according to the circumscribed volume, centroid coordinates of the K-1 local cavity minimum circumscribed bodies, and the overall centroid coordinates of the remaining space;

[0072] S6-7, traversing the G candidate cavities to calculate the space compatibility one by one until the space compatibility of the G candidate cavities is obtained.

[0073] The application provides a container loading capacity evaluation method based on a remaining space configuration, which has the following beneficial effects:

[0074] The application constructs a full-space voxel set of a container loading space, and marks the occupation state of each voxel according to the minimum corner point coordinates and the maximum corner point coordinates of the loaded cargo, so as to convert the continuous space region into a set that can be judged whether it is occupied or not voxel by voxel. Further, based on the six-neighborhood connectivity relationship of the remaining space voxels, multiple independent local cavities are identified, and candidate cavities that can accommodate the cargo in the X, Y and Z three-axis directions are selected by comparing the geometric size of the to-be-loaded cargo with the configuration accommodation size of each local cavity. Finally, by calculating the space compatibility corresponding to each candidate loading position, the index comprehensively reflects the proportion of large cavities and the space concentration degree in the remaining space after loading, so that under the premise of meeting the size constraint of the current cargo, the loading position with higher space compatibility is preferentially selected to reserve more continuous and concentrated available regions, thereby improving the overall space utilization efficiency and loading success rate of the container in the multi-round loading process.

[0075] In a second aspect, the application discloses a container loading capacity evaluation system based on a remaining space configuration, which executes the container loading capacity evaluation method of the first aspect, and the evaluation system comprises the following units:

[0076] A double-coordinate acquisition unit is configured to acquire minimum corner point coordinates and maximum corner point coordinates of a loading space in a container.

[0077] A voxel set construction unit is configured to acquire a predefined voxel edge length, and construct a full-space voxel set covering the loading space based on the minimum corner point coordinates and the maximum corner point coordinates of the loading space.

[0078] A voxel set division unit is configured to anchor loaded goods in the loading space, and divide the full-space voxel set into an occupied space voxel set and a remaining space voxel set based on the loaded goods.

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

[0080] A candidate cavity screening unit is configured to screen G candidate cavities from the K local cavities based on the voxel subsets of the local cavities, the G candidate cavities satisfying loading conditions of a to-be-loaded cargo.

[0081] A compatibility calculation unit is configured to calculate respective space compatibilities of the G candidate cavities; wherein the space compatibility is represented as a comprehensive evaluation of a large cavity proportion of the remaining space and a spatial concentration degree when the to-be-loaded cargo is loaded into a target candidate cavity.

[0082] A position recommendation unit is configured to select a candidate cavity corresponding to a maximum space compatibility from the G candidate cavities as a recommended loading position of the to-be-loaded cargo.

[0083] Compared with the prior art, the container loading capacity evaluation system based on a remaining space configuration of the present application has the same beneficial effects as the container loading capacity evaluation method based on a remaining space configuration of the present application, and thus will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 FIG. 1 is a flowchart of the container loading capacity evaluation method based on a remaining space configuration of the present application;

[0085] Figure 2 FIG. 4 is a flowchart of the obtaining process of the local cavity of the present application;

[0086] Figure 3 FIG. 6 is a flowchart of the definition process of the configuration accommodation size of the present application;

[0087] Figure 4 FIG. 8 is a flowchart of the calculation process of the space compatibility of the present application;

[0088] Figure 5 FIG. 10 is a structural block diagram of the container loading capacity evaluation system based on a remaining space configuration of the present application. 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 space compatibility of each of the G candidate cavities; wherein the space compatibility is represented as: the comprehensive evaluation of the large cavity proportion of the remaining space and the space concentration degree when the to-be-loaded cargo is loaded into the target candidate cavity.

[0100] S7, select the candidate cavity corresponding to the maximum space compatibility in the G candidate cavities as the recommended loading position of the to-be-loaded cargo.

[0101] The embodiment discretizes the container loading space into a voxel grid, divides the space range occupied by the loaded cargo and the remaining area, further identifies the local cavity in the remaining space, screens out the candidate cavity that can accommodate the to-be-loaded cargo, calculates the space compatibility of each candidate cavity based on the large cavity proportion of the remaining space and the space concentration degree after loading, and finally takes the cavity corresponding to the maximum compatibility as the recommended loading position, thereby realizing the quantitative evaluation of the remaining space configuration of the container and the structured judgment of the loading capacity.

[0102] Specifically, the step S2 includes:

[0103] S2-1, the minimum corner coordinates of the loading space are and the maximum corner coordinates are Among them, the maximum coordinate component and the minimum coordinate component of the loading space on each space axis are extracted; wherein each space axis is the X axis, the Y axis and the Z axis which are mutually orthogonal;

[0104] S2-2, based on the maximum coordinate component and the minimum coordinate component of each space axis, the coordinate component difference of each space axis is calculated;

[0105] S2-3, the coordinate component difference is subjected to ratio operation with the voxel edge length to determine the voxel number of each space axis;

[0106] Specifically, the calculation formula of the voxel number is:

[0107] ;

[0108] Among them, , , respectively represent the voxel number in the X axis direction, the Y axis direction and the Z axis direction, and the value range satisfies the down rounding, represents the preset voxel edge length.

[0109] S2-4, based on the voxel number of each space axis, the three-axis index range of the loading space is determined respectively;

[0110] Specifically, the three-axis index range is represented as:

[0111] The X axis index range is: ;

[0112] Y-axis index range: ;

[0113] Z-axis index range: ;

[0114] S2-5, performing permutation combination on each index in the three-axis index range, and traversing to generate all triple index ;

[0115] wherein, , , respectively represent the index of the X-axis, the Y-axis and the Z-axis;

[0116] S2-6, for each triple index, calculating its corresponding minimum corner point coordinate;

[0117] The calculation formula of the minimum corner point coordinate is:

[0118] ;

[0119] wherein, , , respectively represent the coordinate components of the minimum corner point coordinate;

[0120] S2-7, taking the minimum corner point coordinate of each triple index as the reference, extending the voxel edge length along each spatial axis to generate a voxel of a cube;

[0121] S2-8, for each generated voxel, judging whether the coordinate component of the minimum corner point coordinate on any axis is greater than or equal to the corresponding axis coordinate component of the maximum corner point coordinate of the loading space;

[0122] S2-9, if greater than or equal to, the generated voxel is removed; otherwise, the generated voxel is retained;

[0123] S2-10, binding each retained voxel with its triple index and minimum corner point coordinate to obtain the full-space voxel set.

[0124] The embodiment calculates the number of voxels and generates triple index according to the minimum corner point coordinate, the maximum corner point coordinate and the preset voxel edge length of the container loading space, then accurately allocates the minimum corner point coordinate for each voxel, and removes the voxels exceeding the boundary of the loading space after generation, finally constructs the full-space voxel set which is strictly aligned with the actual loading space and has no redundancy, so as to convert the continuous geometric space of the container loading space into the voxel set with clear spatial coordinates and index identification.

[0125] Specifically, in the embodiment, the step S3 comprises:

[0126] S3-1, obtaining minimum corner point coordinates and maximum corner point coordinates of D loaded cargos in the loading space and maximum corner point coordinates ;

[0127] It should be noted that the minimum corner point coordinates and the maximum corner point coordinates can be used to calculate the minimum axis-aligned cuboid representing the cargo and the size range occupied in the loading space.

[0128] S3-2, based on the minimum corner point coordinates and the maximum corner point coordinates of the D loaded cargos, marking the occupied voxels of each loaded cargo and the unoccupied voxels of the remaining space in the full-space voxel set;

[0129] S3-3, collecting the occupied voxels of each loaded cargo and the unoccupied voxels of the remaining space respectively to generate an occupied space voxel set and a remaining space voxel set.

[0130] The embodiment realizes the division of the full-space voxel set into two types of voxel sets, namely occupied and remaining, by taking the space range defined by the minimum corner point coordinates and the maximum corner point coordinates of the loaded cargo as the basis, so that the used space and the available space in the container are clearly separated at the voxel level.

[0131] The step S3-2 further comprises:

[0132] S3-2-1, for each loaded cargo, extracting the minimum coordinate component and the maximum coordinate component of the loaded cargo on each spatial axis from the minimum corner point coordinates and the maximum corner point coordinates of the loaded cargo;

[0133] S3-2-2, calculating the voxel index lower bound and the voxel index upper bound of each spatial axis of the loaded cargo according to the minimum coordinate component and the maximum coordinate component of the loaded cargo on each spatial axis, the minimum corner point coordinates of the loading space, and the voxel edge length;

[0134] Specifically, the calculation formula of the voxel index lower bound and the voxel index upper bound is:

[0135] ;

[0136] wherein:

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

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

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

[0140] S3-2-3, determining the index traversal range of the loaded cargo on each spatial axis based on the lower and upper bounds of the voxel index of the loaded cargo on each spatial axis;

[0141] Specifically, the index traversal range on each spatial axis is respectively represented as:

[0142] X-axis index range: ;

[0143] Y-axis index range: ;

[0144] Z-axis index range: ;

[0145] S3-2-4, performing permutation and combination on each index in the index traversal range on each spatial axis to generate a candidate triple index set corresponding to the loaded cargo;

[0146] S3-2-5, selecting a target triple index from the candidate triple index set corresponding to the loaded cargo;

[0147] S3-2-6, anchoring the target triple index to the target voxel corresponding to the target triple index in the full space voxel set;

[0148] S3-2-7, extracting the minimum corner point coordinates bound by the target voxel , and calculating the voxel coordinate range of the target voxel based on the voxel edge length;

[0149] Specifically, the coordinate interval of the voxel coordinate range on the X-axis is , the coordinate interval on the Y-axis is , and the coordinate interval on the Z-axis is ;

[0150] S3-2-8, judging whether the voxel coordinate range of the target voxel overlaps with the cargo coordinate range constructed by the minimum corner point coordinates and the maximum corner point coordinates of the loaded cargo;

[0151] The overlap is represented as each spatial axis satisfying:

[0152] Equation ① and ;

[0153] Equation ② and ;

[0154] Equation ③ and ;

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

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

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

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

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

[0160] S3-2-10, traverse the minimum corner point coordinate and the maximum corner point coordinate of each of the D loaded cargos until the occupied voxels of each loaded cargo and the unoccupied voxels of the remaining space in the full-space voxel set are marked.

[0161] The embodiment generates candidate triple indexes by calculating the voxel index upper and lower bounds of the loaded cargos in each space axis, and marks the occupancy state of each voxel in the full-space voxel set based on the overlap relationship between the voxel coordinate range and the cargo coordinate range, thereby realizing the distinction between occupied voxels and unoccupied voxels.

[0162] Specifically, the step S4 includes:

[0163] S4-1, mark each voxel 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] Each time an unvisited voxel is encountered, the connected component counter K is increased by 1, the unvisited voxel is taken as a current starting voxel, and a voxel subset corresponding to the Kth local cavity is created

[0166] S4-3, add the current starting voxel to the Kth voxel subset, and update the mark of the current starting voxel to visited;

[0167] S4-4, based on the visited voxels in the Kth voxel subset, find the adjacent voxels of the six-neighborhood in the remaining space voxel set;

[0168] The six-neighborhood represents six face directions determined along the positive and negative directions of the X-axis, Y-axis and Z-axis with the accessed voxel as the center;

[0169] The adjacent voxel represents an unvisited voxel whose three-tuple index differs by 1 in only one spatial axis and whose remaining two spatial axis indexes are the same;

[0170] S4-5, adding the adjacent voxel to the Kth voxel subset and updating the label of the adjacent voxel to visited;

[0171] S4-6, continuously performing the searching and adding operations of the adjacent voxel until the Kth voxel subset no longer adds voxels

[0172] S4-7, traversing each unvisited voxel in the remaining spatial voxel set until each voxel is visited, obtaining K local cavities; wherein each local cavity is bound to its corresponding voxel subset.

[0173] The embodiment clusters voxels connected to each other into independent voxel subsets by performing connectivity traversal based on the six-neighborhood relationship in the remaining spatial voxel set, thereby identifying K local cavities that are not connected to each other, and decomposing the remaining space into a plurality of structurally separated local cavities at the voxel level.

[0174] Specifically, the step S5 includes:

[0175] S5-1, selecting a target local cavity from the K local cavities;

[0176] S5-2, calculating the configuration containing size of the target local cavity according to the voxel subset bound to the target local cavity;

[0177] The configuration containing size represents the side length of the minimum circumscribed body of the target local cavity in the X-axis, Y-axis and Z-axis directions.

[0178] S5-3, obtaining the geometric size of the to-be-loaded cargo; wherein the geometric size includes the length, width and height side lengths of the to-be-loaded cargo in the X-axis, Y-axis and Z-axis directions;

[0179] S5-4, comparing the geometric size of the to-be-loaded cargo with the configuration containing size of the target local cavity;

[0180] S5-5, if the geometric size satisfies the loading constraint condition, determining that the target local cavity satisfies the loading requirement of the to-be-loaded cargo;

[0181] The loading constraint condition is that the size of the to-be-loaded cargo in any spatial axis is not greater than the component of the configuration containing size in the corresponding axis;

[0182] S5-6, mark the target local cavity meeting the loading requirement as a candidate cavity; otherwise, mark it as a cavity that does not meet the requirement; wherein the candidate cavity inherits the voxel subset of the local cavity to which it belongs;

[0183] S5-7, traverse the K local cavities until all G candidate cavities meeting the loading requirement are screened out.

[0184] The embodiment ensures that the candidate cavities screened out can be actually loaded in terms of spatial size by judging the axial matching between the geometric size of the to-be-loaded cargo and the configuration containing size of each local cavity, and only retaining the local cavities meeting the containing condition in three axial directions as candidate cavities.

[0185] The step S5-2 further comprises:

[0186] S5-2-1, extract all the minimum corner point coordinates bound by the voxels from the voxel subset of the target local cavity;

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

[0188] S5-2-3, based on the minimum value and the maximum value, and in combination with the pre-defined voxel side length, calculate the side length of the minimum circumscribed body of the target local cavity in each spatial axial direction;

[0189] The calculation formula of the side length in each spatial axial direction is:

[0190]

[0191] wherein: , and respectively represent the side length of the minimum circumscribed body of the target local cavity in the X-axis, Y-axis and Z-axis directions;

[0192] S5-2-4, define the side length of the minimum circumscribed body in each spatial axial direction as the configuration containing size of the target local cavity.

[0193] It should be noted that the configuration containing size refers to the side length of the minimum circumscribed body that can be contained by the local cavity in the X, Y and Z three axial directions. Since each voxel extends the voxel side length in the positive direction based on the minimum corner point coordinate thereof, the physical extension length of the local cavity in any spatial axis is equal to the range of the minimum corner point coordinates of all the voxels in the axis plus the voxel side length.

[0194] Specifically in the embodiment, the step S6 comprises:

[0195] S6-1, select a target candidate cavity from the G candidate cavities;

[0196] The target candidate cavity is a candidate cavity in which the to-be-loaded cargo is to be loaded.

[0197] S6-2, excluding the target candidate cavity from the K local cavities to obtain a remaining space composed of the remaining K-1 local cavities;

[0198] S6-3, extracting a voxel subset of each of the K-1 local cavities in the remaining space;

[0199] S6-4, determining a minimum circumscribed body of each local cavity based on the minimum corner point coordinates in each voxel subset, and calculating the circumscribed volume and centroid coordinates of the minimum circumscribed body;

[0200] Specifically, for each local cavity, first find the minimum corner point coordinates of all voxel bindings thereof, and determine the minimum and maximum values of these coordinates in the X, Y and Z three-axis directions; then, taking the minimum value of each axis as the lower boundary and the maximum value plus the voxel edge length as the upper boundary, a minimum axis-aligned cuboid that can completely enclose all voxels of the local cavity, i.e., the minimum circumscribed body, is constructed.

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

[0202] The centroid coordinates are the average values of the minimum and maximum corner point coordinates of the minimum circumscribed body in the X, Y and Z directions, thereby determining the geometric center position of the cuboid.

[0203] S6-5, calculating the overall centroid coordinates of the remaining space based on the centroid coordinates of the K-1 minimum circumscribed bodies;

[0204] S6-6, calculating the space compatibility of the remaining space according to the circumscribed volumes and centroid coordinates of the K-1 local cavity minimum circumscribed bodies and the overall centroid coordinates of the remaining space;

[0205] The calculation formula of the space compatibility is:

[0206] ;

[0207] Wherein:

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

[0209] represents the number of local cavities with a circumscribed 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] represents the overall centroid coordinate of the remaining space, that is, the arithmetic mean of all local cavity centroid coordinates;

[0212] represents the Euclidean distance between the centroid coordinate of the minimum circumscribed body of the i-th local cavity and the overall centroid coordinate;

[0213] represents the diagonal length of the container loading space, which is calculated based on the minimum corner point coordinate and the maximum corner point coordinate of the loading space;

[0214] and are weight coefficients of the large cavity proportion and the space concentration degree, respectively, and both are positive.

[0215] Specifically, reflects the large cavity proportion in the remaining space, and the larger the value, the more continuous space is retained, which is more conducive to efficient loading of goods;

[0216] represents the average deviation degree of each local cavity centroid relative to the overall center, and the smaller the value, the closer the cavity centroid is to the overall center, the more concentrated the space distribution is, and the higher the loading flexibility is; therefore, represents the space concentration degree, and the larger the value, the more concentrated the space is, and the lower the fragmentation risk is.

[0217] S6-7, traverse G candidate cavities, and calculate the space compatibility degree one by one until the space compatibility degrees of the G candidate cavities are obtained.

[0218] In this embodiment, after excluding the target candidate cavity, the space compatibility degree which comprehensively reflects the large cavity proportion and the space concentration degree is calculated based on the minimum circumscribed body volume of the remaining local cavity and the average deviation degree of the centroid thereof relative to the overall centroid coordinate, so that the structural influence of different candidate cavities on the remaining space can be quantitatively compared, thereby providing a quantitatively comparable selection item for recommending a loading position.

[0219] Embodiment 2: The technical solution of this embodiment 2 is different from that of embodiment 1 in that embodiment 2 further discloses a container loading capacity evaluation system based on a remaining space configuration, which is used to implement the above-mentioned method embodiments, and details thereof have been described above. The terms "module", "unit", "sub-unit" and the like used below can be a combination of software and / or hardware that can realize a predetermined function. Although the system described in the following embodiments is preferably realized in software, hardware, or a combination of software and hardware is also possible and is conceived.

[0220] As shown in Figure 5 , Figure 5is a structural diagram of a container loading capacity evaluation system based on a residual space configuration according to the present application, which comprises:

[0221] A double-coordinate acquisition unit is configured to acquire minimum corner point coordinates and maximum corner point coordinates of a loading space in a container.

[0222] A voxel set construction unit is configured to acquire a predefined voxel edge length and construct a full-space voxel set covering the loading space based on the minimum corner point coordinates and the maximum corner point coordinates of the loading space.

[0223] A voxel set division unit is configured to anchor loaded goods in the loading space and divide the full-space voxel set into an occupied space voxel set and a residual space voxel set based on the loaded goods.

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

[0225] A candidate cavity screening unit is configured to screen G candidate cavities from the K local cavities based on the voxel subsets of the local cavities, which meet the loading conditions of the to-be-loaded goods.

[0226] A compatibility calculation unit is configured to calculate the spatial compatibility of each of the G candidate cavities; wherein the spatial compatibility is represented as: a comprehensive evaluation of the large cavity proportion of the residual space and the spatial concentration when the to-be-loaded goods are loaded into the target candidate cavity.

[0227] A position recommendation unit is configured to select the candidate cavity corresponding to the maximum spatial compatibility from the G candidate cavities as the recommended loading position of the to-be-loaded goods.

[0228] In the above system, the minimum corner point coordinates and the maximum corner point coordinates are acquired by the double-coordinate acquisition unit; the full-space voxel set is constructed by the voxel set construction unit; the occupied space voxel set and the residual space voxel set are divided by the voxel set division unit; the K independent local cavities are determined by the local cavity determination unit; wherein each local cavity is bound to its corresponding voxel subset; the G candidate cavities are screened by the candidate cavity screening unit; the spatial compatibility of each of the G candidate cavities is calculated by the compatibility calculation unit; and the recommended loading position of the to-be-loaded goods is selected by the position recommendation unit, thereby solving the problem of loading capacity waste.

[0229] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are merely illustrative. For example, multiple units or components can be combined or integrated into another system, or some features can be omitted or not implemented. In addition, the coupling or direct coupling or communication connection between the shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0230] The above descriptions are merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present 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; 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. The container loading capacity assessment method based on residual space configuration according to claim 1, characterized in that, Calculate the spatial compatibility of each of the G candidate cavities, including: 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. 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.

9. 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 8 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 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.

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