Cargo consolidation loading method, device, equipment and storage medium

Abstract

Embodiments of the present application disclose a cargo consolidation loading method, device, equipment and storage medium. The method comprises: obtaining a plurality of dispersed cargos and a target container for consolidating and loading the cargos; performing cargo clustering based on a preset cargo category number, actual cargo size information corresponding to each cargo and cargo weight identification information, to obtain a target cargo set corresponding to each cargo category; determining virtual cargo size information corresponding to each cargo based on the target cargo set; performing cargo loading optimization on the target container based on the virtual cargo size information corresponding to each cargo and actual size information of the target container, to obtain a target cargo loading strategy, and consolidating and loading the target cargos into the target container based on the target cargo loading strategy. Through the technical solution of the embodiments of the present application, the calculation amount can be reduced, and the efficiency of cargo consolidation loading can be improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to logistics technology, and in particular to a cargo consolidation loading method, device, equipment and storage medium. BACKGROUND

[0002] In the logistics scene, especially in the production and manufacturing related logistics scene, there are often many small size scattered goods, such as parts, etc. In order to facilitate transportation, it is necessary to consolidate the scattered goods into larger containers, which is called consolidation operation.

[0003] At present, in the process of optimizing the consolidation of goods in a single container, all categories of goods need to be traversed at each position of the container, so as to obtain the optimal cargo loading strategy. However, in the process of implementing the present application, the inventors have found that at least the following problems exist in the prior art:

[0004] Due to the large number of categories of scattered goods, the computational complexity is greatly increased, thereby reducing the efficiency of cargo consolidation loading. SUMMARY

[0005] Embodiments of the present application provide a cargo consolidation loading method, device, equipment and storage medium to reduce the amount of calculation and improve the efficiency of cargo consolidation loading.

[0006] In a first aspect, embodiments of the present application provide a cargo consolidation loading method, comprising:

[0007] obtaining a plurality of scattered goods and a target container for consolidating the goods;

[0008] performing cargo clustering based on a preset number of cargo categories, actual cargo size information corresponding to each cargo and cargo weight identification information, to obtain a target cargo set corresponding to each cargo category;

[0009] determining virtual cargo size information corresponding to each cargo based on the target cargo set;

[0010] performing cargo loading optimization on the target container based on the virtual cargo size information corresponding to each cargo and actual size information of the target container, to obtain a target cargo loading strategy, and consolidating the target cargo into the target container based on the target cargo loading strategy.

[0011] In a second aspect, embodiments of the present application also provide a cargo consolidation loading device, comprising:

[0012] an information acquisition module configured to obtain a plurality of scattered goods and a target container for consolidating the goods;

[0013] a cargo clustering module, configured to perform cargo clustering based on a preset number of cargo categories, actual cargo size information corresponding to each cargo, and cargo weight identification information, to obtain a target cargo set corresponding to each cargo category;

[0014] a virtual size determination module, configured to determine virtual cargo size information corresponding to each cargo based on the target cargo set;

[0015] a consolidated loading module, configured to perform cargo loading optimization on the target container based on the virtual cargo size information corresponding to each cargo and actual size information of the target container, to obtain a target cargo loading strategy, and to consolidate and load target cargos into the target container based on the target cargo loading strategy.

[0016] In a third aspect, an electronic device is provided, and the electronic device includes:

[0017] one or more processors;

[0018] a memory configured to store one or more programs;

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the cargo consolidated loading method provided in any of the embodiments of the present application.

[0020] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, which, when executed by a processor, implements the cargo consolidated loading method provided in any of the embodiments of the present application.

[0021] An embodiment of the above application has the following advantages or beneficial effects:

[0022] By performing cargo clustering based on a preset number of cargo categories, actual cargo size information corresponding to each cargo, and cargo weight identification information, a target cargo set corresponding to each cargo category under the preset number of cargo categories is obtained, and virtual cargo size information corresponding to each cargo is determined based on the target cargo set; cargo loading optimization is performed on the target container based on the virtual cargo size information corresponding to each cargo and actual size information of the target container, and target cargos are consolidated and loaded into the target container based on a target cargo loading strategy, so that, by means of cargo clustering, the number of cargo categories that need to be considered in the cargo loading optimization process can be reduced, thereby reducing the computational complexity in the cargo loading optimization process, reducing the amount of calculation, and improving the efficiency of cargo consolidated loading. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to make the technical solutions of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained on the basis of these drawings without creative effort.

[0024] Figure 1 is a flow chart of a cargo consolidation loading method provided by an embodiment of the present application;

[0025] Figure 2 is an example diagram of cargo loading optimization related to an embodiment of the present application;

[0026] Figure 3 is a flow chart of another cargo consolidation loading method provided by an embodiment of the present application;

[0027] Figure 4 is a structural schematic diagram of a cargo consolidation loading device provided by an embodiment of the present application;

[0028] Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] The present application will be further described below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, but not all the structures.

[0030] Figure 1 is a flow chart of a cargo consolidation loading method provided by an embodiment of the present application, and the present embodiment can be applied to cargo loading optimization of scattered cargos, and the most suitable scattered cargos are consolidated and loaded into the same container. The method can be executed by a cargo consolidation loading device, which can be realized by software and / or hardware, and integrated in an electronic device. As shown in the figure, the method specifically includes the following steps: Figure 1

[0031] S110, obtaining a plurality of scattered cargos and a target container for consolidating and loading the cargos.

[0032] ​The goods can be any object that needs to be consolidated. For example, the goods can be parts loaded using small rectangular boxes. Different categories of goods can refer to different models of goods. Different categories of goods have different sizes. For example, the length, width and height of the nth goods are l(n), w(n) and h(n), respectively. The quantity of each type of goods can be one or more. The target container can be any container for consolidating goods. For example, the target container can refer to a standard logistics box.

[0033] S120, based on the preset number of goods categories, the actual goods size information corresponding to each type of goods and the goods weight identification information, the goods clustering is performed to obtain the target goods set corresponding to each type of goods category.

[0034] The preset number of goods categories can be the number of goods categories that the goods need to be clustered based on business requirements and scenarios. The preset number of goods categories M is less than the original number of goods categories N. For example, when there are N different categories of goods, the N goods need to be clustered into M categories, thereby reducing the number of goods categories. The actual goods size information corresponding to each type of goods can be the real size information of the goods, i.e. l(n), w(n) and h(n). The goods weight identification information can be identification information for indicating whether the goods are heavy or light. For example, the goods weight identification information can be 0 or 1, where 0 indicates light goods and 1 indicates heavy goods. The target goods set can refer to each goods set in the final clustering result. The number of target goods sets is the preset number of goods categories. Each target goods set includes multiple goods with similar sizes.

[0035] Specifically, by a preset clustering method, such as a K-means clustering method or a hierarchical clustering method, based on the preset number of goods categories, the actual goods size information corresponding to each type of goods and the goods weight identification information, the goods clustering is performed to obtain the target goods set of the preset number of goods categories after clustering. For example, multiple goods with the closest sizes and meeting the preset weight consolidation condition can be consolidated to obtain the target goods set corresponding to each type of goods category with the minimum consolidation loss value.

[0036] Exemplarily, S120 can include: based on the beam search method, the preset number of goods categories, the actual goods size information corresponding to each type of goods and the goods weight identification information, the goods clustering is performed to obtain the target goods set corresponding to each type of goods category.

[0037] The beam search mode is used to select the range of each search result by limiting the beam width, so as to search for the optimal solution within a certain beam width range each time, thereby improving the search efficiency of the optimal solution. Specifically, by using the beam search mode for cargo clustering, a globally optimal clustering result can be quickly obtained, thereby improving the cargo clustering efficiency.

[0038] For example, based on the beam search mode, the preset number of cargo categories, the actual cargo size information corresponding to each cargo, and the cargo weight identification information, the cargo clustering is performed to obtain the target cargo set corresponding to each cargo category, which can include: initializing each cargo as a current cargo set corresponding to a cargo category, and obtaining the cargo weight identification information corresponding to each current cargo set; based on the cargo weight identification information corresponding to each current cargo set and the actual cargo size information corresponding to each cargo, performing successive merging and beam search of merging loss on two current cargo sets that meet the preset weight merging condition to obtain the target cargo set corresponding to each cargo category under the preset number of cargo categories.

[0039] Specifically, each of the N cargos is taken as a current cargo set corresponding to a cargo category, and at this time, the current cargo set only contains one cargo. The cargo weight identification information corresponding to each cargo can be directly determined as the cargo weight identification information of the corresponding current cargo set. When performing clustering and merging for the first time, there are N current cargo sets, and each current cargo set contains only one cargo. Based on the cargo weight identification information corresponding to each current cargo set and the actual cargo size information corresponding to each cargo, successive merging and beam search of merging loss are performed on two current cargo sets that meet the preset weight merging condition in the N current cargo sets, each merging reduces the number of current cargo sets, and the merging ends when the number of current cargo sets is reduced to the preset number of cargo categories, and based on the beam search result after the merging ends, the target cargo set corresponding to each cargo category under the preset number of cargo categories can be determined.

[0040] S130, based on the target cargo set, determining the virtual cargo size information corresponding to each cargo.

[0041] The virtual cargo size information refers to size information similar to the actual cargo size. Specifically, the size of all cargos in each target cargo set can be unified, and the same virtual cargo size information is used to approximate the actual cargo size information of each cargo in the set, so that the N cargos can be integrated into M cargos, reducing the number of cargo categories.

[0042] S130, determining virtual cargo size information corresponding to the target cargo set.

[0043] Specifically, for each target cargo set, the maximum cargo size information corresponding to the target cargo set can be determined based on the actual cargo size information corresponding to each cargo in the target cargo set, and the maximum cargo size information is determined as the virtual cargo size information corresponding to the target cargo set. For example, the actual length l(n), the actual width w(n) and the actual height h(n) of each cargo in the target cargo set can be compared to determine the maximum actual length, the maximum actual width and the maximum actual height in the target cargo set, and the maximum actual length, the maximum actual width and the maximum actual height are determined as the virtual length L(m), the virtual width W(m) and the virtual height H(m) corresponding to the target cargo set, respectively, thereby obtaining the virtual cargo size information corresponding to the target cargo set. The virtual cargo size information corresponding to each cargo in the target cargo set is determined as the virtual cargo size information corresponding to the target cargo set, so that each cargo in the target cargo set is treated as a cargo with dimensions L(m), W(m) and H(m), thereby greatly reducing the number of cargo categories and reducing the calculation amount.

[0044] S140, based on the virtual cargo size information corresponding to each cargo and the actual size information of the target container, performing cargo loading optimization on the target container to obtain a target cargo loading strategy, and based on the target cargo loading strategy, loading the target cargo into the target container.

[0045] The target cargo loading strategy can be the optimal consolidation loading strategy. For example, the target cargo loading strategy can be the cargo loading strategy when the target container loading rate is maximum. The target cargo loading strategy can include target cargo that needs to be consolidated in the target container and the loading position and attitude of each target cargo, so that the loading rate of the target container is maximum (i.e., the number of loaded cargos is maximum).

[0046] Specifically, an integer programming algorithm, a genetic algorithm or reinforcement learning can be used to maximize the loading rate as the optimization objective, based on the virtual cargo size information corresponding to each cargo and the actual size information of the target container, to perform cargo loading optimization on the target container and obtain the target cargo loading strategy when the loading rate is maximum. Since the number of cargo categories can be reduced after cargo clustering, the number of iterations can be reduced when traversing all categories of cargos at each position of the target container, thereby reducing the calculation amount and obtaining the optimal target cargo loading strategy more quickly, and greatly improving the efficiency of cargo consolidation loading.

[0047] The technical scheme of the embodiment clusters multiple types of goods based on the preset number of goods categories, the actual goods size information corresponding to each type of goods, and the goods weight identification information, obtains a target goods set corresponding to each goods category under the preset number of goods categories, and determines the virtual goods size information corresponding to each type of goods based on the target goods set; the target container is optimized for goods loading based on the virtual goods size information corresponding to each type of goods and the actual size information of the target container, and the target goods are loaded into the target container based on the target goods loading strategy, so that the number of goods categories that need to be considered in the goods loading optimization process can be reduced by means of goods clustering, and the calculation complexity in the goods loading optimization process is reduced, the calculation amount is reduced, and the efficiency of goods loading is improved.

[0048] On the basis of the above technical scheme, the "target container is optimized for goods loading based on the virtual goods size information corresponding to each type of goods and the actual size information of the target container, and a target goods loading strategy is obtained" in S140 can include: the target container is optimized for goods loading based on the virtual goods size information corresponding to each type of goods and the actual size information of the target container, and a target goods loading strategy is obtained when the loading rate is maximum.

[0049] Specifically, the tree search method is to use goods to accumulate into a column that can verify the transverse and longitudinal directions of the container, and then fill the inside of the container. As shown in Figure 2 The tree search method is to use the structure of a binary tree, to store the result of filling a transverse column based on the current node in the left child tree, and to store the result of filling a longitudinal column based on the current node in the right child tree, and to return the one with the maximum loading rate from the two results. By using the structure of a binary tree, comparison can be made from bottom to top, and finally, when the root node is reached, the output is the goods loading strategy with the maximum loading rate. Each transverse and longitudinal column is formed by pushing and accumulating individual goods, that is, each column is formed by stacking individual goods from bottom to top. When generating each column, the solution method of the knapsack problem can be used to traverse all types of goods at each position and select the best way to form the column. By filling the remaining space in the target container with a transverse column or a longitudinal column, the one with the higher loading rate is selected from the two options, so that by using the tree search method, the target goods loading strategy with the maximum loading rate can be accurately obtained under the constraints of the virtual goods size information of each type of goods and the actual size information of the target container.

[0050] It should be noted that the complexity of the tree search method for goods loading optimization is O(N M), wherein N is the number of cargo categories, and M is related to the size of the target container. By reducing the number of cargo categories, the complexity of cargo loading optimization can be reduced, thereby reducing the amount of calculation and improving the efficiency of cargo consolidated loading.

[0051] Figure 3 A flowchart of another cargo consolidated loading method provided by an embodiment of the present application is described in detail, which is based on the above-mentioned embodiments and describes the specific process of cluster search. The same or corresponding terms as those in the above-mentioned embodiments are not described herein.

[0052] Referring to Figure 3 The cargo consolidated loading method provided by the embodiment specifically includes the following steps.

[0053] S310, obtaining a plurality of dispersed cargos and a target container for consolidated loading of the cargos.

[0054] S320, initializing each cargo as a current cargo set corresponding to a cargo category, and obtaining cargo weight identification information corresponding to each current cargo set.

[0055] Specifically, each cargo of the N cargos is initialized as a current cargo set corresponding to a cargo category, and at this time, the current cargo set only contains one cargo. The cargo weight identification information corresponding to each cargo can be directly determined as the cargo weight identification information of the corresponding current cargo set. When the cargos are first clustered and consolidated, there are N current cargo sets, and each current cargo set contains only one cargo.

[0056] S330, based on the cargo weight identification information corresponding to each current cargo set, two current cargo sets satisfying a preset weight consolidation condition are iteratively consolidated to obtain a plurality of first clustering results after the current consolidation, and the number of current cargo categories after the current consolidation is updated.

[0057] The preset weight consolidation condition can be set in advance based on business requirements and scenarios, and the weight condition that needs to be met by the cargos that can be consolidated. For example, in an actual business scenario, heavy goods cannot be placed on light goods, so light goods and heavy goods cannot be consolidated and need to be placed separately in different categories. Based on this, the preset weight consolidation condition can be that heavy goods are consolidated with heavy goods and light goods are consolidated with light goods. The first clustering result can be a clustering result obtained after only two current cargo sets in all current cargo sets are consolidated. The two current cargo sets consolidated in different first clustering results are different. Each current cargo set corresponds to a cargo category. The number of current cargo categories can be the number of current cargo sets after the current consolidation.

[0058] Specifically, in the first clustering and merging, two current cargo sets satisfying the preset weight merging condition can be iteratively merged based on the cargo weight identification information corresponding to each current cargo set, to obtain a plurality of first clustering results after the current merging. Since only two current cargo sets are merged each time, each first clustering result obtained after the first merging includes N-1 current cargo sets. In this regard, the current cargo category number N can be updated by subtracting 1, i.e., the current cargo category number is updated to N-1 after the current merging.

[0059] For example, in S330, the "iteratively merging two current cargo sets satisfying the preset weight merging condition based on the cargo weight identification information corresponding to each current cargo set, to obtain a plurality of first clustering results after the current merging" can include: iteratively merging two current cargo sets having the same cargo weight identification information, to obtain a plurality of first clustering results after the current merging; wherein each first clustering result includes only one merging of the two current cargo sets.

[0060] Specifically, two current cargo sets belonging to light cargo or heavy cargo can be merged in all cases based on the cargo weight identification information corresponding to each current cargo set, to obtain a first clustering result corresponding to each merging case. For example, if there are 6 cargoes (i.e., N equals 6), namely cargo A, cargo B, cargo C, cargo D, cargo E and cargo F, there are 6 current cargo sets during the first clustering and merging, namely {A}, {B}, {C}, {D}, {E} and {F}, wherein {A}, {B} and {C} are light cargo, and {D}, {E} and {F} are heavy cargo. Iterative merging of two light cargoes or two heavy cargoes can obtain 6 first clustering results, namely: [{AB}, {C}, {D}, {E}, {F}]; [{A}, {BC}, {D}, {E}, {F}]; [{AC}, {B}, {D}, {E}, {F}]; [{A}, {B}, {C}, {DE}, {F}]; [{A}, {B}, {C}, {D}, {EF}]; and [{A}, {B}, {C}, {E}, {DF}]. It can be seen that the current cargo category number after the first merging is updated to 5.

[0061] S340, based on the actual cargo size information corresponding to each cargo in each first clustering result, determining a merging loss value corresponding to each first clustering result, and based on the merging loss value, determining a preset number of second clustering results from the first clustering results.

[0062] The merging loss value refers to the precision loss value after the actual size of each kind of goods is approximated to the virtual size of the goods set. The closer the actual size of each kind of goods is to the virtual size of the goods set, the smaller the merging loss value is. The preset bundle quantity B can be the result range of each clustering search, that is, the number of clustering results to be retained after each merging. The second clustering result can refer to the first clustering result with a smaller merging loss value. The number of second clustering results is the preset bundle quantity.

[0063] Specifically, for each first clustering result after the current merging, the merging loss value of each kind of goods in the first clustering result after the actual size of each kind of goods is approximated to the virtual size of the goods set can be determined based on the actual goods size information corresponding to each kind of goods in the first clustering result. The merging loss values corresponding to each first clustering result are arranged in ascending order from small to large to obtain a first clustering result sequence with increasing merging loss values. The first clustering results in the front of the preset bundle quantity in the first clustering result sequence are determined as the second clustering results, and the current clustering merging operation is completed. In the above example, if the preset bundle quantity is 4, the first 4 first clustering results with the smallest merging loss value are selected from the 6 first clustering results as the second clustering results after the current merging.

[0064] For example, in S340, "determining the merging loss value corresponding to each first clustering result based on the actual goods size information corresponding to each kind of goods in the first clustering result", can include steps S341 and S342:

[0065] S341, for each first clustering result, determining the virtual goods size information corresponding to each current goods set in the first clustering result based on the actual goods size information corresponding to each kind of goods in the current goods set.

[0066] The virtual goods size information refers to size information similar to the actual goods size. Specifically, for each first clustering result, the size of all goods in each current goods set in the first clustering result can be unified, and the actual goods size information of each kind of goods in each current goods set can be approximated using the same virtual goods size information, so that the number of goods categories can be reduced.

[0067] For example, S341 can include: for each current goods set in the first clustering result, determining the maximum goods size information corresponding to the current goods set based on the actual goods size information corresponding to each kind of goods in the current goods set; and determining the maximum goods size information as the virtual goods size information corresponding to the current goods set.

[0068] Specifically, for each current cargo set in the first clustering result, the maximum cargo size information corresponding to the current cargo set can be determined based on the actual cargo size information corresponding to each cargo in the current cargo set, and the maximum cargo size information is determined as the virtual cargo size information corresponding to the current cargo set. For example, the actual length l(n), the actual width w(n) and the actual height h(n) of each cargo in the current cargo set can be compared to determine the maximum actual length, the maximum actual width and the maximum actual height in the current cargo set, and the maximum actual length, the maximum actual width and the maximum actual height are determined as the virtual length L(m), the virtual width W(m) and the virtual height H(m) corresponding to the current cargo set, respectively, thereby obtaining the virtual cargo size information corresponding to the current cargo set. The virtual cargo size information corresponding to each cargo in the current cargo set is determined as the virtual cargo size information corresponding to the current cargo set, so that each cargo in the current cargo set is treated as a cargo with the size of L(m), W(m) and H(m), thereby greatly reducing the number of cargo categories and further reducing the calculation amount.

[0069] S342, determining the merging loss value corresponding to the first clustering result based on the virtual cargo size information corresponding to each current cargo set in the first clustering result and the actual cargo size information corresponding to each cargo.

[0070] Specifically, the virtual cargo size information corresponding to each current cargo set is the virtual cargo size information corresponding to each cargo contained in each current cargo set. Based on the virtual cargo size information and the actual cargo size information corresponding to each cargo in each current cargo set in the first clustering result, the merging loss value after the actual size of each cargo is approximated to the virtual size of the cargo set in the first clustering result can be determined.

[0071] Illustratively, S342 can include: determining the volume loss value corresponding to each current cargo set in the first clustering result based on the virtual cargo size information corresponding to each current cargo set in the first clustering result and the actual cargo size information corresponding to each cargo; determining the merging loss value corresponding to the first clustering result based on the volume loss value corresponding to each current cargo set in the first clustering result.

[0072] Specifically, based on the virtual cargo size information corresponding to each current cargo set in the first clustering result, the virtual volume corresponding to each cargo in each current cargo set can be determined, such as the virtual volume V m corresponding to the i-th cargo in the m-th set G(m) is L(m)×W(m)×H(m). Based on the actual cargo size information corresponding to each cargo, the actual volume corresponding to each cargo is determined, such as the actual volume Vi = l(i) x w(i) x h(i). The absolute value of the difference obtained by subtracting the corresponding actual volume from the corresponding virtual volume of each cargo is determined as the volume loss value corresponding to the corresponding cargo, that is, V(i, m) = |V m -V i | The volume loss value corresponding to each cargo contained in each current cargo set in the first clustering result is added to obtain the volume loss value corresponding to each current cargo set, that is, ∑ i∈G(m) V(i, m). The volume loss value corresponding to each current cargo set in the first clustering result is added to obtain the merging loss value corresponding to the first clustering result, that is, ∑ m ∑ i∈G(m) V(i, m). The merging loss value corresponding to each first clustering result is obtained by the above-mentioned manner.

[0073] S350, based on the cargo weight identification information corresponding to each current cargo set in each second clustering result, the next merging and the bundle search of the merging loss are performed until the updated current cargo category number is the preset cargo category number.

[0074] Specifically, the two current cargo sets satisfying the preset weight merging condition can be merged by traversal based on the cargo weight identification information corresponding to each current cargo set in each second clustering result in the first clustering merging manner described in steps S330-S340, a plurality of first clustering results after the second merging are obtained, and the current cargo category number after the second merging is updated, that is, the current cargo category number is reduced by 1 after each merging. Based on the actual cargo size information corresponding to each cargo in each first clustering result after the second merging, the merging loss value corresponding to each first clustering result is determined, and based on the merging loss value, the second clustering result of the preset bundle number after the second merging is determined from the first clustering result, and the second clustering merging operation is completed. Similarly, based on the cargo weight identification information corresponding to each current cargo set in each second clustering result after the second merging, the third merging and the bundle search of the merging loss are performed until the updated current cargo category number is the preset cargo category number, that is, the clustering ends when the specified cargo category number is reached, thereby completing the clustering of N kinds of cargos into M categories, and reducing the cargo category number.

[0075] S360, based on the second clustering result of the preset bundle number after the merging ends, the target cargo set corresponding to each cargo category under the preset cargo category number is determined.

[0076] Specifically, the merging loss values corresponding to the second clustering results of the preset bundle quantity after merging can be compared, and the second clustering result with the smallest merging loss value is determined as the final target clustering result. The target clustering result includes a plurality of goods sets of the preset quantity of goods categories. Each goods set included in the target clustering result is determined as the target goods set corresponding to each goods category, thereby obtaining the optimal goods clustering result, only possibly reducing the precision loss caused by size unification, and ensuring the accuracy of goods merging and loading.

[0077] S370, based on the target goods set, determining the virtual goods size information corresponding to each goods.

[0078] S380, based on the virtual goods size information corresponding to each goods and the actual size information of the target container, performing goods loading optimization on the target container, obtaining a target goods loading strategy, and based on the target goods loading strategy, merging and loading the target goods into the target container.

[0079] The technical scheme of the embodiment, by means of the goods weight identification information corresponding to each current goods set, the two current goods sets meeting the preset weight merging condition are iteratively merged to obtain a plurality of first clustering results after merging, and based on the actual goods size information corresponding to each goods in each first clustering result, the merging loss value corresponding to each first clustering result is determined, and based on the merging loss value, the second clustering result of the preset bundle quantity is determined from the first clustering result, thereby completing the first merging and loading of the cluster search, and based on the goods weight identification information corresponding to each current goods set in each second clustering result, the next merging and merging loss cluster search is performed, until the updated current goods category quantity is the preset goods category quantity, and the merging is ended, thereby accurately clustering the goods by means of the cluster search method, and ensuring the efficiency and accuracy of goods merging and loading.

[0080] The following is an embodiment of a goods merging and loading device provided by the embodiment of the application. The device belongs to the same inventive concept as the goods merging and loading method described above. Details not described in the embodiment of the goods merging and loading device can be referred to the embodiment of the goods merging and loading method described above.

[0081] Figure 4 A structural schematic diagram of a goods merging and loading device provided by the embodiment of the application. The embodiment can be applied to the case of merging and loading the most suitable scattered goods into the same container. As shown in the figure, the device specifically includes an information acquisition module 410, a goods clustering module 420, a virtual size determination module 430, and a merging and loading module 440. Figure 4

[0082] ​The information acquisition module 410 is configured to acquire a plurality of scattered goods and a target container for consolidated loading of the goods. The goods clustering module 420 is configured to perform goods clustering based on a preset number of goods categories, actual goods size information corresponding to each kind of goods, and goods weight identification information, to obtain a target goods set corresponding to each kind of goods category. The virtual size determination module 430 is configured to determine virtual goods size information corresponding to each kind of goods based on the target goods set. The consolidated loading module 440 is configured to perform goods loading optimization on the target container based on the virtual goods size information corresponding to each kind of goods and actual size information of the target container, to obtain a target goods loading strategy, and to consolidate and load the target goods into the target container based on the target goods loading strategy.

[0083] The technical scheme of the embodiment is configured to perform clustering on a plurality of scattered goods based on a preset number of goods categories, actual goods size information corresponding to each kind of goods, and goods weight identification information, to obtain a target goods set corresponding to each kind of goods category under the preset number of goods categories, and to determine virtual goods size information corresponding to each kind of goods based on the target goods set. The target container is subjected to goods loading optimization based on the virtual goods size information corresponding to each kind of goods and actual size information of the target container, and the target goods are consolidated and loaded into the target container based on a target goods loading strategy. In this way, the number of goods categories that need to be considered in the goods loading optimization process can be reduced through goods clustering, thereby reducing the computational complexity in the goods loading optimization process, reducing the amount of calculation, and improving the efficiency of consolidated loading of the goods.

[0084] Optionally, the goods clustering module 420 comprises:

[0085] The goods initialization submodule is configured to initialize each kind of goods as a current goods set corresponding to a goods category, and to acquire goods weight identification information corresponding to each current goods set.

[0086] The goods clustering submodule is configured to perform successive consolidation and bundle search of consolidation loss on two current goods sets that meet a preset weight consolidation condition based on the goods weight identification information corresponding to each current goods set and the actual goods size information corresponding to each kind of goods, to obtain a target goods set corresponding to each kind of goods category under the preset number of goods categories.

[0087] Optionally, the goods clustering submodule comprises:

[0088] The first clustering result determination unit is configured to perform traversal consolidation on two current goods sets that meet a preset weight consolidation condition based on the goods weight identification information corresponding to each current goods set, to obtain a plurality of first clustering results after the current consolidation, and to update the number of current goods categories after the current consolidation.

[0089] The second clustering result determination unit is configured to determine a merging loss value corresponding to each first clustering result based on actual cargo size information corresponding to each cargo in the first clustering result, and determine a preset number of second clustering results from the first clustering results based on the merging loss value.

[0090] The next time merging searching unit is configured to perform next time merging and merging loss beam search based on cargo weight identification information corresponding to each current cargo set in each second clustering result until merging ends when the updated current cargo category number is the preset cargo category number.

[0091] The target cargo set determination unit is configured to determine a target cargo set corresponding to each cargo category under the preset cargo category number based on the preset number of second clustering results after merging ends.

[0092] Optionally, the first clustering result determination unit is specifically configured to:

[0093] The two current cargo sets with the same cargo weight identification information are merged to obtain a plurality of first clustering results after this time merging; wherein, in each first clustering result, there is only one merging of the two current cargo sets.

[0094] Optionally, the second clustering result determination unit comprises:

[0095] The virtual cargo size determination subunit is configured to, for each first clustering result, determine virtual cargo size information corresponding to each current cargo set in the first clustering result based on actual cargo size information corresponding to each cargo in the current cargo set.

[0096] The merging loss value determination subunit is configured to determine a merging loss value corresponding to the first clustering result based on the virtual cargo size information corresponding to each current cargo set in the first clustering result and the actual cargo size information corresponding to each cargo.

[0097] Optionally, the virtual cargo size determination subunit is specifically configured to:

[0098] For each current cargo set in the first clustering result, the virtual cargo size determination subunit is configured to determine maximum cargo size information corresponding to the current cargo set based on actual cargo size information corresponding to each cargo in the current cargo set, and determine the maximum cargo size information as virtual cargo size information corresponding to the current cargo set.

[0099] Optionally, the merging loss value determination subunit is specifically configured to:

[0100] Based on the virtual cargo size information corresponding to each current cargo set in the first clustering result and the actual cargo size information corresponding to each type of cargo, the volume loss value corresponding to each current cargo set in the first clustering result is determined; based on the volume loss value corresponding to each current cargo set in the first clustering result, the merged loss value corresponding to the first clustering result is determined.

[0101] Optionally, the virtual size determination module 430 is specifically used for:

[0102] Determine the virtual cargo size information corresponding to the target cargo set; determine the virtual cargo size information corresponding to the target cargo set as the virtual cargo size information corresponding to each type of cargo in the target cargo set.

[0103] Optionally, the merging loading module 440 is specifically used for:

[0104] Based on a tree-structured search method, the virtual cargo size information corresponding to each type of cargo, and the actual size information of the target container, cargo loading optimization is performed on the target container to obtain the target cargo loading strategy with the maximum loading rate.

[0105] The cargo merging and loading device provided in the embodiments of the present invention can execute the cargo merging and loading method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the cargo merging and loading method.

[0106] It is worth noting that in the embodiments of the cargo consolidation and loading device described above, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0107] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Figure 5 A block diagram is shown of an exemplary electronic device 12 suitable for implementing embodiments of the present invention. Figure 5 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0108] like Figure 5 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0109] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration bus, a processor or local bus using any of a variety of bus architectures. By way of example, these architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0110] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that is accessible by electronic device 12 and includes both volatile and non-volatile media, removable and non-removable media.

[0111] System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a "hard drive"). Figure 5 Although not shown, a magnetic disk drive can also be utilized in some embodiments to access and read / write from one or more magnetic disk drives (not shown) that can also be part of electronic device 12. As stated above, a disk drive can also be used to read from or write to a temporary non-removable, non-volatile magnetic medium (not shown) included in a removable memory port. Such approaches can also be used with programs written in JAVATM, other like object Figure 5 oriented programming languages, and / or other programming languages. Program / utility 40, having a set (at least one) of program modules 42, can be stored in system memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data, and can include an implementation of a network environment. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, can include an implementation of a network environment. Program modules 42 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0112] Program / utility 40 having a set (at least one) of program modules 42 can be stored in system memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data, and can include an implementation of a network environment. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, can include an implementation of a network environment. Program modules 42 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0113] The electronic device 12 can also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; and can communicate with one or more devices that enable a user to interact with the electronic device 12 and / or one or more devices that enable the electronic device 12 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 22. Still yet, the electronic device 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network such as the Internet, via a network adapter 20. As depicted, the network adapter 20 communicates with the other components of the electronic device 12 via the bus 18. It should be appreciated that the electronic device 12 can be a part of another device or can be a stand-alone device. In addition, the electronic device 12 can be connected to one or more devices in series, parallel, or some combination thereof. It is further noted that, while the following embodiments will be described in the context of a fully functioning electronic device, those skilled in the art will appreciate that the embodiments are merely exemplary and that the application need not be implemented with respect to any particular type of electronic device. In addition, various functional units can have been described as discrete components, in practice, they can have been implemented as part of an integrated circuit or other controller. Furthermore, specific functions can have been described as being performed by one or more functional units, in practice, the functions can have been carried out by a single functional unit, or two or more functional units, in various combinations or sub-combinations.

[0114] The processing unit 16 performs various overall functions of the electronic device 12 by executing programs stored in the system memory 28, such as implementing a method for consolidating loading of goods, the method comprising:

[0115] obtaining a plurality of scattered goods and a target container for consolidating loading of the goods;

[0116] performing clustering of the goods based on a preset number of categories of the goods, actual size information of each category of the goods, and weight identification information of the goods, to obtain a target set of goods corresponding to each category of the goods;

[0117] determining virtual size information of each category of the goods based on the target set of goods;

[0118] performing optimization of loading of the goods into the target container based on the virtual size information of each category of the goods and actual size information of the target container, to obtain a target loading strategy of the goods, and consolidating loading of the target goods into the target container based on the target loading strategy of the goods.

[0119] Of course, those skilled in the art can understand that the processor can also implement the technical solutions of the method for consolidating loading of goods provided by any of the embodiments of the present application.

[0120] The present embodiment provides a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of the method for consolidating loading of goods provided by any of the embodiments of the present application, the method comprising:

[0121] obtaining a plurality of scattered goods and a target container for consolidating loading of the goods;

[0122] The preset number of goods categories, the actual goods size information corresponding to each goods and the goods weight identification information are used for goods clustering, and a target goods set corresponding to each goods category is obtained.

[0123] Based on the target goods set, virtual goods size information corresponding to each goods is determined.

[0124] Based on the virtual goods size information corresponding to each goods and the actual size information of the target container, the target container is optimized for goods loading, a target goods loading strategy is obtained, and target goods are loaded into the target container based on the target goods loading strategy.

[0125] The computer storage medium of the embodiment of the application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0126] The computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, in which a computer readable program code is carried. Such a propagated data signal can take on many forms, including but not limited to electro-magnetic, optical or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can transmit, propagate or transport a program for use by or in connection with an instruction execution system, device or component.

[0127] The program code contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0128] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0129] Those skilled in the art will appreciate that the modules or steps of the application described above can be implemented in a general purpose computer, and can be centralized in a single computer or distributed over a network of multiple computers, and optionally, they can be implemented in program code executable by a computer, and thus can be stored in a storage device and executed by a computer, or they can be made into individual integrated circuit modules, or a plurality of modules or steps can be made into a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0130] Note that the above are only the preferred embodiments of the present application and the principles of the applied technology. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, reconfigurations and substitutions can be made by those skilled in the art without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for consolidating and loading cargo, characterized in that, include: Acquire multiple dispersed goods and target containers for consolidating and loading them; Based on the preset number of goods categories, the actual size information of each goods category, and the weight identification information of each goods category, goods are clustered to obtain the target goods set corresponding to each goods category. Based on the target cargo set, determine the virtual cargo size information corresponding to each type of cargo; Based on the virtual cargo size information corresponding to each type of cargo and the actual size information of the target container, cargo loading optimization is performed on the target container to obtain a target cargo loading strategy. Based on the target cargo loading strategy, the target cargo is combined and loaded into the target container.

2. The method according to claim 1, characterized in that, The process involves clustering goods based on a preset number of goods categories, the actual dimensions of each type of goods, and the weight information of each type of goods, to obtain a target goods set for each goods category, including: Based on the cluster search method, the preset number of cargo categories, the actual cargo size information and cargo weight identification information corresponding to each cargo, cargo clustering is performed to obtain the target cargo set corresponding to each cargo category.

3. The method according to claim 2, characterized in that, The process involves clustering goods based on a cluster search method, a preset number of goods categories, and the actual dimensions and weight information of each type of goods to obtain a target goods set for each category, including: Each type of goods is initialized as a current goods set corresponding to a goods category, and the weight identification information of each current goods set is obtained; Based on the cargo weight identification information corresponding to each current cargo set and the actual cargo size information corresponding to each type of cargo, a cluster search is performed on two current cargo sets that meet the preset weight merging conditions, including successive merging and merging loss, to obtain the target cargo set corresponding to each cargo category under the preset number of cargo categories.

4. The method according to claim 3, characterized in that, Based on the cargo weight identification information corresponding to each current cargo set and the actual cargo size information corresponding to each type of cargo, a cluster search is performed on two current cargo sets that meet the preset weight merging conditions, involving successive merging and merging loss, to obtain the target cargo set corresponding to each cargo category under the preset number of cargo categories, including: Based on the cargo weight identification information corresponding to each current cargo set, the two current cargo sets that meet the preset weight merging conditions are traversed and merged to obtain multiple first clustering results after the current merge, and the number of current cargo categories after the current merge is updated. Based on the actual cargo size information corresponding to each cargo in each first clustering result, determine the merging loss value corresponding to each first clustering result, and based on the merging loss value, determine the second clustering result with a preset bundle width from the first clustering results; Based on the cargo weight identification information corresponding to each current cargo set in each second clustering result, perform the next merging and clustering loss cluster search until the updated number of current cargo categories is the preset number of cargo categories, and the merging ends. Based on the second clustering results after the merging process and the preset bundle width, the target cargo set corresponding to each cargo category is determined under the preset cargo category number.

5. The method according to claim 4, characterized in that, Based on the cargo weight identification information corresponding to each current cargo set, the two current cargo sets that meet the preset weight merging conditions are traversed and merged to obtain various first clustering results after the current merge, including: Two current cargo sets with the same cargo weight identification information are traversed and merged to obtain multiple first clustering results after the current merge; among them, there is only one merge of the two current cargo sets in each first clustering result.

6. The method according to claim 4, characterized in that, The step of determining the merged loss value corresponding to each first clustering result based on the actual cargo size information corresponding to each cargo in each first clustering result includes: For each first clustering result, based on the actual cargo size information corresponding to each cargo in each current cargo set in the first clustering result, the virtual cargo size information corresponding to each current cargo set in the first clustering result is determined; Based on the virtual cargo size information corresponding to each current cargo set and the actual cargo size information corresponding to each type of cargo in the first clustering result, the merged loss value corresponding to the first clustering result is determined.

7. The method according to claim 6, characterized in that, The step of determining the virtual cargo size information corresponding to each current cargo set in the first clustering result based on the actual cargo size information corresponding to each cargo in each current cargo set includes: For each current cargo set in the first clustering result, the maximum cargo size information corresponding to the current cargo set is determined based on the actual cargo size information corresponding to each cargo in the current cargo set; The maximum cargo size information is determined as the virtual cargo size information corresponding to the current cargo set.

8. The method according to claim 6, characterized in that, The step of determining the merging loss value corresponding to the first clustering result based on the virtual cargo size information corresponding to each current cargo set and the actual cargo size information corresponding to each type of cargo in the first clustering result includes: Based on the virtual cargo size information corresponding to each current cargo set in the first clustering result and the actual cargo size information corresponding to each type of cargo, the volume loss value corresponding to each current cargo set in the first clustering result is determined; Based on the volume loss value corresponding to each current cargo set in the first clustering result, the merged loss value corresponding to the first clustering result is determined.

9. The method according to claim 1, characterized in that, The step of determining the virtual cargo size information corresponding to each type of cargo based on the target cargo set includes: Determine the virtual cargo size information corresponding to the target cargo set; The virtual cargo size information corresponding to the target cargo set is determined as the virtual cargo size information corresponding to each type of cargo in the target cargo set.

10. The method according to claim 1, characterized in that, The step of optimizing cargo loading for the target container based on the virtual cargo size information corresponding to each type of cargo and the actual size information of the target container to obtain a target cargo loading strategy includes: Based on a tree-structured search method, the virtual cargo size information corresponding to each type of cargo, and the actual size information of the target container, cargo loading optimization is performed on the target container to obtain the target cargo loading strategy with the maximum loading rate.

11. A cargo consolidation and loading device, characterized in that, include: The information acquisition module is used to acquire information on various dispersed goods and target containers for merging and loading goods. The cargo clustering module is used to cluster cargo based on the preset number of cargo categories, the actual cargo size information and cargo weight identification information corresponding to each cargo category, and to obtain the target cargo set corresponding to each cargo category. The virtual size determination module is used to determine the virtual cargo size information corresponding to each type of cargo based on the target cargo set; The merge loading module is used to optimize the loading of the target container based on the virtual cargo size information corresponding to each type of cargo and the actual size information of the target container, obtain the target cargo loading strategy, and merge the target cargo into the target container based on the target cargo loading strategy.

12. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the cargo consolidation loading method as described in any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the cargo consolidation loading method as described in any one of claims 1-10.

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