Intelligent boxing processing method and system based on commercial order cargo information extraction
By collecting and optimizing cargo information, and using packing constraint rules and heuristic rules to generate efficient packing solutions, the problems of low packing efficiency and low space utilization in existing technologies have been solved, achieving more efficient logistics packing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-03-10
AI Technical Summary
Current logistics packing processes suffer from low packing efficiency, low space utilization, and are prone to errors. In particular, when dealing with goods of multiple categories, specifications, and constraints, traditional methods struggle to achieve efficient and reasonable packing solutions.
By collecting cargo and container information, iterative optimization is performed using packing constraint rules and heuristic rules to generate the packing scheme with the highest fitness, and the three-dimensional coordinates of each cargo are output. This includes data cleaning, classification and standardization processing, and iterative optimization of packing information is performed using genetic algorithms and greedy rules.
It improves the utilization rate of packing space, reduces transportation costs, reduces manual intervention, and improves packing efficiency and accuracy.
Smart Images
Figure CN121638541A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of logistics, in particular to an intelligent packing processing method based on commercial order cargo information extraction, and further relates to an intelligent packing processing system based on commercial order cargo information extraction. BACKGROUND
[0002] In the existing logistics packing process, the goods are usually packed by relying on manual experience, which has the problems of low packing efficiency, low space utilization, easy error, and inability to dynamically adapt to different cargo attributes. Especially when facing goods of multiple categories, multiple specifications and multiple constraint conditions, the traditional method is difficult to achieve an efficient and reasonable packing scheme. SUMMARY
[0003] In order to solve the problems in the prior art, the present application provides an intelligent packing processing method and system based on commercial order cargo information extraction, which improves the packing space utilization, reduces the transportation cost, reduces the manual intervention, and improves the packing efficiency and accuracy. For this purpose, the present application also provides an intelligent packing processing system based on commercial order cargo information extraction.
[0004] In the first aspect, the present application provides an intelligent packing processing method based on commercial order cargo information extraction, comprising: collecting cargo information and container information to generate a cargo information list and a container information list; iteratively optimizing the cargo information list and the container information list by packing constraint rules and heuristic rules; wherein the packing constraint rules include container boundary constraints, container load constraints, container stacking constraints, container center of gravity constraints and cargo category constraints; obtaining the highest fitness packing scheme after iteratively optimizing the packing information, and outputting the three-dimensional coordinates of each cargo in the container.
[0005] Preferably, the intelligent packing processing method based on commercial order cargo information extraction provided by the present application comprises: performing data cleaning, classification and standardization processing on the collected cargo information and container information.
[0006] Preferably, the intelligent packing processing method based on commercial order cargo information extraction provided by the present application iteratively optimizes the cargo information list and the container information list by packing constraint rules and heuristic rules, comprising: When the fitness function and decoding calculation are performed for the current goods, all opened containers and available container types are traversed, the remaining space of the goods after being placed in the container is segmented into several rectangular spaces along the X, Y and Z axis directions, and a space list is generated, and the packing constraint rules are detected, and if the packing constraint rules are not met, the current goods are considered as unplaceable.
[0007] Preferably, the application provides an intelligent packing processing method based on merchant order goods information extraction, which initializes and sorts the goods information list before packing information iterative optimization, and preferentially places fragile goods.
[0008] Preferably, the application provides an intelligent packing processing method based on merchant order goods information extraction, which includes goods length, goods width, goods height, goods weight, goods type, goods fragility and goods maximum stacking number.
[0009] Preferably, the application provides an intelligent packing processing method based on merchant order goods information extraction, which includes container length, container width, container height, container maximum load and container type.
[0010] In a second aspect, the application further provides an intelligent packing processing system based on merchant order goods information extraction, comprising: The acquisition module is used to acquire goods information and container information, and generate a goods information list and a container information list; The iterative optimization module is used to perform packing information iterative optimization on the goods information list and the container information list through packing constraint rules and heuristic rules; The packing constraint module is used to constrain container boundaries, container load, container stacking, container center of gravity and container category during packing; The output module is used to output the three-dimensional coordinates of each goods in the container through the packing scheme with the highest fitness after packing information iterative optimization.
[0011] In a third aspect, the application further provides an intelligent packing processing device based on merchant order goods information extraction, comprising at least one processor; and a memory coupled to the at least one processor, the memory storing executable instructions, the executable instructions being executed by the at least one processor to implement the steps of any one of the methods of the first aspect.
[0012] In a fourth aspect, the application further provides a chip for executing the steps of the method of the first aspect. Specifically, the chip comprises a processor for calling and running a computer program from a memory, so that the device installed with the chip is used to execute the steps of the method of the first aspect.
[0013] In a fifth aspect, the present application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of any method of the first aspect. BRIEF DESCRIPTION OF DRAWINGS
[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor. Figure 1 A flow chart of a smart container processing method based on commercial order cargo information extraction is shown. DETAILED DESCRIPTION
[0015] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0016] It should be noted that, in this document, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions. In this document, the term "comprises", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, an element defined by the statement "comprising a" does not exclude the existence of additional identical elements in the process, method, article or apparatus that includes the element.
[0017] Embodiment 1 In the prior art, in the existing logistics packing process, the goods are usually packed relying on manual experience, and there are problems such as low packing efficiency, low space utilization, easy to make mistakes, and unable to dynamically adapt to different goods attributes. Especially when facing goods of multiple categories, multiple specifications and multiple constraint conditions, the traditional method is difficult to achieve an efficient and reasonable packing scheme. Embodiment 1 of the present application provides the following scheme: As shown in Figure 1 The present embodiment provides an intelligent packing processing method based on commercial order goods information extraction, comprising: Step one, collect goods information and container information, and generate a goods information list and a container information list.
[0018] In some embodiments, the collected goods information and container information are subjected to data cleaning, classification and standardization processing, thereby generating a unified goods attribute data set.
[0019] In some embodiments, the goods information includes goods length, goods width, goods height, goods weight, goods type, goods fragility and maximum goods stacking number.
[0020] In some embodiments, the container information includes container length, container width, container height, container maximum load and container type.
[0021] Step two, iteratively optimize the goods information list and the container information list through packing constraint rules and heuristic rules, wherein the packing constraint rules include container boundary constraint, container load constraint, container stacking constraint, container center of gravity constraint and goods category constraint.
[0022] It should be noted that the container boundary constraint is that the goods cannot exceed the container boundary; the container load constraint is that the total weight of the goods in the container cannot exceed the maximum load; the container stacking constraint is that heavy objects cannot be placed on fragile goods, and the number of goods stacking cannot exceed the maximum load; the container center of gravity constraint is that the overall center of gravity of the goods in the container needs to fall within a safe range, such as the middle area of the container bottom area; and the goods category constraint is that chemical goods and food goods cannot be placed in the same container.
[0023] Specifically, the heuristic rule can adopt a genetic algorithm, each gene represents a goods number, a chromosome contains multiple genes, an initial population of size N is randomly generated, each chromosome in the population is subjected to actual packing using a decoder, the decoder can adopt a greedy rule, and the fitness is calculated by a fitness function calculator. Then, through selection, crossover, mutation, and elite preservation strategy, iteratively optimize, and finally output the packing scheme with the highest fitness.
[0024] In some embodiments, when performing fitness function and decoding calculation for the current goods, all opened and available container types are traversed, the remaining space after the goods are placed in the container is divided into several rectangular spaces along the X, Y and Z axes, and a space list is generated, and meanwhile, the packing constraint rules are detected, and if the packing constraint rules are not met, the current goods are considered as unplaceable.
[0025] It should be noted that if all opened containers cannot be accommodated, a new container is opened, thereby improving the accuracy of optimizing the optimal packing scheme.
[0026] Step three, the highest fitness packing scheme is obtained through iterative optimization of the packing information, and the three-dimensional coordinates of each goods in the container are output, and the overall space utilization, the number of containers used and the total cost can also be output in combination with the optimal packing scheme.
[0027] Embodiment 2 The embodiment provides an intelligent packing processing system based on commercial order goods information extraction, comprising: The acquisition module is configured to acquire goods information and container information, and generate a goods information list and a container information list.
[0028] In some embodiments, the acquired goods information and container information are subjected to data cleaning, classification and standardization processing, thereby generating a unified goods attribute data set.
[0029] In some embodiments, the goods information includes goods length, goods width, goods height, goods weight, goods type, goods fragility and maximum goods stacking number.
[0030] In some embodiments, the container information includes container length, container width, container height, container maximum load and container type.
[0031] The iterative optimization module is configured to perform iterative optimization of the goods information list and the container information list by the packing constraint rules and the heuristic rules.
[0032] The packing constraint module is configured to constrain the container boundary, container load, container stacking, container center of gravity and container category during packing.
[0033] It should be noted that the container boundary constraint is that the goods cannot exceed the container boundary; the container load constraint is that the total weight of the goods in the container cannot exceed the maximum load; the container stacking constraint is that heavy objects cannot be placed on fragile goods, and the number of goods stacking layers cannot exceed the maximum load; the container center of gravity constraint is that the overall center of gravity of the goods in the container needs to fall within a safe range, for example, the middle area of the container bottom area; and the goods category constraint is that chemical goods and food goods cannot be placed in the same container.
[0034] Specifically, the heuristic rule can adopt a genetic algorithm, each gene represents the number of a cargo, a plurality of genes are contained in a chromosome, an initial population of size N is randomly generated, an actual packing is performed on each chromosome in the population using a decoder, the decoder can adopt a greedy rule, and the fitness is calculated by a fitness function calculator. Then, iterative optimization is performed through selection, crossover, mutation, and elite preservation strategy, and finally the highest fitness packing scheme is output.
[0035] In some embodiments, when performing fitness function and decoding calculation on the current cargo, all opened containers and available container types are traversed, the remaining space after the cargo is placed in the container is divided into a plurality of rectangular spaces along the X, Y and Z axis directions, and a space list is generated. At the same time, the packing constraint rules are detected, and if the packing constraint rules are not met, the current cargo is considered as unplaceable.
[0036] It should be noted that if all opened containers cannot be accommodated, a new container is opened, thereby improving the accuracy of optimizing the optimal packing scheme.
[0037] The output module is configured to output the three-dimensional coordinates of each cargo in the container according to the highest fitness packing scheme obtained after iterative optimization of the packing information, and output the overall space utilization, the number of containers used, and the total cost according to the optimal packing scheme.
[0038] Embodiment 3 The embodiment provides an intelligent packing processing device based on commercial order cargo information extraction, which comprises: at least one processor; and a memory coupled to the at least one processor, the memory storing executable instructions, wherein the executable instructions, when executed by the at least one processor, cause the implementation of the method steps of the embodiment 1 of the application.
[0039] The intelligent packing processing device based on commercial order cargo information extraction provided by the embodiment of the application can be separately arranged or integrated together.
[0040] For example, the memory can include random access memory, flash memory, read-only memory, programmable read-only memory, non-volatile memory, or registers, etc. The processor can be a central processing unit (CPU) or the like. Or a graphic processing unit (GPU) The memory can store executable instructions. The processor can execute the executable instructions stored in the memory, thereby implementing the various processes described herein.
[0041] It can be appreciated that the memory in the embodiments can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Among them, the nonvolatile memory can be a ROM (Read-Only Memory), a PROM (Programmable ROM), an EPROM (Erasable PROM), an EEPROM (Electrically EPROM), or a flash memory. The volatile memory can be a RAM (Random Access Memory) used as an external cache. By way of example and not limitation, many forms of RAM can be used, such as SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), DDR SDRAM (Double Data Rate SDRAM), ESDRAM (Enhanced SDRAM), SLDRAM (Synchlink DRAM), and DRRAM (Direct Rambus RAM). The memory described herein is intended to include, without being limited to, these and any other suitable types of memory.
[0042] In some embodiments, the memory stores elements, executable units or data structures, or a subset thereof, or an extended set thereof: operating systems and application programs.
[0043] Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program includes various application programs for implementing various application services. The program for implementing the method of the embodiments of the application can be included in the application program.
[0044] In the embodiments of the application, the processor invokes the program or instruction stored in the memory, specifically, the program or instruction stored in the application program, and the processor is used to execute the method steps of Embodiment 1 of the application.
[0045] Embodiment 4 This embodiment provides a chip for executing the method of Embodiment 1 of the application described above. Specifically, the chip includes a processor for invoking and running a computer program from a memory, so that a device installed with the chip is used to execute the method of Embodiment 1 of the application.
[0046] Embodiment 5 The embodiment provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement steps of the method in the embodiment 1 of the application.
[0047] For example, the machine readable storage medium can include, but is not limited to, various known and unknown types of non-volatile memory.
[0048] To sum up, the embodiments 1-5 of the application provide an intelligent container loading processing method and system based on business order cargo information extraction. The container loading information iteration optimization is performed on the cargo information list and the container information list by using the container loading constraint rule and the heuristic rule, so that the container loading scheme with the highest fitness is obtained. The container loading space utilization rate is improved, the transportation cost is reduced, the manual intervention is reduced, and the container loading efficiency and accuracy are improved.
[0049] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or in combination of software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different ways to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.
[0050] In the embodiments of the present application, the disclosed system, device and method can be implemented in other ways. For example, the division of units is only a logical function division, and in actual implementation, there can be another division manner. For example, a plurality of units or components can be combined or integrated into another system. In addition, the coupling between the units can be direct coupling or indirect coupling. In addition, the functional units in the embodiments of the present application can be integrated in one processing unit, or can be separate physical units, etc.
[0051] It should be understood that, in various embodiments of the present application, the size of the serial number of each process does not mean the execution order, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0052] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a machine readable storage medium. Therefore, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a machine readable storage medium, and can include a plurality of instructions to make an electronic device execute all or part of the processes described in the embodiments of the present application. The above storage medium can include ROM, RAM, removable disks, hard disks, magnetic disks or optical disks, and various media that can store program codes. The above is only a specific embodiment of the present application, and the protection scope of the present application is not limited thereto. Those skilled in the art can make changes or replacements within the technical scope disclosed in the present application, and these changes or replacements should be within the protection scope of the present application.
Claims
1. An intelligent case packing processing method based on commercial invoice cargo information extraction, characterized in that, The method comprises the following steps: Collecting cargo information and container information to generate a cargo information list and a container information list; Iteratively optimizing the cargo information list and the container information list according to container constraint rules and heuristic rules; The container constraint rules include container boundary constraints, container load constraints, container stacking constraints, container gravity center constraints and cargo category constraints; The container scheme with the highest fitness is obtained after the iteratively optimizing the container information, and the three-dimensional coordinates of each cargo in the container are outputted. 2.The intelligent packing processing method based on business order cargo information extraction according to claim 1, wherein, The collecting of the cargo information and the container information comprises the following steps: The collected cargo information and container information are subjected to data cleaning, classification and standardization processing. 3.The intelligent case packing method based on commercial invoice information extraction of claim 1, wherein, The iteratively optimizing the cargo information list and the container information list according to container constraint rules and heuristic rules comprises the following steps: When the fitness function and decoding calculation are performed on the current cargo, all opened containers and available container types are traversed, the remaining space of the cargo after being placed in the container is divided into several rectangular spaces along the X, Y and Z axes, and a space list is generated, and the container constraint rules are detected, and if the container constraint rules are not met, the current cargo is considered as unplaceable. 4.The intelligent case packing method based on commercial invoice information extraction of claim 1, wherein, The cargo information list is initialized and sorted before the iteratively optimizing the container information, and fragile cargos are placed preferentially. 5.The intelligent case packing method based on commercial invoice information extraction of claim 1, wherein, The cargo information includes cargo length, cargo width, cargo height, cargo weight, cargo type, cargo fragility and maximum cargo stacking number. 6.The intelligent case packing method based on commercial invoice information extraction of claim 1, wherein, The container information includes container length, container width, container height, maximum container load and container type.
7. An intelligent case packing processing system based on commercial invoice information extraction, characterized in that, The method comprises the following steps: A collecting module is configured to collect cargo information and container information to generate a cargo information list and a container information list; An iteratively optimizing module is configured to iteratively optimize the cargo information list and the container information list according to container constraint rules and heuristic rules; A container constraint module is configured to constrain the container boundary, container load, container stacking, container gravity center and container category during container loading; An output module is configured to obtain the container scheme with the highest fitness after iteratively optimizing the container information, and output the three-dimensional coordinates of each cargo in the container.
8. An intelligent case packing processing device based on commercial invoice information extraction, comprising at least one processor; a memory coupled to the at least one processor, the memory storing executable instructions, characterized in that, The executable instructions, when executed by the at least one processor, cause the steps of the method according to any one of claims 1 to 7 to be implemented.
9. A chip, characterized by The computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 7.