A method and apparatus for loading cargo into a configurable multi-type freight car formation.

Abstract

This invention proposes a method and apparatus for loading cargo into configurable multi-type freight car formations. The method includes: acquiring transport vehicle formation information and cargo information to be transported; for each type of cargo to be transported, pre-setting different vehicle placement priorities according to the corresponding constraint rules; employing a feature extraction method for placeable anchor points based on discrete point cloud description to determine a set of anchor points suitable for placing other cargo based on the current cargo space occupancy; continuing to place cargo to be transported according to the anchor point set until the current loading situation meets a preset stopping condition; changing the placement method of the cargo to be transported to obtain new alternative placement results, and determining the loading method with the lowest transportation cost. The method provided by this invention automatically solves the optimal placement method based on constraint rules and given cargo information, improving the utilization rate of freight car space and reducing transportation costs by using the fewest freight cars for cargo transportation.

Description

Technical Field

[0001] This invention relates to the field of cargo loading technology, and in particular to a method and apparatus for loading cargo into a wagon that can be configured with multiple types of freight car formations. Background Technology

[0002] Currently, the placement of goods during vehicle transportation is generally done by workers based on experience. However, due to differences in parameters such as cargo size, weight, and safety type, and considering the vehicle type, safety conditions, and available space in the loaded wagons, it is necessary to optimize the cargo loading of wagons based on configurable multi-type freight car formations. This is especially important under specific rule constraints, in order to further improve the utilization rate of wagon space and reduce transportation costs. Summary of the Invention

[0003] The technical problem this invention aims to solve is how to improve the utilization rate of freight car space and reduce transportation costs. In view of this, this invention provides a method and apparatus for loading cargo into freight cars that can be configured with multiple types of freight car formations.

[0004] The technical solution adopted in this invention is that the cargo loading of the configurable multi-type freight car formation includes:

[0005] Step S1: Obtain information on the transport vehicle grouping and the cargo to be transported;

[0006] Step S2: For each category of goods to be transported, according to the constraint rules corresponding to the category, different vehicle placement priorities are preset. Each category of goods to be transported has at least one vehicle alternative loading method. The goods to be transported are sorted from largest to smallest according to the size of the goods and the safety distance, and this order is used as the initial input order of the loading optimization algorithm.

[0007] Step S3: The method of extracting placeable anchor points based on discrete point cloud description is used to determine the set of anchor points suitable for placing other goods in the discrete point cloud data according to the space occupancy of the currently placed goods, and to update the set of anchor points according to anchor point features, goods loading constraints and vehicle loading constraints.

[0008] Step S4: Continue to place the goods to be transported according to the set of anchor points until the current loading status meets the preset stopping conditions;

[0009] Step S5: Change the placement method of the goods to be transported, obtain new alternative placement results, and determine the loading method that minimizes transportation costs.

[0010] In one embodiment, the transport vehicle grouping information includes: transport vehicle type, vehicle size, maximum load capacity, and type of cargo that can be loaded; the cargo information to be transported includes the type, quantity, shape, size, weight, vehicle placement priority, and safe distance between cargo.

[0011] In one implementation, in step S3, the selection of goods for placement is carried out by one of random selection or traversal selection; the selection of anchor points for goods placement and the placement method are determined based on the maximum envelope space utilization rate of the placed goods after placement; the anchor point set search is implemented by one of binary tree, Monte Carlo algorithm, or spatial traversal.

[0012] In one implementation, the stopping condition in step S4 includes:

[0013] The remaining load of the vehicle is calculated in real time during the placement process, and the placement is stopped when the minimum weight of the goods to be placed exceeds the remaining load of the vehicle.

[0014] When the remaining space cannot be arranged in order, iterate through all unplaced goods until all goods cannot be placed.

[0015] Update the vehicle's remaining space and cargo inventory list, and continue placing other types of cargo until the vehicle's remaining space is full.

[0016] Another aspect of the present invention provides a cargo loading device for configurable multi-type freight car formations, comprising:

[0017] The acquisition module is configured to acquire information on the transport vehicle grouping and the cargo to be transported.

[0018] The preprocessing module is configured to, for each category of goods to be transported, preset different vehicle placement priorities according to the constraint rules corresponding to the category, and each category of goods to be transported has at least one vehicle alternative loading method. The goods to be transported are sorted from largest to smallest according to the size of the goods and the safety distance, and this order is used as the initial input order of the loading optimization algorithm.

[0019] The update module is configured to use a placeable anchor point feature extraction method based on discrete point cloud description to determine a set of anchor points suitable for placing other goods in the discrete point cloud data based on the space occupancy of the currently placed goods, and update the anchor point set based on anchor point features, goods loading constraints and vehicle loading constraints.

[0020] The stop module is configured to continue placing the goods to be transported according to the set of anchor points until the current loading condition meets the preset stop condition.

[0021] The comparison module is configured to change the placement of the goods to be transported, obtain new alternative placement results, and determine the loading method that minimizes transportation costs.

[0022] In one embodiment, the transport vehicle grouping information includes: transport vehicle type, vehicle size, maximum load capacity, and type of cargo that can be loaded; the cargo information to be transported includes the type, quantity, shape, size, weight, vehicle placement priority, and safe distance between cargo.

[0023] In one implementation, the update module selects the goods for placement using either random selection or traversal selection; the selection of anchor points and the placement method are determined based on the maximum envelope space utilization of the placed goods; and the anchor point set search is implemented using either a binary tree, Monte Carlo algorithm, or spatial traversal.

[0024] In one implementation, the stopping conditions in the stopping module include:

[0025] The remaining load of the vehicle is calculated in real time during the placement process, and the placement is stopped when the minimum weight of the goods to be placed exceeds the remaining load of the vehicle.

[0026] When the remaining space cannot be arranged in order, iterate through all unplaced goods until all goods cannot be placed.

[0027] Update the vehicle's remaining space and cargo inventory list, and continue placing other types of cargo until the vehicle's remaining space is full.

[0028] Another aspect of the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the cargo loading method for configurable multi-type freight car formations as described in any of the preceding claims.

[0029] Another aspect of the present invention provides a computer storage medium storing a computer program that, when executed by a processor, implements the steps of the cargo loading method for configurable multi-type freight car formations as described in any of the preceding claims.

[0030] By adopting the above technical solution, the cargo loading method for configurable multi-type freight car formations provided by the present invention automatically solves the optimal placement method according to the constraint rules and given cargo information, improves the utilization rate of freight car space, and reduces transportation costs by using the fewest freight cars for cargo transportation. Attached Figure Description

[0031] Figure 1 This is a flowchart of a cargo loading method for configurable multi-type freight car formations according to an embodiment of the present invention;

[0032] Figure 2This is a logic flowchart of a cargo loading method for configurable multi-type freight car formations according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of the composition structure of a cargo loading device for configurable multi-type freight car formations according to an embodiment of the present invention;

[0034] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0035] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments.

[0036] In the accompanying drawings, the thickness, size, and shape of the objects have been slightly exaggerated for ease of illustration. The drawings are for illustrative purposes only and are not drawn to scale.

[0037] It should also be understood that the terms "comprising," "including," "having," "containing," and / or "comprising," when used in this specification, indicate the presence of the stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or combinations thereof. Furthermore, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire listed feature, not individual elements in the list. Additionally, when describing embodiments of this application, the word "may" is used to mean "one or more embodiments of this application." And the term "exemplary" is intended to refer to an example or illustration.

[0038] As used herein, the terms “basically,” “approximately,” and similar terms are used as terms of approximation rather than terms of degree, and are intended to describe inherent biases in measured or calculated values ​​that will be recognized by those skilled in the art.

[0039] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms (e.g., those defined in common dictionaries) shall be interpreted as having the meaning consistent with their meaning in the context of the relevant art and shall not be interpreted in an idealized or overly formal sense unless expressly so specified herein.

[0040] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0041] The steps described in the specification and the flowcharts in the accompanying drawings of this invention are not necessarily to be strictly followed according to the step numbers; the execution order of the steps can be changed. Furthermore, certain steps can be omitted, multiple steps can be combined into one step, and / or one step can be broken down into multiple steps.

[0042] The first embodiment of the present invention provides a method for loading cargo into a wagon carriage that can be configured with multiple types of freight car formations, such as... Figure 1 As shown, the specific steps include the following:

[0043] Step S1: Obtain information on the transport vehicle grouping and the cargo to be transported;

[0044] Step S2: For each category of goods to be transported, according to the constraint rules corresponding to the category, different vehicle placement priorities are preset. Each category of goods to be transported has at least one vehicle alternative loading method. The goods to be transported are sorted from largest to smallest according to the size of the goods and the safety distance, and this order is used as the initial input order of the loading optimization algorithm.

[0045] Step S3: The method of extracting placeable anchor points based on discrete point cloud description is used to determine the set of anchor points suitable for placing other goods in the discrete point cloud data according to the space occupancy of the currently placed goods, and to update the set of anchor points according to anchor point features, goods loading constraints and vehicle loading constraints.

[0046] Step S4: Continue to place the goods to be transported according to the set of anchor points until the current loading status meets the preset stopping conditions;

[0047] Step S5: Change the placement method of the goods to be transported, obtain new alternative placement results, and determine the loading method that minimizes transportation costs.

[0048] The following will combine Figures 1 to 2 The method provided in this embodiment will be described in detail.

[0049] In this embodiment, the transport vehicle grouping information and the cargo information to be transported are obtained. The transport vehicle grouping information includes the type of transport vehicle, vehicle size, maximum load capacity, and types of cargo that can be loaded. The cargo information to be transported includes the type, quantity, shape, size, weight, vehicle placement priority, and safe distance between cargo.

[0050] Among them, vehicle information and the list of goods to be transported can be manually entered by relevant personnel on the host computer. The configurable input mode is adopted for the loading and assembly of carriages, and the carriage types include different sizes, load capacities, safety types, etc.

[0051] The shapes of goods to be transported generally include cylinders and cuboids;

[0052] The dimensions of the goods to be transported include the length, width and height dimensions. For cylindrical goods, the length and width dimensions are both considered as the diameter of the circular base.

[0053] Understandably, a single transportation process may include different vehicles transporting different types and quantities of goods to be transported. In one embodiment, the vehicles include three different types of vehicles: A, B, and C; the cargo list includes three different types of goods: A, B, and C. Goods A consists of cylindrical goods of different sizes, with a total of 10 goods; goods B consists of rectangular goods of the same size, with a total of 15 goods; and goods C includes 10 cylindrical goods and 10 rectangular goods of different sizes.

[0054] Optionally, special constraint rules can be set for different goods to be transported during the transportation process. In the above embodiment, it can be stipulated that goods A are placed in vehicle A first, goods B can be placed in vehicles B and C, and goods C can be placed in any vehicle. The placement order is A, B, C, and different types of goods cannot be mixed.

[0055] For each type of goods to be transported, different vehicle placement priorities are preset, and each type of goods to be transported has at least one alternative vehicle loading method; in particular, special constraint rules can be set for each type of goods.

[0056] Each type of goods has at least one way of being displayed;

[0057] Understandably, different shapes of goods have different placement requirements. Generally speaking, cylindrical goods can only be placed vertically, that is, with the circular cross-section parallel to the ground; when placing cuboid goods, all faces except the two smallest faces can be placed on the bottom.

[0058] Optionally, different levels of safety distance can be set between different goods, and the space occupied by the safety distance is added in addition to the size of the goods when calculating the remaining space of the vehicle;

[0059] Optionally, special constraint rules can be set for specific types of goods. In one embodiment, cylindrical goods are placed according to the principle of similar height, rectangular goods are placed according to the principle of similar length, the bottom area of ​​the upper layer of stackable goods is smaller than the area of ​​the lower layer, and goods with related products are given priority to be placed in vehicle A.

[0060] For each type of goods to be transported, the goods list is sorted from largest to smallest according to the goods size and safety distance, and this order is used as the initial input order for the loading optimization algorithm;

[0061] The single-car loading optimization algorithm employs a feature extraction method for placeable anchor points based on discrete point cloud description. This method extracts a set of anchor points suitable for placing other goods from the discrete point cloud data, based on the space occupancy of the placed goods. The anchor point set is continuously updated based on anchor point features, goods loading constraints, and vehicle loading constraints. Specifically, the selection of placed goods utilizes methods such as random selection and traversal selection; the selection and placement method of goods placement anchor points are determined based on the maximum envelope space utilization rate of the placed goods; and the anchor point set search is implemented using methods such as binary trees, Monte Carlo algorithms, and spatial traversal.

[0062] Furthermore, the new rectangles created by removing the previous row of rectangles are considered the updated vehicle space, and the process continues until the remaining space in the vehicle is insufficient to accommodate all the unplaced goods, at which point the vehicle's cargo placement is complete.

[0063] The remaining load of the vehicle is calculated in real time during the placement process, and the placement is stopped when the minimum weight of the goods to be placed exceeds the remaining load of the vehicle.

[0064] Preferably, when the remaining space cannot be arranged in order, all unplaced goods are traversed until all goods cannot be placed.

[0065] Update the vehicle's remaining space and cargo list, and continue to place other types of cargo until the vehicle's remaining space is full.

[0066] Change the direction of the goods, compare the number of vehicles occupied by horizontal and vertical placement, and select the method with the fewest vehicles as the optimal placement method;

[0067] Based on information such as transportation costs and space utilization of different types of vehicles, and combined with the information on the transported goods and vehicle formation, the optimal cargo loading combination and vehicle formation combination are selected.

[0068] Optionally, the transportation parameters of the goods to be transported include the allowable double-layer placement of the goods to be transported. For goods that can be placed in double layers, the lower layer goods are still placed in the above manner. After each lower layer goods are placed, all unplaced goods are traversed, and the goods with the closest size are placed on the upper layer.

[0069] Optionally, the transportation parameters of the goods to be transported include the presence of related products that need to be placed in the same vehicle as the goods to be transported. For such goods, the related products are packaged and sorted together during sorting, and the two are treated as a whole during placement.

[0070] In summary, compared with the prior art, the method provided in this embodiment has at least the following advantages:

[0071] The system automatically solves for the optimal cargo placement based on constraints and given cargo information, improving cargo space utilization and reducing transportation costs by using the fewest possible trucks.

[0072] The second embodiment of the present invention, corresponding to the first embodiment, introduces a cargo loading device for configurable multi-type freight car formations, such as... Figure 3 As shown, it includes the following components:

[0073] The acquisition module is configured to acquire information on the transport vehicle grouping and the cargo to be transported.

[0074] The preprocessing module is configured to, for each category of goods to be transported, preset different vehicle placement priorities according to the constraint rules corresponding to the category, and each category of goods to be transported has at least one vehicle alternative loading method. The goods to be transported are sorted from largest to smallest according to the size of the goods and the safety distance, and this order is used as the initial input order of the loading optimization algorithm.

[0075] The update module is configured to use a placeable anchor point feature extraction method based on discrete point cloud description to determine a set of anchor points suitable for placing other goods in the discrete point cloud data based on the space occupancy of the currently placed goods, and update the anchor point set based on anchor point features, goods loading constraints and vehicle loading constraints.

[0076] The stop module is configured to continue placing the goods to be transported according to the set of anchor points until the current loading condition meets the preset stop condition.

[0077] The comparison module is configured to change the placement of the goods to be transported, obtain new alternative placement results, and determine the loading method that minimizes transportation costs.

[0078] In this embodiment, the transport vehicle grouping information includes: transport vehicle type, vehicle size, maximum load capacity, and type of cargo that can be loaded; the cargo information to be transported includes the cargo type, quantity, shape, size, weight, vehicle placement priority, and safe distance between cargo.

[0079] In this embodiment, the selection of goods in the update module is carried out by random selection or traversal selection; the selection of goods placement anchor points and placement method are determined based on the maximum envelope space utilization rate of the placed goods after placement; the anchor point set search is implemented by one of binary tree, Monte Carlo algorithm, or spatial traversal.

[0080] In this embodiment, the stopping conditions in the stopping module include:

[0081] The remaining load of the vehicle is calculated in real time during the placement process, and the placement is stopped when the minimum weight of the goods to be placed exceeds the remaining load of the vehicle.

[0082] When the remaining space cannot be arranged in order, iterate through all unplaced goods until all goods cannot be placed.

[0083] Update the vehicle's remaining space and cargo inventory list, and continue placing other types of cargo until the vehicle's remaining space is full.

[0084] A third embodiment of the present invention provides an electronic device, such as... Figure 4 As shown, it can be understood as a physical device, including a processor and a memory storing processor-executable instructions. When the instructions are executed by the processor, the following operations are performed:

[0085] Step S1: Obtain information on the transport vehicle grouping and the cargo to be transported;

[0086] Step S2: For each category of goods to be transported, according to the constraint rules corresponding to the category, different vehicle placement priorities are preset. Each category of goods to be transported has at least one vehicle alternative loading method. The goods to be transported are sorted from largest to smallest according to the size of the goods and the safety distance, and this order is used as the initial input order of the loading optimization algorithm.

[0087] Step S3: The method of extracting placeable anchor points based on discrete point cloud description is used to determine the set of anchor points suitable for placing other goods in the discrete point cloud data according to the space occupancy of the currently placed goods, and to update the set of anchor points according to anchor point features, goods loading constraints and vehicle loading constraints.

[0088] Step S4: Continue to place the goods to be transported according to the set of anchor points until the current loading status meets the preset stopping conditions;

[0089] Step S5: Change the placement method of the goods to be transported, obtain new alternative placement results, and determine the loading method that minimizes transportation costs.

[0090] In the fourth embodiment of the present invention, the process of the cargo loading method for configurable multi-type freight car formations is the same as that of the first, second, or third embodiments. The difference lies in the engineering implementation: this embodiment can be implemented using software plus necessary general-purpose hardware platforms. While hardware implementation is also possible, the former is often a preferred method. Based on this understanding, the method of the present invention can be embodied in the form of a computer software product stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including several instructions to cause a device to execute the method described in the embodiments of the present invention.

[0091] Through the description of specific embodiments, a more in-depth and specific understanding should be gained of the technical means and effects adopted by the present invention to achieve the intended purpose. However, the accompanying drawings are only for reference and illustration and are not intended to limit the present invention.

Claims

1. A method for loading cargo into a wagon carriage that can be configured with multiple types of freight car formations, characterized in that, include: Step S1: Obtain information on the transport vehicle grouping and the cargo to be transported; Step S2: For each category of goods to be transported, according to the constraint rules corresponding to the category, different vehicle placement priorities are preset. Each category of goods to be transported has at least one vehicle alternative loading method. The goods to be transported are sorted from largest to smallest according to the size of the goods and the safety distance, and this order is used as the initial input order of the loading optimization algorithm. Step S3: The method of extracting placeable anchor points based on discrete point cloud description is used to determine the set of anchor points suitable for placing other goods in the discrete point cloud data according to the space occupancy of the currently placed goods, and to update the set of anchor points according to anchor point features, goods loading constraints and vehicle loading constraints. The selection of goods for placement is carried out using one of random selection or traversal selection; the selection of anchor points and placement method are determined based on the maximum envelope space utilization rate of the placed goods; the anchor point set search is implemented using one of binary tree, Monte Carlo algorithm, or spatial traversal. Step S4: Continue to place the goods to be transported according to the set of anchor points until the current loading status meets the preset stopping conditions; Step S5: Change the placement method of the goods to be transported, obtain new alternative placement results, and determine the loading method that minimizes transportation costs.

2. The method for loading cargo into configurable multi-type freight car formations according to claim 1, characterized in that, The transport vehicle grouping information includes: transport vehicle type, vehicle size, maximum load capacity, and type of cargo that can be loaded; The information on the goods to be transported includes the type, quantity, shape, size, weight, vehicle placement priority, and safe distance between the goods.

3. The method for loading cargo into configurable multi-type freight car formations according to claim 1, characterized in that, The stopping conditions in step S4 include: The remaining load of the vehicle is calculated in real time during the placement process, and the placement is stopped when the minimum weight of the goods to be placed exceeds the remaining load of the vehicle. When the remaining space cannot be arranged in order, iterate through all unplaced goods until all goods cannot be placed. Update the vehicle's remaining space and cargo inventory list, and continue placing other types of cargo until the vehicle's remaining space is full.

4. A cargo loading device for configurable with multiple types of freight car formations, characterized in that, include: The acquisition module is configured to acquire information on the transport vehicle grouping and the cargo to be transported. The preprocessing module is configured to, for each category of goods to be transported, preset different vehicle placement priorities according to the constraint rules corresponding to the category, and each category of goods to be transported has at least one vehicle alternative loading method. The goods to be transported are sorted from largest to smallest according to the size of the goods and the safety distance, and this order is used as the initial input order of the loading optimization algorithm. The update module is configured to use a placeable anchor point feature extraction method based on discrete point cloud description to determine a set of anchor points suitable for placing other goods in the discrete point cloud data based on the space occupancy of the currently placed goods, and update the anchor point set based on anchor point features, goods loading constraints and vehicle loading constraints. The selection of goods for placement is carried out using one of random selection or traversal selection; the selection of anchor points and placement method are determined based on the maximum envelope space utilization rate of the placed goods; the anchor point set search is implemented using one of binary tree, Monte Carlo algorithm, or spatial traversal. The stop module is configured to continue placing the goods to be transported according to the set of anchor points until the current loading condition meets the preset stop condition. The comparison module is configured to change the placement of the goods to be transported, obtain new alternative placement results, and determine the loading method that minimizes transportation costs.

5. The cargo loading device for configurable multi-type freight car formations according to claim 4, characterized in that, The transport vehicle grouping information includes: transport vehicle type, vehicle size, maximum load capacity, and type of cargo that can be loaded; The information on the goods to be transported includes the type, quantity, shape, size, weight, vehicle placement priority, and safe distance between the goods.

6. The cargo loading device for configurable multi-type freight car formations according to claim 4, characterized in that, The stopping conditions in the stopping module include: The remaining load of the vehicle is calculated in real time during the placement process, and the placement is stopped when the minimum weight of the goods to be placed exceeds the remaining load of the vehicle. When the remaining space cannot be arranged in order, iterate through all unplaced goods until all goods cannot be placed. Update the vehicle's remaining space and cargo inventory list, and continue placing other types of cargo until the vehicle's remaining space is full.

7. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the cargo loading method for configurable multi-type freight car formations as described in any one of claims 1 to 3.

8. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the cargo loading method for configurable multi-type freight car formations as described in any one of claims 1 to 3.

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