A data disassembly and assembly bagging method and system

Abstract

The present application relates to the technical field of data analysis, and particularly relates to a data-based unpacking and packing operation method and system, which comprises an order decomposition unit, based on the commodity composition in a purchase order and in combination with the contents of different warehouses, the purchase order is decomposed into multiple sub-pickup orders, and a pickup database corresponding to each warehouse is constructed; a warehouse analysis unit, each warehouse is independently analyzed, and pickup arrays in the pickup database located in a to-be-determined time interval range are divided into a to-be-determined array set; a to-be-determined array set is extracted, a packing combination constructed by multiple pickup arrays is simulated for three-dimensional placement and transportation route simulation, a space recommendation value and a route recommendation value of each packing combination are analyzed, and a pickup packing combination is screened out; a model simulation unit, a three-dimensional model is constructed and sequentially placed and simulated, and the placement simulation is used to eliminate part of the packing combinations that cannot be placed; and a packing pickup unit, the pickup packing combination is cycled for pickup and sorting.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a data-driven method and system for unpacking and repackaging operations. Background Technology

[0002] Data-driven package splitting and repackaging is a modern warehousing operation method that relies on information technology (such as WMS systems, PDAs, and barcode scanning). It intelligently guides operators through data instructions to break down source packages and reassemble them into new order packages, achieving precision, efficiency, and traceability throughout the entire process. It is a core technology for handling massive orders in e-commerce warehousing. Its main application is processing large volumes of C-end orders; for example, a customer's order may contain goods from different suppliers and stored in different warehouse locations, requiring "package splitting and repackaging" to complete the entire order.

[0003] To store massive amounts of goods, e-commerce platforms typically operate multiple warehouses. Each time a customer places an order, the goods need to be retrieved from these warehouses before being shipped out. Current warehousing and retrieval processes usually rely on a time-sequence principle, retrieving goods sequentially according to order time and demand. This process lacks planning for placement and transportation, resulting in a limited number of items that can be processed in each retrieval cycle, impacting efficiency and presenting certain drawbacks. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, this invention provides a data-driven unpacking and repacking operation method and system, which can effectively solve the problems of insufficient placement and transportation planning and poor retrieval efficiency in the e-commerce warehousing and retrieval process in existing technologies.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention provides a data-driven method for unpacking and repackaging operations, comprising at least:

[0007] The order decomposition unit, based on the product composition of the purchase order and the content of different warehouses, decomposes the purchase order into multiple sub-pickup orders and builds a pickup database corresponding to each warehouse;

[0008] The sharding analysis unit performs independent analysis for each warehouse, obtains the quantity of each order's goods and the corresponding inventory serial number to form a pickup array, and divides the pickup arrays in the pickup database that are within the undetermined time interval into the undetermined array set based on the purchase time of the order's goods.

[0009] Extract the consolidation packages constructed from multiple pickup arrays in the undetermined array set. Each consolidation package meets the transportation conditions. Perform three-dimensional placement simulation and transportation route simulation on all order items in the consolidation package. Analyze the spatial recommendation value and route recommendation value of each consolidation package and select the pickup consolidation packages.

[0010] The model simulation unit constructs a 3D model based on the volume and dimensions of all order items in the bundled package and performs a sequential placement simulation. The placement simulation is used to eliminate some bundled packages that cannot be placed.

[0011] The consolidated pickup unit picks up goods in a cyclical manner according to the consolidated pickup combinations, transports multiple order items corresponding to the consolidated pickup combinations to the sorting area, sorts the order items in the sorting area according to the sub-pickup orders, and consolidates multiple order items corresponding to the same user's purchase order into a single package for shipment.

[0012] Furthermore, the process of building the pickup database is as follows:

[0013] Acquire purchase data generated by customers when purchasing on the platform. The purchase data contains multiple product SKUs. Each product SKU is bound to a set of purchase information data, including purchase time and purchase quantity. Each product SKU is recorded as an order item.

[0014] Obtain the inventory set corresponding to each warehouse. The inventory set contains multiple product SKUs and the corresponding stock of each product SKU. Build a pickup database for each warehouse. Based on the inventory set to which the order products belong in the purchase data, enter the corresponding order products into the pickup database.

[0015] Furthermore, the process of constructing the set of arrays to be determined is as follows:

[0016] The goods in the warehouse are recorded as inventory goods, and each inventory goods is assigned an inventory number. Each inventory number corresponds to a location coordinate. The order goods in the pickup database are sorted by time, and the quantity of each order goods and its corresponding inventory number are used to form a pickup array. , ), where i represents the sorting number of the items in the order. This represents the inventory sequence number corresponding to the item in order number i. This indicates the quantity of items in order number i;

[0017] Each warehouse corresponds to a pickup interval threshold, based on the pickup array ( , The corresponding purchase time is combined with the pickup interval threshold to construct a pending time interval, and multiple pickup arrays located within the pending time interval are denoted as a pending array set.

[0018] Furthermore, the process for obtaining the pickup interval threshold is as follows:

[0019] Get the average speed of the pickup truck, record the route of traversing all shelves in the warehouse as the traversal route, divide the traversal route by the average speed to get the traversal time, and get the picking base time corresponding to all inventory items in the warehouse. The picking base time is a preset value.

[0020] Obtain the truck space threshold and the unit volume corresponding to each inventory item, construct a maximum volume array composed of various inventory items, and denote each inventory item in the maximum volume array as the threshold item. Let n represent the index of each threshold item in the maximum volume array, n = 1, 2, ..., m, where m represents the total number of threshold item types in the maximum volume array. The maximum volume array satisfies the following formula: ,in:

[0021] This represents the volume corresponding to the threshold item with serial number n;

[0022] This represents the quantity of the threshold item with serial number n;

[0023] Indicates the truck space threshold;

[0024] Through formula Calculate the time threshold for each maximum volume array. ,in This represents the baseline pickup time for the threshold item with sequence number n. Indicates the traversal time;

[0025] The maximum value among the undetermined time thresholds is selected as the pickup interval threshold.

[0026] Furthermore, the assembly process of the package is as follows:

[0027] Calculate the product of the volume of a single item in the order corresponding to the pickup array and the quantity of items in the order to obtain the placement volume corresponding to the pickup array. Obtain the weight of a single item in the order corresponding to the pickup array and the quantity of items in the order to obtain the placement weight corresponding to the pickup array. Ensure that multiple pickup arrays in the grouped package combination satisfy the following conditions:

[0028] Condition 1: The sum of the placement volumes corresponding to multiple pickup arrays is less than or equal to the preset truck space threshold;

[0029] Condition 2: The sum of the weights of the goods corresponding to multiple pickup arrays is less than or equal to the preset truck weight threshold.

[0030] Furthermore, the process for selecting consolidation packages for pickup is as follows:

[0031] A 3D model is used to simulate the capacity of the package, some of the package combinations are removed and the recommended space value for each package combination is calculated. The space ratio range [k,1] is preset, where k is a constant less than 1 and greater than 0.

[0032] When the space recommendation value corresponding to all group-buying combinations is less than k, the group-buying combination with the largest space recommendation value is selected as the pick-up group-buying combination.

[0033] When multiple group-buying combinations whose space recommendation values ​​fall within the preset space ratio range are selected, they are recorded as pending group-buying combinations. The optimal route corresponding to the pending group-buying combinations is analyzed and the route recommendation value is calculated. The optimal route is the shortest route through all warehouse shelves corresponding to the pending group-buying combinations. The pending group-buying combination with the minimum route recommendation value is selected as the picking group-buying combination.

[0034] Furthermore, the process of eliminating some of the assembled packages in the model simulation unit is as follows:

[0035] S1: Obtain the 3D model of each inventory item and call it the item model. Construct an equivalent hexahedron for each item model. The equivalent hexahedron is the smallest cuboid that can accommodate the item model. Add the equivalent hexahedron corresponding to each item in the package to the hexahedron data set one by one.

[0036] S2: Obtain the three-dimensional space corresponding to the cargo box of the pickup truck and record it as the accommodation space. Place the equivalent hexahedrons in the hexahedron data set into the accommodation space one by one according to steps S3 and S4. After each addition, remove the corresponding equivalent hexahedrons from the hexahedron data set. Generate a new accommodation space based on the three-dimensional model formed after each addition and replace the original accommodation space.

[0037] S3: The added equivalent hexahedron is denoted as the target hexahedron. The target hexahedron is placed multiple times in the accommodating space. The optimal fitting area is calculated based on the contact area between the target hexahedron and the inner surface of the accommodating space during each placement simulation. The equivalent hexahedron corresponding to the maximum value of the optimal fitting area is placed according to the placement position corresponding to the placement simulation.

[0038] S4: Stop placing items when any of the following conditions are met, including:

[0039] Condition A: When the height of any equivalent hexahedron is greater than the space to be contained;

[0040] Condition B: All equivalent hexahedrons are located within the three-dimensional space corresponding to the cargo compartment of the pickup truck;

[0041] S5: When condition A is met and placement stops, remove the group of packages. When condition B is met and placement stops, output the 3D model corresponding to that placement method.

[0042] Furthermore, the preferred process for calculating the bonding area is as follows:

[0043] A contact coefficient is assigned to all horizontal and vertical surfaces in the containment space. The contact coefficient of the vertical surface is a preset reference coefficient R. The contact coefficient of the horizontal surface is inversely proportional to the height of the horizontal surface and is equal to R*(1+(Hh) / H), where H represents the height of the upper edge of the containment space and h represents the height of the horizontal surface. During each placement simulation, the contact area between the target hexahedron and the vertical surface and the horizontal surface at different heights in the containment space is obtained. Each contact area is multiplied by the corresponding contact coefficient and summed to obtain the optimal fitting area.

[0044] Furthermore, the spatial recommendation value is calculated as follows:

[0045] The three-dimensional model generated after the simulation is obtained. The sum of the volumes of all equivalent hexahedrons in the three-dimensional model is recorded as the occupied volume. The ratio of the occupied volume to the truck space threshold is recorded as the space recommendation value.

[0046] A data-driven method for unpacking and repackaging operations includes the following steps:

[0047] Step 1: Based on the order decomposition unit, the user's purchase order is split into multiple sub-pickup orders, each sub-pickup order corresponds to a warehouse, and a pickup database is built based on the multiple sub-pickup orders corresponding to each warehouse.

[0048] Step 2: Based on the database analysis unit, the order items in the pickup database are combined to construct pickup bundles. Pickups are then carried out in a loop according to the composition of the order items in the pickup bundles. The model simulation unit is used to obtain a 3D model of the product placement corresponding to the pickup bundles.

[0049] Step 3: A warehouse floor plan is pre-set. Mark the coordinates of each order item in the pickup and consolidation package on the warehouse floor plan, obtain the current location of the pickup truck, and mark the order item closest to the pickup truck as the target item;

[0050] Step 4: Display the 3D model of the cargo compartment and the 3D model of the goods arrangement using the display device on the pickup truck. Highlight the equivalent hexahedron corresponding to the target product, and use dashed lines to represent the outline of the equivalent hexahedron corresponding to other order products.

[0051] The technical solution provided by this invention has the following advantages compared with the known prior art:

[0052] 1. This invention starts with data from multiple dimensions, such as purchase time window, three-dimensional space utilization, and transportation routes, to conduct collaborative analysis and construct pickup and consolidation combinations. Based on these combinations, the pickup process is rationally planned. Compared to existing technologies that combine orders for pickup according to order time or simple rules, this invention no longer processes individual orders in isolation but treats multiple orders within a period of time as a whole, thereby achieving macro-control. This ensures that the pickup truck's capacity is fully utilized and the transportation route is optimized during each pickup process, significantly reducing pickup costs and improving pickup efficiency. Furthermore, through three-dimensional placement simulation, combinations that meet some logical rules but are actually impossible to place or are uneconomical to transport can be identified and eliminated in advance. This reduces secondary operations, delays, and errors caused by the inability to consolidate packages during actual pickup, improving the feasibility of consolidation planning in the pickup process.

[0053] 2. This invention constructs a 3D model of the product placement corresponding to the pickup and packaging combination, and adds 3D annotations to the 3D model of the cargo compartment. The placement position of the target product is displayed in the 3D model, which helps staff to understand more intuitively where the target product should be placed, thereby reducing the time required to stack the product during the pickup process and improving the pickup efficiency. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0055] Figure 1 This is a system module block diagram of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0057] The present invention will be further described below with reference to embodiments.

[0058] See Figure 1 A data-driven unpacking and repacking operation system, applicable to unpacking and repacking operations during the warehousing and picking stage, includes at least:

[0059] The order decomposition unit, based on the product composition of the purchase order and the content of different warehouses, decomposes the purchase order into multiple sub-pickup orders and builds a pickup database corresponding to each warehouse.

[0060] Specifically, the system acquires purchase data generated by customers when they make purchases on the platform. The purchase data includes multiple product SKUs, and each product SKU is bound to a set of purchase information data, including purchase time and quantity. Each product SKU is recorded as an order item.

[0061] It should be noted that SKU stands for stock keeping unit, which is defined as the smallest available unit for inventory control. A product SKU refers to a single product, and each product has a unique SKU to help e-commerce brands identify their products.

[0062] There are multiple warehouses, each storing multiple products. The system retrieves the inventory product set corresponding to each warehouse, which contains multiple product SKUs and the corresponding stock of each product SKU. A pickup database is built for each warehouse. Based on the inventory product set to which the order products belong in the purchase data, the corresponding order products are entered into the pickup database. The pickup database contains products that need to be picked up and shipped.

[0063] It should be noted that e-commerce warehousing areas typically include multiple different warehouses to store massive amounts of inventory. Since the goods purchased by customers usually belong to different warehouses, it is necessary to pick up and ship goods from multiple warehouses. Poor coordination among multiple warehouses can lead to a decrease in shipping efficiency and affect the efficiency of e-commerce goods transportation.

[0064] The sharding analysis unit analyzes the order products in the pickup database based on the pickup database corresponding to each warehouse, combined with the distribution of goods shelves in the warehouse and the order product data itself, and divides the order products in the pickup database into multiple pickup and consolidation combinations.

[0065] Specifically, the process of creating a consolidated package for pickup is as follows:

[0066] P1: Record the goods in the warehouse as inventory goods, assign an inventory number to each inventory goods, and each inventory number corresponds to a location coordinate. Sort the order goods in the pickup database by time, and obtain the quantity of order goods and the corresponding inventory number to form a pickup array. , ), where i represents the sorting number of the items in the order. This represents the inventory sequence number corresponding to the item in order number i. This indicates the quantity of items in order number i;

[0067] It should be noted that the purchase time, product SKU, and corresponding customer ID of different orders in the pickup database are all different. In other words, if any one or more of the purchase time, product SKU, and corresponding customer ID are different, they do not belong to the same order. Even two orders with the same product SKU cannot be merged, thus ensuring the independence of each order and avoiding mis-sending, omission, or over-sending.

[0068] P2: Each warehouse has a corresponding pickup interval threshold, which represents the maximum time from order placement to pickup completion for a single item, based on the pickup array ( , The corresponding purchase time is combined with the pickup interval threshold to construct a pending time interval. Multiple pickup arrays located within the pending time interval are recorded as a pending array set. That is, the purchase time corresponding to multiple pickup arrays is located within the pending time interval.

[0069] More specifically, the process for obtaining the pickup interval threshold is as follows:

[0070] The average speed of the pickup truck is obtained. The route taken to traverse all shelves in the warehouse is recorded as the traversal route (i.e., the route passing in front of all shelves). The traversal route is divided by the average speed to obtain the traversal time. The retrieval baseline time for each inventory item in the warehouse is obtained. The retrieval baseline time is a preset value, representing the average time required to retrieve or place the inventory item from the shelf. It is set by staff based on experience (i.e., the time allocated by staff for retrieval and placement). The truck space threshold and the unit volume corresponding to each inventory item are obtained. A maximum volume array composed of multiple inventory items is constructed, and each inventory item in the maximum volume array is recorded as a threshold item. Let n represent the index of each threshold item in the maximum volume array, n = 1, 2, ..., m, where m represents the total number of threshold item types in the maximum volume array. The maximum volume array satisfies the following formula: ,in:

[0071] This represents the volume corresponding to the threshold item with serial number n;

[0072] This represents the quantity of the threshold item with serial number n;

[0073] Indicates the truck space threshold;

[0074] Through formula Calculate the time threshold for each maximum volume array. ,in This represents the baseline pickup time for the threshold item with sequence number n. This indicates the traversal time, and the maximum value among the undetermined time thresholds is selected as the pickup interval threshold.

[0075] It should be noted that the pickup interval threshold represents the total pickup time required for the warehouse under the longest route and the longest pickup time. It can usually be used as the upper limit of the pickup time for the warehouse. The pickup time corresponding to any pickup package formed by multiple products should be within this time range. This is because, under reasonable planning, no matter how the products are combined or the route is adjusted, the total time required will not exceed the pickup interval threshold. Therefore, dividing the pickup array with this threshold can obtain relatively efficient pickup packages, while limiting the pickup time of the products and improving pickup efficiency.

[0076] P3: Select multiple pickup arrays from the undetermined array set to form a combined package. For each pickup array in the combined package, calculate the product of the volume of the single item in the order and the quantity of the order items to obtain the placement volume of the pickup array. Obtain the weight of the single item in the order and the quantity of the order items to obtain the placement weight of the pickup array, satisfying the following conditions:

[0077] Condition 1: The sum of the placement volumes corresponding to multiple pickup arrays is less than or equal to the preset truck space threshold (i.e., the upper limit of the cargo transport space of the pickup truck).

[0078] Condition 2: The sum of the weights of goods corresponding to multiple pickup arrays is less than or equal to the preset truck weight threshold (i.e., the upper limit of the weight of goods transported by pickup trucks).

[0079] P4: Perform a 3D model to accommodate the space, remove some bundled packages and calculate the recommended space value. There is a preset space ratio range [k,1], where k is a constant less than 1 and greater than 0, which is set by staff based on experience (in a specific embodiment, the value is 0.8). By setting the space ratio range, the proportion of bundled packages occupying the transportation space of the pickup truck can be limited, thereby ensuring the transportation efficiency of the pickup truck when transporting the order goods corresponding to the bundled packages and making full use of the transportation space.

[0080] When the space recommendation value corresponding to all group-buying combinations is less than k, the group-buying combination with the largest space recommendation value is selected as the pick-up group-buying combination.

[0081] When multiple group-buying combinations whose space recommendation values ​​fall within the preset space ratio range are selected, they are recorded as pending group-buying combinations. The best routes corresponding to the pending group-buying combinations are analyzed and route recommendation values ​​are calculated. The pending group-buying combination with the minimum route recommendation value is selected as the pickup group-buying combination.

[0082] It is worth noting that the optimal route value refers to the shortest route through all the product shelves corresponding to the pending package combination. The recommended route value is equal to the sum of the route travel time and the pickup time corresponding to the pending package combination. The route travel time is calculated by the total route length and the average speed of the truck. The sum of the pickup times is calculated based on the pickup baseline time corresponding to each product in the pending package combination.

[0083] P5: Remove the order items from the pickup pooling database and repeat P1-P5.

[0084] The model simulation unit performs a three-dimensional model accommodation simulation based on the volume and size of the goods in the package. The three-dimensional model accommodation simulation is used to eliminate some package combinations that are difficult to place.

[0085] The simulation steps for using a 3D model are as follows:

[0086] S1: Obtain the 3D model of each inventory item and denote it as the item model. Construct an equivalent hexahedron for each item model. The equivalent hexahedron is the smallest cuboid that can accommodate the item model. Add the equivalent hexahedron corresponding to each item in the group purchase combination to the hexahedron data set one by one (that is, the hexahedron data set contains the equivalent hexahedrons corresponding to all items in the group purchase combination, and the number of equivalent hexahedrons in the hexahedron data set is equal to the total number of corresponding items in all orders in the group purchase combination).

[0087] S2: Obtain the three-dimensional space corresponding to the cargo box of the pickup truck and record it as the accommodation space (the accommodation space is a groove-shaped space composed of a bottom surface and four sides, the bottom surface is a horizontal surface and the sides are vertical surfaces). Place the equivalent hexahedrons in the hexahedron data set into the accommodation space one by one according to steps S3 and S4. After each addition, remove the corresponding equivalent hexahedron from the hexahedron data set. Generate a new accommodation space based on the three-dimensional model formed after each addition and replace the original accommodation space.

[0088] S3: Denote the added equivalent hexahedron as the target hexahedron (the target hexahedron can be any equivalent hexahedron in the hexahedron data set), perform multiple placement simulations of the target hexahedron in the accommodating space, calculate the optimal fitting area based on the contact area between the target hexahedron and the inner surface of the accommodating space during each placement simulation, and place the equivalent hexahedron corresponding to the maximum value of the optimal fitting area according to the placement position corresponding to the simulation placement;

[0089] It is worth noting that placement simulation refers to placing the target hexahedron at any position in the containment space in a 3D model. During placement simulation, there is no interference between the target hexahedron and the containment space, thus a variety of placement positions can be obtained. Each placement position corresponds to a fitting area. Placement simulation can be performed in any 3D software (such as SolidWorks). The specific process is existing technology and will not be elaborated on here.

[0090] S4: Stop placing items when any of the following conditions are met, including:

[0091] Condition A: When the height of any equivalent hexahedron is greater than the space to be contained;

[0092] Condition B: All equivalent hexahedrons are located within the three-dimensional space corresponding to the cargo compartment of the pickup truck;

[0093] S5: When condition A is met and placement stops, remove the group of packages. When condition B is met and placement stops, output the 3D model corresponding to that placement method.

[0094] The optimal bonding area calculation process is as follows:

[0095] A contact coefficient is assigned to all horizontal and vertical surfaces in the containment space. The contact coefficient of the vertical surface is a preset reference coefficient R (1 in a specific embodiment). The contact coefficient of the horizontal surface is inversely proportional to the height of the horizontal surface and is equal to R*(1+(Hh) / H), where H represents the height of the upper edge of the containment space and h represents the height of the horizontal surface. During each placement simulation, the contact area between the target hexahedron and the vertical surface and the horizontal surface at different heights in the containment space is obtained. Each contact area is multiplied by the corresponding contact coefficient and summed to obtain the preferred fitting area.

[0096] It should be noted that by setting different contact coefficients, the equivalent hexahedrons placed during the placement process can be made to contact the inner surface of the storage space as much as possible, thereby making full use of the storage space for placement, achieving orderly placement, and outputting the optimal placement method to help staff to assemble packages and improve the efficiency of picking up goods.

[0097] The calculation process for the space recommendation value is as follows:

[0098] The three-dimensional model generated after the simulation is obtained. The sum of the volumes of all equivalent hexahedrons in the three-dimensional model is recorded as the occupied volume. The ratio of the occupied volume to the truck space threshold is recorded as the space recommendation value.

[0099] The consolidated pickup unit picks up goods in a cyclical manner according to the consolidated pickup combinations. During each pickup process, multiple order items corresponding to the consolidated pickup combination are transported to the sorting area as a single batch of goods. The order items in the sorting area are sorted according to the sub-pickup order, and multiple order items corresponding to the same user's purchase order are consolidated into a single package for shipment.

[0100] It should be noted that cyclical pickup specifically refers to staff driving pickup trucks to collect goods in batches according to the order in which the pickup bundles were generated. Multiple pickup trucks can be set up. During the pickup process, the trucks pass through all the shelves corresponding to the pickup bundles, thereby transporting all the order items in the pickup bundles to the designated sorting area and sorting them according to the orders. Each cyclical pickup is an independent bundling process, combining different order items into boxes and transporting them to the sorting area to maximize the efficiency of each pickup.

[0101] Example 2:

[0102] Based on the data-driven unpacking and repacking operation system in Embodiment 1, although the system can provide warehouse workers with more efficient packing and combination solutions, thereby improving the retrieval efficiency in the warehousing process, warehouse workers still need to manually stack goods during the retrieval process, and the placement of goods needs to be selected independently during the stacking process. This may result in additional stacking and thinking time during the retrieval process, affecting the retrieval efficiency and having certain drawbacks.

[0103] To address the aforementioned shortcomings, a data-driven unpacking and repacking operation method is proposed, applied to a data-driven unpacking and repacking operation system in Embodiment 1, comprising the following steps:

[0104] Step 1: Based on the order decomposition unit, the user's purchase order is split into multiple sub-pickup orders, each sub-pickup order corresponds to a warehouse, and a pickup database is built based on the multiple sub-pickup orders corresponding to each warehouse.

[0105] Step 2: Based on the database analysis unit, the order items in the pickup database are combined to construct pickup bundles. Pickups are then carried out in a loop according to the composition of the order items in the pickup bundles. The model simulation unit is used to obtain a 3D model of the product placement corresponding to the pickup bundles.

[0106] Step 3: A warehouse floor plan is pre-set. Mark the coordinates of each order item in the pickup and consolidation package on the warehouse floor plan, obtain the current location of the pickup truck, and mark the order item closest to the pickup truck as the target item;

[0107] Determining the location of pickup trucks and obtaining warehouse floor plans through positioning systems are existing technologies, and will not be elaborated further here.

[0108] Step 4: Display the 3D model of the cargo compartment and the 3D model of the goods arrangement using the display device on the pickup truck. Highlight the equivalent hexahedron corresponding to the target product, and use dashed lines to represent the outline of the equivalent hexahedron corresponding to other order products.

[0109] It should be noted that displaying a 3D model to show the placement of the target product can help staff understand more intuitively where the target product should be placed, thereby reducing the time required to stack the product during the retrieval process and improving the efficiency of product retrieval.

[0110] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method.

[0111] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method.

[0112] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data-based unpacking and packing operation system, characterized by, The method comprises the following steps: An order decomposition unit decomposes a purchase order into multiple sub-pickup orders based on the composition of goods in the purchase order and the contents of different warehouses, and constructs a pickup database corresponding to each warehouse; A warehouse analysis unit independently analyzes each warehouse to obtain the quantity of each order item and the corresponding inventory serial number to form a pickup array, and divides the pickup arrays in the pickup database within a pending time interval into a pending array set based on the purchase time of the order items; The construction process of the pending array set is as follows: The goods in the warehouse are recorded as inventory goods, each inventory good is assigned an inventory serial number, each inventory serial number corresponds to a position coordinate, the order goods in the order database are sorted by time, and the quantity of the order goods and the corresponding inventory serial number are obtained to form an order array , where i represents the order number of the order goods, represents the inventory serial number corresponding to the order goods with serial number i, represents the quantity of the order goods with serial number i; Each warehouse corresponds to a pickup interval threshold, and a pickup array is constructed based on the pickup interval threshold and the purchase time of the pickup array. The purchase time corresponding to the pickup array is combined with the pickup interval threshold to construct a pending time interval, and a plurality of pickup arrays located within the range of the pending time interval are obtained as a pending array set. The pickup interval threshold is obtained as follows: Obtain the average speed of the pickup truck, and the driving route of all shelves in the warehouse is recorded as the traversal route. Divide the traversal route by the average speed to obtain the traversal time. Obtain the pickup reference time of all inventory goods in the warehouse. The pickup reference time is a preset value. Acquire the truck space threshold value and the unit volume corresponding to each inventory commodity, construct a maximum volume array composed of multiple inventory commodities, and mark each inventory commodity in the maximum volume array as a threshold commodity , n represents the serial number corresponding to each threshold commodity in the maximum volume array, n = 1, 2, …, m, m represents the total number of types of threshold commodities in the maximum volume array, and the maximum volume array satisfies the following formula , wherein: Vn represents the volume corresponding to the threshold product of order n; represents the quantity corresponding to the threshold commodity with the serial number n; represents a truck space threshold value; The time pending threshold value corresponding to each maximum volume array is calculated by the formula The time pending threshold value corresponding to each maximum volume array is calculated by the formula Wherein The pickup reference duration corresponding to the threshold commodity with the serial number n is represented by Tn, The traversal duration is represented by T. Select the maximum value in the time pending threshold as the pickup interval threshold. Extract the package combinations constructed by multiple pickup arrays in the pending array set. Each package combination meets the transportation conditions. Perform three-dimensional placement simulation and transportation route simulation on all order items in the package combination. Analyze the space recommendation value and route recommendation value of each package combination, and select the pickup package combination; A model simulation unit constructs a three-dimensional model based on the volume and size of all order items in the package combination and performs sequential placement simulation. Placement simulation is used to eliminate some package combinations that cannot be placed. A package pickup unit performs cyclic pickup according to the pickup package combination, transports multiple order items corresponding to the pickup package combination to the sorting area, sorts the order items in the sorting area according to the sub-pickup order, and combines multiple order items corresponding to the same user purchase order into a package for delivery.

2. The data-based unpacking and packing work system according to claim 1, characterized by, The construction process of the pickup database is as follows: Obtain the purchase data generated when the customer purchases on the purchase platform. The purchase data includes multiple goods SKUs. Each goods SKU is bound to a group of purchase information data, which includes purchase time and purchase quantity. Each goods SKU is recorded as an order item; Obtain the inventory goods set corresponding to each warehouse. The inventory goods set includes multiple goods SKUs and the inventory of each goods SKU. Construct a pickup database for each warehouse. Based on the inventory goods set to which the order items in the purchase data belong, record the corresponding order items in the pickup database.

3. The data-based unpacking and packing work system according to claim 2, characterized by, The construction process of the package combination is as follows: Calculate the product of the volume of a single order item and the quantity of the order item corresponding to the pickup array to obtain the placement volume of the pickup array. Calculate the product of the weight of a single order item and the quantity of the order item corresponding to the pickup array to obtain the placement weight of the pickup array. The multiple pickup arrays in the package combination meet the following conditions: Condition one: the sum of the placement volumes of the multiple pickup arrays is less than or equal to a preset truck space threshold; Condition two: the sum of the weights of the goods corresponding to the multiple pickup arrays is less than or equal to a preset truck weight threshold.

4. The data-based unpacking and packing work system according to claim 3, characterized by, The pickup package combination selection process is as follows: Perform three-dimensional model containment simulation to eliminate some package combinations and calculate the space recommendation value of each package combination. There is a space ratio interval [k, 1], where k is a constant greater than 0 and less than 1. When all the space recommendation values corresponding to the package combinations are less than k, the package combination with the largest space recommendation value is selected as the pickup package combination; When multiple package combinations with space recommendation values located in the preset space proportion interval are screened out as pending package combinations, the best routes corresponding to the pending package combinations are analyzed, and route recommendation values are calculated. The best route is the shortest route passing through all the warehouse shelves corresponding to the pending package combinations. The pending package combination corresponding to the minimum route recommendation value is selected as the pickup package combination.

5. The data-based unpacking and packing work system according to claim 1, characterized by, The process of eliminating part of the package combinations by the model simulation unit is as follows: S1: Obtain the three-dimensional model of each inventory commodity, denoted as a commodity model. Construct an equivalent hexahedron for each commodity model. The equivalent hexahedron is the smallest cuboid that can accommodate the commodity model. Add the equivalent hexahedron corresponding to each commodity in the package combination to the hexahedron data set one by one. S2: Obtain the three-dimensional space corresponding to the pickup truck cargo compartment, denoted as the accommodation space. Place the equivalent hexahedrons in the hexahedron data set into the accommodation space one by one according to steps S3 and S4. After each addition, the corresponding equivalent hexahedron is removed from the hexahedron data set. Based on the three-dimensional model formed after each addition, a new accommodation space is generated and replaces the original accommodation space. S3: Denote the added equivalent hexahedron as the target hexahedron. Perform multiple placement simulations of the target hexahedron in the accommodation space. Calculate the optimal contact area based on the contact area between the target hexahedron and the inner surface of the accommodation space at each placement simulation. Place the equivalent hexahedron with the maximum optimal contact area at the corresponding placement position of the placement simulation. S4: Stop placing when any of the following conditions is met, including: Condition A: The height of any equivalent hexahedron after placement is higher than the accommodation space; Condition B: All equivalent hexahedrons are located within the three-dimensional space corresponding to the pickup truck cargo compartment; S5: When the placement is stopped under condition A, the package combination is eliminated. When the placement is stopped under condition B, the three-dimensional model corresponding to the placement method is output.

6. The data-based unpacking and packing work system according to claim 5, characterized by The process of calculating the optimal contact area is as follows: A contact coefficient is given to all horizontal and vertical surfaces in the accommodation space, the contact coefficient of the vertical surface is a preset reference coefficient R, the contact coefficient of the horizontal surface is inversely proportional to the height value of the horizontal surface, and the value is equal to H represents the upper edge height of the accommodation space, h represents the height value of the horizontal surface, in each placement simulation process, the contact areas of the target hexahedron and the vertical surfaces and the horizontal surfaces with different heights in the accommodation space are obtained, each contact area is multiplied by the corresponding contact coefficient and summed to obtain the preferred fitting area.

7. The data-based unpacking and packing work system according to claim 6, characterized by, The process of calculating the space recommendation value is as follows: Obtain the three-dimensional model generated after the accommodation simulation. Calculate the sum of the volumes of all equivalent hexahedrons in the three-dimensional model, denoted as the occupied volume. Calculate the ratio of the occupied volume to the truck space threshold, denoted as the space recommendation value.

8. A data-based unpacking and packing operation method applied to the data-based unpacking and packing operation system of any one of claims 1-7, characterized in that, The following steps are included: Step one: Based on the order decomposition unit, split the user's purchase order into multiple sub-pickup orders, each corresponding to a warehouse. Construct a pickup database based on the multiple sub-pickup orders corresponding to each warehouse. Step two: Based on the database analysis unit, combine the order commodities in the pickup database. Construct a pickup package combination and perform loop picking according to the composition of the order commodities in the pickup package combination. Obtain the three-dimensional model of the commodity placement corresponding to the pickup package combination through the model simulation unit. Step three: There is a preset warehouse floor plan. Label the coordinate positions of each order commodity in the pickup package combination on the warehouse floor plan. Obtain the current location of the pickup truck. The order commodity closest to the pickup truck is denoted as the target commodity. Step four: display the three-dimensional model of the cargo compartment and the three-dimensional model of the goods placement through the display device carried by the pickup truck, and highlight the equivalent hexahedron corresponding to the target goods, and use a dotted line to represent the contour line of the equivalent hexahedron corresponding to other order goods.

Images