Warehouse picking method and system for goods delivery

Abstract

The invention relates to the technical field of warehousing, and discloses a warehouse picking method and system for cargo delivery, and the method comprises the steps: setting warehouse picking elements: dividing warehouses into different types according to the distance between each warehouse and a cargo production place, and setting the stored cargo type, the distribution range, the delivery priority and the daily delivery limit corresponding to each warehouse type; obtaining cargo information in the order, and judging whether the cargo information comprises a batch number; when the batch number exists, taking the goods substitute code, the goods quantity, the goods delivery address and the batch number as query conditions, and performing query to obtain an inventory data set of the order demand; when no batch information exists in the cargo information, taking an order delivery factory, a cargo substitute code, a cargo quantity and a cargo delivery address as query conditions, and querying to obtain an inventory data set meeting the order demand; based on the inventory data set meeting the order demand, the delivery warehouse is determined in combination with the warehouse picking elements and the batch first-in first-out principle, the warehouse picking efficiency is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of warehousing technology, and in particular to a warehouse selection method and system for cargo delivery. Background Art

[0002] During the logistics order selection process, relevant functional departments are required to manually compare inventory and orders to determine the shipping order from the external warehouse and the shipping carrier for the local warehouse. This manual operation mode is inefficient, time-consuming, prone to human error, and difficult to quickly respond to the large number of order processing needs. On the one hand, the integrity of warehouse batch information is poor. Only the pilot warehouse has batch data, while non-pilot warehouses lack batch information. This makes it difficult to accurately trace and allocate goods when processing orders involving batch management. On the other hand, although the proportion of designated batch shipment items is low and non-mandatory, the existing process lacks an effective mechanism for batch management. If the demand for batch management increases in the future, it will be difficult to meet business requirements.

[0003] After receiving an order, logistics companies are responsible for selecting warehouses. However, there is a lack of clear, unified standards for determining warehouse adjustments, inter-company transactions, and transfer requests during this process, leading to a high degree of arbitrariness. Different operators may make different decisions due to varying understandings and judgments, resulting in inconsistent selection results and impacting logistics delivery efficiency and cost control. Furthermore, the scattered inventory distribution of local warehouse companies places a heavy burden on logistics companies for transfers. Due to the short-term difficulties in repatriating transfer management responsibilities to the relevant departments for unified control, the existing decentralized transfer model lacks overall coordination, leading to issues such as illogical transfer routes and wasted resources. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for picking goods for shipment, so as to solve the problem that the existing technology uses picking decisions based on manual experience, resulting in unstable picking results, poor picking efficiency, and thus affecting logistics distribution efficiency and cost control.

[0005] In a first aspect, the present invention provides a method for selecting a warehouse for shipment of goods, the method comprising:

[0006] Set up warehouse selection factors, including: classifying warehouses into different warehouse types based on their distance from the production site, and setting the corresponding storage type, delivery range, delivery priority, and daily delivery limit for each warehouse type;

[0007] Get the goods information in the order and determine whether the goods information includes batch number information;

[0008] When the goods information includes batch number information, the goods code, goods quantity, goods delivery address, and batch number are used as query conditions to obtain the inventory data set required by the order. When the goods information does not contain batch information, the order shipping factory, goods code, goods quantity, and goods delivery address are used as query conditions to obtain the inventory data set that meets the order requirements.

[0009] Based on the inventory data set that meets the order requirements, the shipping warehouse is determined in combination with the above-mentioned picking factors and the first-in-first-out principle of batches.

[0010] The warehouse picking method for goods shipment provided by the embodiment of the present invention divides warehouse types according to the distance between the warehouse and the production site of the goods and clarifies the relevant attributes of each type of warehouse. This can quickly narrow the selection range when picking warehouses, reduce unnecessary information screening, and thus improve the efficiency of warehouse picking; different query conditions are used to obtain inventory data sets according to whether there is a batch number in the order goods information. This targeted query method can accurately locate the inventory that meets the order requirements; the shipment priority and daily shipment limit settings in the warehouse picking elements help to reasonably allocate warehouse resources, avoid inventory backlogs or shortages due to excessive use of certain warehouses, and determine the shipping warehouse in combination with the batch first-in-first-out principle, which can effectively reduce the risks of expiration and damage of goods due to long-term backlogs, reduce inventory losses, and reduce the operating costs of the enterprise.

[0011] In an optional embodiment, determining a shipping warehouse based on an inventory dataset that meets order requirements, combined with the bin selection factors and the batch first-in-first-out principle, includes:

[0012] If the inventory required to meet the order batch requirements is distributed across multiple warehouses, calculate the logistics cost from each warehouse to the order address and prioritize each warehouse based on the first-in, first-out principle of lowest logistics cost and compliance with the goods production date batch.

[0013] If the warehouse's daily shipping quota is full, it will be automatically allocated to the next priority warehouse and trigger a quota warning.

[0014] In the embodiment of the present invention, when the inventory that meets the needs of an order batch is scattered across multiple warehouses, by calculating the logistics cost from each warehouse to the order address and giving priority to warehouses with low logistics costs for shipment, it helps the enterprise reduce logistics and transportation costs and improve economic benefits. By combining the batch first-in-first-out principle to determine the warehouse priority, it can ensure that the first-produced goods are shipped first, avoiding the quality degradation or expiration and deterioration of goods due to long-term storage, and effectively reducing the loss of goods. Taking into account the warehouse's daily shipment limit, it can make the shipment volume of each warehouse more balanced, avoid the situation where some warehouses are overused and some warehouses are idle, and improve the overall utilization efficiency of the enterprise's warehouse resources. At the same time, triggering a limit warning allows the enterprise to understand the warehouse shipment status in a timely manner, facilitating the adjustment and optimization of the allocation of subsequent orders and warehouse management.

[0015] In an optional embodiment, the calculating of the logistics cost from each warehouse to the order address and determining the priority of each warehouse based on the first-in-first-out principle of the lowest logistics cost and compliance with the production date batch of the goods include:

[0016] Build a database containing cost data for various transportation modes, combine warehouse and order addresses to obtain transportation distances, and calculate transportation costs for different transportation modes;

[0017] Obtain the production date information of the goods in the warehouse. For warehouses with the same logistics costs, prioritize the warehouse with the earliest production date of the goods. Locate the earliest batch of goods that meets the order requirements in each warehouse and determine the warehouse priority.

[0018] The embodiment of the present invention constructs a database containing cost data of multiple transportation modes, and calculates costs in combination with transportation distance. It can comprehensively and accurately evaluate the logistics costs of shipments from different warehouses, and prioritize warehouses based on the detailed calculated logistics costs, which can avoid shipping decision errors caused by rough cost estimates; by giving priority to the consumption of early batches of goods, it promotes rapid inventory turnover and reduces storage space and capital occupation; by comprehensively considering logistics costs and warehouse priorities, while controlling costs, it will also select reasonable transportation methods and warehouses to ensure timely delivery of goods.

[0019] In an optional embodiment, the method further includes:

[0020] Generate a pre-occupancy record for the shipping warehouse, which includes: order number, detail serial number, product code, occupancy data, product type, warehouse location, order shipping factory, batch number, and record creation and expiration time;

[0021] After the goods are shipped out, the real-time available inventory is determined by deducting the pre-occupied inventory and / or replenishing the inventory;

[0022] When there is a new order to be picked, it is picked based on the dynamic real-time available inventory.

[0023] The embodiment of the present invention generates a pre-occupied record and locks the corresponding inventory after the order is determined to be shipped from the warehouse, thereby avoiding duplicate allocation of inventory. The pre-occupied inventory or replenished inventory is synchronously deducted after the shipment is shipped out of the warehouse, which can reflect the actual available resources of the warehouse in real time. This dynamic update mechanism ensures that the company always has accurate real-time available inventory data, avoids problems such as overselling and wrong shipments caused by lagging inventory data, and effectively improves the accuracy and refinement of inventory management. When picking new orders, operations are performed based on real-time available inventory, without the need to repeatedly check inventory status, thus shortening the picking time and improving order processing efficiency.

[0024] In an optional embodiment, the method further includes:

[0025] When the picking fails based on the inventory data set that meets the order requirements and the first-in-first-out principle of the batch, a picking failure prompt is triggered and a picking failure reason is generated;

[0026] Send the reason for the failed order and the corresponding order to the manual intervention queue for manual order processing, including: modifying the warehouse type or directly specifying the warehouse location;

[0027] When a shipping warehouse corresponding to the order exists after manual warehouse picking, the original inventory occupancy record is updated.

[0028] In the embodiment of the present invention, when the system fails to automatically select a warehouse, a prompt for the failure to select a warehouse is immediately triggered and a reason is generated, which enables relevant personnel to quickly perceive the abnormal situation. The problem order is promptly sent to the manual intervention queue to ensure that the abnormal order will not be shelved. It is quickly processed through manual intervention to avoid long-term stagnation of orders due to failure to select a warehouse, thereby ensuring the smooth progress of the entire delivery business and reducing the impact on customer delivery time. After the manual selection process, the original inventory occupancy record is updated to ensure the accuracy and consistency of the inventory data. It avoids the disconnection between inventory information and actual delivery status due to manual intervention, ensures that subsequent operations such as order selection and inventory query are based on correct data, and maintains the reliability of the entire inventory management system.

[0029] In a second aspect, the present invention provides a cargo delivery picking system, the system comprising:

[0030] The warehouse picking factor configuration module is used to set warehouse picking factors, including: classifying warehouses into different warehouse types based on their distance from the goods production site, and setting the corresponding storage goods type, distribution range, delivery priority, and daily delivery limit for each warehouse type;

[0031] The order goods information module is used to obtain the goods information in the order and determine whether the goods information includes batch number information;

[0032] The inventory data set acquisition module is used to query the inventory data set required by the order by using the cargo code, cargo quantity, cargo delivery address, and batch number as query conditions when the cargo information includes batch number information; and to query the inventory data set required by the order by using the order shipping factory, cargo code, cargo quantity, and cargo delivery address as query conditions when the cargo information does not include batch information.

[0033] The shipping warehouse determination module is used to determine the shipping warehouse based on the inventory data set that meets the order requirements, combined with the picking factors and the batch first-in-first-out principle.

[0034] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the cargo shipment picking method of the first aspect or any corresponding embodiment thereof.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for picking warehouses for shipment of goods according to the first aspect or any corresponding embodiment thereof.

[0036] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the method for selecting warehouses for shipment of goods according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 1 is a flow chart of a method for selecting a warehouse for shipment of goods according to an embodiment of the present invention;

[0039] Figure 2 is a flow chart of another method for selecting warehouses for cargo shipment according to an embodiment of the present invention;

[0040] Figure 3 This is a structural block diagram of a warehouse picking system for goods shipment according to an embodiment of the present invention;

[0041] Figure 4 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0043] In this embodiment, a method for selecting a warehouse for shipment of goods is provided. Figure 11 is a flow chart of a method for picking warehouses for cargo shipment according to an embodiment of the present invention. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer device such as a set of computer executable instructions, and although a logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in an order different from that shown here. Figure 1 As shown, the process includes the following steps:

[0044] Step S101, setting the warehouse selection factors, including: dividing the warehouses into different warehouse types according to the distance between each warehouse and the goods production site, and setting the storage goods type, distribution range, delivery priority, and daily delivery limit corresponding to each warehouse type.

[0045] Specifically, the embodiments of the present invention clarify the delivery range of each warehouse type, which can avoid long-distance transportation caused by improper warehouse selection and reduce transportation time and cost; the setting of delivery priority can ensure that urgent orders are shipped from appropriate warehouses first, thereby improving order response speed; the setting of daily delivery limits helps to balance the workload of each warehouse and prevent the delivery efficiency from being affected by the heavy workload of a certain warehouse.

[0046] By incorporating factors like the distance between the warehouse and the goods' production site into the warehouse selection process, a comprehensive and objective basis is provided for warehouse selection decisions. When processing orders, the system or operators can quickly make reasonable warehouse selections based on these clear rules, reducing human interference and improving the scientific nature and accuracy of decision-making. This structured warehouse selection factor setting enables companies to flexibly adapt to various business scenarios by adjusting warehouse type parameters in response to changes in market demand and inventory fluctuations, thereby enhancing their market adaptability and competitiveness.

[0047] Taking liquor warehousing as an example, warehouse types and corresponding cargo types are categorized as follows: Based on the distance between the distillery (where the goods are produced) and the warehouse, warehouses are divided into "on-site warehouses," "local warehouses," and "off-site warehouses." "On-site warehouses" are closer to the raw liquor and base liquor and are used to temporarily store finished liquor during the bottling line, enabling direct shipment to clients / consumers and reducing logistics transit times. "Local warehouses" are closer to the distillery and are used to store finished liquor. "Off-site warehouses" are located in major sales regions and store mid- to high-end liquor and some popular liquors sold in that region.

[0048] Distribution scope: The "on-site warehouse" is mainly responsible for allocating goods to the "local warehouse" for short-distance transportation; the "local warehouse" is responsible for shipping goods to dealers, large supermarkets and other customers within its coverage area, and the distribution scope is generally surrounding provinces and cities; the "out-of-town warehouse" undertakes short-distance distribution tasks within or around the city, and directly delivers the goods to terminal retail outlets or small customers.

[0049] Shipping Priority and Daily Quota: During peak liquor sales seasons like the Mid-Autumn Festival and Spring Festival, urgent distributor replenishment orders will be prioritized for shipment from "off-site warehouses" based on shipping priority. If inventory is insufficient, orders will be transferred from the "local warehouse." Ordinary orders will be handled according to standard priorities. Furthermore, to minimize warehouse workload, a daily shipping limit of 5,000 cases for the "local warehouse" and 1,000 cases for the "off-site warehouse" will be set to ensure orderly delivery.

[0050] Step S102: Obtain the goods information in the order and determine whether the goods information includes batch number information.

[0051] Specifically, the batch number is a unique identifier that distinguishes different batches of goods. By determining whether the batch number information is included in the order, the inventory of a specific batch of goods can be accurately located, enabling refined inventory management. In the wine industry, different batches of wine may vary due to factors such as brewing time and raw materials. Accurate batch inventory management helps companies accurately understand the quantity and status of each batch of wine, avoiding confusion or incorrect shipments. Based on the batch number information in the order, companies can plan logistics and distribution plans in advance, centralizing the distribution of goods from the same or similar batches, improving logistics efficiency and reducing transportation costs.

[0052] In step S103, when the goods information includes batch number information, the goods code, goods quantity, goods delivery address, and batch number are used as query conditions to obtain the inventory data set required by the order; when the goods information does not include batch information, the order shipping factory, goods code, goods quantity, and goods delivery address are used as query conditions to obtain the inventory data set that meets the order requirements.

[0053] Specifically, different orders may have different requirements. Some orders have strict regulations on shipment batches, such as specific batches for high-end wines. Other orders may focus more on the shipping factory or overall supply of goods. This flexible query method can adapt to diverse business scenarios, improve the company's ability to respond to different customer needs, and enhance customer satisfaction.

[0054] When an order includes batch number information, inventory queries based on detailed criteria such as the cargo code, quantity, delivery address, and batch number can accurately locate inventory that meets specific requirements, avoiding problems such as misdelivery and missed shipments caused by inaccurate inventory data. For example, a high-end liquor distributor placed an order with a distillery, specifying a specific batch (batch number 20191913) for 100 cases, with a delivery address in Shanghai. After receiving the order, the distillery's inventory management system accurately searches the inventory data based on query criteria such as cargo code, quantity, delivery address, and batch number. In this way, the distillery can quickly determine whether the inventory location and quantity of the batch of liquor meet requirements, and then make subsequent shipment arrangements to ensure that the distributor receives the products that meet the requirements.

[0055] Even when batch number information is unavailable, querying by order shipping factory, cargo code, cargo quantity, and delivery address can still filter out goods from the overall inventory that meet the order requirements, ensuring an accurate match between order and inventory. For example, a customer places an order for 200 cases of a certain brand of low-end liquor, without specifying a batch, requesting shipment from a shipping factory in Sichuan to a sales outlet in Beijing. In this case, the inventory management system uses query criteria such as the order shipping factory (for example, the Sichuan factory), cargo code, cargo quantity, and delivery address to filter out inventory data that matches the order, either located in the Sichuan factory or available for distribution from that factory. The system can quickly determine the inventory location and quantity that meets the order requirements and then arrange shipment, fulfilling the customer's order while also improving inventory turnover efficiency.

[0056] Step S104: Determine the shipping warehouse based on the inventory data set that meets the order requirements, combined with the picking factors and the batch first-in-first-out principle.

[0057] Specifically, based on the inventory data set combined with the picking factors, warehouses that meet order requirements can be quickly screened out. For example, based on the distribution range and delivery priority corresponding to the warehouse type, warehouses with close distances and high priorities are given priority to shorten delivery time; the batch first-in-first-out principle ensures that goods are shipped in order according to production batches, avoiding the confusion of manual selection and improving delivery accuracy. The reasonable allocation of delivery warehouses based on the picking factors can balance the inventory of each warehouse, avoid inventory backlogs in some warehouses and frequent replenishment in some warehouses, and reduce warehousing and logistics costs; the dynamic selection of delivery warehouses based on the picking factors and inventory conditions can cope with market demand fluctuations, warehouse emergencies and other problems. For example, when a warehouse is unable to ship due to equipment failure, the system can quickly ship from other suitable warehouses according to the rules to ensure order fulfillment and improve supply chain resilience.

[0058] For example, in one scenario, a liquor distributor orders 500 cases of a certain brand of mid-range liquor (without a designated batch) from a liquor company, with the delivery address located in East China. Based on the inventory dataset, the liquor company's inventory management system selects multiple warehouses that meet the order quantity requirements. Both the East China Regional Central Warehouse and the local production area warehouse have inventory. Considering the selection criteria, the East China Regional Central Warehouse is a regional warehouse with a distribution range covering East China, high shipping priority, and an in-specified daily shipping quota. The local production area warehouse is farther away and has high transportation costs. Furthermore, of the two warehouses, the East China Regional Central Warehouse has an earlier batch of liquor. Therefore, based on the selection criteria and the first-in, first-out principle for batches, the system prioritizes the East China Regional Central Warehouse as the shipping warehouse, shortening delivery time while ensuring that first-produced liquor is shipped first.

[0059] In another scenario, a high-end liquor collector placed an order for 10 cases of a specific batch of aged liquor. Based on the cargo code, batch number, and other information, the system locked this batch of liquor in the inventory data set to a warehouse in the local production area. Due to the unique nature of this batch of liquor, even though the local production area warehouse was far from the delivery address, the system still determined that the warehouse stored high-end liquor and the order had strict batch requirements. The system also prioritized transportation, ensuring accurate order fulfillment and meeting the customer's demand for this special batch of liquor.

[0060] This embodiment also provides a method for selecting a warehouse for shipment of goods, such as Figure 2 As shown, the process includes the following steps:

[0061] Step S201 sets the warehouse selection factors, including: classifying warehouses into different warehouse types based on their distance from the production site, and setting the corresponding storage type, delivery range, delivery priority, and daily delivery limit for each warehouse type. Specifically, the following steps are included:

[0062] In step S2011, the Euclidean distance between the warehouse and the production site is calculated using geo-fencing technology, and the warehouse types are divided according to the threshold, including: on-site warehouse, local warehouse, and off-site warehouse.

[0063] Specifically, geo-fencing technology divides warehouse types based on Euclidean distance, making the functions of each warehouse clear. On-site warehouses can respond quickly to production, local warehouses can flexibly allocate inventory, and external warehouses can achieve cross-regional coverage, effectively avoiding resource waste and improving overall operational efficiency. For example, the Luzhou Laojiao production base is located in Luzhou, Sichuan. Using geo-fencing technology, warehouses at the production site are set as on-site warehouses, such as warehouses in Luzhou and surrounding areas, which are used to store newly produced finished wines; warehouses within a range of 50-200 kilometers are set as local warehouses, covering warehouses in surrounding cities such as Chengdu, storing some finished wines to meet the fast delivery needs of the provincial market; warehouses above 200 kilometers are external warehouses, distributed in major core cities across the country, such as Beijing, Shanghai, Guangzhou, etc., to reserve best-selling wines in advance and radiate the surrounding regional markets.

[0064] Step S2012: Analyze historical order data to obtain cargo type preference experience data, and determine the cargo type stored in each warehouse type based on the cargo type preference experience data.

[0065] Specifically, we analyze product type preferences based on historical order data to guide each warehouse's storage decisions and align them with market demand. This reduces the backlog of unsold goods, accelerates inventory turnover, and reduces storage costs and capital tie-up. For example, an analysis of Luzhou Laojiao's historical order data revealed that the northern market prefers strong-flavor liquors, such as the classic strong-flavor Guojiao 1573, while parts of the south have a higher demand for low-alcohol liquors and health-promoting wines. Based on this, on-site warehouses primarily store strong-flavor base liquors, local warehouses store finished liquors of various alcohol content and some health-promoting wines, and off-site warehouses tailor their inventory to local preferences. For example, northern warehouses tend to stock more strong-flavor Guojiao 1573, while southern warehouses appropriately increase their inventory of low-alcohol liquors and health-promoting wines.

[0066] Step S2013: Determine the delivery range based on the administrative geographic information of the region where each warehouse type is located, logistics transportation route data, transportation tool information, and traffic restriction information of each region;

[0067] Specifically, the embodiment of the present invention integrates information such as regional geography, transportation routes, tools and traffic restrictions to plan a reasonable distribution plan. It avoids transportation risks, shortens delivery time, improves customer satisfaction, and enhances the company's market competitiveness. For example, considering the high-end positioning of Luzhou Laojiao products, distribution needs to take into account both timeliness and safety. In terms of regional administrative geographic information, transportation routes will avoid areas with poor road conditions and prone to congestion; logistics transportation route data shows that some highways are suitable for long-distance transportation. Combined with transportation tool information, special transportation vehicles with constant temperature and shockproof are selected to ensure the quality of wine products; in response to traffic restrictions in cities such as Beijing and Shanghai, night delivery is arranged or small delivery vehicles that meet regulations are used to ensure that wine products are delivered to distributors and end customers in a timely manner, and a reasonable distribution range is defined.

[0068] Step S2014: Based on the warehouse's human and material resource data and historical shipment data, a time series analysis algorithm is used to predict the maximum shipment volume that the warehouse can handle daily and determine the daily shipment limit.

[0069] Specifically, a time series analysis algorithm is used, combined with warehouse resources and historical shipping data, to accurately predict daily shipment volumes. Production and shipments are rationally arranged to meet market demand while avoiding operational pressure caused by excessive shipments. Taking a Luzhou Laojiao warehouse in another city as an example, based on the warehouse's existing material and human resource data, such as the number of handling equipment, warehouse area, and number of staff, combined with historical shipping data from holidays and peak sales seasons in previous years, a time series analysis algorithm is used to predict the maximum daily shipment volume. For example, during the peak sales season before the Spring Festival, the warehouse's maximum daily shipment volume is predicted to be 800 boxes. Setting a daily shipment limit of 750 boxes can not only ensure market supply, but also reserve buffer space to avoid shipment chaos caused by emergencies, ensuring stable and orderly operations.

[0070] Step S202, obtaining the goods information in the order and determining whether the goods information includes the batch number information; see the above step S102 for details, which will not be repeated here.

[0071] In step S203, when the goods information includes batch number information, the goods code, goods quantity, goods delivery address, and batch number are used as query conditions to obtain the inventory data set required by the order; when the goods information does not include batch information, the order shipping factory, goods code, goods quantity, and goods delivery address are used as query conditions to obtain the inventory data set that meets the order requirements; see the above step S102 for details, which will not be repeated here.

[0072] Step S204: Determine the shipping warehouse based on the inventory data set that meets the order requirements, combined with the picking factors and the batch first-in-first-out principle.

[0073] Specifically, if the inventory that meets the batch requirements in the order is scattered across multiple warehouses, the logistics cost from each warehouse to the order address is calculated, and the priority of each warehouse is determined based on the first-in-first-out principle of low logistics cost and meeting the batch of goods production date; if the warehouse's daily shipping limit is full, it is automatically allocated to the second-priority warehouse and a limit warning is triggered. The embodiment of the present invention combines inventory data sets, warehouse selection factors, and the first-in-first-out principle to quickly and accurately determine the shipping warehouse, avoid order delays caused by improper warehouse selection, significantly improve order fulfillment efficiency, and ensure customer satisfaction. When the warehouse's daily shipping limit is full, it is automatically allocated to the second-priority warehouse and a limit warning is triggered, which can achieve dynamic management of warehouse resources, ensure a smooth shipping process, and improve the stability and risk resistance of the enterprise's operations.

[0074] In one application scenario, for example, suppose a distributor orders a batch of 52-degree Guojiao 1573 from Luzhou Laojiao, with an order demand of 100 boxes and a specific batch. After querying the inventory data set, it was found that the Chengdu local warehouse had 40 boxes of this batch, and the Chongqing out-of-town warehouse had 60 boxes. At this time, Luzhou Laojiao calculated the logistics costs from the two warehouses to the order address (assuming it is Wuhan), taking into account factors such as transportation distance and transportation method, and found that the logistics cost from the Chongqing out-of-town warehouse to Wuhan was lower. According to the principle of low logistics cost and first-in-first-out, 60 boxes were shipped from the Chongqing out-of-town warehouse first, and then 40 boxes were shipped from the Chengdu local warehouse to complete the order delivery, which reduced logistics costs while ensuring product quality.

[0075] In another application scenario, during the Spring Festival sales peak, Luzhou Laojiao's Beijing warehouse had a daily shipping limit of 800 cases. One day, after completing 780 cases, the warehouse received an order for 200 cases. The system detected that the Beijing warehouse's daily shipping limit had been reached and automatically assigned the order to the lower-priority Tianjin warehouse, triggering a quota alert for the Beijing warehouse. Upon receiving the alert, company managers were able to promptly adjust the shipping schedule for subsequent orders while also monitoring and adjusting the Beijing warehouse's operations to ensure the entire shipping process was not impacted and maintain stable market supply.

[0076] Step S205, generating a pre-occupancy record of the shipping warehouse, the pre-occupancy record including: order number, detail serial number, goods code, occupancy data, goods type, warehouse point, order shipping factory, batch number, record creation and expiration time.

[0077] The embodiment of the present invention uses pre-occupancy records to clearly lock the goods required for a specific order and the corresponding warehouse points, preventing the same inventory from being repeatedly allocated to different orders, avoiding shortages or wrong shipments of goods during delivery, and ensuring that orders are accurately executed as planned. Records cover multi-dimensional information such as orders, goods, and warehouses. Enterprises can grasp the pre-occupancy status of inventory in real time, intuitively understand the resource allocation of each warehouse, and flexibly adjust the scheduling according to actual needs to improve the efficiency of inventory resource utilization; the creation and expiration time in the pre-occupancy records provide time clues for the order execution process. When problems such as delivery delays and abnormal goods occur, records can be used to quickly trace each link, accurately locate the root cause of the problem, and take timely solutions.

[0078] For example, a distributor places an order with Luzhou Laojiao, order number LZ20250425001, for 50 cases of 52-proof Guojiao 1573 (product code: GJ1573-52). The system determines a plan based on the shipping warehouse and pre-occupies this batch of goods in the local Chengdu warehouse (warehouse point: CD-01). The order is shipped from a Luzhou Laojiao production base, with batch number 20250101. The system automatically generates a pre-occupancy record, created at 10:00 on April 25, 2025, and set to expire after the estimated delivery time (for example, 15:00 on April 27, 2025). During this period, other orders cannot occupy this batch of inventory, ensuring the supply of goods for the distributor's order.

[0079] Step S206: After the goods are shipped out, the pre-occupied inventory and / or replenished inventory are synchronously deducted to determine the real-time available inventory. When there is a new order to be picked, the warehouse is picked based on the dynamic real-time available inventory.

[0080] In practice, when picking fails based on an inventory data set that meets order requirements, combined with picking factors and the first-in-first-out principle of batches, a picking failure prompt is triggered and a reason for the picking failure is generated; the reason for the picking failure and the corresponding order are sent to the manual intervention queue for manual picking processing, including: modifying the warehouse type or directly specifying the warehouse point; when a shipping warehouse corresponding to the order exists after manual picking processing, the original inventory occupancy record is updated.

[0081] The embodiment of the present invention synchronously deducts the pre-occupied inventory after shipment, and promptly updates the real-time available inventory after replenishment, so that the inventory data always reflects the real status. New order picking is based on dynamic inventory, which can avoid overselling orders or waste of resources due to lagging inventory data, ensure the balance of supply and demand for the enterprise, and improve inventory management efficiency. When the picking fails, the system automatically triggers a prompt and generates a reason, which can quickly locate the problem and avoid stagnation of order processing due to system "stuck". Sending the problem order and the reason to the manual intervention queue provides a flexible solution for order processing and minimizes order delays. This human-computer collaboration mode can make full use of manual experience and judgment, and make precise adjustments for complex order requirements or special inventory situations, significantly improving the success rate of picking and ensuring the smooth execution of orders.

[0082] In a dynamic inventory management scenario, for example, a Luzhou Laojiao distributor orders 100 cases of Tequ liquor. Based on real-time available inventory, the warehouse in Chongqing is successfully selected and a pre-occupancy record is generated. After shipment, the pre-occupied inventory at the Chongqing warehouse is deducted, and the real-time available inventory data is updated. If a new order for Tequ liquor follows, the warehouse is selected based on the updated real-time available inventory, avoiding duplicate allocation of already shipped inventory and ensuring inventory data accuracy and normal order processing.

[0083] In a scenario involving failed stock picking and manual intervention, for example, during the Mid-Autumn Festival sales peak, a large supermarket placed a large order for a variety of liquor products with Luzhou Laojiao. While selecting stocks based on inventory datasets, stock picking factors, and the first-in, first-out (FIFO) principle, stock picking failed due to issues such as insufficient inventory batches at various warehouses for some liquor products and the warehouse's daily shipping quota being reached. The system immediately triggered a stock picking failure notification, generated a reason (e.g., "Insufficient inventory of a certain batch of Guojiao 1573 52% alcohol at various warehouses, and the warehouse's shipping quota was reached"), and sent the order and reason to the manual intervention queue. After reviewing the situation, the operator changed the shipping warehouse for some of the order's goods from the original Chengdu local warehouse to a warehouse in Wuhan (which had inventory meeting the requirements and within the shipping quota) and directly designated the specific warehouse location in Wuhan. After processing, the system updated the original inventory occupancy record, ensuring smooth subsequent order delivery. This also helped the company gain experience in handling orders during these challenging times and optimize future stock picking strategies.

[0084] This embodiment also provides a cargo delivery picking system, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0085] This embodiment provides a system for picking goods for shipment. Figure 3 Shown, including:

[0086] The warehouse selection factor configuration module 301 is used to set the warehouse selection factors, including: classifying warehouses into different warehouse types based on their distance from the goods production site, and setting the corresponding storage goods type, distribution range, delivery priority, and daily delivery limit for each warehouse type;

[0087] The order goods information module 302 is used to obtain the goods information in the order and determine whether the goods information includes batch number information;

[0088] The inventory data set acquisition module 303 is configured to, when the goods information includes batch number information, use the goods code, goods quantity, goods delivery address, and batch number as query conditions to obtain the inventory data set required by the order; when the goods information does not include batch information, use the order shipping factory, goods code, goods quantity, and goods delivery address as query conditions to obtain the inventory data set that meets the order requirements;

[0089] The shipping warehouse determination module 304 is configured to determine the shipping warehouse based on the inventory data set that meets the order requirements, combined with the bin selection factors and the first-in-first-out principle of the batch.

[0090] In some optional implementations, the bin selection factor configuration module 301 includes:

[0091] The warehouse type classification unit is used to calculate the Euclidean distance between the warehouse and the production site using geo-fencing technology and classify warehouse types according to thresholds, including on-site warehouses, local warehouses, and off-site warehouses;

[0092] A goods type matching unit is used to analyze historical order data to obtain goods type preference experience data, and determine the goods type stored in each warehouse type based on the goods type preference experience data;

[0093] The distribution range division unit is used to determine the distribution range based on the administrative geographic information of the region where each warehouse type is located, logistics transportation route data, transportation tool information, and traffic restriction information of each region;

[0094] The daily shipping limit setting unit is used to predict the maximum daily shipping volume that the warehouse can withstand based on the warehouse's human and material resource data and historical shipping data, using a time series analysis algorithm to determine the daily shipping limit.

[0095] In some optional implementations, the shipping warehouse determination module 304 includes:

[0096] The multi-warehouse priority determination unit is used to calculate the logistics cost of each warehouse to the order address if the inventory that meets the batch requirements of the order is scattered across multiple warehouses. The priority of each warehouse is determined based on the first-in-first-out principle of low logistics cost and meeting the batch requirements of the goods production date.

[0097] The quota warning unit is used to automatically allocate to the next priority warehouse and trigger a quota warning if the warehouse's daily shipping limit is reached.

[0098] In some optional implementations, the multi-warehouse priority determination unit includes:

[0099] The transportation cost calculation subunit builds a database containing cost data of various transportation modes, combines the warehouse and order address to obtain the transportation distance, and calculates the transportation cost for different transportation modes;

[0100] The priority determination subunit is used to obtain the production date information of the goods in the warehouse. For warehouses with the same logistics costs, the warehouse with the earliest production date of the goods is prioritized. The earliest batch of goods that meets the order requirements in each warehouse is located to determine the priority of the warehouse.

[0101] In some optional embodiments, the above system further includes:

[0102] A pre-occupancy record unit is used to generate a pre-occupancy record of the shipping warehouse, wherein the pre-occupancy record includes: order number, detail serial number, goods code, occupancy data, goods type, warehouse location, order shipping factory, batch number, record creation and expiration time;

[0103] The dynamic picking unit is used to determine the real-time available inventory after deducting the pre-occupied inventory and / or replenishing the inventory after delivery. When there is a new order for picking, the picking is based on the dynamic real-time available inventory.

[0104] In some optional embodiments, the above system further includes:

[0105] a picking failure reason generating unit, configured to trigger a picking failure prompt and generate a picking failure reason when picking fails based on an inventory data set that meets order requirements, in combination with the picking factors and the batch first-in-first-out principle;

[0106] The manual intervention unit is used to send the reasons for the picking failure and the corresponding orders to the manual intervention queue for manual picking processing, including: modifying the warehouse type or directly specifying the warehouse point; when there is a shipping warehouse corresponding to the order after manual picking processing, the original inventory occupancy record is updated.

[0107] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0108] The cargo delivery picking system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0109] The embodiment of the present invention also provides a computer device having the above Figure 3 The picking system for goods shipment is shown.

[0110] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0111] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0112] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0113] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0114] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0115] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0116] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0117] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0118] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for selecting warehouses for cargo shipment, characterized in that: include: Set up warehouse selection factors, including: classifying warehouses into different warehouse types based on their distance from the production site, and setting the corresponding storage type, delivery range, delivery priority, and daily delivery limit for each warehouse type; Get the goods information in the order and determine whether the goods information includes batch number information; When the goods information includes batch number information, the goods code, goods quantity, goods delivery address, and batch number are used as query conditions to obtain the inventory data set required by the order. When the goods information does not contain batch information, the order shipping factory, goods code, goods quantity, and goods delivery address are used as query conditions to obtain the inventory data set that meets the order requirements. Based on the inventory data set that meets the order requirements, the shipping warehouse is determined in combination with the above-mentioned picking factors and the first-in-first-out principle of batches.

2. The method according to claim 1, characterized in that According to the distance between each warehouse and the production site of the goods, the warehouses are divided into different warehouse types, and the storage goods type, distribution range, delivery priority, and daily delivery limit corresponding to each warehouse type are set, including: Geographic fencing technology is used to calculate the Euclidean distance between the warehouse and the production site, and warehouse types are divided according to thresholds, including: on-site warehouse, local warehouse, and off-site warehouse; Analyze historical order data to obtain empirical data on cargo type preferences, and use this empirical data to determine the cargo types stored in each warehouse type. Determine the delivery range based on the administrative geographic information of the region where each warehouse type is located, logistics transportation route data, transportation tool information, and traffic restriction information of each region; Based on the warehouse's human and material resource data and historical shipment data, a time series analysis algorithm is used to predict the maximum daily shipment volume that the warehouse can withstand and determine the daily shipment limit.

3. The method according to claim 1 or 2, characterized in that The inventory data set based on meeting order requirements, combined with the bin selection factors and the batch first-in-first-out principle, determines the shipping warehouse, including: If the inventory required to meet the order batch requirements is distributed across multiple warehouses, calculate the logistics cost from each warehouse to the order address and prioritize each warehouse based on the first-in, first-out principle of lowest logistics cost and compliance with the goods production date batch. If the warehouse's daily shipping quota is full, it will be automatically allocated to the next priority warehouse and trigger a quota warning.

4. The method according to claim 3, characterized in that The logistics cost from each warehouse to the order address is calculated, and the priority of each warehouse is determined based on the first-in-first-out principle of low logistics cost and meeting the production date batch of the goods, including: Build a database containing cost data for various transportation modes, combine warehouse and order addresses to obtain transportation distances, and calculate transportation costs for different transportation modes; Obtain the production date information of the goods in the warehouse. For warehouses with the same logistics costs, prioritize the warehouse with the earliest production date of the goods. Locate the earliest batch of goods that meets the order requirements in each warehouse and determine the warehouse priority.

5. The method according to claim 1, characterized in that Also includes: Generate a pre-occupancy record for the shipping warehouse, which includes: order number, detail serial number, product code, occupancy data, product type, warehouse location, order shipping factory, batch number, and record creation and expiration time; After the goods are shipped out, the pre-occupied inventory and / or replenished inventory are deducted synchronously to determine the real-time available inventory. When there is a new order to be picked, the order is picked based on the dynamic real-time available inventory.

6. The method according to claim 1 or 5, characterized in that Also includes: When the picking fails based on the inventory data set that meets the order requirements and the first-in-first-out principle of the batch, a picking failure prompt is triggered and a picking failure reason is generated; Send the reason for the failed order and the corresponding order to the manual intervention queue for manual order processing, including: modifying the warehouse type or directly specifying the warehouse location; When a shipping warehouse corresponding to the order exists after manual warehouse picking, the original inventory occupancy record is updated.

7. A cargo delivery picking system, characterized in that: include: The warehouse picking factor configuration module is used to set warehouse picking factors, including: classifying warehouses into different warehouse types based on their distance from the goods production site, and setting the corresponding storage goods type, distribution range, delivery priority, and daily delivery limit for each warehouse type; The order goods information module is used to obtain the goods information in the order and determine whether the goods information includes batch number information; The inventory data set acquisition module is used to query the inventory data set required by the order by using the cargo code, cargo quantity, cargo delivery address, and batch number as query conditions when the cargo information includes batch number information; and to query the inventory data set required by the order by using the order shipping factory, cargo code, cargo quantity, and cargo delivery address as query conditions when the cargo information does not include batch information. The shipping warehouse determination module is used to determine the shipping warehouse based on the inventory data set that meets the order requirements, combined with the picking factors and the batch first-in-first-out principle.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the cargo shipment picking method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for selecting warehouses for cargo shipment according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for picking warehouses for cargo shipment according to any one of claims 1 to 6.

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