Method for scheduling warehouse containers and electronic device

Abstract

This application provides a method for scheduling warehouse containers and an electronic device thereof. The method includes acquiring order demand information from multiple pending orders and inventory information from multiple candidate warehouse containers; aggregating the order demand information from the multiple pending orders to generate multiple merged demands; wherein each merged demand includes: a demanded goods characteristic and a corresponding demand quantity; comparing the inventory information of each candidate warehouse container with the multiple merged demands to calculate the demand matching degree of each candidate warehouse container; and determining the target outbound container from the multiple candidate warehouse containers based on the demand matching degree. By aggregating the order demands of multiple pending orders and combining the overall matching degree of each candidate warehouse container, it achieves collaborative optimization of multiple order demand information, effectively reducing the number of ineffective handling operations by warehouse robots, and improving the system's picking efficiency and overall throughput.

Description

Technical Field

[0001] This application relates to the field of warehousing equipment technology, and in particular to a method for scheduling warehousing containers and an electronic device. Background Technology

[0002] In automated or semi-automated warehouse logistics systems, a storage container (such as a bin or pallet) typically holds a mix of inventory items from different stock keeping units (SKUs), batches, and attributes. Meanwhile, the logistics system often contains dozens to hundreds of pending orders, forming a pool of orders to be picked.

[0003] However, when determining outbound storage containers, current logistics systems can only focus on whether the storage container can meet the needs of a single specific order, while ignoring a global assessment of the match between the remaining inventory in the storage container and other potential needs in the pool of orders to be picked.

[0004] This prevents the full value of each storage container from being fully explored and identified, resulting in repeated and ineffective handling of storage containers by storage robots. This significantly increases the energy consumption of the logistics system and reduces overall handling efficiency and outbound throughput. Summary of the Invention

[0005] This application aims to address the problem that the overall value of storage containers cannot be assessed and determined during the scheduling process of existing storage containers.

[0006] In a first aspect, embodiments of this application provide a method for scheduling warehouse containers. The scheduling method includes: acquiring order demand information of multiple pending picking orders and inventory information of multiple candidate warehouse containers; aggregating the order demand information of the multiple pending picking orders to generate multiple merged demands; wherein each merged demand includes: a demanded goods characteristic and a corresponding demand quantity; comparing the inventory information of each candidate warehouse container with the multiple merged demands to calculate the demand matching degree of each candidate warehouse container; and determining a target outbound container from the multiple candidate warehouse containers based on the demand matching degree.

[0007] Optionally, the step of aggregating the order demand information of the multiple orders to be picked to generate multiple merged demands specifically includes: classifying the order demand information of the multiple orders to be picked to obtain multiple sets of order demand information, each set of order demand information having the same goods demand characteristics; accumulating and calculating the total demand quantity of each set of order demand information; associating the total demand quantity of a set of order demand information with the corresponding goods demand characteristics to form a merged demand.

[0008] Optionally, the inventory information includes: at least one inventory item characteristic and a corresponding item quantity; the step of comparing the inventory information of each candidate storage container with the multiple merged demands and calculating the demand matching degree of each candidate storage container specifically includes: selecting a first merged demand from the multiple merged demands whose demand item characteristics match the inventory item characteristics of the candidate storage containers; calculating the demand fulfillment ratio based on the demand quantity of each first merged demand and the item quantity of the candidate storage containers; and determining the demand matching degree of the candidate storage containers based on the quantity of the first merged demands and the demand fulfillment ratio of each first merged demand.

[0009] Optionally, the step of calculating the demand fulfillment ratio based on the demand quantity of each first merged demand and the quantity of goods in the candidate storage containers specifically includes: calculating the ratio between the quantity of goods and the demand quantity of the first merged demand; determining the demand fulfillment ratio as 1 when the ratio is greater than or equal to 1; and determining the demand fulfillment ratio as the ratio when the ratio is less than 1.

[0010] Optionally, determining the demand matching degree of the candidate storage containers based on the number of the first merged demands and the demand satisfaction ratio of each first merged demand specifically includes: calculating the average ratio of the demand satisfaction ratio of each first merged demand; and performing a weighted calculation on the number of the first merged demands and the average ratio to determine the demand matching degree of the candidate storage containers.

[0011] Optionally, the step of selecting the first merging demand that matches the characteristics of the goods in demand with the characteristics of the inventory goods in the candidate storage containers from among the multiple merging demands specifically includes: generating a corresponding first feature fingerprint for the characteristics of the goods in demand for each merging demand using a preset coding rule; constructing a demand mapping table containing all merging demands using the first feature fingerprint as an index key; generating a corresponding second feature fingerprint for each type of inventory goods in the candidate storage containers using the coding rule; searching for an index key in the demand mapping table that matches the second feature fingerprint, and determining the merging demand corresponding to the index key that matches the second feature fingerprint as the first merging demand.

[0012] Optionally, generating a corresponding feature fingerprint based on multiple preset attribute dimensions specifically includes: obtaining the value of the requirement in each attribute dimension; combining the values ​​of each attribute dimension to form a composite key as the feature fingerprint; wherein, when two records have the same value in all attribute dimensions, their feature fingerprints are determined to be the same; when the value of any attribute dimension is inconsistent, their feature fingerprints are determined to be different.

[0013] Optionally, each of the demand goods characteristics and each of the inventory goods characteristics includes multiple different dimensions of goods attribute information; wherein, the goods attribute information includes at least one of the following: a unique commodity identification code for identifying the type of goods; a goods batch number for identifying the production or warehousing batch of goods; a warehouse area code for indicating the storage location of goods; and a temperature zone requirement for indicating the storage environment temperature of goods.

[0014] Optionally, determining the target outbound container from the plurality of candidate storage containers based on the demand matching degree specifically includes: obtaining the job execution parameters of each candidate storage container, the job execution parameters being used to characterize the execution cost of the candidate storage container during physical operations; calculating the scheduling score of each candidate storage container by combining the demand matching degree and the job execution parameters; and determining the candidate storage container with the highest scheduling score among the plurality of candidate storage containers as the target outbound container.

[0015] Optionally, the operation execution parameters include: the distance index of the candidate storage container relative to the target operation point and the lane balance index of the lane where the candidate storage container is located; wherein, the step of calculating the scheduling score of each candidate storage container by combining the demand matching degree and the operation execution parameters specifically includes: normalizing the demand matching degree, the distance index and the lane balance index respectively, resulting in a first value, a second value and a third value; and calculating the weighted sum of the first value, the second value and the third value according to a preset weight coefficient to obtain the scheduling score of the candidate storage container.

[0016] Secondly, embodiments of this application also provide an electronic device. The electronic device includes a processor and a memory, the memory storing computer program instructions so that when the computer program instructions are invoked by the processor, the storage container scheduling method described above is executed.

[0017] At least one technical effect of the warehouse container scheduling method provided in this application embodiment is that by aggregating multiple orders to be picked to generate a merged demand, and determining and screening outbound containers based on the comparison between inventory and merged demand to calculate the degree of demand matching, the method can maximize the ability of a single outbound container to meet the current order pool, effectively improve the output ratio of a single handling operation of warehouse containers, reduce repeated handling and ineffective circulation of warehouse containers, thereby improving the system picking efficiency and overall throughput. Attached Figure Description

[0018] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0019] Figure 1 A schematic diagram of an automated warehouse provided in an embodiment of this application; Figure 2 A flowchart illustrating the method for scheduling storage containers provided in this application embodiment; Figure 3 A schematic diagram of a sub-process of step S240 provided in an embodiment of this application; Figure 4 A schematic diagram of a sub-process of step S220 provided in an embodiment of this application; Figure 5 A schematic diagram of a sub-process of step S230 provided in an embodiment of this application; Figure 6 A flowchart illustrating the method for screening the first merging requirement provided in this application embodiment; Figure 7 Functional block diagram of the scheduling device provided in the embodiments of this application; Figure 8 A schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To facilitate understanding of this application, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. It should be noted that when an element is described as being "fixed to" another element, it can be directly on the other element, or one or more intermediate elements may exist between them. When an element is described as being "connected" to another element, it can be directly connected to the other element, or one or more intermediate elements may exist between them. The terms "upper," "lower," "inner," "outer," "bottom," etc., used in this specification indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0021] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0022] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0023] Figure 1 This is a schematic diagram of a typical automated warehouse. (Example) Figure 1 As shown, the automated warehouse typically includes: a warehouse area 11, a warehouse robot 12, a workstation 13, a scheduling device 14, and multiple storage containers 15.

[0024] The warehouse area 11 is equipped with one or more shelves for storing goods. Based on the needs of practical applications, the shelves can adopt various structural forms such as shelving, beam, and cantilever, and be arranged in a specific layout to form multiple regularly distributed goods storage locations (also known as "warehouse locations") for storing storage containers 15.

[0025] Storage containers 15 are logistics carriers used to store goods, including but not limited to bins, pallets, and turnover boxes. Storage containers 15 are placed in the goods storage locations within warehouse area 11. Each storage container 15 can store multiple goods with different stock keeping units (SKUs), different batches, or different attributes.

[0026] Within warehouse area 11, a passage of suitable width can be formed between two adjacent shelves 11 to allow warehouse robots 12 to move. In some applications, shelves 11 can also be raised high enough to allow warehouse robots 12 to move from the bottom of the shelves.

[0027] The warehouse robot 12 is an automated robot capable of moving autonomously within the warehouse area 11 and completing handling tasks. During the handling process, the warehouse robot 12 uses its onboard positioning and navigation modules to move in an orderly manner between various goods storage locations and workstations 13, transporting the storage container 15 from the goods storage location to the workstation 13, or transporting the storage container 15 from the workstation 13 back to the goods storage location.

[0028] In some application scenarios, the warehouse robot 12 can be capable of picking up and placing storage containers 15 on shelves, and can complete the operation process of taking out storage containers 15 from the shelves and placing candidate containers 15 on the shelves by itself.

[0029] Alternatively, an additional cargo handling robot can be configured in the automated warehouse specifically for picking up and placing containers, while the warehouse robot 12 only has the ability to handle the storage containers 15. The cargo handling robot works in conjunction with the warehouse robot 12 to remove the storage containers 15 from the shelves and place them on the warehouse robot 12, or to remove candidate containers 15 from the warehouse robot 12 and place them in the corresponding storage location on the shelves.

[0030] Workstation 13 is a station used to perform inbound and outbound operations. It can be located at the edge of the warehouse area 11 and the goods handling required for inbound and outbound operations is completed through manual operation and / or automated goods handling equipment (e.g., robotic arms, conveyor belts, and scanning equipment).

[0031] For example, when performing an inbound operation, workstation 13 receives goods transported from outside, and after sorting, packaging or reorganizing the goods at workstation 13, the processed inbound goods are placed in storage container 15, and then the storage robot 12 transports the storage container 15 to the designated goods storage location.

[0032] When performing outbound operations, the warehouse robot 12 transports the warehouse container 15 to the workstation 13, where the workstation 13 inspects, packages, or sorts the goods in the warehouse container 15 to form the required outbound goods and complete the outbound operation.

[0033] The scheduling device 14 is the control center for unified scheduling and management of various devices within the automated warehouse. It can be implemented by an electronic platform integrating processors, memory, and communication modules, and receives and analyzes specific goods entry and exit requirements by interacting with the upstream warehouse management system. It then transforms these requirements into specific scheduling tasks, coordinating multiple automated operation units such as warehouse robots 12 and workstations 13 in the automated warehouse to achieve efficient goods flow and warehousing operations.

[0034] Based on the needs of practical applications, the scheduling device 14 can be designed in various forms such as a fixed workstation, a mobile controller, or a distributed control system. It can be used for the operation and management of a single warehouse, or it can be extended to a smart logistics network with multi-warehouse collaboration.

[0035] In the outbound operation execution phase of the aforementioned automated warehousing, one of the core tasks of scheduling equipment in implementing scheduling control strategies is how to accurately make decisions and select the optimal target outbound container from a massive number of in-stock storage containers.

[0036] Typically, the scheduling equipment maintains a data set known as the order pool. This order pool gathers all business orders issued by the upstream business system that have not yet been picked at the current moment, constituting the source of demand for outbound operations in automated warehousing.

[0037] When faced with an order pool containing multiple orders to be picked, existing scheduling equipment typically uses serial processing logic triggered by a single order to schedule storage containers.

[0038] First, the scheduling equipment extracts the order demand information of individual orders one by one from the current order pool to be picked, and selects multiple candidate storage containers containing the corresponding goods from the warehouse based on the single order demand information (e.g., SKU code).

[0039] Then, the scheduling equipment sorts these candidate storage containers according to pre-configured evaluation rules. These evaluation rules include sorting by the distance of the candidate storage containers from the workstation, or by the order in which the goods were received.

[0040] Finally, based on the sorting results, the scheduling equipment selects the storage container that ranks first as the target outbound container, and schedules the storage robot to move the target outbound container to the corresponding workstation to complete the outbound operation.

[0041] In implementing this application, the applicant discovered that although the above-mentioned method of scheduling storage containers based on a serial processing logic triggered by a single order to be picked has a relatively simple implementation logic, the scheduling decision is limited to the current single order demand and cannot take into account other potential similar demands in the order pool to be picked, thus failing to fully and comprehensively measure or describe the outbound value of a single storage container.

[0042] Furthermore, since each designated outbound container serves only a single order demand, when there are multiple orders in the order pool that require the same SKU or similar inventory, the same storage container often needs to be repeatedly dispatched from the storage area to the workstation.

[0043] This necessitates warehouse robots making multiple trips between the warehouse area and workstations. This high-frequency repetitive handling not only significantly increases the ineffective travel distance and energy consumption of the warehouse robots and exacerbates the risk of aisle congestion, but also consumes the robots' operating time, severely impacting the overall operational efficiency of automated warehouses.

[0044] To address the aforementioned issues, the applicant's research revealed that by aggregating multiple pending orders to generate merged requirements, and then calculating the degree of demand matching based on a comparison between inventory and merged requirements to determine and filter target outbound containers, a comprehensive evaluation and measurement of the ability of individual candidate storage containers to meet the current pending order pool or the degree of demand matching can be achieved. This effectively improves the output ratio of a single handling operation for storage containers, reduces redundant handling and ineffective circulation of storage containers, and enhances the picking efficiency and overall throughput of automated warehouses.

[0045] Figure 2 This is a flowchart of the scheduling method for storage containers provided in an embodiment of this application. Figure 2 As shown, the method includes the following steps: S210. Obtain order demand information for multiple pending picking orders and inventory information for multiple candidate storage containers.

[0046] Among them, "pending picking orders" are business orders that have not yet been picked at the current moment. Each business order records the specific requirements data for the corresponding outbound operation, such as the specific goods needed and the quantity of goods.

[0047] "Candidate storage containers" are those selected by the scheduling equipment that can fulfill the order requirements of at least one pending picking order. Each candidate storage container records the current status of the goods actually stored, such as the specific goods stored and their quantities.

[0048] S220. Aggregate the order demand information of multiple orders to be picked to generate multiple merged requirements.

[0049] "Aggregation" is a process of reorganizing and merging data information. It summarizes and merges the same goods requirements from multiple different picking orders, thereby generating multiple merged requirements.

[0050] In this embodiment, each merged demand includes: a demand goods characteristic and a corresponding demand quantity. The demand goods characteristic is a set of attributes used to identify and distinguish the required goods. In other words, the scheduling device can distinguish whether they belong to the same type of goods based on the demand goods characteristic.

[0051] Specifically, the characteristics of the goods in demand can be composed of one or more different dimensions of goods attribute information. For example, a unique commodity identification code to identify the type of goods, a batch number to identify the production or warehousing batch of goods, a warehouse area code to indicate the storage location of goods, and a temperature zone requirement to indicate the storage environment temperature of goods.

[0052] S230. Compare the inventory information of each candidate storage container with multiple combined requirements, and calculate the degree of requirement matching for each candidate storage container.

[0053] The degree of demand matching is a metric used to quantify and evaluate the contribution of candidate containers to the overall consolidation needs of the current multiple consolidation requirements. In other words, it indicates the extent to which the current physical storage of candidate containers can meet the current multiple consolidation needs.

[0054] S240. Based on the degree of demand matching, determine the target outbound container from multiple candidate storage containers.

[0055] The target outbound container is the storage container selected by the scheduling equipment from multiple candidate storage containers to perform the outbound operation, based on the degree of demand matching. It represents the scheduling decision result that can meet the current order demand with optimal efficiency at the current moment.

[0056] In some embodiments, such as Figure 3 As shown, step S240 specifically includes the following sub-processes: S241. Obtain the operation execution parameters for each of the candidate storage containers.

[0057] The execution parameters are used to characterize the execution cost of the candidate storage container during physical operations. Examples include the distance index between the candidate storage container and the target operation point, and the lane balance index of the candidate storage container's lanes.

[0058] S242. Calculate the scheduling score for each candidate storage container by combining the degree of demand matching and the job execution parameters.

[0059] The "scheduling score" is a comprehensive evaluation value that combines demand matching and physical operation costs. It can be used to quantify or assess the value of moving a candidate storage container. For example, a candidate storage container with a high degree of demand matching and low physical operation costs will have a higher scheduling score, and vice versa.

[0060] S243. Among multiple candidate storage containers, determine the candidate storage container with the highest scheduling score as the target outbound container.

[0061] Among them, the candidate storage container with the highest scheduling score indicates that using it as the current scheduling target for outbound operations will yield the best overall benefits. Therefore, using it as the target outbound container can efficiently meet order demands while minimizing equipment operating energy consumption or time costs, thus achieving optimal scheduling.

[0062] Specifically, step S242 can integrate indicators from different dimensions, such as job execution parameters and demand matching degree, through normalization and weighted summation to obtain the required comprehensive evaluation value.

[0063] First, the demand matching degree, distance index, and lane balance index are normalized to obtain the corresponding first, second, and third values. Then, based on preset weight coefficients, the first, second, and third values ​​are weighted and summed to obtain the scheduling score of the candidate storage container.

[0064] "Normalization" refers to a preprocessing method that uses specific mathematical formulas (e.g., Min-Max scaling) to map the original data to the same specific numerical range (e.g., the range of 0 to 1), thereby eliminating the influence of different units and unifying the evaluation criteria.

[0065] Furthermore, the degree of matching of needs is a positive indicator, while the distance indicator is a negative indicator. Therefore, the normalization process also includes unifying the directionality of the indicators. That is, during the normalization process, the system will transform negative indicators into positive indicators, thereby ensuring that the values ​​of all indicators in different dimensions follow the evaluation rule of "the higher the value, the better" after normalization.

[0066] In some embodiments, such as Figure 4 As shown, the order demand information of multiple orders to be picked can be aggregated through the following steps: S221. Classify the order demand information of multiple orders to be picked to obtain multiple sets of order demand information.

[0067] "Classification" refers to traversing all pending orders, extracting order requirements that point to the same type of goods from these orders, and grouping them into the same order requirement group. In other words, within each group of order requirement information, there are identical characteristics of the goods required.

[0068] S222. Accumulate and calculate the total demand quantity for each group of order demand information.

[0069] "Accumulated calculation" refers to, without considering the differences in order sources, based on the grouping results of step S221, performing arithmetic summation on the quantity of goods required for a type of goods indicated by each group of order demand information, thereby obtaining a total demand quantity.

[0070] For example, suppose a set of order requirements includes three orders from different pending orders, requiring 2, 3, and 5 of the same item respectively. Therefore, by summing these requirements, the total required quantity is calculated to be 10.

[0071] S223. Associate the total demand quantity of a set of order demand information with the corresponding goods demand characteristics to form a merged demand.

[0072] By associating the total demand quantity with a set of order demand information, a standardized data result (i.e., merged demand) is formed, which can serve as the basis for subsequent calculations of demand matching degree.

[0073] In some embodiments, corresponding to the standardized data format of the aforementioned consolidation requirements, the inventory information of candidate storage containers also includes: at least one inventory item characteristic and the quantity of goods corresponding to each inventory item characteristic. For example... Figure 5 As shown, the sub-process of step S230 specifically includes: S231. Among multiple merge requests, select the first merge request that matches the characteristics of the goods in the request with the characteristics of the inventory goods in the candidate storage containers.

[0074] The "first merged demand" refers to the merged demand among multiple merged demands where the characteristics of the demanded goods and the characteristics of the inventory goods are consistent. In other words, the first merged demand marks the intersection between the inventory characteristics of the candidate storage container and multiple merged demands.

[0075] Specifically, in the process of screening the first consolidation requirement, the number of the first consolidation requirement can be counted by cumulative calculation to determine the coverage of multiple consolidation requirements for candidate storage containers.

[0076] S232. Calculate the demand fulfillment ratio based on the demand quantity for each first merged demand and the quantity of goods in the candidate storage containers.

[0077] The "demand fulfillment ratio" is a metric used to measure or evaluate the ability of candidate storage containers to cover a first consolidated demand. It can be calculated by comparing the quantity of goods stored in the candidate storage containers with the demand quantity in the consolidated demand.

[0078] Specifically, the demand fulfillment ratio can be the ratio between the quantity of goods currently stored in the candidate storage container and the quantity of demand for the first consolidated demand. In other words, the ratio obtained by dividing the quantity of goods by the quantity of demand is the demand fulfillment ratio.

[0079] Understandably, when the quantity of goods in a candidate storage container is greater than or equal to the demand quantity, the quantity of goods exceeding the demand will not generate additional marginal value for the goal of "eliminating the current demand gap".

[0080] Therefore, the maximum value of the demand fulfillment ratio is 1 to ensure that the overall matching score of the candidate storage container is not artificially inflated due to a serious oversupply of a certain type of goods. Specifically, when the calculated ratio is greater than or equal to 1, the demand fulfillment ratio is determined to be 1; when the calculated ratio is less than 1, it is determined to be the demand fulfillment ratio.

[0081] S233. Based on the number of first merger requirements and the demand satisfaction ratio of each first merger requirement, determine the demand matching degree of candidate storage containers.

[0082] Among them, the "degree of demand matching" is calculated by integrating two different dimensions of indicators: the breadth and depth of coverage of candidate storage containers for multiple combined demands. This enables a comprehensive and complete evaluation of candidate storage containers and fully explores their value.

[0083] Specifically, the two different dimensions of coverage breadth and coverage depth can be integrated through weighted calculation to obtain the degree of demand matching of candidate storage containers.

[0084] First, calculate the average proportion of demand fulfillment for each first-order merged demand. Then, perform a weighted calculation on the number of first-order merged demands and the average proportion to determine the demand matching degree of the candidate storage containers.

[0085] "Weighted calculation" refers to assigning a weight coefficient to the quantity and average proportion of the first merged demand, and then summing the results by multiplying the quantity and average proportion of the first merged demand by their respective weight coefficients. The specific weight coefficients assigned can be set according to the needs of the actual application scenario, and are not specifically limited here.

[0086] Preferably, in order to minimize the total number of handling operations, the weighting factor for the quantity of the first consolidation requirement can be set to be significantly greater than the weighting factor for the proportional average (for example, the weighting factor for the quantity of the first consolidation requirement can be set to 0.9, while the weighting factor for the proportional average can be set to 0.1).

[0087] In some embodiments, such as Figure 6 As shown, the specific process for screening the first merging requirement includes the following steps: S2311. Generate a corresponding first feature fingerprint for the required goods features of each merged requirement using preset coding rules.

[0088] Among them, "encoding rules" are pre-configured or predetermined data transformation logic. They can compress the characteristics of demanded goods, which contain multiple dimensions of goods attribute information (such as SKU number, batch number, storage area code, and stabilization requirements, etc.), into unique identifiers.

[0089] The specific data transformation rules can be chosen based on the actual needs, and no specific restrictions are imposed here. For example, a hash algorithm or a specific string concatenation rule can be used.

[0090] In this application, the term "feature fingerprint" is used to refer to the unique identifier obtained after conversion by the preset encoding rules. In other words, each feature fingerprint represents a cargo feature, and different cargo features have different feature fingerprints.

[0091] S2312. Using the first feature fingerprint as the index key, construct a requirement mapping table containing all merging requirements.

[0092] The "demand mapping table" is a key-value data structure that enables fast retrieval. The first characteristic fingerprint of each merged demand serves as the index key for data retrieval or searching, while the corresponding merged demand information (e.g., the number of demands) serves as the index value.

[0093] S2313. Generate a corresponding second feature fingerprint for each type of inventory item in the candidate storage container by using coding rules.

[0094] The "second feature fingerprint" is a feature fingerprint generated from the inventory goods feature using the same encoding rules. In this embodiment, for ease of description, the "first feature fingerprint" and "second feature fingerprint" are used to describe the feature fingerprints generated from the demand goods feature and the inventory goods feature, respectively.

[0095] S2314. In the demand mapping table, find the index key that matches the second feature fingerprint.

[0096] Based on the rules followed when constructing the demand mapping table: each feature fingerprint represents a type of cargo characteristic, and different cargo characteristics have different feature fingerprints. Therefore, by quickly querying the demand mapping table to see if there is an index key (i.e., the first feature fingerprint) that is the same as the second feature fingerprint, matching merged demands can be found quickly.

[0097] S2315. The merging requirement corresponding to the index key that is consistent with the second feature fingerprint is determined as the first merging requirement.

[0098] Once a matching index key is found, the complete requirement information associated with that index key can be extracted from the requirement mapping table and marked as the aforementioned "first merged requirement," thus completing the filtering operation for the first merged requirement.

[0099] The method for filtering the first merging demand provided in this application firstly uses feature fingerprint technology to transform the complex comparison operation involving multiple dimensions of goods attribute information such as SKU, batch, and warehouse area into a simple matching of a single identifier, greatly simplifying the data processing logic. Moreover, by using a demand mapping table (key-value structure) for index lookup, the time complexity of lookup and matching is reduced to the constant level. Even when facing massive merging demands and large-scale inventory data, it can achieve rapid response, significantly improving data processing efficiency and the real-time nature of scheduling decisions, while reducing the required computing resource consumption.

[0100] In other embodiments, other feasible data processing methods can be used to filter the first matching merger requirement from multiple merger requirements, as long as the matching relationship between the inventory characteristics of candidate storage containers and the required goods characteristics of the merger requirements can be identified and extracted. For example, a traversal polling method can be used to perform multi-field matching verification between the inventory characteristics of candidate storage containers and the required goods characteristics of each merger requirement. Another example is to perform a retrieval based on a tree-structured data structure to determine the matching status between inventory characteristics and required goods characteristics.

[0101] To enable those skilled in the art to more clearly understand the inventive concept and execution logic of the scheduling method provided in the embodiments of this application, the following will combine a specific business scenario example to explain in detail the above data processing and calculation process and demonstrate the complete data flow process.

[0102] 1) The order pool contains multiple orders to be picked, as shown in Table 1 below:

[0103] Table 1 2) Multiple candidate storage containers and their inventory information are shown in Table 2 below:

[0104] Table 2 As shown in Table 2, two types of inventory SKUs (SKU-A and SKU-C) were mixed in storage container C001, and two types of inventory SKUs (SKU-A and SKU-B) were also mixed in storage container C002. However, storage container C003 contained only one type of inventory SKU, but its batch number was LOT002, which was different from the batch number LOT001 required for picking orders D001 and D003.

[0105] 3) Aggregate the order demand information of multiple orders to be picked in Table 1 to form three merged requirements, as shown in Table 3 below:

[0106] Table 3 4) Calculate the demand matching degree for each candidate storage container in Table 2: First, by comparing the data in Tables 2 and 3, it can be determined that: the first merged demand quantity for candidate storage container C001 is 2, and the average demand fulfillment rate is 75%; the first merged demand quantity for candidate storage container C002 is also 2, and the average demand fulfillment rate is 66.7%; while the first merged demand quantity for candidate storage container C003 is 0, indicating no first merged demand.

[0107] Then, the weight of the first merged demand quantity is set to 0.9, and the weight of the average proportion of demand satisfaction ratio is set to 0.1. By weighted summation, the demand matching degree of candidate storage container C001 is 1.875, the demand matching degree of candidate storage container C002 is 1.867, and the demand matching degree of candidate storage container C003 is 0.

[0108] 5) Determine the target outbound container from multiple candidate storage containers: The distance indicators from each candidate storage container to the target operation point are shown in Table 4 below:

[0109] Table 4 The roadway equilibrium indicators for each candidate storage container are shown in Table 5 below:

[0110] Table 5 First, linear normalization is used to map both the distance index and the roadway balance index to a numerical range of 0 to 1. The formula for this linear normalization is as follows: Normalized value = (Original score - Minimum value) / (Maximum value - Minimum value), where "Maximum value" and "Minimum value" refer to the maximum and minimum values ​​of this indicator among all candidate storage containers, respectively.

[0111] The first value obtained after normalizing the demand matching degree of each candidate storage container is shown in Table 6-1 below:

[0112] Table 6-1 The normalized distance metrics for each candidate storage container are shown in Table 6-2 below:

[0113] Table 6-2 The normalized values ​​of the aisle balance index for each candidate storage container are shown in Table 6-3 below:

[0114] Table 6-3 Based on preset weight coefficients, the first, second, and third values ​​are weighted and summed according to the following formula to obtain the scheduling score: Scheduling score = W1 × first value + W2 × second value + W3 × third value Where W1, W2, and W3 are different weighting coefficients, and W1 + W2 + W3 = 1.

[0115] Specifically, the three weighting coefficients are set as follows: W1 = 0.6, W2 = 0.25, and W3 = 0.15, respectively, to indicate that in the scheduling score, the degree of demand matching accounts for 60%, the distance index accounts for 25%, and the roadway balance index accounts for 15%.

[0116] The scheduling scores for each candidate storage container are as follows: Scheduling score for candidate storage container C001: 0.6 × 1.0 + 0.25 × 0.6 + 0.15 × 1.0 = 0.9 Scheduling score for candidate storage container C002: 0.6 × 0.996 + 0.25 × 1.0 + 0.15 × 0 = 0.848 Scheduling score for candidate storage container C003: 0.6 × 0 + 0.25 × 0 + 0.15 × 1.0 = 0.15.

[0117] Finally, based on the ranking of the three candidate storage containers, the scheduling device 14 can select candidate storage container C001 as the target outbound container and send a handling instruction to the storage robot 12 to move it to the corresponding target work point.

[0118] Based on the storage container scheduling method provided in the embodiments of this application, this application further provides a storage container scheduling device. In some embodiments, such as Figure 7 As shown, the scheduling device 70 includes: an acquisition module 710, a demand merging module 720, a comparison module 730, and an output module 740.

[0119] The acquisition module 710 acquires order demand information from multiple pending picking orders and inventory information from multiple candidate storage containers. The demand merging module 720 aggregates the order demand information from multiple pending picking orders to generate multiple merged demands. Each merged demand includes a demanded goods characteristic and a corresponding demand quantity. The comparison module 730 compares the inventory information of each candidate storage container with the multiple merged demands to calculate the demand matching degree for each candidate storage container. The output module 740 determines the target outbound container from the multiple candidate storage containers based on the demand matching degree.

[0120] In some embodiments, the demand merging module 720 is specifically used to: classify the order demand information of multiple orders to be picked to obtain multiple sets of order demand information, each set of order demand information having the same goods demand characteristics; accumulate and calculate the total demand quantity of each set of order demand information; associate the total demand quantity of a set of order demand information with the corresponding goods demand characteristics to form a merged demand.

[0121] In some embodiments, the inventory information for each candidate storage container includes: at least one inventory item characteristic and the corresponding quantity of the item. Please continue reading. Figure 7 The comparison module 730 includes: a filtering unit 731, a ratio calculation unit 732, and a demand calculation unit 733.

[0122] The screening unit 731 is used to screen, among multiple consolidation demands, the first consolidation demand whose demand goods characteristics match the inventory goods characteristics of the candidate storage containers. The ratio calculation unit 732 is used to calculate the demand fulfillment ratio based on the demand quantity of each first consolidation demand and the goods quantity of the candidate storage containers. The ratio calculation unit 732 is used to determine the demand matching degree of the candidate storage containers based on the quantity of the first consolidation demands and the demand fulfillment ratio of each first consolidation demand.

[0123] Specifically, the demand fulfillment ratio is the ratio between the quantity of goods and the quantity of the first consolidated demand. When the ratio is greater than or equal to 1, the demand fulfillment ratio is determined to be 1.

[0124] In some embodiments, the demand calculation unit 733 is specifically used to: calculate the average proportion of demand satisfaction for each first merged demand; and perform a weighted calculation on the number and average proportion of the first merged demands to determine the demand matching degree of the candidate storage containers.

[0125] In some embodiments, the filtering unit 731 is specifically configured to: generate a corresponding first feature fingerprint for the required goods feature of each merged requirement according to a preset encoding rule; construct a demand mapping table containing all merged requirements using the first feature fingerprint as an index key; generate a corresponding second feature fingerprint for each type of inventory goods feature in the candidate storage container according to the encoding rule; search for an index key that matches the second feature fingerprint in the demand mapping table, and determine the merged requirement corresponding to the index key that matches the second feature fingerprint as the first merged requirement.

[0126] Specifically, each demand goods characteristic and each of the aforementioned inventory goods characteristics includes multiple dimensions of goods attribute information. This goods attribute information includes at least one of the following: a unique commodity identification code for identifying the type of goods; a goods batch number for identifying the production or warehousing batch of the goods; a warehouse area code for indicating the storage location of the goods; and a temperature zone requirement for indicating the storage environment temperature of the goods.

[0127] Preferably, the cargo attribute information may also include code requirements for defining the outbound logic. This outbound logic describes additional outbound constraints or selection rules. For example, the code requirements may include different outbound logics such as strict expiration date priority and last-in-first-out (LIFO).

[0128] By defining code requirements for outbound logic, the system enables rich customization and flexible configuration of outbound scheduling strategies. It abstracts complex logical rules into attribute code, thus easily adapting to the needs of different business scenarios and significantly improving the versatility and scalability of the warehouse management system.

[0129] It should be noted that the scheduling device provided in this application embodiment can be executed by any suitable type of electronic computing platform. This electronic computing platform can implement or execute the above-described functional modules using electronic hardware, computer software programs, or a combination of both. Whether these functional modules are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0130] Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of this application. This electronic device can be used to execute the scheduling method for storage containers described in one or more of the above embodiments, as... Figure 1 The scheduling equipment shown is in use.

[0131] It should be noted that, Figure 8 The components, their connections and relationships, and their functions shown are for illustrative purposes only and are not intended to impose limitations on the specific implementation of the scheduling device.

[0132] like Figure 8 As shown, the electronic device 80 may include a processor 801, a memory 802, a storage device 803, a high-speed interface 805 connected to the memory 802 and a plurality of high-speed expansion ports 804, and a low-speed interface 807 connected to a low-speed expansion port 806 and the storage device 803.

[0133] Each of the processor 801, memory 802, storage device 803, high-speed interface 805, high-speed expansion port 804, and low-speed interface 807 is interconnected using various buses and can be mounted on a common motherboard or other suitable methods.

[0134] Processor 801 can process computer program instructions stored in memory 802 or on storage device 803 for use with external input / output devices (e.g., display 808 coupled to high-speed interface 805).

[0135] In some embodiments, multiple processors and / or multiple buses may be used together with multiple memories and multiple types of memories, and multiple electronic devices may be further connected, each of which provides a portion of the necessary operation.

[0136] The memory 802 stores information within the control terminal. It may be one or more volatile memory cells, non-volatile memory cells, or another form of computer-readable media, such as a magnetic disk or optical disk.

[0137] Storage device 803 can provide large-capacity storage for the control terminal. It can contain computer-readable media, such as floppy disk devices, hard disk devices, optical disk devices, magnetic tape devices, flash memory, or other similar solid-state storage devices.

[0138] Computer program instructions may be stored in an information carrier. When executed by one or more processing devices (e.g., processor 801), the computer program instructions implement the scheduling method described in one or more of the above embodiments.

[0139] High-speed interface 805 manages bandwidth-intensive operations for controlling the terminal, while low-speed interface 807 manages lower bandwidth-intensive operations. In some embodiments, high-speed interface 805 is coupled to memory 802, display 808, and high-speed expansion port 804 which accepts various expansion cards. Low-speed interface 807 is coupled to storage device 803 and low-speed expansion port 806.

[0140] The low-speed expansion port 806 includes communication ports (e.g., USB, Ethernet, wireless Ethernet) and can be coupled to one or more input / output devices, such as keyboards, pointing devices, scanners, or networked devices like switches.

[0141] Specifically, such as Figure 8 As shown, in order to provide interaction with the user, the electronic device 80 has a display 808 for displaying information to the user, a pointing device 809 that the user can use to provide input to the computer, and other types of interactive devices.

[0142] Of course, the feedback provided to the user can be any form of sensory feedback (such as visual feedback, auditory feedback, or tactile feedback), and the input from the user can be received in any form, including acoustic, voice, or tactile input.

[0143] The scheduling method described in one or more embodiments of this application can be implemented in digital electronic circuits, integrated circuits, specially designed ASICs (Application-Specific Integrated Circuits), computer hardware, firmware, software, and / or combinations thereof. It may include embodiments of one or more computer programs that can be executed and / or interpreted on a programmable system comprising at least one programmable processor, which may be dedicated or general-purpose, coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transmit data and instructions to the storage system, at least one input device, and at least one output device.

[0144] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level programming and / or goal-oriented programming languages ​​and / or assembly / machine language. In this embodiment, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" can refer to any signal used to provide machine instructions and / or data to a programmable processor.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of this application as described above, which are not provided in detail for the sake of brevity; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for scheduling storage containers, characterized in that, include: Obtain order demand information for multiple pending picking orders and inventory information for multiple candidate storage containers; The order demand information of the multiple orders to be picked is aggregated to generate multiple merged demands; Each of the merged requirements includes: a required goods characteristic and a corresponding required quantity; The inventory information of each candidate storage container is compared with the multiple merged requirements to calculate the demand matching degree of each candidate storage container. Based on the degree of demand matching, a target outbound container is determined from the plurality of candidate storage containers.

2. The scheduling method according to claim 1, characterized in that, The aggregation of order demand information from the multiple orders to be picked, generating multiple merged requirements, specifically includes: The order demand information of the multiple orders to be picked is classified to obtain multiple sets of order demand information, and each set of order demand information has the same goods demand characteristics. Accumulate and calculate the total demand quantity for each group of order demand information; The total demand quantity of a set of order demand information is associated with the corresponding goods demand characteristics to form a merged demand.

3. The scheduling method according to claim 1, characterized in that, The inventory information includes: at least one characteristic of the inventory goods and the corresponding quantity of the goods; The step of comparing the inventory information of each candidate storage container with the multiple merged demands and calculating the demand matching degree of each candidate storage container specifically includes: Among the multiple merged requirements, the first merged requirement is selected based on whether the characteristics of the required goods match the characteristics of the inventory goods in the candidate storage containers. Calculate the demand fulfillment ratio based on the demand quantity for each first merged demand and the quantity of goods in the candidate storage containers; The degree of demand matching for the candidate storage containers is determined based on the number of the first merged demands and the demand satisfaction ratio of each first merged demand.

4. The scheduling method according to claim 3, characterized in that, The calculation of the demand fulfillment ratio based on the demand quantity of each first merged demand and the quantity of goods in the candidate storage containers specifically includes: Calculate the ratio between the quantity of the goods and the quantity required by the first consolidated demand; When the ratio is greater than or equal to 1, the demand satisfaction ratio is determined to be 1. When the ratio is less than 1, the ratio is determined to be the proportion in which the demand is met.

5. The scheduling method according to claim 3, characterized in that, The determination of the demand matching degree of the candidate storage containers based on the number of the first merged demands and the demand satisfaction ratio of each first merged demand specifically includes: Calculate the average percentage of demand fulfillment for each first-consolidation requirement; The demand matching degree of the candidate storage containers is determined by weighting the quantity of the first merged demand and the average ratio.

6. The scheduling method according to claim 3, characterized in that, The first merging demand, which selects the demand goods characteristics from the candidate storage containers based on their matching characteristics with the inventory goods characteristics of the multiple merging demands, specifically includes: By using preset encoding rules, a corresponding first feature fingerprint is generated for the required goods features of each of the aforementioned merged requirements; Using the first feature fingerprint as the index key, construct a requirement mapping table containing all merging requirements; Using the encoding rules, a corresponding second feature fingerprint is generated for each type of inventory item in the candidate storage container; In the demand mapping table, find the index key that matches the second feature fingerprint, and The merging requirement corresponding to the index key that matches the second feature fingerprint is determined as the first merging requirement.

7. The scheduling method according to claim 3, characterized in that, Each of the aforementioned demand goods characteristics and each of the aforementioned inventory goods characteristics contains multiple dimensions of goods attribute information; The cargo attribute information includes at least one of the following; Unique Product Identifier (UPI) code used to identify the type of goods; Batch number used to identify the production or warehousing batch of goods; Warehouse area codes used to indicate the storage location of goods, and Temperature zone requirements used to indicate the ambient temperature of goods storage environment.

8. The scheduling method according to claim 1, characterized in that, The step of determining the target outbound container from the plurality of candidate storage containers based on the degree of demand matching specifically includes: Obtain the job execution parameters for each candidate storage container, wherein the job execution parameters are used to characterize the execution cost of the candidate storage container during physical operations; Based on the demand matching degree and the job execution parameters, calculate the scheduling score for each candidate storage container; Among the multiple candidate storage containers, the candidate storage container with the highest scheduling score is determined as the target outbound container.

9. The scheduling method according to claim 8, characterized in that, The operation execution parameters include: the distance index of the candidate storage container relative to the target operation point and the lane balance index of the lane where the candidate storage container is located; Specifically, calculating the scheduling score for each candidate storage container by combining the demand matching degree and the job execution parameters includes: The demand matching degree, the distance index, and the roadway balance index are normalized respectively to obtain the corresponding first value, second value, and third value. Based on preset weighting coefficients, the first value, the second value, and the third value are weighted and summed to obtain the scheduling score of the candidate storage container.

10. An electronic device, characterized in that, include: A processor and a memory, the memory storing computer program instructions such that when the computer program instructions are invoked by the processor, the scheduling method for storage containers as described in any one of claims 1-9 is executed.

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