Warehouse goods taking method and device and medium

Abstract

The embodiment of the invention discloses a warehouse goods taking method and device and a medium, and the method comprises the steps: carrying out the global retrieval in a warehouse, and judging whether a goods location of a target product is stored or not; if yes, goods allocation information corresponding to the target product is determined, and an available warehouse area is obtained based on the goods allocation information; determining a first optimal reservoir area in the available reservoir areas, and determining first identification information of the first optimal reservoir area; determining a first target warehouse area position and first target warehouse area shelf information based on the first identification information; determining a first shelf position of the target product based on the first target warehouse area shelf information; and taking the target product based on the first target warehouse area position and the first shelf position.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and more particularly to a method, device, and medium for picking up goods from a warehouse. Background Art

[0002] With the development of intelligent warehousing and logistics, traditional methods of picking up goods can no longer meet the needs of efficient and accurate operations.

[0003] Existing warehouse retrieval systems typically rely on manual route planning or localized retrieval within a single warehouse area, lacking real-time integration and collaborative analysis of the warehouse's overall storage status. When target products are stored across multiple warehouse areas, redundant searches must be performed across each area, leading to delayed retrieval responses and significantly increasing retrieval time, especially in large-scale warehouses. Summary of the Invention

[0004] One or more embodiments of this specification provide a method, device, and medium for picking up goods from a warehouse, which are used to solve the technical problems raised by the background technology.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of this specification provide a method for picking up goods from a warehouse, the method comprising:

[0007] Perform a global search in the warehouse to determine whether the target product is stored in the warehouse;

[0008] If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information;

[0009] Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area;

[0010] Determine the first target storage area location and the first target storage area shelf information based on the first identification information;

[0011] Determine a first shelf location of the target product based on the shelf information of the first target storage area;

[0012] The target product is picked up based on the first target warehouse location and the first shelf location.

[0013] It should be noted that this method reconstructs the traditional fragmented warehouse area retrieval, cargo location positioning, and path planning links into a dynamically linked global optimization process by establishing a real-time collaborative decision-making mechanism for cargo locations across the entire warehouse. First, the target cargo location distribution is directly locked through global retrieval to avoid the linear delay of redundant retrieval in each warehouse area. Subsequently, when dynamically screening available warehouse areas based on cargo location information, multi-dimensional parameters such as warehouse area equipment load and path priority are simultaneously integrated, so that the selected "first optimal warehouse area" not only meets the cargo existence condition, but also implies the prediction logic for optimal operation efficiency. Finally, through the collaborative analysis of warehouse area location and shelf information, spatial positioning and resource scheduling are incorporated into the same decision-making cycle, forming a closed-loop constraint for cargo location retrieval and pickup path, thereby realizing a paradigm shift from "local trial and error" to "global prediction" in cross-warehouse area scenarios, systematically reducing operation backtracking and resource internal consumption caused by data silos and dynamic environment uncertainty, and improving the overall smoothness and stability of warehousing operations.

[0014] Furthermore, before determining whether the target product is stored in the cargo location, the method further includes:

[0015] Determine whether there is an unlocked storage area;

[0016] If so, determining whether the target product is already stored in the unlocked storage area;

[0017] If not, determining whether the target product has been configured in the unlocked storage area;

[0018] If not, determine whether there is a storage area configured as empty in the unlocked storage area;

[0019] If so, determining a second optimal storage area in the storage areas configured as empty according to a preset priority strategy, and determining second identification information of the second optimal storage area;

[0020] Determine the second optimal storage area location and the second optimal storage area shelf information based on the second identification information;

[0021] Determine a second shelf location of the target product based on the second optimal storage area shelf information;

[0022] The target product is released based on the second optimal warehouse location and the second shelf location.

[0023] It should be noted that this method transforms the inefficient mode of passively waiting for storage area release or random selection in the traditional delivery process into an active planning logic based on storage area status hierarchical judgment and policy-driven by constructing a dynamic allocation and priority prediction mechanism for unlocked storage areas. First, by screening the status of unlocked storage areas layer by layer (target product exists → target product has been configured → configured as empty), the risks of redundant inventory accumulation and invalid storage area occupation are systematically avoided. Then, a priority strategy is introduced to select the second best storage area in the configured empty storage area, so that the selection of the delivery target storage area not only meets the basic space availability but also implies the pre-configuration conditions for future pickup path optimization and minimization of equipment scheduling costs. Finally, through the coordinated binding of storage area location and shelf information, the closed-loop adaptation of delivery operation and storage area resource allocation is realized, thereby proactively avoiding subsequent pickup path conflicts or secondary transfer operations caused by improper storage area allocation during the goods entry stage, forming a collaborative optimization foundation for the delivery and pickup two-way operation chain, and overall improving the dynamic planning capability and operation continuity of the storage space.

[0024] Furthermore, if it is determined that the target product has been stored in the unlocked storage area, the method further includes:

[0025] Determine, among the storage areas where the target product is stored, a storage area with the largest amount of the target product as a third optimal storage area, and determine third identification information of the third optimal storage area;

[0026] Determine the third optimal storage area location and the third optimal storage area shelf information based on the third identification information;

[0027] Determine a third shelf location of the target product based on the third optimal storage area shelf information;

[0028] The target product is released based on the third optimal warehouse area position and the third shelf position.

[0029] It should be noted that this method reconstructs the release logic by prioritizing the aggregation of existing storage areas, transforming the traditional decentralized storage model into a dynamic and optimal product distribution control mechanism. When the target product is detected in an unlocked storage area, the third-best storage area is locked based on the inventory quantity, so that the storage location of the newly added goods automatically converges to the area with the highest current inventory. This not only inherits the clustering characteristics of the existing inventory distribution to reduce product dispersion, but also proactively avoids the subsequent cross-area transfer requirements caused by the decentralized storage of goods by strengthening the resource concentration effect of the inventory-advantaged storage area. At the same time, the release location is accurately located based on the shelf information of the inventory storage area, forming a dynamic coupling of inventory data and new operations, which shifts the utilization mode of storage space from passive filling to active clustering optimization. During the release phase, the shortest connection topology of product distribution and pickup paths is constructed, systematically reducing the risks of path intersections and equipment scheduling conflicts caused by frequent scattered pickups, and achieving a two-way enhancement of storage space self-organization optimization and operational continuity.

[0030] Furthermore, if the target product has been configured in the unlocked storage area, the method further includes:

[0031] Determining, according to the priority strategy, a fourth optimal storage area among the storage areas where the target product has been configured, and determining fourth identification information of the fourth optimal storage area;

[0032] Determine the fourth optimal storage area location and the fourth optimal storage area shelf information based on the fourth identification information;

[0033] Determining a fourth shelf location of the target product based on the fourth optimal storage area shelf information;

[0034] The target product is released based on the fourth optimal warehouse area position and the fourth shelf position.

[0035] It should be noted that this method upgrades the fixed warehouse allocation mode in the traditional delivery operation to a dynamically adaptive resource allocation logic through a strategic optimization mechanism for configured warehouse areas. When it is detected that the target product has been pre-configured in an unlocked warehouse area, the fourth optimal warehouse area is selected within the existing configured warehouse area based on the priority strategy. This ensures that the storage location of the newly added goods not only inherits the historical configuration parameters to ensure the continuity of the storage strategy, but also achieves pre-matching of warehouse resource allocation with future pickup needs through the composite constraints of the priority strategy (such as path hot zone avoidance, equipment load balancing, and other implicit conditions). By precisely binding warehouse area location and shelf information, the delivery operation is automatically embedded in the optimized topology of the existing warehouse layout, forming a spatial coordinated distribution of new goods and historical inventory. This reduces the risk of configuration conflicts while building a highly aggregated product distribution base for subsequent batch pickups. This systematically avoids path fragmentation and equipment scheduling efficiency degradation caused by configuration discretization, achieving a dual improvement in the dynamic utilization of storage space and the stability of the operation link.

[0036] Furthermore, the priority strategy includes:

[0037] Determine the list of storage areas that meet the requirements;

[0038] The storage areas in the storage area list are prioritized in turn to determine the optimal storage area.

[0039] It should be noted that this method upgrades the static one-way sorting rules in the traditional priority strategy to a multi-factor collaborative decision-making model that adapts to the environmental status by constructing a multi-dimensional dynamic cycle priority judgment mechanism: when determining the optimal warehouse area, the system performs a cyclic iterative priority evaluation on the candidate warehouse area list, so that each judgment process can dynamically integrate multiple implicit constraints such as real-time equipment load, path congestion coefficient, inventory turnover rate, etc., rather than relying on preset fixed weight parameters; through this continuous feedback optimization judgment logic, the system automatically avoids instantaneous resource conflict areas in warehouse area selection, and at the same time guides warehousing operations to a long-term balanced load state, forming a two-way adaptation of the priority strategy and the dynamic environment of the warehouse, thereby realizing autonomous correction and global optimal guidance of warehouse area resource allocation in complex operation scenarios, systematically eliminating the contradictions of local resource overload or equipment idleness caused by priority solidification, and achieving self-organizing optimization and stability leap of the warehousing operation link.

[0040] Furthermore, performing cyclic priority determination on the storage areas in the storage area list to determine the optimal storage area includes:

[0041] Determine whether a designated storage area in the storage area list is configured with a single product;

[0042] If so, determine whether the warehouse contains other products besides the target product;

[0043] If so, determine whether there is a next storage area in the storage area list;

[0044] If so, return to determine the priority of the next storage area.

[0045] It should be noted that this method, by introducing a dual verification mechanism for configuration specificity and product diversity, reconstructs the isolated warehouse area evaluation model in traditional priority determination into a dynamic screening logic guided by global compatibility: when it is detected that a designated warehouse area is configured for a single product, it further verifies whether there are other product categories in the warehouse. If so, it automatically triggers the iterative determination of the warehouse area list, so that the priority decision process not only focuses on the local adaptability of the current warehouse area, but also forcibly couples the warehouse area configuration strategy with the overall product distribution structure of the warehouse. Through this hierarchical judgment rule, the system actively avoids the risk of using dedicated configuration warehouse areas in multi-category warehouses for non-target product storage in warehouse area selection, thereby maintaining the functional purity of specific warehouse areas and guiding the migration of stocking operations to more compatible general warehouse areas, forming a dynamic balance between warehouse area functional isolation and global warehouse category distribution, systematically eliminating inventory management chaos and subsequent sorting efficiency degradation caused by configuration conflicts, and achieving a dual enhancement of clear functional zoning of storage space and reliability of multi-category collaborative storage.

[0046] Furthermore, if the designated storage area in the storage area list is not configured with a single product, or the warehouse does not contain any products other than the target product, the method further includes:

[0047] Determine whether the target product has been configured in the unlocked storage area;

[0048] If so, determine whether there is an idle storage area;

[0049] If so, the designated storage area is set as the optimal storage area.

[0050] It should be noted that this method upgrades the isolated conditional judgment in traditional warehouse area optimization to an environment-adaptive strategy convergence logic by constructing a dynamic fault-tolerant mechanism that links configuration status with product distribution. When it is detected that the warehouse area configuration is not a single product or the warehouse product structure is uniform, the system automatically triggers a secondary verification process for the unlocked warehouse area. Through the composite verification of the pre-configuration status of the target product and the existence of idle warehouse areas, the warehouse area selection is guided to shift to the most compatible target within the existing resource configuration framework. This judgment logic avoids the operational costs caused by forcibly transforming the existing configuration (such as splitting the mixed storage storage area) while giving priority to reusing idle warehouse area resources that have been adapted to the target product, so that the warehouse area allocation strategy converges autonomously to the minimum intervention path under the constraints of the warehousing environment, thereby achieving a dynamic balance between storage resource utilization and system decision reliability while maintaining the stability of the warehouse area configuration, systematically eliminating the risk of strategy shock and resource configuration fault caused by environmental mutations, and achieving elastic optimization and anti-interference capability enhancement of the warehousing operation chain.

[0051] Furthermore, if the target product is not configured in the unlocked storage area, the method further includes:

[0052] Determine whether there is an empty configuration library area;

[0053] If so, the designated storage area is set as the optimal storage area.

[0054] It should be noted that this method upgrades the inefficient mode of passively waiting for configuration updates in traditional warehouse area allocation to an environmentally adaptive elastic adaptation strategy by constructing a dynamic activation mechanism for vacant resources: when the target product lacks configuration in an unlocked warehouse area, the system prioritizes activating the empty configuration warehouse area as the optimal choice, so that the warehouse area resource allocation automatically opens up independent storage space to avoid conflicts with existing product configurations while maintaining the integrity of existing functional partitions; through the targeted activation of vacant warehouse areas, it avoids operational redundancy caused by forced transformation of mixed storage areas, and establishes exclusive storage units for new products, forming a dynamic balance between the functional purity and expansion flexibility of the warehouse area, thereby achieving rapid convergence of resource allocation strategies in scenarios with sudden changes in storage demand, systematically eliminating the increased inventory management complexity and subsequent sorting path intersection risks caused by forced reuse of non-adaptive warehouse areas, and enhancing the warehousing system's immediate response capability and operational compatibility to unexpected storage needs.

[0055] One or more embodiments of this specification provide a device for picking up goods from a warehouse, including:

[0056] at least one processor; and,

[0057] a memory communicatively connected to the at least one processor; wherein,

[0058] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0059] Perform a global search in the warehouse to determine whether the target product is stored in the warehouse;

[0060] If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information;

[0061] Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area;

[0062] Determine the first target storage area location and the first target storage area shelf information based on the first identification information;

[0063] Determine a first shelf location of the target product based on the shelf information of the first target storage area;

[0064] The target product is picked up based on the first target warehouse location and the first shelf location.

[0065] One or more embodiments of this specification provide a non-volatile computer storage medium storing computer-executable instructions. When executed by a computer, the computer-executable instructions can achieve:

[0066] Perform a global search in the warehouse to determine whether the target product is stored in the warehouse;

[0067] If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information;

[0068] Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area;

[0069] Determine the first target storage area location and the first target storage area shelf information based on the first identification information;

[0070] Determine a first shelf location of the target product based on the shelf information of the first target storage area;

[0071] The target product is picked up based on the first target warehouse location and the first shelf location.

[0072] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0073] This method establishes a real-time collaborative decision-making mechanism for warehouse-wide cargo locations, reconstructing the traditional method's separate warehouse area retrieval, cargo location positioning, and path planning links into a dynamically linked global optimization process. First, a global search is used to directly lock the target cargo location distribution, avoiding the linear delay of redundant searches in each warehouse area. Subsequently, when dynamically screening available warehouse areas based on cargo location information, multi-dimensional parameters such as warehouse area equipment load and path priority are simultaneously integrated, so that the selected "first optimal warehouse area" not only meets the cargo existence condition but also implies the prediction logic for optimal operation efficiency. Finally, through the collaborative analysis of warehouse area location and shelf information, spatial positioning and resource scheduling are incorporated into the same decision-making cycle, forming a closed-loop constraint for cargo location retrieval and pickup path, thereby achieving a paradigm shift from "local trial and error" to "global prediction" in cross-warehouse scenarios, systematically reducing operation backtracking and resource internal consumption caused by data silos and dynamic environment uncertainty, and improving the overall fluency and stability of warehousing operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some of the embodiments described in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. In the drawings:

[0075] Figure 1 A flowchart of a method for picking up goods from a warehouse provided in one or more embodiments of this specification;

[0076] Figure 2 A flowchart of the optimal pickup location function provided for one or more embodiments of this specification;

[0077] Figure 3 A flowchart of the optimal cargo placement function provided for one or more embodiments of this specification;

[0078] Figure 4 A sub-flowchart of the optimal cargo placement function provided in one or more embodiments of this specification;

[0079] Figure 5 A schematic diagram of the structure of a warehouse cargo pickup device provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0080] The embodiments of this specification provide a method, device, and medium for picking up goods from a warehouse.

[0081] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0082] Figure 1 This is a flowchart of a method for picking up goods from a warehouse provided in one or more embodiments of this specification. The process can be executed by a warehouse goods picking system. Certain input parameters or intermediate results in the process allow for manual intervention and adjustment to help improve accuracy.

[0083] The method steps of the embodiment of this specification are as follows:

[0084] S101, perform a global search in the warehouse to determine whether there is a storage location for the target product.

[0085] In the embodiments of this specification, a real-time status map of all warehouse locations is established by linking the coding database in the location management system with the IoT sensor network. An optimization algorithm is used to globally scan the location storage information, matching target product IDs with location records, and generating a spatial distribution map of all target locations. Sensor data (such as RFID tag readings) can be integrated to verify the actual inventory status of the locations, ensuring that global search results are consistent with the physical inventory.

[0086] S102: If yes, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information.

[0087] In this embodiment, the storage area ownership information is extracted from the target storage location record and, combined with the storage structure binding relationship table (product-storage area mapping rules), the unlocked storage areas that meet the equipment operation conditions are selected. A priority strategy is used for pre-filtering (e.g., prioritizing storage areas with strong binding to the target product) to generate a candidate set of available storage areas. If no storage location for the target product exists, the process ends.

[0088] S103: Determine a first optimal storage area in the available storage areas, and determine first identification information of the first optimal storage area.

[0089] In the embodiment of this specification, the available storage area can be dynamically scored based on a multi-dimensional optimization algorithm, and the multi-dimensional details are as follows:

[0090] Warehousing structure priority: path topological distance between the warehouse area and the loading and unloading point;

[0091] Inventory level: Prioritize warehouses with moderate inventory levels (avoid fully loaded warehouses that affect replenishment efficiency);

[0092] Equipment coordination: the idle rate of the associated equipment in the warehouse area (such as the number of AGVs on standby in the warehouse area);

[0093] Dynamic weight adjustment: Temporarily increase the weight of specific dimensions based on real-time order types (such as urgent orders).

[0094] The warehouse area with the highest score is output as the first optimal warehouse area, and a unique identification code (warehouse area ID + operation batch number) is generated.

[0095] S104: Determine the location of a first target storage area and shelf information of the first target storage area based on the first identification information.

[0096] In an embodiment of this specification, the warehouse digital twin model can be queried based on the warehouse area identification code to obtain the 3D spatial coordinate boundaries of the warehouse area (such as the warehouse area entrance coordinates, shelf layout matrix), and the shelf configuration table of the warehouse area (shelf load limit, accessibility status) can be loaded synchronously.

[0097] S105: Determine a first shelf location of the target product based on the shelf information of the first target storage area.

[0098] In the embodiment of this specification, the shelf units storing the target product can be screened from the shelf configuration table in the warehouse area, and the shelves with excessive offsets can be eliminated by combining the shelf posture sensor data (such as a laser rangefinder), and finally the shelves that meet the following conditions are locked:

[0099] Operability: The shelf is within the reach of the robotic arm;

[0100] Inventory validity: The shelf inventory status is "accessible" (not frozen or being counted).

[0101] Output the shelf location code (e.g. Area A - Row 3 - Layer 5).

[0102] S106: Pick up the target product based on the first target storage area location and the first shelf location.

[0103] In the embodiment of this specification, the warehouse location and shelf coordinates can be encapsulated into standardized pickup instructions and distributed to the automation equipment control center:

[0104] AGV path planning: Generates a global navigation path based on the warehouse entrance coordinates, dynamically avoiding hot spots where other equipment is operating;

[0105] Collaborative operation of stacker crane: triggering vertical lifting and fork extension and retraction according to shelf layer coordinates;

[0106] Status synchronization update: After the pickup is completed, the inventory level and storage area locking status are updated in real time, and the operation results are fed back to the storage location management system.

[0107] It should be noted that the above content can be found in Figure 2 The optimal pickup location function flow chart is shown.

[0108] It should be noted that this method reconstructs the traditional fragmented warehouse area retrieval, cargo location positioning, and path planning links into a dynamically linked global optimization process by establishing a real-time collaborative decision-making mechanism for cargo locations across the entire warehouse. First, the target cargo location distribution is directly locked through global retrieval to avoid the linear delay of redundant retrieval in each warehouse area. Subsequently, when dynamically screening available warehouse areas based on cargo location information, multi-dimensional parameters such as warehouse area equipment load and path priority are simultaneously integrated, so that the selected "first optimal warehouse area" not only meets the cargo existence condition, but also implies the prediction logic for optimal operation efficiency. Finally, through the collaborative analysis of warehouse area location and shelf information, spatial positioning and resource scheduling are incorporated into the same decision-making cycle, forming a closed-loop constraint for cargo location retrieval and pickup path, thereby realizing a paradigm shift from "local trial and error" to "global prediction" in cross-warehouse area scenarios, systematically reducing operation backtracking and resource internal consumption caused by data silos and dynamic environment uncertainty, and improving the overall smoothness and stability of warehousing operations.

[0109] Furthermore, before determining whether there is a storage location for the target product, it is possible to determine whether there is an unlocked storage area; if so, determine whether the target product has been stored in the unlocked storage area; if not, determine whether the target product has been configured in the unlocked storage area; if not, determine whether there is a storage area configured as empty in the unlocked storage area; if so, determine the second optimal storage area in the storage area configured as empty according to a pre-set priority strategy, and determine the second identification information of the second optimal storage area; determine the second optimal storage area position and the second optimal storage area shelf information based on the second identification information; determine the second shelf position of the target product based on the second optimal storage area shelf information; and release the target product based on the second optimal storage area position and the second shelf position.

[0110] It should be noted that the embodiments of this specification may provide the following specific implementation plans for the above content:

[0111] S1. Determine whether there is an unlocked storage area

[0112] Call the real-time status interface of the warehouse management system to obtain a list of all warehouse lock status (locked / unlocked). Filter and generate a set of unlocked warehouses. If the set is empty, trigger the warehouse unlock negotiation mechanism or terminate the process.

[0113] S2. Determine whether the target product is stored in the unlocked storage area

[0114] Search the inventory database for current inventory records in unlocked storage areas and match them to the target product ID. If a storage record exists, proceed to the existing inventory processing sub-process (such as consolidated storage); if not, proceed to the next step.

[0115] S3. Determine whether the target product has been configured in the unlocked storage area

[0116] Query the inventory area-product configuration relationship table to check whether the unlocked inventory area is configured as a permitted inventory area for the target product. If so, proceed to the Configure Inventory Area Optimization sub-process; if not, proceed to the next step.

[0117] S4. Determine whether there is an empty storage area in the unlocked storage area

[0118] Filter the unlocked warehouses with a configuration status of "Vacant" (i.e., no product type is bound to them). If any exist, generate a candidate set of vacant warehouses; if not, trigger warehouse reconfiguration or manual intervention.

[0119] S5. Priority strategy determines the second best storage area

[0120] Launch a dynamic priority scoring engine to conduct a multi-dimensional assessment of vacant storage areas:

[0121] Path efficiency: the distance between the storage area and the entrance and the path complexity;

[0122] Equipment adaptability: the types of storage devices supported by the warehouse area (such as AGV passage width);

[0123] Future expandability: Reservoir capacity margin and surrounding expansion space.

[0124] The storage area with the highest score is output as the second best storage area, and a unique identification code (such as storage area ID + strategy version) is generated.

[0125] S6. Analyze the second optimal warehouse location and shelf information

[0126] Based on the warehouse area identification code, the warehouse area boundary coordinates, shelf layout rules and load-bearing restrictions are extracted from the warehouse digital twin model to generate a warehouse area operation space map (such as entrance coordinates and shelf height distribution).

[0127] S7. Determine the second shelf location of the target product

[0128] Based on the shelf layout rules, select shelf units that meet the storage conditions of the target products (such as temperature and humidity zones), give priority to the use of middle shelves (taking into account access efficiency and equipment load balancing), and output the shelf location code (such as Zone B - Row 2 - Layer 4).

[0129] Technical relevance: Integrate shelf sensor data (such as temperature and humidity monitoring) to verify storage condition compliance.

[0130] S8. Execute the goods release operation

[0131] The warehouse location and shelf coordinates are packaged into a delivery instruction and sent to the automation equipment control center:

[0132] AGV navigation: Generate a global path based on the warehouse entrance coordinates to avoid dynamic obstacles;

[0133] Shelf positioning: The stacker crane performs precise lifting and fork alignment according to the shelf layer coordinates;

[0134] Inventory update: After the release is completed, the inventory database and warehouse configuration status are updated (from "Vacant" to "Configured with target products").

[0135] It should be noted that this method transforms the inefficient mode of passively waiting for storage area release or random selection in the traditional delivery process into an active planning logic based on storage area status hierarchical judgment and policy-driven by constructing a dynamic allocation and priority prediction mechanism for unlocked storage areas. First, by screening the status of unlocked storage areas layer by layer (target product exists → target product has been configured → configured as empty), the risks of redundant inventory accumulation and invalid storage area occupation are systematically avoided. Then, a priority strategy is introduced to select the second best storage area in the configured empty storage area, so that the selection of the delivery target storage area not only meets the basic space availability but also implies the pre-configuration conditions for future pickup path optimization and minimization of equipment scheduling costs. Finally, through the coordinated binding of storage area location and shelf information, the closed-loop adaptation of delivery operation and storage area resource allocation is realized, thereby proactively avoiding subsequent pickup path conflicts or secondary transfer operations caused by improper storage area allocation during the goods entry stage, forming a collaborative optimization foundation for the delivery and pickup two-way operation chain, and overall improving the dynamic planning capability and operation continuity of the storage space.

[0136] Furthermore, if it is determined that the target product has been stored in the unlocked storage area, the storage area with the largest number of target products among the storage areas where the target products have been stored is determined as the third optimal storage area, and the third identification information of the third optimal storage area is determined; the position of the third optimal storage area and the shelf information of the third optimal storage area are determined based on the third identification information; the third shelf position of the target product is determined based on the shelf information of the third optimal storage area; and the target product is released based on the position of the third optimal storage area and the third shelf position.

[0137] It should be noted that the above content can be found in Figure 3 The functional flow chart of optimal cargo placement is shown.

[0138] It should be noted that this method reconstructs the release logic by prioritizing the aggregation of existing storage areas, transforming the traditional decentralized storage model into a dynamic and optimal product distribution control mechanism. When the target product is detected in an unlocked storage area, the third-best storage area is locked based on the inventory quantity, so that the storage location of the newly added goods automatically converges to the area with the highest current inventory. This not only inherits the clustering characteristics of the existing inventory distribution to reduce product dispersion, but also proactively avoids the subsequent cross-area transfer requirements caused by the decentralized storage of goods by strengthening the resource concentration effect of the inventory-advantaged storage area. At the same time, the release location is accurately located based on the shelf information of the inventory storage area, forming a dynamic coupling of inventory data and new operations, which shifts the utilization mode of storage space from passive filling to active clustering optimization. During the release phase, the shortest connection topology of product distribution and pickup paths is constructed, systematically reducing the risks of path intersections and equipment scheduling conflicts caused by frequent scattered pickups, and achieving a two-way enhancement of storage space self-organization optimization and operational continuity.

[0139] Furthermore, if the target product has been configured in the unlocked warehouse area, according to the priority strategy, a fourth optimal warehouse area is determined in the warehouse area where the target product has been configured, and the fourth identification information of the fourth optimal warehouse area is determined; the fourth optimal warehouse area position and the fourth optimal warehouse area shelf information are determined based on the fourth identification information; the fourth shelf position of the target product is determined based on the fourth optimal warehouse area shelf information; and the target product is released based on the fourth optimal warehouse area position and the fourth shelf position.

[0140] It should be noted that this method upgrades the fixed warehouse allocation mode in the traditional delivery operation to a dynamically adaptive resource allocation logic through a strategic optimization mechanism for configured warehouse areas. When it is detected that the target product has been pre-configured in an unlocked warehouse area, the fourth optimal warehouse area is selected within the existing configured warehouse area based on the priority strategy. This ensures that the storage location of the newly added goods not only inherits the historical configuration parameters to ensure the continuity of the storage strategy, but also achieves pre-matching of warehouse resource allocation with future pickup needs through the composite constraints of the priority strategy (such as path hot zone avoidance, equipment load balancing, and other implicit conditions). By precisely binding warehouse area location and shelf information, the delivery operation is automatically embedded in the optimized topology of the existing warehouse layout, forming a spatial coordinated distribution of new goods and historical inventory. This reduces the risk of configuration conflicts while building a highly aggregated product distribution base for subsequent batch pickups. This systematically avoids path fragmentation and equipment scheduling efficiency degradation caused by configuration discretization, achieving a dual improvement in the dynamic utilization of storage space and the stability of the operation link.

[0141] Furthermore, the priority strategy includes: determining a list of storage areas that meet the requirements; and performing cyclic priority determination on the storage areas in the storage area list in turn to determine the optimal storage area.

[0142] It should be noted that the embodiments of this specification may provide the following specific implementation plans for the above content:

[0143] Step 1: Generate a list of library areas that meet the requirements

[0144] Basic condition filtering: Filter the storage areas that meet the preset hard conditions (such as unlocked status, equipment compatibility, temperature and humidity compliance, etc.) from the entire storage area set to generate an initial candidate set.

[0145] Dynamic constraint injection: Based on the real-time storage status (such as AGV hot zone congestion alarm and stacker crane fault zone marking), inoperable storage areas are eliminated to form a final list of storage areas that meet the requirements.

[0146] Technical association: Call the warehouse attribute database of the storage location management system and the real-time status interface of the Internet of Things to ensure that the candidate set dynamically adapts to the current environment.

[0147] Step 2: Cycle priority determination and optimal storage area selection

[0148] Multi-dimensional scoring model: Each area in the area list is evaluated in the following order:

[0149] Path efficiency: the topological distance and path complexity between the storage area and the current operation point (such as the loading and unloading port);

[0150] Equipment load: The task queue length and expected idle time of the associated equipment (such as stacker);

[0151] Inventory distribution: the concentration of target products within the warehouse area (prioritizing warehouse areas with high inventory to reduce dispersed storage);

[0152] Strategy weighting: Apply preset rules (such as mandatory local allocation for urgent orders) to adjust the scoring weighting.

[0153] Dynamic circulation mechanism: sort the storage areas from high to low according to the scores. If the highest-scoring storage area fails due to an emergency (such as a sensor error), the next best storage area replacement process will be immediately started.

[0154] Identification generation: Generate a unique operation identifier (such as storage area ID + timestamp) for the selected optimal storage area to ensure that subsequent operation links are traceable.

[0155] It should be noted that this method upgrades the static one-way sorting rules in the traditional priority strategy to a multi-factor collaborative decision-making model that adapts to the environmental status by constructing a multi-dimensional dynamic cycle priority judgment mechanism: when determining the optimal warehouse area, the system performs a cyclic iterative priority evaluation on the candidate warehouse area list, so that each judgment process can dynamically integrate multiple implicit constraints such as real-time equipment load, path congestion coefficient, inventory turnover rate, etc., rather than relying on preset fixed weight parameters; through this continuous feedback optimization judgment logic, the system automatically avoids instantaneous resource conflict areas in warehouse area selection, and at the same time guides warehousing operations to a long-term balanced load state, forming a two-way adaptation of the priority strategy and the dynamic environment of the warehouse, thereby realizing autonomous correction and global optimal guidance of warehouse area resource allocation in complex operation scenarios, systematically eliminating the contradictions of local resource overload or equipment idleness caused by priority solidification, and achieving self-organizing optimization and stability leap of the warehousing operation link.

[0156] Furthermore, the priority of the storage areas in the storage area list is determined cyclically. When determining the optimal storage area, it is determined whether the specified storage area in the storage area list is configured with a single product; if so, it is determined whether the warehouse contains other products besides the target product; if so, it is determined whether there is the next storage area in the storage area list; if so, it returns to perform priority determination on the next storage area.

[0157] It should be noted that the embodiments of this specification may provide the following specific implementation plans for the above content:

[0158] Step 1: Verify the uniqueness of the repository configuration

[0159] Call the warehouse area-product configuration relationship table to check the configuration type of the current determined warehouse area:

[0160] If it is configured as a single product (only one specific product is allowed to be stored), the warehouse product diversity check is triggered; if it is configured as mixed storage of multiple products, the subsequent check is skipped and the priority scoring process is directly entered.

[0161] Step 2: Determine the diversity of warehouse products

[0162] Query the entire warehouse inventory distribution database to determine whether there are other product categories besides the target product:

[0163] If there are other products (for example, the warehouse stores three categories of products A, B, and C at the same time), it is necessary to protect the functional purity of the single product configuration warehouse area and force entry into the next warehouse area for judgment; if the warehouse only stores the target product (no other categories), the use of this single configuration warehouse area is allowed.

[0164] Step 3: Iterative control of the library list

[0165] Check whether the current storage area is the last one in the list to be determined:

[0166] If there is a next storage area, terminate the current storage area determination process and return to the priority determination main loop to process the next storage area; if there is no next storage area, trigger the priority policy degradation mechanism (such as relaxing the configuration constraints and regenerating the candidate list).

[0167] It should be noted that this method, by introducing a dual verification mechanism for configuration specificity and product diversity, reconstructs the isolated warehouse area evaluation model in traditional priority determination into a dynamic screening logic guided by global compatibility: when it is detected that a designated warehouse area is configured for a single product, it further verifies whether there are other product categories in the warehouse. If so, it automatically triggers the iterative determination of the warehouse area list, so that the priority decision process not only focuses on the local adaptability of the current warehouse area, but also forcibly couples the warehouse area configuration strategy with the overall product distribution structure of the warehouse. Through this hierarchical judgment rule, the system actively avoids the risk of using dedicated configuration warehouse areas in multi-category warehouses for non-target product storage in warehouse area selection, thereby maintaining the functional purity of specific warehouse areas and guiding the migration of stocking operations to more compatible general warehouse areas, forming a dynamic balance between warehouse area functional isolation and global warehouse category distribution, systematically eliminating inventory management chaos and subsequent sorting efficiency degradation caused by configuration conflicts, and achieving a dual enhancement of clear functional zoning of storage space and reliability of multi-category collaborative storage.

[0168] Furthermore, if the designated storage area in the storage area list is not configured with a single product, or the warehouse does not contain other products besides the target product, determine whether the target product has been configured in the unlocked storage area; if so, determine whether there is an idle storage area; if so, set the designated storage area as the optimal storage area.

[0169] It should be noted that the embodiments of this specification may provide the following specific implementation plans for the above content:

[0170] Step 1: Verify the warehouse configuration type and warehouse product distribution

[0171] Configuration type determination: Query the warehouse area-product configuration table to confirm whether the specified warehouse area is a non-single product configuration (that is, it allows storage of multiple categories or is not bound to a specific product).

[0172] Warehouse product distribution analysis: Call the entire warehouse inventory database to verify whether only the target product (no other categories) exists in the warehouse.

[0173] If the condition is met (the warehouse area is not configured with a single product or there are no other products in the warehouse): proceed to step 2;

[0174] If the condition is not met, the subsequent process is skipped and the decision is made according to the default priority policy.

[0175] Step 2: Check the configuration status of target products in the unlocked storage area

[0176] Filter the list of unlocked storage areas to check whether there is a storage area that has been pre-configured with the target product (that is, the storage area policy allows the storage of this product):

[0177] If a configured library area exists: go to step 3;

[0178] Does not exist: triggers the dynamic configuration or manual allocation process of the warehouse area.

[0179] Step 3: Verify the existence of free storage area

[0180] Idle status definition: Based on the warehouse operation log and equipment status, determine whether the warehouse area is in the "immediately operational" state (no ongoing tasks, no equipment occupancy alarms).

[0181] Free stock area filtering: Extract a subset of free stock areas from the unlocked stock areas of the configured target products.

[0182] If there is an idle storage area: go to step 4;

[0183] Does not exist: triggers equipment scheduling optimization (such as terminating low-priority tasks to release storage areas) or manual intervention.

[0184] Step 4: Set the optimal storage area

[0185] Policy binding: Mark the designated storage area (meeting the conditions in steps 1-3) as the optimal choice for the current operation and generate a unique identification code (such as storage area ID + operation batch).

[0186] Status synchronization: Update the lock status of the storage area to "pre-occupied" to prevent conflicts with other tasks.

[0187] It should be noted that this method upgrades the isolated conditional judgment in traditional warehouse area optimization to an environment-adaptive strategy convergence logic by constructing a dynamic fault-tolerant mechanism that links configuration status with product distribution. When it is detected that the warehouse area configuration is not a single product or the warehouse product structure is uniform, the system automatically triggers a secondary verification process for the unlocked warehouse area. Through the composite verification of the pre-configuration status of the target product and the existence of idle warehouse areas, the warehouse area selection is guided to shift to the most compatible target within the existing resource configuration framework. This judgment logic avoids the operational costs caused by forcibly transforming the existing configuration (such as splitting the mixed storage storage area) while giving priority to reusing idle warehouse area resources that have been adapted to the target product, so that the warehouse area allocation strategy converges autonomously to the minimum intervention path under the constraints of the warehousing environment, thereby achieving a dynamic balance between storage resource utilization and system decision reliability while maintaining the stability of the warehouse area configuration, systematically eliminating the risk of strategy shock and resource configuration fault caused by environmental mutations, and achieving elastic optimization and anti-interference capability enhancement of the warehousing operation chain.

[0188] Furthermore, if the target product is not configured in the unlocked storage area, the method further includes:

[0189] Determine whether there is an empty configuration library area;

[0190] If so, the designated storage area is set as the optimal storage area.

[0191] It should be noted that the above content can be found in Figure 4 The optimal cargo placement function sub-flow chart is shown.

[0192] It should be noted that this method upgrades the inefficient mode of passively waiting for configuration updates in traditional warehouse area allocation to an environmentally adaptive elastic adaptation strategy by constructing a dynamic activation mechanism for vacant resources: when the target product lacks configuration in an unlocked warehouse area, the system prioritizes activating the empty configuration warehouse area as the optimal choice, so that the warehouse area resource allocation automatically opens up independent storage space to avoid conflicts with existing product configurations while maintaining the integrity of existing functional partitions; through the targeted activation of vacant warehouse areas, it avoids operational redundancy caused by forced transformation of mixed storage areas, and establishes exclusive storage units for new products, forming a dynamic balance between the functional purity and expansion flexibility of the warehouse area, thereby achieving rapid convergence of resource allocation strategies in scenarios with sudden changes in storage demand, systematically eliminating the increased inventory management complexity and subsequent sorting path intersection risks caused by forced reuse of non-adaptive warehouse areas, and enhancing the warehousing system's immediate response capability and operational compatibility to unexpected storage needs.

[0193] The technical problem addressed by this invention is optimizing inventory allocation. Specifically, this solution addresses the technical problem of efficiently allocating inventory to different locations to maximize vehicle access and retrieval of goods. This involves determining the optimal access location for each product based on factors such as the warehouse structure's priority, the lock status of the warehouse structure, the product relationships bound to the warehouse structure, and current inventory levels. By optimizing inventory allocation, vehicle transport efficiency can be increased, thereby improving work efficiency. Warehouse managers can also better understand inventory conditions, enabling better inventory management and allocation.

[0194] The technical background of the optimal inventory allocation function stems from the continuous pursuit of warehouse logistics efficiency and the need for optimization. With the increasing complexity of supply chains and the growth of logistics scale, traditional warehousing operations can no longer meet the requirements for efficiency, accuracy, and flexibility. Therefore, to improve warehouse efficiency and reduce human error, the optimal inventory allocation function has been developed and applied. The optimal inventory allocation function aims to achieve more efficient warehouse operations by optimizing the storage and retrieval process of goods. It combines automation technology, data analysis and optimization algorithms, and intelligent control systems. Through precise location coding and identification, automated storage and retrieval equipment can accurately locate and retrieve goods, thereby improving operational accuracy and speed. Sensors and intelligent control systems can monitor the status and inventory of storage locations in real time, and intelligently schedule and optimize based on demand and data. The development of the optimal inventory allocation function also stems from the continuous pursuit of warehouse efficiency and cost reduction. By optimizing inventory allocation, operation time and labor costs can be reduced, and goods turnover and storage space utilization can be improved. This has important implications for the logistics industry and supply chain management, improving overall operational efficiency and customer satisfaction.

[0195] The existing technical solutions are as follows:

[0196] 1. Manual experience and knowledge: Warehouse managers manually determine the optimal storage and retrieval locations based on their experience and knowledge, taking into account factors such as product attributes, inventory requirements, and warehouse layout. This is usually based on their understanding of warehouse operations and product characteristics.

[0197] 2. Cargo attributes and characteristics: Determine the most suitable storage location for storing or removing cargo based on its attributes and characteristics, such as size, weight, and fragility. For example, fragile cargo can be stored in a fragile cargo area, and heavy cargo can be stored in a stronger cargo area.

[0198] 3. Warehouse layout and process optimization: Determine the optimal storage and retrieval locations based on the warehouse layout and operational processes. Consider factors such as the flow of goods, operational efficiency, and worker safety to manually determine the optimal storage and retrieval locations.

[0199] 4. Real-time monitoring and adjustment: By monitoring the inventory status and warehouse operations of the storage locations in real time, manual adjustments and optimizations can be made as needed. For example, based on inventory demand and goods flow, the storage and retrieval order of the storage locations can be manually adjusted or the storage locations of goods can be reallocated.

[0200] To solve the above problems, in practical applications, combining automation and intelligent technologies can further improve the efficiency and accuracy of storage and retrieval. The following functions are provided:

[0201] Storage location management system: Optimal inventory allocation relies on advanced storage location management systems. These systems enable precise inventory management and control by coding, identifying, and tracking storage locations.

[0202] Automated warehousing equipment: To achieve optimal inventory allocation, automated warehousing equipment such as stackers and automated guided vehicles is often used. These devices can automatically store and retrieve goods between locations based on inventory requirements and operational instructions.

[0203] Sensors and IoT technologies: Utilizing sensors and IoT technologies, inventory levels and status can be monitored in real time. Sensor data collection and IoT connectivity provide accurate location information, supporting optimal inventory allocation decisions and operations.

[0204] Human-machine interface and intelligent control: To facilitate operation and management, optimal inventory allocation functions often feature a human-machine interface and intelligent control. Through the intuitive interface and intelligent control system, operators can easily access and store goods, and monitor and manage the status of goods in real time.

[0205] Data Analysis and Optimization Algorithms: Optimal inventory allocation relies on data analysis and optimization algorithms for decision-making and optimization. By configuring and analyzing the priority of storage structures, the lock status of storage structures, the product relationships bound to storage structures, and current inventory levels, we can determine the optimal storage and retrieval strategy, improve storage and retrieval efficiency, and reduce operating costs.

[0206] Manually determining optimal inventory allocation relies on the experience and judgment of warehouse managers, allowing for flexible adjustments and decisions based on specific cargo attributes and warehouse conditions. However, this approach can be limited by subjectivity, human error, and time costs.

[0207] Optimal inventory allocation technology eliminates the variability and inconsistencies that can occur in manual processes, addresses human errors, and reduces duplication. It ensures tasks are consistently performed according to predetermined standards, improving the quality, reliability, and efficiency of output, and can be easily scaled up or down to adapt to changing business needs.

[0208] Figure 5 A schematic diagram of a warehouse cargo pickup device provided for one or more embodiments of this specification includes:

[0209] at least one processor; and,

[0210] a memory communicatively connected to the at least one processor; wherein,

[0211] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0212] Perform a global search in the warehouse to determine whether the target product is stored in the warehouse;

[0213] If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information;

[0214] Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area;

[0215] Determine the first target storage area location and the first target storage area shelf information based on the first identification information;

[0216] Determine a first shelf location of the target product based on the shelf information of the first target storage area;

[0217] The target product is picked up based on the first target warehouse location and the first shelf location.

[0218] One or more embodiments of this specification provide a non-volatile computer storage medium storing computer-executable instructions. When executed by a computer, the computer-executable instructions can achieve:

[0219] Perform a global search in the warehouse to determine whether the target product is stored in the warehouse;

[0220] If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information;

[0221] Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area;

[0222] Determine the first target storage area location and the first target storage area shelf information based on the first identification information;

[0223] Determine a first shelf location of the target product based on the shelf information of the first target storage area;

[0224] The target product is picked up based on the first target warehouse location and the first shelf location.

[0225] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0226] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. In particular, the device embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0227] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0228] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0229] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0230] In addition, the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above units may be implemented in the form of hardware or software.

[0231] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0232] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for picking up goods from a warehouse, characterized in that: include: Perform a global search in the warehouse to determine whether the target product is stored in the warehouse; If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information; Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area; Determine the first target storage area location and the first target storage area shelf information based on the first identification information; Determine a first shelf location of the target product based on the shelf information of the first target storage area; The target product is picked up based on the first target warehouse location and the first shelf location.

2. The method according to claim 1, characterized in that Before determining whether a cargo location for the target product is stored, the method further includes: Determine whether there is an unlocked storage area; If so, determining whether the target product is already stored in the unlocked storage area; If not, determining whether the target product has been configured in the unlocked storage area; If not, determine whether there is a storage area configured as empty in the unlocked storage area; If so, determining a second optimal storage area in the storage areas configured as empty according to a preset priority strategy, and determining second identification information of the second optimal storage area; Determine the second optimal storage area location and the second optimal storage area shelf information based on the second identification information; Determine a second shelf location of the target product based on the second optimal storage area shelf information; The target product is released based on the second optimal warehouse location and the second shelf location.

3. The method according to claim 2, characterized in that If it is determined that the target product is already stored in the unlocked storage area, the method further includes: Determine, among the storage areas where the target product is stored, a storage area with the largest amount of the target product as a third optimal storage area, and determine third identification information of the third optimal storage area; Determine the third optimal storage area location and the third optimal storage area shelf information based on the third identification information; Determine a third shelf location of the target product based on the third optimal storage area shelf information; The target product is released based on the third optimal warehouse area position and the third shelf position.

4. The method according to claim 2, characterized in that If the target product has been configured in the unlocked storage area, the method further includes: Determining, according to the priority strategy, a fourth optimal storage area among the storage areas where the target product has been configured, and determining fourth identification information of the fourth optimal storage area; Determine the fourth optimal storage area location and the fourth optimal storage area shelf information based on the fourth identification information; Determining a fourth shelf location of the target product based on the fourth optimal storage area shelf information; The target product is released based on the fourth optimal warehouse area position and the fourth shelf position.

5. The method according to claim 2, characterized in that The priority strategy includes: Determine the list of storage areas that meet the requirements; The storage areas in the storage area list are prioritized in turn to determine the optimal storage area.

6. The method according to claim 5, characterized in that The step of performing cyclic priority determination on the storage areas in the storage area list to determine the optimal storage area includes: Determine whether a designated storage area in the storage area list is configured with a single product; If so, determine whether the warehouse contains other products besides the target product; If so, determine whether there is a next storage area in the storage area list; If so, return to determine the priority of the next storage area.

7. The method according to claim 6, characterized in that If the designated storage area in the storage area list does not have a single product configured, or the warehouse does not contain any products other than the target product, the method further includes: Determine whether the target product has been configured in the unlocked storage area; If so, determine whether there is an idle storage area; If so, the designated storage area is set as the optimal storage area.

8. The method according to claim 7, characterized in that If the target product is not configured in the unlocked storage area, the method further includes: Determine whether there is an empty configuration library area; If so, the designated storage area is set as the optimal storage area.

9. A device for picking up goods from a warehouse, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Perform a global search in the warehouse to determine whether the target product is stored in the warehouse; If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information; Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area; Determine the first target storage area location and the first target storage area shelf information based on the first identification information; Determine a first shelf location of the target product based on the shelf information of the first target storage area; The target product is picked up based on the first target warehouse location and the first shelf location.

10. A non-volatile computer storage medium, characterized in that The computer-executable instructions are stored, and when the computer-executable instructions are executed by a computer, they can achieve: Perform a global search in the warehouse to determine whether the target product is stored in the warehouse; If so, determine the cargo location information corresponding to the target product, and obtain the available storage area based on the cargo location information; Determining a first optimal storage area in the available storage areas, and determining first identification information of the first optimal storage area; Determine the first target storage area location and the first target storage area shelf information based on the first identification information; Determine a first shelf location of the target product based on the shelf information of the first target storage area; The target product is picked up based on the first target warehouse location and the first shelf location.

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