A method, apparatus, computer equipment, and storage medium for determining target warehouse locations.

Abstract

This invention relates to the field of computer technology and discloses a method, apparatus, computer equipment, and storage medium for determining target warehouse locations. The method includes: upon receiving order information for target goods, parsing the order information to obtain order attributes; using a global fuzzy search algorithm to initially retrieve the warehouse area corresponding to the target goods from the database based on the order attributes and preset weights; using a dynamic programming algorithm to perform a secondary search within the warehouse area based on the priority order corresponding to the preset weights to determine the target warehouse area; upon detecting that a vehicle has arrived at a parking space in the target warehouse area, receiving a confirmation operation sent by the user through a terminal device, and generating a delivery signal using an edge computing algorithm; using a dynamic programming algorithm to determine the target warehouse location corresponding to the target goods, and associating the target warehouse location with the delivery signal to lock the warehouse location. This invention employs multiple intelligent algorithms in combination to achieve rapid warehouse location locking, improve scheduling efficiency, reduce server pressure, and reduce operation and maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and specifically to a method, apparatus, computer equipment, and storage medium for determining target warehouse locations. Background Technology

[0002] Currently, the scale of alcohol production is gradually expanding. After receiving customer orders, the sales department confirms order information such as product type, quantity, and delivery date. The production department formulates an outbound plan based on the order information, arranges production lines to produce products, and after the products are produced, they are packaged, inspected for quality, outbound, and transported to complete the product outbound process.

[0003] With the rapid development of technology, automated outbound delivery has gradually replaced manual outbound delivery. However, how outbound vehicles can find suitable warehouse locations for picking up and leaving the warehouse, and improve scheduling efficiency, is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, computer equipment and storage medium for determining target warehouse locations, in order to solve the problem of determining suitable warehouse locations for outbound vehicles for picking up and shipping goods.

[0005] In a first aspect, the present invention provides a method for determining a target position, the method comprising:

[0006] When order information for the target goods is received, the order information is parsed to obtain the order attributes;

[0007] The global fuzzy search algorithm is used to retrieve the warehouse area corresponding to the target goods in the database for the first time based on order attributes and pre-set weights;

[0008] The target storage area is determined by performing a secondary search in the storage area based on the priority order corresponding to the pre-set weights using a dynamic programming algorithm.

[0009] Once the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the system receives the confirmation from the user via the terminal device, uses edge computing algorithms to lock the target warehouse space, generates a delivery signal, and associates the target warehouse space with the delivery signal.

[0010] The optimal equipment scheduling path is determined by using a dynamic programming algorithm based on the target warehouse location and the delivery signal.

[0011] This invention parses order information of target goods to obtain order attributes, and then uses a global fuzzy search algorithm to perform an initial retrieval in the database based on order attributes and pre-set weights, aiming to improve retrieval efficiency. It utilizes a dynamic programming algorithm combined with the priority order of weights to quickly determine the target warehouse area, adapting to the dynamic state of the warehouse during the retrieval process. An edge computing algorithm generates a delivery signal after the vehicle arrives at the parking space in the target warehouse area, and the optimal equipment scheduling path is determined by combining this with the dynamic programming algorithm. Multiple intelligent algorithms work together to quickly lock in warehouse locations, improve scheduling efficiency, reduce server pressure, and lower operation and maintenance costs.

[0012] In one optional implementation, a global fuzzy search algorithm is used to initially retrieve the warehouse area corresponding to the target goods from the database based on order attributes and pre-set weights, including:

[0013] Determine the query conditions corresponding to the order attributes, and use pre-set weights to calculate the matching degree between each storage area in the database and the query conditions;

[0014] The warehouse areas are sorted according to their matching degree, and the warehouse area corresponding to the target goods is determined according to the sorting results.

[0015] This invention utilizes a global fuzzy search algorithm to quickly locate results matching the order attribute query conditions in a massive database, calculates the matching degree between each storage area and the query conditions, and allows for a certain degree of imprecise matching to improve the flexibility and inclusiveness of the search. The storage area is determined according to the ranking results of the matching degree to achieve automatic determination of the storage area and improve the efficiency of the algorithm.

[0016] In one optional implementation, a dynamic programming algorithm is used to perform a secondary search in the database area according to the priority order corresponding to pre-set weights to determine the target database area, including:

[0017] The allocation status of the target goods is represented by state variables, which include allocated and unallocated.

[0018] With all goods unallocated as the initial state, a state transition equation is constructed based on the priority order corresponding to the state variables and pre-set weights.

[0019] The optimal solution to the state transition equation is obtained by using dynamic programming algorithm, and the reservoir area corresponding to the optimal solution of the state transition equation is determined as the candidate target reservoir area;

[0020] If the warehouse currently receiving outbound goods does not require restocking, then the alternative target warehouse area will be designated as the target warehouse area.

[0021] If the warehouse currently handling outbound shipments needs to prepare inventory, then the candidate target warehouse area will be designated as the advance inventory preparation warehouse area.

[0022] The edge computing algorithm is used to access the nearest server to obtain the production batch of the product leaving the warehouse, and to determine whether the product leaving the warehouse is the last batch or the whole batch based on the production batch.

[0023] If the product shipment is a last-minute shipment, then the AGV warehouse will be designated as the target warehouse area for the product shipment.

[0024] If the product is shipped out in whole pallets, then the vertical warehouse will be designated as the target warehouse area for the product shipment.

[0025] This invention utilizes a dynamic programming algorithm to construct a state transition equation based on the priority of the state variables and weights of goods, and solves the optimal solution of the state transition equation to optimize decision-making and improve decision quality. Based on the optimal solution, target warehouse areas are determined to flexibly adapt to business needs. Warehouses that do not require stockpiling are directly designated as target warehouse areas to facilitate the recommendation of warehouse locations. Warehouse areas that require stockpiling are designated as pre-stocking warehouse areas so that products can be shipped out after stockpiling is completed. By determining whether the product shipment is a final pallet or a full pallet shipment based on the production batch, the available warehouse areas for product shipment are determined under different circumstances to ensure warehouse availability and enhance scalability.

[0026] In one optional implementation, upon detecting that a vehicle has arrived at the parking space corresponding to the target warehouse area, a confirmation operation sent by the user via a terminal device is received, and the target warehouse space is located using an edge computing algorithm, generating a delivery signal, including:

[0027] Once a vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the RFID reader identifies the vehicle information, platform information, and terminal equipment, and receives confirmation from the user via the terminal equipment.

[0028] Select the warehouse locations in the target warehouse area that match the warehouse location selection requirements and determine them as the target warehouse locations;

[0029] If the product type is simple and the warehouse location selection requirement is to prioritize single-depth warehouses, then the available single-depth warehouses in the target warehouse area will be selected as the target warehouses.

[0030] If the product types are not uniform, the warehouse location selection requirement is to prioritize double-deep warehouse locations. The available double-deep warehouse locations in the target warehouse area are identified as target warehouse locations, and the warehouse locations of the target warehouse locations are determined according to the pre-obtained warehouse location recommendation rules. The warehouse location recommendation rules for double-deep warehouse locations prioritize shallow warehouse locations.

[0031] Using edge computing algorithms, based on vehicle information, platform information, and user confirmation, and combined with warehouse location determination logic, the system locks the target warehouse location and generates a delivery signal containing vehicle location information and specified platform information.

[0032] This invention uses vehicle information, platform information, and terminal equipment read by RFID readers to select target warehouses from the target warehouse area based on product type. It prioritizes recommending suitable single-deep or double-deep warehouses under different circumstances, thereby improving warehouse utilization. It utilizes edge computing algorithms to generate shipping signals to respond promptly to shipping demands. The timely preparation of shipping signal generation strategies by edge computing devices optimizes operational processes, adapts to various business scenarios, and reduces the access and computational pressure on the top-level server by accessing the nearest server, thus reducing the risk of top-level server crashes and improving the overall operating efficiency of the algorithm.

[0033] In one optional implementation, the target position and the delivery signal are associated to lock the position, including:

[0034] Associate the target position with the delivery signal, and then change the status of the target position after associating the delivery signal to unavailable.

[0035] This invention updates the status of the warehouse after associating the target warehouse location with the delivery signal, thereby locking the warehouse location to prevent inventory chaos, prevent scheduling chaos, and improve operational efficiency.

[0036] In one optional implementation, a dynamic programming algorithm is used to determine the optimal equipment scheduling path based on the target warehouse location and the delivery signal, including:

[0037] The objective function is to minimize the distance between the target goods and the target warehouse location.

[0038] The objective function is to minimize the number of tasks currently being executed by the device.

[0039] The boundary conditions are product type, target warehouse coordinates, target warehouse type, equipment location, and equipment operation path.

[0040] The optimal solution of the objective function under boundary conditions is obtained by recursively solving the dynamic programming algorithm, and the optimal equipment scheduling path is determined based on the optimal solution of the objective function.

[0041] This invention employs a dynamic programming algorithm to perform dynamic programming, taking into account the distance between the target goods and the target warehouse, as well as the product type, target warehouse coordinates, target warehouse type, equipment location, and equipment operation path. It solves for the optimal solution of the objective function under boundary conditions, determines the optimal equipment scheduling path, and adapts to the changing state of tasks and equipment in dynamic programming. This enhances the flexibility and optimality of equipment scheduling, improves equipment scheduling efficiency, and reduces equipment operating costs.

[0042] In one alternative implementation, the method further includes:

[0043] Obtain advance stocking tasks and create picking and pallet handling tasks based on them;

[0044] Based on picking and palletizing tasks and full pallet handling tasks, control the transportation of products from the pre-stocked warehouse to the AGV buffer zone.

[0045] This invention addresses the outbound shipment of products requiring pre-stocking by controlling the transportation of products to the AGV buffer zone based on created picking and pallet handling tasks, thus facilitating the outbound shipment of products that need to be prepared in advance.

[0046] In a second aspect, the present invention provides a target warehouse location determination device, the device comprising:

[0047] The parsing module is used to parse the order information to obtain the order attributes when the order information of the target goods is received.

[0048] The retrieval module is used to retrieve the warehouse area corresponding to the target goods in the database for the first time based on the order attributes and pre-set weights using a global fuzzy search algorithm.

[0049] The determination module is used to perform a secondary search in the reservoir area based on the priority order corresponding to the pre-set weights using a dynamic programming algorithm to determine the target reservoir area;

[0050] The association module is used to receive the confirmation operation sent by the user through the terminal device when the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, and to use the edge computing algorithm to lock the target warehouse space, generate a delivery signal, and associate the target warehouse space with the delivery signal.

[0051] The scheduling module is used to determine the optimal equipment scheduling path based on the target warehouse location and the delivery signal using a dynamic programming algorithm.

[0052] Thirdly, the present invention provides a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the target warehouse location determination method described in the first aspect or any corresponding embodiment thereof.

[0053] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the target warehouse location determination method described in the first aspect or any corresponding embodiment thereof.

[0054] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the target warehouse location determination method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0056] Figure 1 This is a flowchart illustrating the target warehouse location determination method according to an embodiment of the present invention;

[0057] Figure 2 This is a flowchart illustrating another method for determining target warehouse locations according to an embodiment of the present invention;

[0058] Figure 3 This is a flowchart illustrating another method for determining target warehouse locations according to an embodiment of the present invention;

[0059] Figure 4 This is a flowchart illustrating another method for determining target warehouse locations according to an embodiment of the present invention;

[0060] Figure 5 This is a schematic diagram illustrating the application of the target warehouse location determination method according to an embodiment of the present invention;

[0061] Figure 6 This is a structural block diagram of a target warehouse location determination device according to an embodiment of the present invention;

[0062] Figure 7 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] According to an embodiment of the present invention, a method for determining a target warehouse location is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0065] This embodiment provides a method for determining target positions. Figure 1This is a flowchart of a target position determination method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0066] Step S101: When the order information for the target goods is received, the order information is parsed to obtain the order attributes.

[0067] In this embodiment of the invention, a customer places an order for alcoholic beverages through a sales platform or other means. The target goods (i.e., goods to be shipped) are the alcoholic beverages ordered by the user. The order information for the target goods includes the type, quantity, and delivery date of the alcoholic beverages. Upon receiving the order information for the target goods, the order information is extracted by text extraction, and keyword extraction technology is used to extract keywords from the order information in order to parse the order information and obtain order attributes. The order attributes include, but are not limited to, order number, customer information, delivery method, and order remarks.

[0068] Step S102: Using a global fuzzy search algorithm, the warehouse area corresponding to the target goods is retrieved from the database for the first time based on the order attributes and pre-set weights.

[0069] In this embodiment of the invention, different orders are assigned weights based on their order attributes. For example, if the priority of customers of type A is higher than that of customers of type B, then orders of customers of type A are retrieved first. A global fuzzy search algorithm is used to perform an initial warehouse area search in the database, that is, to retrieve which warehouse area the goods are located in and perform fuzzy calculations, in order to improve search efficiency and quickly locate the warehouse area.

[0070] Step S103: Use dynamic programming algorithm to perform a secondary search in the storage area according to the priority order corresponding to the pre-set weights to determine the target storage area.

[0071] In this embodiment of the invention, during the warehouse area retrieval process, the entire warehouse is dynamic and not static. Therefore, dynamic programming algorithm is used for dynamic programming. The warehouse area is retrieved a second time according to the priority order of the pre-set weights. The available warehouse area where the goods are located is quickly calculated using dynamic programming algorithm. The available warehouse area is determined as the target warehouse area. At the same time, the state of each warehouse area is determined during the dynamic programming process. The state of the warehouse area is divided into two states: "with goods" and "without goods".

[0072] Step S104: When the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the system receives the confirmation operation sent by the user through the terminal device, uses the edge computing algorithm to lock the target warehouse space, generates a delivery signal, and associates the target warehouse space with the delivery signal.

[0073] In this embodiment of the invention, sensors are installed at each parking space in the storage area. When a vehicle is detected to have arrived at a parking space in the target storage area, operators or other users use terminal devices to confirm. Upon receiving the confirmation from the user via the terminal device, the edge computing algorithm generates a delivery signal. This delivery signal includes order information, vehicle information, platform information, and nearby equipment information to access the nearest server.

[0074] Edge computing algorithms are used to quickly calculate the specific warehouse location of the target goods, associate the target warehouse location with the delivery signal, and lock the warehouse location. Once the warehouse location is locked, it can only be used for the outbound delivery of the goods corresponding to that delivery signal, and no other scheduling is performed, thereby preventing the confusion between inventory information and scheduling information.

[0075] During the warehouse location determination process, since the nearest server is accessed, the data access speed and calculation speed are greatly increased. At the same time, since it is not necessary to access the top-level server for every outbound shipment, the computing pressure on the top-level server is greatly reduced, the probability of the top-level server crashing is reduced, and the operation and maintenance costs are reduced.

[0076] Step S105: Use dynamic programming algorithm to determine the optimal equipment scheduling path based on the target warehouse location and delivery signal.

[0077] In this embodiment of the invention, a dynamic programming algorithm is used to calculate the shortest path or the lowest cost path between the target warehouse and the warehouse corresponding to the delivery signal, etc. In the case of multiple tasks in parallel, the dynamic programming algorithm comprehensively considers the load, location and task execution of each device to achieve optimal device scheduling, ensure the operation process, flexibly adapt to the warehouse environment and improve efficiency.

[0078] The target warehouse location determination method provided in this embodiment obtains order attributes by parsing the order information of the target goods. It then uses a global fuzzy search algorithm to perform an initial search in the database based on the order attributes and pre-set weights, aiming to improve search efficiency. The method utilizes a dynamic programming algorithm combined with the priority order of weights to quickly determine the target warehouse area, adapting to the dynamic state of the warehouse during the search process. An edge computing algorithm is used to generate a delivery signal after the vehicle arrives at the parking space in the target warehouse area. Combined with the dynamic programming algorithm, the optimal equipment scheduling path is determined. By employing multiple intelligent algorithms in combination, the method can quickly lock the warehouse location, improve scheduling efficiency, reduce server pressure, and reduce operation and maintenance costs.

[0079] This embodiment provides a method for determining target positions. Figure 2 This is a flowchart of a target position determination method according to an embodiment of the present invention, such as... Figure 2 As shown, the method specifically includes the following steps:

[0080] Step S201: When the order information for the target goods is received, the order information is parsed to obtain the order attributes.

[0081] Please see details Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0082] Step S202: Using a global fuzzy search algorithm, the warehouse area corresponding to the target goods is retrieved from the database for the first time based on the order attributes and pre-set weights.

[0083] Specifically, step S202 includes:

[0084] Step S2021: Determine the query conditions corresponding to the order attributes, and use the pre-set weights to calculate the matching degree between each warehouse area in the database and the query conditions.

[0085] Step S2022: Sort the warehouse areas according to the matching degree, and determine the warehouse area corresponding to the target goods according to the sorting results.

[0086] In this embodiment of the invention, query conditions for order attributes obtained by parsing order information are determined, such as the category, volume, and barcode of alcoholic beverages. Based on outbound requirements, weights for each attribute are pre-set; for example, alcoholic beverages requiring priority outbound have higher weights. Using a global fuzzy search algorithm, combined with the query conditions and pre-set weights, the matching degree between each warehouse area in the database and the query conditions is calculated, and the warehouse areas are sorted in descending order of matching degree, prioritizing those with high matching degrees as the warehouse areas corresponding to the target goods. The global fuzzy search algorithm allows for a certain degree of imprecise matching, thereby improving the flexibility and inclusiveness of the search.

[0087] The global fuzzy search algorithm is used to quickly locate results that match the order attribute query conditions in a large database. The matching degree of each warehouse area and query conditions is calculated. The global fuzzy search algorithm allows a certain degree of imprecise matching to improve the flexibility and inclusiveness of the search. The warehouse area is determined according to the ranking results of the matching degree to achieve automatic warehouse area determination and improve the efficiency of the algorithm.

[0088] Step S203: Use dynamic programming algorithm to perform a secondary search in the database area according to the priority order corresponding to the pre-set weights to determine the target database area.

[0089] Specifically, step S203 includes:

[0090] Step S2031: Use state variables to represent the allocation status of the target goods. The allocation status includes allocated and unallocated.

[0091] Step S2032: With all goods unallocated as the initial state, construct the state transition equation according to the priority order corresponding to the state variables and the pre-set weights.

[0092] Step S2033: Solve the optimal solution of the state transition equation using the dynamic programming algorithm, and determine the reservoir area corresponding to the optimal solution of the state transition equation as the candidate target reservoir area.

[0093] Step S2034: If the warehouse currently issuing goods does not need to prepare goods, then the alternative target warehouse area is determined as the target warehouse area.

[0094] Step S2035: If the warehouse currently issuing goods needs to prepare goods, then the alternative target warehouse area is determined as the warehouse area for advance preparation.

[0095] Step S2036: Use the edge computing algorithm to access the nearest server, obtain the production batch of the product leaving the warehouse, and determine whether the product leaving the warehouse is the last batch or the whole batch based on the production batch.

[0096] Step S2037: If the product outbound is a last-minute shipment, then the AGV warehouse is determined as the target warehouse area for product outbound.

[0097] Step S2038: If the product outbound is a whole pallet outbound, then the vertical warehouse is determined as the target warehouse area for the product outbound.

[0098] In this embodiment of the invention, when performing a secondary retrieval in the warehouse area using a dynamic programming algorithm, a state variable is defined to represent the allocation status of the target goods. The number 1 can be used to represent that the goods have been allocated, and the number 0 can be used to represent that the goods have not been allocated. This is only an example and is not intended to be a limitation.

[0099] The initial state is set to unallocated goods, meaning all goods have a state variable of 0. Based on the probability of allocating goods to a specific warehouse area under each state, a state transition equation is constructed using pre-defined weights and priorities. An evaluation function is then used to assess this equation. Using a bottom-up or top-down approach with dynamic programming, the optimal path from the initial state to the final state is calculated—the optimal solution from unallocated goods to fully allocated goods. The optimal solution obtained through dynamic programming determines the corresponding warehouse area, which is the area where the goods should be allocated, thus identifying potential target warehouse areas.

[0100] If the warehouse currently handling outbound shipments does not require stock preparation and can proceed directly, then that warehouse is designated as the target warehouse area. Warehouse 1 is the target warehouse. After determining the target warehouse, it is determined whether there are loading sequence requirements. If there are loading sequence requirements, wave consolidation and release are performed based on product materials and product batches. If there are no loading sequence requirements, outbound operations can proceed directly. Warehouses 2, 3, and 4 are non-target warehouse areas, meaning stock preparation is required in advance, and outbound shipments can only proceed after stock preparation is complete.

[0101] Warehouses that do not require stocking are designated as target warehouse areas so that warehouse locations can be directly recommended. Warehouses that require stocking are designated as advance stocking warehouse areas so that products can be shipped out after stocking is completed.

[0102] Edge computing algorithms are used to access the nearest server to obtain the production batch of the products to be shipped. Based on the quantity of products, the currently available warehouse for product shipment is determined. If the product shipment is a last-pallet shipment (i.e., not a full pallet), the depalletizing station (AGV warehouse) is prioritized. If the product shipment is a full pallet shipment (i.e., a complete pallet), the vertical warehouse is prioritized. If the vertical warehouse cannot handle the shipment, then the AGV warehouse is considered.

[0103] Before determining the current available warehouse, photoelectric switches can be used to detect whether the warehouse is full of goods, that is, whether there are empty spaces for the product. If there are no empty spaces in the warehouse, the product is directly determined to be an unavailable product.

[0104] In some alternative implementations, after identifying the candidate target warehouse area as the advance stocking warehouse area, the method further includes:

[0105] Step Sa involves obtaining advance stock preparation tasks and creating picking and packing tasks and full pallet handling tasks based on these tasks.

[0106] Step Sb involves controlling the transportation of products from the pre-stocked warehouse to the AGV buffer zone based on picking and pallet handling tasks.

[0107] In this embodiment of the invention, if the warehouse is a pre-stocked warehouse, the delivery note or vehicle information is obtained, a pre-stocked task is created, a picking task is created based on the pre-stocked task, a full pallet handling task is created, and the products of the pre-stocked warehouse, namely the products of warehouses 2, 3 and 4, are transported to the AGV (Automated Guided Vehicle) buffer zone.

[0108] For products that need to be stocked in advance, the system controls the transportation of products to the AGV buffer zone based on the created picking and pallet handling tasks, so that products that need to be stocked in advance can be shipped out.

[0109] Step S204: When the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the system receives the confirmation operation sent by the user through the terminal device, uses the edge computing algorithm to lock the target warehouse space, generates a delivery signal, and associates the target warehouse space with the delivery signal.

[0110] Please see details Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0111] Step S205: Use dynamic programming algorithm to determine the optimal equipment scheduling path based on the target warehouse location and delivery signal.

[0112] Please see details Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0113] The target warehouse location determination method provided in this embodiment utilizes a dynamic programming algorithm to construct a state transition equation based on the priority of the cargo's state variables and weights, and solves the optimal solution of the state transition equation to optimize decision-making and improve decision quality. Based on the optimal solution, target warehouse areas are determined to flexibly adapt to business needs. Warehouses that do not require stockpiling are directly designated as target warehouse areas to facilitate warehouse location recommendations. Warehouse areas requiring stockpiling are designated as pre-stocking warehouse areas so that product shipments can be carried out after stockpiling is completed. By determining whether the product shipment is a final pallet or a full pallet based on the production batch, available warehouse areas for product shipments are determined under different circumstances to ensure warehouse availability and enhance scalability.

[0114] This embodiment provides a method for determining target positions. Figure 3 This is a flowchart of a target position determination method according to an embodiment of the present invention, such as... Figure 3 As shown, the method specifically includes the following steps:

[0115] Step S301: When the order information for the target goods is received, the order information is parsed to obtain the order attributes.

[0116] Please see details Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0117] Step S302: Using a global fuzzy search algorithm, the warehouse area corresponding to the target goods is retrieved from the database for the first time based on the order attributes and pre-set weights.

[0118] Please see details Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0119] Step S303: Using a dynamic programming algorithm, a secondary search is performed in the storage area according to the priority order corresponding to the pre-set weights to determine the target storage area.

[0120] Please see details Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0121] Step S304: When the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the system receives the confirmation operation sent by the user through the terminal device, uses the edge computing algorithm to lock the target warehouse space, generates a delivery signal, and associates the target warehouse space with the delivery signal.

[0122] Specifically, in step S304 above, after detecting that a vehicle has arrived at the parking space corresponding to the target warehouse area, receiving the confirmation operation sent by the user through the terminal device, and using edge computing algorithms to lock the target warehouse space and generate a delivery signal includes:

[0123] Step S3041: When the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the vehicle information, platform information and terminal equipment are identified by the RFID reader, and the confirmation operation sent by the user through the terminal equipment is received.

[0124] Step S3042: Select the warehouse location that matches the warehouse location selection requirement from the warehouse locations in the target warehouse area and determine it as the target warehouse location.

[0125] Step S3043: If the product type is simple and the warehouse location selection requirement is to prioritize single-depth warehouse locations, then the available single-depth warehouse locations in the target warehouse area will be determined as the target warehouse locations.

[0126] Step S3044: If the product types are not uniform, the storage location selection requirement is to prioritize double-deep storage locations. The available double-deep storage locations in the target storage area are determined as target storage locations, and the storage location of the target storage location is determined according to the pre-obtained storage location recommendation rules for double-deep storage locations.

[0127] Step S3045: Using an edge computing algorithm, based on vehicle information, platform information, and confirmation operations sent by the user, and combined with the warehouse location determination logic, the target warehouse location is locked, and a delivery signal containing vehicle location information and specified platform information is generated.

[0128] In this embodiment of the invention, RFID (Radio Frequency Identification) readers are pre-installed at parking spaces in the warehouse area, and RFID tags are installed on vehicles. The RFID readers read the RFID tags of the vehicles to identify vehicle information, platform information, and terminal equipment, thereby determining the goods that need to be pre-loaded on the vehicle and confirming the platform position where the vehicle should park, ensuring that the vehicle is parked in the correct position.

[0129] Specifically, the storage location recommendation rule for double-deep storage is to prioritize shallow storage locations. After determining the target storage area, select a suitable storage location from the available locations within that area to store the products. Obtain the types of products currently being shipped to determine if the product types are uniform. If the product types are uniform, single-deep storage locations are preferred, storing the same type of product. If the product types are not uniform (two or more types), double-deep storage locations are preferred, storing different types of products.

[0130] For products with a single type, the mechanical structure of a single-deep storage unit is relatively simple, and the stacking height of a single-deep storage unit is relatively low, making operation simpler, safer, and easier to control.

[0131] For products with multiple types, double-deep storage units have a higher storage density than single-deep storage units. With the same storage area, double-deep storage units can store more products than single-deep storage units. Furthermore, during product transportation, different products can be retrieved and moved to the same storage unit simultaneously, effectively improving the efficiency of product outbound operations.

[0132] If the available double-deep storage space in the target warehouse area is determined as the target storage space, then according to the storage space recommendation rules for double-deep storage spaces, shallow storage spaces will be recommended first, followed by deep storage spaces.

[0133] If the current available positions in the database are determined to be the target positions, then the recommended positions can be determined directly based on the position selection requirements.

[0134] After operators and other users confirm the information using the terminal user, the edge computing algorithm is used to analyze the vehicle information, platform information, and the confirmation operation sent by the user. Combined with the current warehouse operation status, a delivery signal is quickly generated. The delivery signal includes the vehicle location information and the specified platform information, and the delivery signal is sent out to carry out the operation.

[0135] Based on vehicle information, platform information, and terminal equipment read by RFID readers, and according to product type, the system selects target warehouses from the warehouse locations that match the warehouse selection requirements. This prioritizes recommending reasonable single-deep or double-deep warehouses under different circumstances, thereby improving warehouse utilization. Edge computing algorithms are used to generate shipping signals to respond to shipping needs in a timely manner. The timely preparation of shipping signal generation strategies by edge computing devices optimizes the operation process, adapts to various business scenarios, and reduces the access and computing pressure on the top-level server by accessing the nearest server, thereby reducing the risk of top-level server crashes and improving the overall operating efficiency of the algorithm.

[0136] Specifically, the association between the target warehouse location and the delivery signal in step S304 above includes:

[0137] Step S3046: Associate the target warehouse location with the delivery signal, and change the status of the target warehouse location after associating with the delivery signal to an unavailable status.

[0138] In this embodiment of the invention, the target warehouse location and the delivery signal are associated. Based on the information carried by the delivery signal, including but not limited to vehicle location information and designated platform information, after the target warehouse location and the delivery signal are associated, the warehouse location is locked and the status of the warehouse location is changed to an unavailable state. After the warehouse location is locked, it can only be used for the outbound shipment of goods corresponding to the delivery signal and cannot be used for other scheduling.

[0139] Step S305: Use dynamic programming algorithm to determine the optimal equipment scheduling path based on the target warehouse location and delivery signal.

[0140] Please see details Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0141] The target warehouse location determination method provided in this embodiment updates the warehouse location status after associating the target warehouse location with the delivery signal, thereby locking the warehouse location to prevent inventory chaos, prevent scheduling chaos, and improve operational efficiency.

[0142] This embodiment provides a method for determining target positions. Figure 4 This is a flowchart of a target position determination method according to an embodiment of the present invention, such as... Figure 4 As shown, the method specifically includes the following steps:

[0143] Step S401: When the order information for the target goods is received, the order information is parsed to obtain the order attributes.

[0144] Please see details Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0145] Step S402: Using a global fuzzy search algorithm, the warehouse area corresponding to the target goods is retrieved from the database for the first time based on the order attributes and pre-set weights.

[0146] Please see details Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0147] Step S403: Using a dynamic programming algorithm, a secondary search is performed in the storage area according to the priority order corresponding to the pre-set weights to determine the target storage area.

[0148] Please see details Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0149] Step S404: When the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the system receives the confirmation operation sent by the user through the terminal device, uses the edge computing algorithm to lock the target warehouse space, generates a delivery signal, and associates the target warehouse space with the delivery signal.

[0150] Please see details Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0151] Step S405: Use dynamic programming algorithm to determine the optimal equipment scheduling path based on the target warehouse location and delivery signal.

[0152] Specifically, step S405 above, which uses a dynamic programming algorithm to determine the target warehouse location corresponding to the target goods, includes:

[0153] Step S4051: The objective function is to minimize the distance between the target goods and the target warehouse.

[0154] Step S4052: The objective function is to minimize the number of tasks currently being executed by the device.

[0155] Step S4053 uses product type, target warehouse coordinates, target warehouse type, equipment location, and equipment operation path as boundary conditions.

[0156] Step S4054: Use dynamic programming algorithm to recursively solve for the optimal solution of the objective function under the boundary conditions, and determine the optimal equipment scheduling path based on the optimal solution of the objective function.

[0157] In this embodiment of the invention, when determining the optimal equipment scheduling path using dynamic programming, the objective is first defined: minimizing the distance between the target goods and the target warehouse location, and minimizing the number of tasks currently being performed by the equipment, in order to minimize outbound costs and improve outbound efficiency. In dynamic programming, the initial state of the warehouses is empty; goods are placed in the warehouses. The boundary conditions are product type, target warehouse location coordinates, target warehouse location type, equipment location, and equipment operating path. The optimal solution to the objective function under the boundary conditions is recursively solved using dynamic programming to determine the optimal equipment scheduling path and formulate the optimal equipment scheduling plan. In the actual equipment scheduling process, real-time factors, such as equipment failures, also need to be considered, and adjustments made as necessary.

[0158] The target warehouse location determination method provided in this embodiment uses a dynamic programming algorithm to perform dynamic programming, taking into account the distance between the target goods and the target warehouse, the product type, the target warehouse coordinates, the target warehouse type, the location of the equipment, and the equipment operation path. It solves the optimal solution of the objective function under boundary conditions, determines the optimal equipment scheduling path, and adapts to the changing state of tasks and equipment in dynamic programming. This enhances the flexibility and optimality of equipment scheduling, improves equipment scheduling efficiency, and reduces equipment operating costs.

[0159] As a specific application embodiment of the present invention, such as Figure 5 As shown, firstly, the corresponding warehouse area and batch are determined according to the warehouse area allocation logic to respond to the warehouse location recommendation request sent from the cloud. Then, it is determined whether the warehouse document is warehouse number 1.

[0160] If the document is for Warehouse 1, determine if there are loading sequence requirements. If so, proceed with consolidation and release based on product materials and production batches. Determine if a manual operation platform on the first floor is needed. If so, prioritize retrieving items from the automated warehouse based on batch size. If not, determine whether the product shipment is a final shipment or a full shipment. If it's a final shipment, prioritize the AGV warehouse, thus identifying it as an available warehouse. If it's a full shipment, prioritize the automated warehouse, thus identifying it as an available warehouse. For automated warehouses, warehouses that meet batch requirements are sorted by aisle cross-location, and a suitable warehouse is selected from the available warehouses. Determine if double-deep locations are recommended based on product type. If double-deep locations are recommended, prioritize shallow locations, then deep locations. If double-deep locations are not recommended, manually recommend warehouse locations for shipment.

[0161] If the document is not from warehouse 1 (i.e., warehouses 2, 3, or 4), determine whether stock preparation is needed. If stock preparation is not needed, determine whether a manual work station on the first floor is required, following the same process as described above. If stock preparation is needed, specify the delivery note or vehicle, create an advance stock preparation task, a picking task, and a full pallet handling task, and control the transportation of products from the advance stock warehouse to the AGV buffer area.

[0162] This embodiment also provides a target warehouse location determination device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0163] This embodiment provides a target warehouse location determination device, such as... Figure 6 As shown, it includes:

[0164] The parsing module 601 is used to parse the order information to obtain the order attributes when the order information of the target goods is received.

[0165] The retrieval module 602 is used to retrieve the warehouse area corresponding to the target goods in the database for the first time based on the order attributes and the pre-set weights using a global fuzzy search algorithm.

[0166] The determination module 603 is used to perform a secondary search in the database area based on the priority order corresponding to the pre-set weights using a dynamic programming algorithm to determine the target database area.

[0167] The association module 604 is used to receive the confirmation operation sent by the user through the terminal device after detecting that the vehicle has arrived at the parking space corresponding to the target warehouse area, and to use the edge computing algorithm to lock the target warehouse space, generate a delivery signal, and associate the target warehouse space with the delivery signal.

[0168] The scheduling module 605 is used to determine the optimal equipment scheduling path based on the target warehouse location and the delivery signal using a dynamic programming algorithm.

[0169] In some alternative implementations, the parsing module 601 includes:

[0170] The matching unit is used to determine the query conditions corresponding to the order attributes and to calculate the matching degree between each storage area in the database and the query conditions using pre-set weights.

[0171] The sorting unit is used to sort the various storage areas according to their matching degree, and to determine the storage area corresponding to the target goods based on the sorting results.

[0172] In some alternative implementations, the determining module 603 includes:

[0173] The state variable representation unit is used to represent the allocation status of the target goods using state variables. The allocation status includes allocated and unallocated.

[0174] The construction unit is used to construct the state transition equation based on the priority order corresponding to the state variables and the pre-set weights, with all goods unassigned as the initial state.

[0175] The solution unit is used to solve the optimal solution of the state transition equation using the dynamic programming algorithm, and to determine the reservoir area corresponding to the optimal solution of the state transition equation as the candidate target reservoir area.

[0176] The first determining unit is used to determine the candidate target warehouse area as the target warehouse area if the warehouse currently issuing goods does not need to prepare goods.

[0177] The second determining unit is used to determine the candidate target warehouse area as the advance stocking warehouse area if the warehouse currently issuing goods needs to prepare goods.

[0178] The judgment unit is used to access the nearest server using edge computing algorithms, obtain the production batch of the product leaving the warehouse, and determine whether the product leaving the warehouse is the last batch or the whole batch based on the production batch.

[0179] The third determining unit is used to determine the AGV warehouse as the target warehouse area for product outbound if the product outbound is a last-minute shipment.

[0180] The fourth determining unit is used to determine the vertical warehouse as the target warehouse area for product outbound if the product outbound is a whole pallet outbound.

[0181] In some alternative implementations, the association module 604 includes:

[0182] The identification unit is used to identify vehicle information, platform information, and terminal equipment through an RFID reader when a vehicle is detected to have arrived at the parking space corresponding to the target parking area, and to receive confirmation from the user through the terminal equipment.

[0183] The fifth determining unit is used to select the warehouse location that matches the warehouse location selection requirements from the warehouse locations in the target warehouse area and determine it as the target warehouse location.

[0184] The sixth determination unit is used to determine the available single-depth warehouse in the target warehouse area as the target warehouse if the product type is simple and the warehouse selection requirement is to prioritize recommending single-depth warehouses.

[0185] The seventh determination unit is used when the product types are not uniform and the warehouse location selection requirement is to prioritize the recommendation of double-deep warehouse locations. It determines the available double-deep warehouse locations in the target warehouse area as target warehouse locations and determines the location of the target warehouse location according to the pre-obtained location recommendation rules for double-deep warehouse locations. The location recommendation rules for double-deep warehouse locations prioritize the recommendation of shallow warehouse locations.

[0186] The signal generation unit is used to use edge computing algorithms to lock the target warehouse based on vehicle information, platform information, and user confirmation operations, combined with warehouse location determination logic, and generate a delivery signal containing vehicle location information and specified platform information.

[0187] In some alternative implementations, the association module 604 further includes:

[0188] The association unit is used to associate the target warehouse location with the delivery signal and modify the status of the target warehouse location after associating the delivery signal to an unavailable status.

[0189] In some alternative implementations, the scheduling module 605 includes:

[0190] The eighth determining unit is used to take the shortest distance between the target cargo and the target warehouse as the objective function.

[0191] The ninth determining unit is used to minimize the number of tasks currently being executed by the device as the objective function.

[0192] The tenth determining unit is used to define the product type, target warehouse coordinates, target warehouse type, equipment location, and equipment running path as boundary conditions.

[0193] The eleventh determination unit is used to recursively solve the objective function under boundary conditions using a dynamic programming algorithm, and to determine the optimal equipment scheduling path based on the optimal solution of the objective function.

[0194] In some alternative embodiments, the device further includes:

[0195] The task creation module is used to obtain pre-stocking tasks and create picking and packing tasks and full pallet handling tasks based on the pre-stocking tasks.

[0196] The transportation module is used to control the transportation of products from the pre-stocked warehouse to the AGV buffer zone based on picking and pallet handling tasks.

[0197] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0198] In this embodiment, the target warehouse location determination device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0199] This invention also provides a computer device having the above-described features. Figure 6 The target warehouse location determination device shown.

[0200] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0201] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0202] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0203] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0204] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0205] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0206] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touch screen. Output device 40 may include a display device, etc.

[0207] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0208] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0209] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for determining target positions, characterized in that, The method includes: When order information for the target goods is received, the order information is parsed to obtain order attributes; Using a global fuzzy search algorithm, the warehouse area corresponding to the target goods is retrieved from the database for the first time based on the order attributes and pre-set weights; The target storage area is determined by performing a secondary search in the storage area based on the priority order corresponding to the pre-set weights using a dynamic programming algorithm. When a vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the system receives a confirmation from the user via a terminal device, uses an edge computing algorithm to lock the target warehouse space, generates a delivery signal, and associates the target warehouse space with the delivery signal. The optimal equipment scheduling path is determined using a dynamic programming algorithm based on the target warehouse location and the delivery signal. The step of using a global fuzzy search algorithm to retrieve the warehouse area corresponding to the target goods in the database for the first time based on the order attributes and pre-set weights includes: Determine the query conditions corresponding to the order attributes, and calculate the matching degree between each storage area in the database and the query conditions using pre-set weights; The warehouse areas are sorted according to the matching degree, and the warehouse area corresponding to the target goods is determined according to the sorting result. The step of using a dynamic programming algorithm to perform a secondary search in the database area according to the priority order corresponding to pre-set weights to determine the target database area includes: The allocation status of the target goods is represented by state variables, which include allocated and unallocated. With all goods unallocated as the initial state, a state transition equation is constructed based on the priority order corresponding to the state variables and pre-set weights. The optimal solution to the state transition equation is obtained by using dynamic programming algorithm, and the reservoir area corresponding to the optimal solution of the state transition equation is determined as the candidate target reservoir area; If the warehouse currently receiving outbound goods does not require restocking, then the alternative target warehouse area will be designated as the target warehouse area. If the warehouse currently handling outbound shipments needs to prepare inventory, then the candidate target warehouse area will be designated as the advance inventory preparation warehouse area. The nearest server is accessed using an edge computing algorithm to obtain the production batch of the product leaving the warehouse, and the product leaving the warehouse is determined as either the last batch or the whole batch based on the production batch. If the product shipment is a last-minute shipment, then the AGV warehouse will be designated as the target warehouse area for the product shipment. If the product is shipped out in whole pallets, then the vertical warehouse will be designated as the target warehouse area for the product shipment.

2. The method according to claim 1, characterized in that, When a vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the system receives a confirmation operation sent by the user through a terminal device, uses an edge computing algorithm to lock the target warehouse space, and generates a delivery signal, including: When a vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, the vehicle information, platform information and terminal equipment are identified by an RFID reader, and the user's confirmation operation sent through the terminal equipment is received. Select the warehouse locations in the target warehouse area that match the warehouse location selection requirements and determine them as the target warehouse locations; If the product type is simple and the warehouse location selection requirement is to prioritize single-depth warehouses, then the available single-depth warehouses in the target warehouse area will be selected as the target warehouses. If the product types are not uniform, the warehouse location selection requirement is to prioritize double-deep warehouse locations. The available double-deep warehouse locations in the target warehouse area are determined as target warehouse locations, and the warehouse locations of the target warehouse locations are determined according to the pre-obtained warehouse location recommendation rules. The warehouse location recommendation rules for double-deep warehouse locations prioritize shallow warehouse locations. Using edge computing algorithms, based on vehicle information, platform information, and user confirmation, and combined with warehouse location determination logic, the system locks the target warehouse location and generates a delivery signal containing vehicle location information and specified platform information.

3. The method according to claim 1, characterized in that, The association between the target warehouse location and the delivery signal includes: Associate the target position with the delivery signal, and then change the status of the target position after associating the delivery signal to unavailable.

4. The method according to claim 1, characterized in that, The process of determining the optimal equipment scheduling path based on the target warehouse location and delivery signal using a dynamic programming algorithm includes: The objective function is to minimize the distance between the target goods and the target warehouse location. The objective function is to minimize the number of tasks currently being executed by the device. The boundary conditions are product type, target warehouse coordinates, target warehouse type, equipment location, and equipment operation path. The optimal solution of the objective function under boundary conditions is obtained by recursively solving the dynamic programming algorithm, and the optimal equipment scheduling path is determined based on the optimal solution of the objective function.

5. The method according to claim 1, characterized in that, After identifying the candidate target warehouse area as the advance stocking warehouse area, the method further includes: Obtain advance stocking tasks, and create picking tasks and pallet handling tasks based on the advance stocking tasks; Based on the picking and palletizing tasks and the pallet handling tasks, control the transportation of products from the pre-stocked warehouse to the AGV buffer zone.

6. A target warehouse location determination device, characterized in that, The device includes: The parsing module is used to parse the order information to obtain order attributes when the order information of the target goods is received. The retrieval module is used to retrieve the warehouse area corresponding to the target goods in the database for the first time based on the order attributes and pre-set weights using a global fuzzy search algorithm. The determination module is used to perform a secondary search in the reservoir area based on the priority order corresponding to the pre-set weights using a dynamic programming algorithm to determine the target reservoir area; The association module is used to receive the confirmation operation sent by the user through the terminal device when the vehicle is detected to have arrived at the parking space corresponding to the target warehouse area, and to lock the target warehouse space using the edge computing algorithm, generate a delivery signal, and associate the target warehouse space with the delivery signal. The scheduling module is used to determine the optimal equipment scheduling path based on the target warehouse location and delivery signal using a dynamic programming algorithm; The retrieval module is specifically used to: determine the query conditions corresponding to the order attributes, and calculate the matching degree between each warehouse area in the database and the query conditions using a pre-set weight; sort each warehouse area according to the matching degree, and determine the warehouse area corresponding to the target goods according to the sorting result; The determining module is specifically used for: using state variables to represent the allocation status of target goods, the allocation status including allocated and unallocated; starting with all goods unallocated as the initial state, constructing a state transition equation according to the priority order corresponding to the state variables and pre-set weights; using a dynamic programming algorithm to solve the optimal solution of the state transition equation, and determining the warehouse area corresponding to the optimal solution of the state transition equation as a candidate target warehouse area; if the warehouse currently issuing goods does not need to prepare goods, then the candidate target warehouse area is determined as the target warehouse area; if the warehouse currently issuing goods needs to prepare goods, then the candidate target warehouse area is determined as the pre-preparation warehouse area; using an edge computing algorithm to access the nearest server, obtain the production batch of the product being issued, and determine whether the product being issued is a last-minute shipment or a full-pallet shipment based on the production batch; if the product being issued is a last-minute shipment, then the AGV warehouse is determined as the target warehouse area for the product being issued; if the product being issued is a full-pallet shipment, then the vertical warehouse is determined as the target warehouse area for the product being issued.

7. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the target warehouse location determination method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the target warehouse location determination method according to any one of claims 1 to 5.

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