Intelligent part management method based on vehicle part storage point collaborative consideration

Abstract

The invention discloses an intelligent part management method based on vehicle part storage and distribution collaborative consideration. The method is applied to a warehouse-in storage end and comprises the steps that historical warehouse-out sorting distribution information of to-be-stored parts is acquired based on part attribute information of the to-be-stored parts, and the to-be-stored parts are used for representing vehicle parts to be subjected to warehouse-in storage management; the historical ex-warehouse sorting distribution information is used for representing record information of historical ex-warehouse sorting distribution of the parts of the same type as the to-be-stored parts; determining a target storage strategy of the to-be-stored parts based on the part attribute information and the historical warehouse-out sorting distribution information; and performing warehousing storage on the to-be-warehoused parts based on the target storage strategy. According to the invention, the technical problems of low efficiency and high cost of warehouse-out sorting of the vehicle parts in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of logistics information processing, in particular to an intelligent spare part management method based on vehicle spare part storage and distribution coordination. BACKGROUND

[0002] In logistics information processing, efficient management and picking of spare parts are key links to improve overall supply chain efficiency and reduce costs. Currently, the picking of vehicle spare parts mainly relies on order similarity improvement. By clustering similar orders, the picking distance in the picking process is reduced. In high-density wave picking, the operation time of the order clustering algorithm is relatively long, and the improvement effect on the picking efficiency of vehicle spare parts is limited, resulting in low efficiency and high cost of vehicle spare part picking in related technologies.

[0003] At present, no effective solution has been proposed for the above problems. SUMMARY

[0004] The embodiments of the present application provide an intelligent spare part management method based on vehicle spare part storage and distribution coordination, to at least solve the technical problems of low efficiency and high cost of vehicle spare part picking in related technologies.

[0005] According to an aspect of an embodiment of the present application, an intelligent spare part management method based on vehicle spare part storage and distribution coordination is provided, applied to a storage end, comprising: based on spare part attribute information of a spare part to be stored, obtaining historical warehouse-out picking distribution information of the spare part to be stored, wherein the spare part to be stored is used to represent a vehicle spare part to be stored and managed, and the historical warehouse-out picking distribution information is used to represent record information of historical warehouse-out picking distribution of a spare part of the same type as the spare part to be stored; based on the spare part attribute information and the historical warehouse-out picking distribution information, determining a target storage strategy of the spare part to be stored; and based on the target storage strategy, storing the spare part to be stored.

[0006] In an embodiment of the present application, based on the spare part attribute information and the historical warehouse-out picking distribution information, the target storage strategy of the spare part to be stored is determined, comprising: determining a spare part volume in the spare part attribute information; based on a preset volume threshold and the spare part volume, classifying the spare part to be stored to obtain a target type of the spare part to be stored, wherein the target type includes a first type or a second type, the spare part of the first type is used to represent a spare part with a volume greater than or equal to the preset volume threshold, and the spare part of the second type is used to represent a spare part with a volume less than the preset volume threshold; and based on the historical warehouse-out picking distribution information and the target type, determining the target storage strategy.

[0007] In this embodiment of the invention, determining a target storage strategy based on historical outbound picking allocation information and target type includes: determining an in-frame storage strategy for parts to be received based on historical outbound picking allocation information and target type, wherein the in-frame storage strategy is used to characterize the strategy of storing the parts to be received within the target frame; determining an inter-frame position setting strategy for parts to be received based on historical outbound picking allocation information, wherein the inter-frame position setting strategy is used to represent the strategy of setting the position of the target frame; and integrating the in-frame storage strategy and the inter-frame position setting strategy to obtain the target storage strategy.

[0008] In this embodiment of the invention, the in-frame storage strategy for parts to be received is determined based on historical outbound picking and allocation information and target type. This includes: when the target type is a first type, a preset in-frame storage strategy is determined as the in-frame storage strategy, wherein the preset in-frame storage strategy is used to represent the strategy of storing the parts to be received in a floor stack storage frame, and the target frame is a floor stack storage frame; when the target type is a second type, the in-frame storage strategy is determined based on historical outbound picking and allocation information, wherein the target frame is a pallet storage frame or a rack storage frame, and the target frame is capable of storing different types of parts of the second type.

[0009] In this embodiment of the invention, a storage strategy within a frame is determined based on historical outbound picking allocation information, including: determining the historical picking frequency in the historical outbound picking allocation information and the component weight in the component attribute information; determining an initial storage strategy within the frame based on the historical picking frequency, wherein the initial storage strategy within the frame is used to indicate that the picking convenience of components with a first historical picking frequency is greater than the picking convenience of components with a second historical picking frequency, the first historical picking frequency being greater than the second historical picking frequency, and the picking convenience being determined based on the type of the target frame and the picking method of the target frame; and adjusting the initial storage strategy within the frame based on the component weight to obtain the storage strategy within the frame.

[0010] In this embodiment of the invention, the initial storage strategy within the framework is adjusted based on the weight of the components to obtain the storage strategy within the framework. This includes: determining the stability of the target framework for storing the components to be stored in the warehouse according to the initial storage strategy based on preset simulation software and the weight of the components; and adjusting the initial storage strategy within the framework based on the stability of the framework to obtain the storage strategy within the framework.

[0011] In this embodiment of the invention, a strategy for setting the inter-frame position of parts to be received is determined based on historical outbound picking allocation information, including: determining the historical picking frequency in the historical outbound picking allocation information and the part weight in the part attribute information; determining the frame picking frequency of the target frame based on the historical picking frequency and determining the frame weight of the target frame based on the part weight; determining an initial inter-frame position setting strategy based on the historical picking frequency, wherein the initial inter-frame position setting strategy is used to represent the outbound travel path of the target frame corresponding to the first frame picking frequency from the outbound exit, the outbound travel path of the target frame corresponding to the second frame picking frequency from the outbound exit, and the first frame picking frequency being greater than the second frame picking frequency; and adjusting the initial inter-frame position setting strategy based on the frame weight to obtain the inter-frame position setting strategy.

[0012] According to another aspect of the present invention, an intelligent parts management method based on the collaborative consideration of vehicle parts storage and distribution is also provided, applied to the outbound selection end, comprising: in response to receiving a parts outbound order, determining the outbound demand frequency of the parts to be outbound in the parts outbound order, wherein the parts to be outbound are used to represent parts stored in the warehouse using the above method; obtaining the storage location information of the parts to be outbound; generating a target selection strategy based on the outbound demand frequency and the storage location information; and selecting the parts to be outbound based on the target selection strategy.

[0013] In this embodiment of the invention, a target selection strategy is generated based on the frequency of outbound demand and storage location information, including: generating an initial selection strategy based on the frequency of outbound demand and storage location information; determining the selection time for executing the initial selection strategy; and adjusting the initial selection strategy based on the selection time to obtain the target selection strategy.

[0014] According to another aspect of the present invention, an intelligent parts management device based on the collaborative consideration of vehicle parts storage and distribution is also provided, applied to the inbound storage end, comprising: an acquisition module, used to acquire historical outbound picking allocation information of the parts to be inbound based on the parts attribute information of the parts to be inbound, wherein the parts to be inbound are used to represent vehicle parts to be managed for inbound storage, and the historical outbound picking allocation information is used to represent the historical outbound picking allocation record information of parts of the same type as the parts to be inbound; a determination module, used to determine the target storage strategy of the parts to be inbound based on the parts attribute information and the historical outbound picking allocation information; and a storage module, used to store the parts to be inbound based on the target storage strategy.

[0015] According to another aspect of the present invention, an intelligent parts management device based on the coordinated consideration of vehicle parts storage and distribution is also provided, applied to an outbound selection end, comprising: a determining module, configured to determine the outbound demand frequency of parts to be outbound in the parts outbound order in response to receiving a parts outbound order, wherein the parts to be outbound are used to represent parts stored in the warehouse using the above method; an acquiring module, configured to acquire the storage location information of the parts to be outbound; a generating module, configured to generate a target selection strategy based on the outbound demand frequency and storage location information; and a selecting module, configured to perform outbound selection of the parts to be outbound based on the target selection strategy.

[0016] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0017] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0019] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0020] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0021] In this embodiment of the invention, firstly, based on the component attribute information of the components to be warehoused, historical outbound picking allocation information of the components to be warehoused is obtained. The components to be warehoused represent vehicle components to be managed for warehouse storage, and the historical outbound picking allocation information represents historical outbound picking allocation records of components of the same type as the components to be warehoused. Next, based on the component attribute information and the historical outbound picking allocation information, a target storage strategy for the components to be warehoused is determined. Finally, based on the target storage strategy, the components to be warehoused are stored. The intelligent component management method based on the coordinated consideration of vehicle component storage and distribution proposed in this application constructs a forward-looking target storage strategy by considering component attribute information and historical outbound picking allocation information at the warehouse storage end. This pre-considers the future outbound picking needs of the components to be warehoused, making the storage layout directly correspond to the improvement of picking efficiency, forming a highly efficient closed-loop management system of storage and distribution coordination. By coordinating storage and picking, this application adjusts the storage layout at the initial stage of component warehouse storage, ensuring that the storage strategy meets the requirements for improved storage efficiency and also contributes to the improvement of the picking process. This storage-distribution collaborative strategy avoids the need for frequent adjustments to the storage layout during subsequent picking processes, reduces picking costs, and thus solves the technical problems of low efficiency and high cost in picking vehicle parts from the warehouse. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0023] Figure 1 This is a flowchart of a multimodal logistics resource integration and scheduling method based on vehicle parts transportation according to an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of another method for integrating and scheduling multimodal logistics resources based on vehicle parts transportation according to an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of an optional intelligent component management process according to an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram illustrating the selection of an optional intelligent component management algorithm according to an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram illustrating an optional intelligent component management algorithm application according to an embodiment of the present invention;

[0028] Figure 6This is a schematic diagram of an intelligent component management device based on the collaborative consideration of vehicle component storage and distribution according to an embodiment of the present invention;

[0029] Figure 7 This is a schematic diagram of an intelligent component management device based on the collaborative consideration of vehicle component storage and distribution according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] According to one aspect of the present invention, an intelligent parts management method based on the collaborative consideration of vehicle parts storage and distribution is provided, applied to the warehousing storage end. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 1 This is a flowchart of an intelligent component management method based on the collaborative consideration of vehicle component storage and distribution according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0034] Step S102: Based on the component attribute information of the components to be put into storage, obtain the historical outbound picking and allocation information of the components to be put into storage.

[0035] Among them, "parts to be put into storage" refers to vehicle parts that are to be put into storage management, and "historical outbound picking and allocation information" refers to the historical outbound picking and allocation records of parts of the same type as those to be put into storage.

[0036] The aforementioned component attribute information refers to the attributes and characteristics of the vehicle components to be put into storage. Component attribute information may include, but is not limited to, the dimensions, volume, weight, and other information of the vehicle components to be put into storage.

[0037] The aforementioned historical outbound picking allocation information can refer to the allocation records of parts of the same or similar type as the parts to be put into storage during the outbound picking process in the past.

[0038] In one optional embodiment, during the warehousing stage of the intelligent component management method, component attribute information and historical outbound picking allocation information of the components to be warehoused can be collected and analyzed. This aims to develop an optimal storage strategy for the components about to be warehoused, thereby significantly improving warehouse efficiency and picking speed. Specifically, component attribute information of the components to be warehoused can be obtained from the supply chain management system, including but not limited to volume, weight, shape, fragility, and storage requirements. Outbound records of past components of the same type as the components to be warehoused can be extracted through database queries to obtain historical outbound picking allocation information. Big data analytics and machine learning algorithms can be used to mine the historical outbound picking allocation information to identify which components are picked more frequently, as well as the picking frequency and time patterns of the components. Furthermore, the impact of component attribute information, such as volume and weight, on storage methods and locations can be analyzed.

[0039] Step S104: Based on the component attribute information and historical outbound picking and allocation information, determine the target storage strategy for the components to be put into storage.

[0040] The aforementioned target storage strategy refers to an optimized storage strategy intelligently generated based on component attribute information and historical outbound picking allocation information. The target storage strategy can include the storage location and method of vehicle components in the warehouse. The formulation of the target storage strategy can consider the following aspects: Based on the outbound frequency and weight of components, they can be allocated to Zone A (closer to the outbound exit), Zone B (middle location), and Zone C (farther away); whether to use automated storage and retrieval systems (AS / RS), large-item floor stacking, small-item pallet storage, or rack storage, and how to allocate bins to pallets or racks when using these storage methods; for small or compact components, how to intelligently palletize them, and how to optimally allocate them to racks after palletizing to maximize picking hit rate and reduce picking time; based on historical picking data, high-frequency and low-frequency components can be placed in different hot zones of the warehouse to improve picking efficiency and reduce picking walking distance.

[0041] In one optional embodiment, a target storage strategy for parts to be warehoused can be determined based on component attribute information and historical outbound picking allocation information. This aims to find an optimal storage solution for vehicle parts to be warehoused through data analysis and intelligent decision-making. Specifically, the attribute information of the parts to be warehoused, such as volume, weight, and shape, can be fused and analyzed with historical outbound picking allocation information. Historical outbound picking allocation information may include the outbound frequency of parts, picking efficiency, and associated order patterns. Based on the fused information, machine learning algorithms or improved models can be used to evaluate the picking efficiency, cost, and safety under different storage strategies. This may include determining whether parts should be stored in bins, pallets, or racks, and their storage location in the warehouse. Through model iteration, a better target storage strategy can be determined, maximizing picking efficiency while considering storage safety and cost control. For example, frequently picked small and lightweight parts can be prioritized for easy picking areas, while infrequently picked parts can be stored in more distant locations.

[0042] In the above process, based on the component attribute information and historical outbound picking and allocation information, the target storage strategy for the components to be put into storage is determined. The intelligent storage strategy avoids unnecessary waste of storage space, improves the layout of components, and reduces the additional handling and storage costs caused by suboptimal storage. The precise storage strategy can avoid frequent bin adjustments and pallet handling, thereby reducing operating costs.

[0043] Step S106: Based on the target storage strategy, store the parts to be stored in the warehouse.

[0044] In one optional embodiment, instructions can be automatically sent to the warehouse management system based on the generated target storage strategy. These instructions include information such as the storage location of the parts, the type of frame used, and whether special handling is required. Upon receiving the instructions, the warehouse management system uses a path planning algorithm to calculate the inbound route, ensuring that the parts can quickly and accurately reach the designated storage location. The parts can be automatically transported to the storage location according to the planned route. During the storage process, the storage progress and status can be monitored in real time to ensure adherence to the target storage strategy. After successful inbound storage, the inventory status can be updated to verify the effectiveness of the storage strategy and provide data support for subsequent picking and storage decisions.

[0045] In the above process, the target storage strategy ensures that parts are stored in appropriate locations, reducing the movement distance during subsequent picking processes. Furthermore, through rational layout, it significantly improves the accuracy and speed of picking. Intelligent storage strategies make more efficient use of warehouse space and storage equipment, avoiding resource waste and reducing storage costs. The target storage strategy can dynamically adjust according to fluctuations in parts demand, ensuring the warehouse can quickly adapt to changes and improving overall operational flexibility.

[0046] In this embodiment of the invention, firstly, based on the component attribute information of the components to be warehoused, historical outbound picking allocation information of the components to be warehoused is obtained. The components to be warehoused represent vehicle components to be managed for warehouse storage, and the historical outbound picking allocation information represents historical outbound picking allocation records of components of the same type as the components to be warehoused. Next, based on the component attribute information and the historical outbound picking allocation information, a target storage strategy for the components to be warehoused is determined. Finally, based on the target storage strategy, the components to be warehoused are stored. The intelligent component management method based on the coordinated consideration of vehicle component storage and distribution proposed in this application constructs a forward-looking target storage strategy by considering component attribute information and historical outbound picking allocation information at the warehouse storage end. This pre-considers the future outbound picking needs of the components to be warehoused, making the storage layout directly correspond to the improvement of picking efficiency, forming a highly efficient closed-loop management system of storage and distribution coordination. By coordinating storage and picking, this application adjusts the storage layout at the initial stage of component warehouse storage, ensuring that the storage strategy meets the requirements for improved storage efficiency and also contributes to the improvement of the picking process. This storage-distribution collaborative strategy avoids the need for frequent adjustments to the storage layout during subsequent picking processes, reduces picking costs, and thus solves the technical problems of low efficiency and high cost in picking vehicle parts from the warehouse.

[0047] In this embodiment of the invention, a target storage strategy for components to be put into storage is determined based on component attribute information and historical outbound picking allocation information. This includes: determining the component volume in the component attribute information; classifying the components to be put into storage based on a preset volume threshold and the component volume to obtain the target type of the components to be put into storage, wherein the target type includes: a first type or a second type, wherein the first type of components is used to represent components whose volume is greater than or equal to the preset volume threshold, and the second type of components is used to represent components whose volume is less than the preset volume threshold; and determining the target storage strategy based on historical outbound picking allocation information and the target type.

[0048] The aforementioned preset volume threshold can refer to a pre-set volume threshold used to classify parts according to their volume.

[0049] The first type mentioned above can refer to components whose volume is greater than or equal to a preset volume threshold. Components of the first type are relatively large; for example, they can include large parts of a vehicle, such as doors and hoods, and can be stored using a bulk stacking storage method.

[0050] The second type mentioned above can refer to components with a volume smaller than a preset volume threshold. These components are smaller, and may include, for example, screws, fasteners, and small electronic components. They are easily stored on small pallets or racks. Intelligent palletizing and racking adjustments can increase storage density and reduce storage space requirements. Furthermore, improvements in categorized storage and picking paths can significantly enhance picking efficiency and reduce picking costs.

[0051] In one optional embodiment, the volume of each component to be warehoused can be extracted from the component attribute information. The component volume can be derived from supplier-provided data, product specification sheets, or obtained in real time through automated measuring equipment. A preset volume threshold can be used as a standard to distinguish between large and small components. Based on the preset volume threshold, components can be divided into two categories: Type 1 and Type 2. Type 1 components occupy more space and require larger storage frames; Type 2 components are more suitable for bin or pallet storage, enabling high-density storage. Historical outbound picking allocation information can be combined to generate optimal storage strategies for different types of components. For larger Type 1 components, the focus can be on reducing picking distance and improving retrieval speed, such as placing them in an automated storage and retrieval system (AS / RS) close to the picking area. For smaller Type 2 components, a bin intelligent palletizing algorithm can be used to adjust the arrangement within the bin, improving picking hit rate and storage efficiency.

[0052] In the above process, parts of different sizes are stored in appropriate spaces, avoiding the waste of space caused by large parts occupying the storage area for small parts, while ensuring that small parts can make full use of the limited space, thus improving the overall space utilization rate of the warehouse. The rapid positioning of large parts and the high hit rate picking of small parts can significantly shorten picking time, reduce ineffective movement in the picking process, and improve picking efficiency, with even more obvious effects when processing batch orders.

[0053] In this embodiment of the invention, determining a target storage strategy based on historical outbound picking allocation information and target type includes: determining an in-frame storage strategy for parts to be received based on historical outbound picking allocation information and target type, wherein the in-frame storage strategy is used to characterize the strategy of storing the parts to be received within the target frame; determining an inter-frame position setting strategy for parts to be received based on historical outbound picking allocation information, wherein the inter-frame position setting strategy is used to represent the strategy of setting the position of the target frame; and integrating the in-frame storage strategy and the inter-frame position setting strategy to obtain the target storage strategy.

[0054] The aforementioned in-frame storage strategy can refer to the strategy of storing and arranging parts within the target frame. It can utilize data from historical outbound picking and allocation information, such as part usage frequency and order combination popularity, combined with part attribute information, such as volume and weight, to make optimal settings for the storage location of parts within the frame.

[0055] The aforementioned inter-frame placement strategy refers to the relative positioning of different storage frames within the warehouse to improve the overall picking path. This strategy can include the placement location and relative distance of target frames, as well as how to adjust the layout based on factors such as the frequency and weight of parts being picked. Based on historical picking allocation information, frames storing frequently picked parts can be placed closer to the warehouse exit, while less frequently picked parts can be placed further away. This shortens the picking path for high-frequency parts, thereby improving picking efficiency.

[0056] In one optional embodiment, the intelligent component management method can improve storage strategies by analyzing historical data and component types to plan the storage layout of components within frames and the optimal location of the frames themselves in the warehouse. The specific implementation process can be as follows: historical outbound picking and allocation information can be analyzed to identify the picking frequency and combination patterns of different components. Combined with the component's volume, weight, and other attributes, the specific layout of the components within the frames can be determined. For example, high-frequency, small-volume components can be arranged in easily accessible locations, while low-frequency components can be stored at the bottom or in harder-to-reach locations. The optimal placement of frames in the warehouse can be determined based on historical picking frequency and frame type. High-frequency component storage frames will be arranged near the picking area for rapid response to demand; while low-frequency components can be stored in more distant areas. Finally, the storage strategy within frames and the location setting strategy between frames can be integrated to form a more comprehensive target storage strategy. This ensures that each component is stored in a location that is easy to pick and conforms to physical properties, while also considering the reasonable layout of the frames to improve overall warehouse operational efficiency.

[0057] In the above process, the inter-frame location strategy ensured a reasonable distribution of warehouse space, avoiding overcrowding in popular areas while minimizing idleness in less popular areas, thus improving space utilization efficiency. Improvements to the in-frame storage strategy and the inter-frame location strategy reduced unnecessary handling and storage costs, lowered the costs of secondary adjustments due to unreasonable layouts, and contributed to cost control.

[0058] In this embodiment of the invention, the in-frame storage strategy for parts to be received is determined based on historical outbound picking and allocation information and target type. This includes: when the target type is a first type, a preset in-frame storage strategy is determined as the in-frame storage strategy, wherein the preset in-frame storage strategy is used to represent the strategy of storing the parts to be received in a floor stack storage frame, and the target frame is a floor stack storage frame; when the target type is a second type, the in-frame storage strategy is determined based on historical outbound picking and allocation information, wherein the target frame is a pallet storage frame or a rack storage frame, and the target frame is capable of storing different types of parts of the second type.

[0059] The aforementioned pre-defined storage strategy within a storage frame can refer to storing components within a floor-mounted storage frame as a storage strategy for the first type of parts. The floor-mounted storage frame can be a storage facility designed for large-volume parts, an open ground-level stacking area, or a floor-mounted rack with structural support. The floor-mounted storage frame can support the weight of larger parts and provide space to meet the volume requirements of the first type of parts.

[0060] In one optional embodiment, the intelligent component management method can determine different in-frame storage strategies based on the component type to improve storage and picking efficiency. If the component belongs to the first type, such as large items, a preset floor-stacking storage frame strategy can be adopted. This preset in-frame storage strategy prioritizes full space utilization and storage stability based on the characteristics of large components, ensuring that components can be stored safely and efficiently within the floor-stacking frame. The floor-stacking frame can be designed with a large load-bearing capacity and multi-layer storage space to accommodate the storage needs of large-volume components. If the component belongs to the second type, such as small items, the in-frame storage strategy can be determined based on historical outbound picking allocation information. For high-frequency combination patterns of small components, second-type components can be intelligently matched to pallets or racks to achieve efficient pallet matching and improve picking hit rate. The layout on the pallets or racks can fully consider the combination frequency of components, ensuring that frequently combined components can be picked quickly.

[0061] In the aforementioned process, the first type of component's floor-stacking frame storage strategy, through its rationally designed structure and layout, ensures the stability of large components during storage, reduces component damage or safety hazards caused by improper storage, and improves the overall safety level of warehouse operations. The second type of component's intelligent palletizing strategy, with improvements for high-frequency combinations, significantly improves the hit rate during picking, reduces search time and walking distance, thereby increasing picking efficiency and reducing the error rate. Adopting appropriate frame storage strategies for different types of components allows for the rational allocation of warehouse space and maximizes storage density based on component characteristics, thus improving warehouse space utilization.

[0062] In this embodiment of the invention, a storage strategy within a frame is determined based on historical outbound picking allocation information, including: determining the historical picking frequency in the historical outbound picking allocation information and the component weight in the component attribute information; determining an initial storage strategy within the frame based on the historical picking frequency, wherein the initial storage strategy within the frame is used to indicate that the picking convenience of components with a first historical picking frequency is greater than the picking convenience of components with a second historical picking frequency, the first historical picking frequency being greater than the second historical picking frequency, and the picking convenience being determined based on the type of the target frame and the picking method of the target frame; and adjusting the initial storage strategy within the frame based on the component weight to obtain the storage strategy within the frame.

[0063] The historical picking frequency mentioned above refers to the number of times a component has been picked and taken out of the warehouse within a past period. Components with a high historical picking frequency can be considered "hot items," while those with a low picking frequency can be considered "cold items." Based on historical picking frequency, future picking demand can be predicted, thereby adjusting storage and picking strategies.

[0064] The initial in-frame storage strategy mentioned above refers to a preliminary storage layout plan for parts within the frame, based on consideration of the historical picking frequency of parts. The initial in-frame storage strategy focuses on placing frequently picked parts in more easily accessible locations to improve picking efficiency.

[0065] The aforementioned ease of picking refers to the ease with which a part can be retrieved from a specific location during the picking operation. A higher ease of picking occurs when parts are stored in easily accessible locations. The assessment of ease of picking can be related to the type of storage frame and the picking method.

[0066] The picking methods mentioned above refer to the operational modes of human or mechanical equipment used in the parts picking process. Picking methods can include manual picking, automated guided vehicle (AGV) picking, and robotic arm picking. Different picking methods have different requirements for the type and layout of storage frames, and will also affect the ease of picking. For example, for heavier parts, robotic arm picking is more convenient than manual picking; while for frequently picked small parts, pallet storage frames combined with AGVs offer greater ease of picking.

[0067] In one optional embodiment, during the determination of the in-frame storage strategy, the intelligent component management method can comprehensively analyze historical picking frequency and component weight to improve the storage layout. Specifically, picking frequency data of components can be extracted from historical outbound picking allocation information, and weight information of components can be obtained from component attribute information. Through data mining techniques, high-frequency components and their weight characteristics are identified. Based on historical picking frequency, an initial in-frame storage strategy can be generated to ensure that the picking convenience of high-frequency components is higher than that of low-frequency components. For example, in the rack, high-frequency components can be placed in the easily accessible front row or top layer to reduce the movement distance and time during the picking process. Furthermore, the impact of component weight on frame stability can be considered, and the initial storage strategy can be adjusted and improved, adjusting the component layout to ensure the stability and safety of the frame structure under high-frequency picking.

[0068] In the aforementioned process, the front-positioning of high-frequency components significantly reduces the movement distance during picking, lowering picking time. The improvement in picking efficiency is even more pronounced when processing bulk orders. By considering the potential impact of component weight on frame stability and adjusting the storage strategy, frame tilting or collapse due to uneven weight distribution can be effectively avoided, ensuring warehouse operational safety and reducing the risk of equipment damage. The modified in-frame storage strategy reduces unnecessary handling and rearrangement needs, lowering storage and subsequent adjustment costs while improving warehouse space utilization.

[0069] In this embodiment of the invention, the initial storage strategy within the framework is adjusted based on the weight of the components to obtain the storage strategy within the framework. This includes: determining the stability of the target framework for storing the components to be stored in the warehouse according to the initial storage strategy based on preset simulation software and the weight of the components; and adjusting the initial storage strategy within the framework based on the stability of the framework to obtain the storage strategy within the framework.

[0070] The aforementioned pre-set simulation software can refer to computer software that is pre-determined based on specific needs and scenarios. It can be used to simulate and predict the stability of the target framework under different conditions, according to the initial framework storage strategy, for storing components to be stored in the warehouse.

[0071] The aforementioned frame stability refers to the structural stability and safety of the storage frame when it is subjected to various components, including components of different weights, during storage and picking operations.

[0072] In one optional embodiment, the structural parameters of the target frame, the initial storage layout, and the weight data of each component to be stored can be input into a pre-defined simulation software. After the simulation software runs, it can simulate various warehousing operation scenarios and evaluate the stability of the frame under different load distributions. Analysis of the simulation results can determine the stability of the frame under the initial storage strategy. If certain weight distributions are found to cause a decrease in frame stability, an early warning message can be generated, indicating the need to adjust the storage strategy. Based on feedback on the frame's stability, the storage position of components within the frame can be automatically or assisted. For example, heavier components can be moved to the bottom of the frame, or the weight can be evenly distributed throughout the frame to enhance overall stability. The adjusted strategy can be verified again using the simulation software to ensure that the new strategy can meet the requirements of convenient picking and storage efficiency while maintaining frame stability.

[0073] In the above process, simulation analysis of frame stability under different weight distributions can effectively prevent structural safety hazards caused by overloading or uneven weight distribution, ensuring the safety of the storage environment. Adjusting storage strategies based on frame stability considerations ensures safe storage of components and, while maintaining structural safety, improves storage layout and increases storage space utilization. Strategy adjustments balance frame stability with ease of picking, avoiding decreased picking efficiency due to frequent frame adjustments, while ensuring safety during the picking process and promoting smoothness and efficiency.

[0074] In this embodiment of the invention, a strategy for setting the inter-frame position of parts to be received is determined based on historical outbound picking allocation information, including: determining the historical picking frequency in the historical outbound picking allocation information and the part weight in the part attribute information; determining the frame picking frequency of the target frame based on the historical picking frequency and determining the frame weight of the target frame based on the part weight; determining an initial inter-frame position setting strategy based on the historical picking frequency, wherein the initial inter-frame position setting strategy is used to represent the outbound travel path of the target frame corresponding to the first frame picking frequency from the outbound exit, the outbound travel path of the target frame corresponding to the second frame picking frequency from the outbound exit, and the first frame picking frequency being greater than the second frame picking frequency; and adjusting the initial inter-frame position setting strategy based on the frame weight to obtain the inter-frame position setting strategy.

[0075] The frame picking frequency mentioned above can refer to the overall or average picking frequency of each component stored in the target frame. A higher frame picking frequency means that the components in the target frame are in higher demand and can be stored closer to the outbound outlet to reduce the cost of picking and handling.

[0076] The frame weight mentioned above can refer to the total or average weight of all components stored in the target frame. Target frames with higher frame weights can be stored closer to the outbound port to reduce costs during picking and handling.

[0077] The initial frame placement strategy mentioned above refers to the preliminary determination of the relative positions of target frames within the warehouse based on historical picking frequency analysis. This strategy involves placing high-frequency picking frames closer to the outbound exit to reduce walking distance and time during picking. Low-frequency picking frames can be placed in more distant areas of the warehouse. This helps improve picking routes, increase picking efficiency, and reduce picking costs.

[0078] In one optional embodiment, the picking frequency of each component can be extracted from historical outbound picking allocation information, combined with the component weight data. The picking frequency and weight of each target frame are calculated to identify the frame's temperature and weight characteristics. Next, thermal analysis can be performed to determine the initial frame placement strategy. Based on historical picking frequencies, the thermal analysis can place frames with high picking frequencies (i.e., hot frames) closer to the outbound exit, reducing their outbound travel path, while placing frames with low picking frequencies (i.e., cold frames) at relatively distant locations, rationally allocating space between frames. Building upon this, and considering the impact of frame weight on handling costs and warehouse layout, the initial frame placement strategy can be fine-tuned. By introducing frame weight as a weighting factor, the initial frame placement strategy can be improved, ensuring that the frame layout considers picking frequency while also taking into account the economy and safety of handling. The adjusted frame placement strategy ensures the rationality of the frame layout, namely, high-frequency and light-weight frames are closer to the outbound gate, high-frequency and heavy-weight frames are next, and low-frequency frames are located far away from the outbound gate, thus obtaining the frame placement strategy.

[0079] In the above process, high-frequency frames are placed closer to the outbound exit, reducing picking distances and significantly improving picking efficiency. By rationally distributing the temperature and weight between frames, unnecessary handling distances are reduced, lowering energy consumption during the picking process and lowering warehousing operating costs. Heavy-duty frames can be appropriately placed closer to the outbound exit, reducing handling risks caused by improper weight distribution, ensuring the safety of warehouse operations, and reducing potential accident risks and insurance costs.

[0080] According to another aspect of the present invention, a multimodal logistics resource integration and scheduling method based on vehicle parts transportation is also provided, which is applied to the outbound picking end.

[0081] Figure 2 This is a flowchart of a multimodal logistics resource integration and scheduling method based on vehicle parts transportation according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0082] Step S202: In response to receiving a parts outbound order, determine the frequency of outbound demand for the parts to be outbound in the parts outbound order.

[0083] Among them, "parts to be shipped out" refers to parts that are stored in the warehouse using the above method.

[0084] The aforementioned parts outbound order can refer to parts outbound documents, which may contain customer or production line demand information for a specific batch of vehicle parts. The parts outbound order may include the required parts type, quantity, specifications, outbound time requirements, and destination.

[0085] The aforementioned outbound demand frequency refers to the number of times a component is requested for shipment within a component outbound order. Based on the outbound demand frequency, component orders with higher demand frequency can be prioritized, picking routes can be improved, and waiting and handling times can be reduced, thereby increasing the overall outbound efficiency of each component required for a component outbound order.

[0086] Step S204: Obtain the storage location information of the parts to be shipped out.

[0087] The aforementioned storage location information can refer to the location where the parts are stored in the warehouse, and may include, but is not limited to, the shelf number, layer number, and storage location number of the parts.

[0088] Step S206: Generate a target selection strategy based on the frequency of outbound demand and storage location information.

[0089] The aforementioned target selection strategy refers to a set of strategies for optimizing the picking process based on outbound demand frequency and storage location information. Aiming to maximize picking efficiency and minimize picking costs, it can include the following aspects: determining which parts should be picked first, which can be determined by outbound demand frequency and storage location information; parts with high demand frequency and parts located nearby can be prioritized. If picking involves intelligent devices, the target selection strategy can also include device scheduling to ensure efficient device utilization and avoid collisions or idleness between devices.

[0090] Step S208: Based on the target selection strategy, select the parts to be shipped out.

[0091] In one optional embodiment, the application of the intelligent parts management method at the outbound picking end can accelerate picking speed and improve accuracy. The specific implementation process is as follows: When an outbound parts order is received, a data analysis module can be activated to determine the outbound demand frequency of each part in the order. This can be based on historical picking data to identify which parts are in high-frequency or low-frequency demand. Database queries can be used to obtain the specific storage location information of the parts within the warehouse. Storage location information can include the frame number of the part and its precise location within the frame, such as layer, row, and column coordinates, for accurate positioning. Based on the outbound demand frequency and storage location information, a target picking strategy can be intelligently generated. Storage locations corresponding to high-frequency demand parts can be prioritized, and an optimal picking route can be automatically calculated to reduce the picking distance. The layout of storage space is also considered to ensure the smoothness and efficiency of the picking operation. Based on the generated target picking strategy, the parts picking task can be completed efficiently and accurately, and the picking process data can be recorded to provide a basis for subsequent strategy improvements.

[0092] In the above process, prioritizing the picking of frequently requested parts and intelligently planning picking routes can significantly reduce picking paths and improve picking efficiency. This enables rapid response to large or urgent orders, shortening order delivery cycles. Precise storage location information, combined with intelligent strategies, improves the accuracy of parts outbound order completion. Intelligent strategies based on outbound demand frequency and storage location enable dynamic and improved allocation of warehouse resources, reducing search time for popular parts while rationally allocating warehouse space, avoiding idleness in less popular areas, and improving overall warehouse operational efficiency.

[0093] In this embodiment of the invention, a target selection strategy is generated based on the frequency of outbound demand and storage location information, including: generating an initial selection strategy based on the frequency of outbound demand and storage location information; determining the selection time for executing the initial selection strategy; and adjusting the initial selection strategy based on the selection time to obtain the target selection strategy.

[0094] The aforementioned selection time may refer to the time required to complete the picking of each component in the parts outbound order.

[0095] In one optional embodiment, an initial picking strategy can be generated using a path planning algorithm based on the demand frequency of the parts to be picked in the outbound order and the storage location information of the parts in the warehouse. This strategy aims to define the optimal path and sequence for outbound picking. Next, the picking time required to execute the initial picking strategy can be calculated. If certain picking sequences are found to be too time-consuming or inefficient, the initial picking strategy can be adjusted. The adjustment process may include reordering the picking order of parts to reduce the movement distance and time of the picking process, or shortening the picking time by processing multiple picking tasks in parallel while meeting outbound requirements. The adjustment can be continuously iterated to find a target picking strategy that meets the time improvement objective. This process can be achieved by simulating multiple picking strategies, comparing the execution time of different picking strategies, and selecting the solution with the shorter execution time or that meets the service level agreement requirements as the target picking strategy.

[0096] In the above process, by generating and improving strategies based on outbound demand frequency and storage location information, picking time can be reduced, enabling more efficient use of warehousing resources and picking equipment, decreasing equipment idle time, and improving resource utilization. The reduction in picking time lowers picking costs, significantly reducing operating costs in large-scale picking operations. Strategy adjustments based on dynamic demand and storage information allow for flexible responses to different order structures and demand fluctuations, improving the flexibility and adaptability of picking operations.

[0097] The technical solution proposed in this application is described below with reference to an optional embodiment. This application proposes an intelligent fusion picking method to improve picking efficiency based on the collaborative consideration of vehicle parts storage and distribution. The technical solution proposed in this application uses order clustering order improvement at the picking end and performs pre-screening of common parts according to industry characteristics to further improve algorithm computation efficiency and picking efficiency. For smaller packages of vehicle parts, the popularity of parts appearing in orders is considered to achieve intelligent pallet allocation for different situations. For the storage end, for smaller packages, the intelligent pallet allocation algorithm is first applied to guide the storage of material boxes within a single layer of the rack. Then, the single-layer stored parts are treated as an independent unit, and matching is performed between layers. During matching, the popularity of single-layer part units is considered, and layers with high popularity are allocated to easily accessible locations. At the same time, rack stability is considered from the perspective of part weight. By considering storage and picking collaboratively, the matched pallets or racks, together with larger packages of parts, apply location popularity allocation. Weights are set for storage and picking to maximize picking efficiency, thereby improving picking efficiency and reducing picking costs.

[0098] The overall framework of this application and the solution design a complete set of intelligent picking methods, which are applied to the processes of warehousing, sorting and picking. It includes multiple algorithms, such as pre-screening of common parts, improved order clustering and sorting, intelligent palletizing of bins, improved palletizing location, and hot allocation of storage locations.

[0099] Figure 3 This is a schematic diagram of an optional intelligent component management process according to an embodiment of the present invention, such as... Figure 3 As shown, the storage location heat allocation algorithm is used during the warehousing process; during the sorting process, the intelligent bin pallet matching algorithm and the improved pallet matching position algorithm are used to process the second type of parts; during the selection process, the output results of the intelligent bin pallet matching algorithm and the improved pallet matching position algorithm are processed by the general parts pre-screening algorithm for the first type of parts, and the output results of the general parts pre-screening algorithm are processed by the order sorting improvement algorithm.

[0100] The algorithm includes several key components: a general-purpose pre-screening algorithm used in the picking area to guide picking operations based on order requirements and preparation time; an order sorting improvement algorithm used in the picking area to guide picking operations based on order requirements and preparation time; a smart bin palletizing algorithm used in the picking area to adjust bins based on the frequency of parts appearing in orders according to historical data; a palletizing location improvement algorithm used in the picking area to adjust pallets based on the frequency of parts usage in historical data; and a location heat allocation algorithm used in both the storage and picking areas to guide delivery based on the frequency of parts usage in historical data according to heat allocation principles. For different business scenarios, multiple algorithms can be used collaboratively or independently through the algorithm center and intelligent equipment management platform to improve the part hit rate during the picking process, thereby increasing picking efficiency and reducing picking costs.

[0101] Figure 4 This is a schematic diagram illustrating the selection of an optional intelligent component management algorithm according to an embodiment of the present invention, such as... Figure 4 As shown, the selection or sorting process in the workshop logistics supermarket uses a general-purpose parts pre-screening algorithm, an improved order sorting algorithm, an intelligent bin palletizing algorithm, an improved palletizing location algorithm, and a location heat allocation algorithm for the first type of parts; and an improved order sorting algorithm and a location heat allocation algorithm for the second type of parts. The selection or sorting process in the line-side warehouse uses the same algorithm for the first type of parts; and for the second type of parts, the same algorithm. The storage process in the warehouse uses a location heat allocation algorithm for both the first and second types of parts.

[0102] The principles and logic of each algorithm are explained. The pre-screening of common parts, developed for specific scenarios, filters order data according to preset common parts rules. This can serve as a preliminary step in improving the order sorting algorithm, thereby increasing the efficiency of the outbound picking algorithm. Specifically, a common parts library is set up based on the vehicle model list. Order data is compared with this library; if they match, the part is considered common, and the order is pre-picked.

[0103] The order sorting improvement addresses scenarios where each order contains multiple parts. Considering order delivery time, it improves the order sorting method by clustering based on the similarity of parts within an order. Parts included in multiple orders are sorted according to their frequency of occurrence, and identical parts are clustered together for picking. The algorithm aims to minimize picking time while meeting order delivery time requirements.

[0104] The intelligent bin matching system uses data mining algorithms to analyze the frequency of part combinations in historical orders and sets support thresholds. A part combination is considered a high-frequency combination if it appears in at least 20% of orders, a medium-frequency combination if it appears in 3%-20% of orders, and a low-frequency combination if it appears in less than 3% of orders. The system also considers the capacity of the matching tray, such as a tray that can hold 3-6 bins. Part combinations with the same frequency are matched together. If the capacity of the matching tray is exceeded, the parts can be split.

[0105] The palletizing locations have been improved. For completed pallets, based on the determination of high, medium, and low frequency combinations, the palletizing locations in the storage area are divided into zones A, B, and C according to their distance from the outbound location. High-frequency parts are assigned to zone A, which is closer to the outbound location, low-frequency parts are assigned to zone C, which is farther from the outbound location, and the rest are assigned to zone B. If it is a rack location, the weight of the pallets needs to be considered.

[0106] The allocation of storage locations is based on historical data of parts outbound frequency. First, a frequency threshold is set to determine the high, medium, and low frequency levels. Outbound frequencies are divided into categories A, B, and C according to high, medium, and low. Then, the storage locations are divided into zones A, B, and C according to their distance from the outbound location and the frequency threshold. Category A parts are assigned to zone A, which is closest to the outbound location, while Category C parts are assigned to zone C, which is furthest from the outbound location. The rest are assigned to zone B.

[0107] Figure 5 This is a schematic diagram illustrating an optional intelligent component management algorithm application according to an embodiment of the present invention, such as... Figure 5As shown, the algorithm input involves the system automatically retrieving data, including order information, parts inventory information, production plan information, parts information, packaging information, time quotas, and historical data. The program performs the entire intelligent selection algorithm, including a general parts pre-screening algorithm, an order sorting algorithm, an intelligent palletizing algorithm, a palletizing location improvement algorithm, and a location popularity allocation algorithm. The algorithm output includes system outputs such as general parts selection instructions, order sorting selection instructions, palletizing records, pallet transfer records, warehousing instructions, and work analysis.

[0108] The collaborative logic works as follows: On the outbound side, based on the vehicle model parts list, a pre-selection algorithm is used to pick common parts. Then, an improved order clustering and sorting algorithm is used to pick other parts. On the inbound and sorting side, for small items, the intelligent bin palletizing algorithm is used first, followed by an improved palletizing position algorithm, and finally, a storage location heat allocation algorithm is used in conjunction with the algorithm for large items.

[0109] At the outbound end, order clustering and sorting improvements can be used, allowing all parts in a wave of orders to participate in the calculation. Alternatively, a pre-screening algorithm for common parts can be called first to pick common parts, allowing for centralized placement of common parts locations. Then, the improved order clustering and sorting algorithm is called, effectively improving overall calculation time and picking efficiency. For small items, the picking area uses multiple types of parts stored on the same rack or pallet. Considering the frequency of part combinations in an order comprehensively improves the picking hit rate of the rack or pallet. Considering common part pre-screening and order clustering and sorting improvements at the outbound end can improve picking efficiency. At the inbound end, considering intelligent bin palletizing and location heat allocation, combined with the outbound end, can effectively improve the picking hit rate, thereby significantly improving picking efficiency.

[0110] The picking method can interface with automated warehouse control systems and be implemented collaboratively with intelligent picking equipment. An application scenario for improved pre-screening of common parts and order clustering sorting is the sorting of baskets at an OEM (Original Equipment Manufacturer), comparing wave picking times. By improving pre-screening of common parts and order clustering sorting, the time spent on picking and walking is reduced, and considering the impact of full-grab placement time, picking time can be reduced. In the inbound and sorting end, the application scenario for intelligent bin palletizing, improved palletizing location, and location heat allocation is the sorting of baskets at an OEM. Automated guided vehicles (AGVs) are used in the storage area to transport small racks and large cases to the picking location. AGVs reduce palletizing handling, and considering improvements in the distance of heat zones and increased inbound distance, AGV handling time is reduced. When intelligent picking methods are comprehensively applied at the inbound and outbound ends, cost improvements are as follows: through the fusion algorithm, picking time and AGV handling time are improved, resulting in a decrease in picking costs compared to before. The picking process utilizes order clustering for improvement and, based on industry characteristics, performs pre-screening of common parts to further enhance algorithm processing efficiency and picking efficiency. For smaller packages of vehicle parts, the frequency of part combinations within an order is considered to achieve intelligent pallet matching for different situations. Storage and picking are considered in tandem; matched pallets or racks are assigned based on location frequency along with larger packages of parts, with weights set for storage and picking to maximize picking efficiency. The intelligent picking method includes multiple algorithms that can be flexibly configured for different business scenarios. These algorithms can be used collaboratively or independently to improve part hit rate during the picking process, increase picking efficiency, and reduce picking costs.

[0111] According to another aspect of the present invention, an intelligent parts management device based on the collaborative consideration of vehicle parts storage and distribution is also provided, applied to the warehousing storage end. This device can execute the intelligent parts management method based on the collaborative consideration of vehicle parts storage and distribution described in the above embodiments. The specific implementation method and preferred application scenarios are the same as those in the above embodiments, and will not be repeated here.

[0112] Figure 6 This is a schematic diagram of an intelligent component management device based on the collaborative consideration of vehicle component storage and distribution, according to an embodiment of this application. Figure 6 As shown, the device includes the following: an acquisition module 602, a determination module 604, and a storage module 606.

[0113] The acquisition module 602 is used to acquire the historical outbound picking allocation information of the parts to be put into storage based on the part attribute information of the parts to be put into storage. The parts to be put into storage represent vehicle parts to be stored and managed, and the historical outbound picking allocation information represents the historical outbound picking allocation record information of parts of the same type as the parts to be put into storage. The determination module 604 is used to determine the target storage strategy of the parts to be put into storage based on the part attribute information and the historical outbound picking allocation information. The storage module 606 is used to store the parts to be put into storage based on the target storage strategy.

[0114] The determination module is also used to determine the volume of the component in the component attribute information; based on the preset volume threshold and the component volume, the components to be put into storage are classified to obtain the target type of the components to be put into storage. The target type includes: a first type or a second type. The first type of component is used to represent components whose volume is greater than or equal to the preset volume threshold, and the second type of component is used to represent components whose volume is less than the preset volume threshold; based on the historical outbound picking allocation information and the target type, the target storage strategy is determined.

[0115] The determination module is further used to determine the in-frame storage strategy for the parts to be received based on historical outbound picking and allocation information and target type. The in-frame storage strategy is used to characterize the strategy of storing the parts to be received in the target frame. Based on historical outbound picking and allocation information, the inter-frame position setting strategy for the parts to be received is determined. The inter-frame position setting strategy is used to represent the strategy of setting the position of the target frame. The in-frame storage strategy and the inter-frame position setting strategy are integrated to obtain the target storage strategy.

[0116] The determining module is further configured to, when the target type is the first type, determine the preset storage strategy within the frame as the storage strategy within the frame, wherein the preset storage strategy within the frame represents the strategy of storing the parts to be put into storage in the floor storage frame, and the target frame is the floor storage frame; when the target type is the second type, determine the storage strategy within the frame based on historical outbound picking allocation information, wherein the target frame is a pallet storage frame or a rack storage frame, and the target frame is capable of storing different types of parts of the second type.

[0117] The determination module is further used to determine the historical picking frequency in the historical outbound picking allocation information and the component weight in the component attribute information; based on the historical picking frequency, an initial frame storage strategy is determined, wherein the initial frame storage strategy is used to indicate that the picking convenience of components with the first historical picking frequency is greater than that of components with the second historical picking frequency, the first historical picking frequency is greater than the second historical picking frequency, and the picking convenience is determined based on the type of the target frame and the picking method of the target frame; based on the component weight, the initial frame storage strategy is adjusted to obtain the frame storage strategy.

[0118] The determination module is also used to determine the stability of the target frame for storing the parts to be put into storage according to the initial frame storage strategy based on the preset simulation software and the weight of the parts; and to adjust the initial frame storage strategy based on the frame stability to obtain the frame storage strategy.

[0119] The determination module is further used to determine the historical picking frequency in the historical outbound picking allocation information and the component weight in the component attribute information; determine the frame picking frequency of the target frame based on the historical picking frequency, and determine the frame weight of the target frame based on the component weight; determine the initial frame inter-position setting strategy based on the historical picking frequency, wherein the initial frame inter-position setting strategy is used to represent the outbound travel path of the target frame corresponding to the first frame picking frequency from the outbound exit, the target frame picking frequency corresponding to the second frame picking frequency from the outbound exit, and the first frame picking frequency is greater than the second frame picking frequency; and adjust the initial frame inter-position setting strategy based on the frame weight to obtain the frame inter-position setting strategy.

[0120] According to another aspect of the present invention, an intelligent parts management device based on the coordinated consideration of vehicle parts storage and distribution is also provided, which is applied to the outbound picking end. This device can execute the intelligent parts management method based on the coordinated consideration of vehicle parts storage and distribution described in the above embodiments. The specific implementation method and preferred application scenarios are the same as those described in the above embodiments, and will not be repeated here.

[0121] Figure 7 This is a schematic diagram of an intelligent component management device based on the collaborative consideration of vehicle component storage and distribution, according to an embodiment of this application. Figure 7 As shown, the device includes the following: a determining module 702, an acquiring module 704, a generating module 706, and a selecting module 708.

[0122] The system includes a determination module 702, which, in response to receiving a parts outbound order, determines the outbound demand frequency of the parts to be outbound in the order, wherein the parts to be outbound represent the parts stored in the warehouse using the method described above; an acquisition module 704, which acquires the storage location information of the parts to be outbound; a generation module 706, which generates a target selection strategy based on the outbound demand frequency and storage location information; and a selection module 708, which performs outbound selection of the parts to be outbound based on the target selection strategy.

[0123] The generation module is also used to generate an initial selection strategy based on the frequency of outbound demand and storage location information; determine the selection time for executing the initial selection strategy; and adjust the initial selection strategy based on the selection time to obtain the target selection strategy.

[0124] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0125] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0126] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0127] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0128] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0129] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0130] The aforementioned memory can refer to devices inside a computer used to store data and programs, including RAM, hard disks, etc. RAM can be used to temporarily store running programs and data, while hard disks can be used to store programs and data long-term. Memory enables the computer to read and write data and execute programs. The aforementioned processor is responsible for executing instructions in computer programs and performing data processing. It can also be responsible for controlling and executing various operations, including arithmetic operations, logical operations, and data transmission.

[0131] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0132] The aforementioned computer storage media can refer to the media used in computer memory to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser discs, etc. Computer-readable storage media include stored programs, which can be a set of instructions that a computer can recognize and execute, running on an electronic computer to meet certain information needs.

[0133] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0134] The aforementioned computer program products can refer to software programs that have been written, tested, and released, and can run on computers or other devices. Computer program products can include application programs, operating systems, utility software, etc., used to achieve specific functions or solve specific problems.

[0135] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0136] The aforementioned non-volatile computer-readable storage medium can refer to a medium for storing data. Non-volatile computer-readable storage media can retain data without loss when power is off and can be used to store long-term data, such as operating systems, applications, and user files. Non-volatile storage media can include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0137] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0138] The aforementioned computer program can refer to a set of instructions used to tell the computer to perform specific tasks or operations. Computer programs can be written by programmers using specific programming languages ​​and can include algorithms, data structures, logic, and control flow. Computer programs can be used for a variety of purposes, including application software, operating systems, etc.

[0139] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection between units or modules can be electrical or other forms.

[0141] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as 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 technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0144] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A smart component management method based on the collaborative consideration of vehicle component storage and distribution, characterized in that, Applied to the inbound storage end, including: Based on the component attribute information of the components to be put into storage, the historical outbound picking and allocation information of the components to be put into storage is obtained. The components to be put into storage are used to represent vehicle components to be stored and managed in storage. The historical outbound picking and allocation information is used to represent the historical outbound picking and allocation record information of components of the same type as the components to be put into storage. Based on the component attribute information and the historical outbound picking allocation information, the target storage strategy for the components to be put into storage is determined. Based on the target storage strategy, the components to be stored are stored in the warehouse.

2. The method according to claim 1, characterized in that, Based on the component attribute information and the historical outbound picking allocation information, the target storage strategy for the components to be put into storage is determined, including: Determine the component volume from the component attribute information; Based on a preset volume threshold and the volume of the component, the components to be put into storage are classified to obtain the target type of the components to be put into storage. The target type includes a first type or a second type. The first type of component is used to represent components whose volume is greater than or equal to the preset volume threshold, and the second type of component is used to represent components whose volume is less than the preset volume threshold. Based on the historical outbound picking allocation information and the target type, the target storage strategy is determined.

3. The method according to claim 2, characterized in that, Based on the historical outbound picking allocation information and the target type, the target storage strategy is determined, including: Based on the historical outbound picking allocation information and the target type, the in-frame storage strategy for the parts to be put into storage is determined, wherein the in-frame storage strategy is used to characterize the strategy of storing the parts to be put into storage in the target frame. Based on the historical outbound picking and allocation information, a frame-to-frame position setting strategy for the parts to be put into storage is determined, wherein the frame-to-frame position setting strategy is used to represent the strategy for setting the position of the target frame. The target storage strategy is obtained by integrating the intra-frame storage strategy and the inter-frame location setting strategy.

4. The method according to claim 3, characterized in that, Based on the historical outbound picking allocation information and the target type, a framework-based storage strategy for the parts to be received is determined, including: When the target type is the first type, the preset frame storage strategy is determined as the frame storage strategy, wherein the preset frame storage strategy is used to represent the strategy of storing the parts to be put into storage in the ground stack storage frame, and the target frame is the ground stack storage frame. When the target type is the second type, the storage strategy within the frame is determined based on the historical outbound picking allocation information, wherein the target frame is a pallet storage frame or a rack storage frame, and the target frame is capable of storing different types of the second type of parts.

5. The method according to claim 3, characterized in that, Based on the historical outbound picking and allocation information, the storage strategy within the framework is determined, including: Determine the historical picking frequency in the historical outbound picking allocation information and the component weight in the component attribute information; Based on the historical picking frequency, an initial frame storage strategy is determined, wherein the initial frame storage strategy is used to indicate that the picking convenience of parts with a first historical picking frequency is greater than that of parts with a second historical picking frequency, the first historical picking frequency being greater than the second historical picking frequency, and the picking convenience being determined based on the type of the target frame and the picking method of the target frame; Based on the weight of the components, the initial in-frame storage strategy is adjusted to obtain the in-frame storage strategy.

6. The method according to claim 5, characterized in that, Based on the weight of the components, the initial in-frame storage strategy is adjusted to obtain the in-frame storage strategy, including: Based on the preset simulation software and the weight of the components, the stability of the target frame storing the components to be put into storage is determined according to the initial frame storage strategy. Based on the stability of the framework, the initial storage strategy within the framework is adjusted to obtain the current storage strategy within the framework.

7. The method according to claim 3, characterized in that, Based on the historical outbound picking and allocation information, a strategy for setting the inter-frame positions of the parts to be received is determined, including: Determine the historical picking frequency in the historical outbound picking allocation information and the component weight in the component attribute information; The frame picking frequency of the target frame is determined based on the historical picking frequency, and the frame weight of the target frame is determined based on the component weight. Based on the historical picking frequency, an initial frame location setting strategy is determined, wherein the initial frame location setting strategy is used to represent the outbound travel path of the target frame corresponding to the first frame picking frequency from the outbound exit, which is less than the outbound travel path of the target frame corresponding to the second frame picking frequency from the outbound exit, and the first frame picking frequency is greater than the second frame picking frequency. Based on the frame weight, the initial inter-frame position setting strategy is adjusted to obtain the inter-frame position setting strategy.

8. An intelligent parts management method based on the collaborative consideration of vehicle parts storage and distribution, applied to the outbound picking end, characterized in that, include: In response to receiving a parts outbound order, the frequency of outbound demand for the parts to be outbound in the parts outbound order is determined, wherein the parts to be outbound are used to represent parts that are stored in the warehouse using the method described in any one of claims 1 to 7; Obtain the storage location information of the parts to be shipped out; Based on the frequency of outbound demand and the storage location information, a target selection strategy is generated; Based on the target selection strategy, the parts to be shipped out are selected for shipment.

9. The method according to claim 8, characterized in that, Based on the frequency of outbound demand and the storage location information, a target selection strategy is generated, including: Based on the frequency of outbound demand and the storage location information, an initial selection strategy is generated; Determine the selection time for executing the initial selection strategy; Based on the selection time, the initial selection strategy is adjusted to obtain the target selection strategy.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor is configured to run the program, wherein the program executes the intelligent component management method based on the collaborative consideration of vehicle component storage and distribution as described in any one of claims 1 to 9.

Images