Warehouse partition storage management method based on warehousing product data

Abstract

The application discloses a warehouse partition storage management method based on warehousing product data and relates to the technical field of warehouse management.The application first divides the warehouse into three virtual functional areas according to a virtual partition ratio, binds virtual attribute parameters to storage locations in each virtual functional area, then extracts product turnover attribute data to be warehoused, matches a corresponding target functional area by means of an intelligent model, and finally sorts product sequences in the target functional area according to warehousing time, and sequentially completes target location allocation.The application discards the traditional fixed physical partition mode, realizes dynamic division of logical partitions without changing the hardware layout of the warehouse, combines location turnover scoring to complete partition classification, matches high turnover goods with the storage location with the highest operation efficiency, relies on an artificial intelligence model to analyze product turnover data to replace artificial experience to determine partition attribution, greatly improves matching accuracy, and allocates locations based on warehousing time to fully utilize idle locations released after delivery and revitalize warehouse resources.

Description

Technical Field

[0001] This invention belongs to the field of warehouse management technology, specifically a warehouse partitioned storage management method based on inbound product data. Background Technology

[0002] Current warehouse storage management models mostly adopt fixed-zone storage, which involves pre-dividing storage areas manually and storing specific product categories. This rigid zoning rule results in extremely poor flexibility. In modern warehousing operations characterized by multiple product categories, small batches, and high turnover, traditional management methods have several drawbacks: First, zoning relies on manual experience, easily leading to problems such as popular products being stored in remote areas, slow-moving products occupying core storage areas, and products with special attributes being stored together, significantly reducing the efficiency of inbound and outbound operations. Second, the static and fixed zoning cannot adapt to dynamic changes in the types of products entering the warehouse, inventory quantities, and turnover frequency, resulting in uneven warehouse space utilization, with some areas experiencing space shortages and others remaining idle for extended periods.

[0003] This invention provides a warehouse partitioned storage management method based on inbound product data to solve the above-mentioned technical problems. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a warehouse partitioned storage management method based on inbound product data.

[0005] To achieve the above objectives, a first aspect of the present invention provides a warehouse partitioned storage management method based on inbound product data, applied to a smart warehouse, comprising: The warehouse is divided into several virtual functional areas according to the virtual partition ratio; among them, the virtual functional areas include high-frequency turnover areas, regular storage areas and idle buffer areas, and the storage locations in the virtual functional areas are all associated with virtual attribute parameters; Retrieve the turnover attribute data of the products to be put into storage, and determine the target functional area to which the products to be put into storage are adapted based on the turnover attribute data; wherein, the turnover attribute data is used to identify the turnover type of the products to be put into storage. Select the target storage location for the product to be stored from the target functional area.

[0006] In one possible implementation, the warehouse is divided into several virtual functional areas according to the virtual partition ratio, including: Predict and obtain the product storage trend of the warehouse within a set time period; where the product storage trend corresponds to product types including high-turnover products, medium-turnover products, and low-turnover products; The product storage trend is converted to obtain the virtual partition ratio, and the storage locations in the warehouse are divided into several virtual functional areas based on the virtual partition ratio.

[0007] In one possible implementation, the storage locations in the repository are divided into several virtual functional areas based on a virtual partitioning ratio, including: Calculate the turnover score of the storage locations in the warehouse; whereby the turnover score is used to evaluate the inbound and outbound efficiency of the storage locations. The storage locations are divided according to the virtual partition ratio and the turnover score of the storage locations, resulting in several virtual functional areas.

[0008] In one possible implementation, the target functional areas for the products to be put into storage are determined based on turnover attribute data, including: Retrieve the partition adaptation model; the partition adaptation model is built based on artificial intelligence model training. Input the turnover attribute data of the products to be put into the warehouse into the partition adaptation model to obtain the target functional area; where the target functional area is one of several virtual functional areas.

[0009] In one possible implementation, the target storage location for the product to be stored is selected from the target functional area, including: The products to be received are sorted according to the receiving time within a set period, and a product sequence is obtained. Extract the products to be put into storage from the product sequence in sequence as target products; allocate storage locations for the target products in the target functional area as target storage locations.

[0010] In one possible implementation, when the target functional area cannot meet the storage requirements of the target product, the target functional area is temporarily expanded, including: Retrieve the expansion functional area associated with the target functional area; among them, the expansion functional area is a virtual functional area with a lower priority than the target functional area, and the priority of the virtual functional areas from high to low is high frequency turnover area, regular storage area and idle buffer; Determine whether the storage space shortage in the target functional area is less than the preset shortage threshold; if yes, temporarily expand the target functional area based on the expansion functional area; if no, determine that the storage space in the target functional area is insufficient.

[0011] In one possible implementation, conflict checking is performed before allocating the target storage location, including: The storage location allocated to the products to be put into storage will be used as the base storage location; Determine if there is a storage conflict when the product to be put into storage is stored in the basic storage location; if yes, redetermine the basic storage location; otherwise, mark the basic storage location as the target storage location.

[0012] In one possible implementation, adjusting the virtual function area within a set time period includes: The system calculates the storage space gap in virtual functional areas over several defined periods within a specified timeframe. The storage space gap is calculated based on the product sequence associated with the virtual functional areas within the defined periods. The decision on whether to restructure and adjust the virtual partition ratio is based on the complementarity of storage space gaps in several set periods; if yes, the virtual partition ratio is adjusted according to the storage space gaps; otherwise, the virtual partition ratio is not adjusted.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention first divides the warehouse into three virtual functional areas based on virtual partition ratios, and binds virtual attribute parameters to storage locations in each virtual functional area; then, it extracts the turnover attribute data of products to be stored, uses an intelligent model to match the corresponding target functional area, and finally sorts the product sequence by storage time within the target functional area to complete the allocation of target storage locations in sequence; this invention abandons the traditional fixed physical partitioning mode, and can achieve dynamic logical partitioning without changing the warehouse hardware layout. It combines storage location turnover scoring to complete partition classification, allowing high-turnover goods to be matched with the storage locations with the highest operational efficiency; it relies on artificial intelligence models to analyze product turnover data, replacing manual experience in determining partition assignment, and greatly improving matching accuracy; it allocates storage locations based on storage time, making full use of idle storage locations released after outbound shipments, and revitalizing warehousing resources.

[0014] 2. In this invention, when the target functional area cannot meet the storage demand, its expansion functional area is invoked. A preset gap threshold is used to determine whether temporary expansion should be performed. The temporarily borrowed storage space restores its original partition attributes after the goods are shipped out. By statistically analyzing the storage space gaps of multiple consecutive set periods within a set time period, and based on the complementarity of storage space gaps in different partitions, it is determined whether to reconstruct and adjust the virtual partition ratio. This invention only enables low-priority storage area replenishment when the storage space gap is less than the threshold, effectively avoiding long-term storage space occupation causing partition function disorder and ensuring that the original positioning of each virtual functional area is not affected. By integrating multi-period storage space gap data and combining the complementary characteristics of partition storage capacity for ratio optimization, it abandons the method of one-sided judgment in a single period and reduces the frequency of ineffective adjustments. This invention solves the problems of chaotic handling, blind partition adjustments, and unbalanced resource allocation when traditional warehousing capacity is insufficient, realizing dynamic borrowing and cyclical reuse of storage spaces, improving the flexibility of warehouse resource scheduling and overall operational stability. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the method steps of the warehouse partitioned storage management method in an embodiment of the present invention; Figure 2This is a schematic diagram illustrating the method steps of dividing a warehouse into several virtual functional areas according to a virtual partition ratio in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the steps of selecting the target storage location for a product to be stored from the target functional area in an embodiment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 are within the scope of protection of the present invention.

[0018] Please see Figure 1 The first aspect of this invention provides a warehouse partitioned storage management method based on inbound product data, applied to a smart warehouse. The implementation process of this method is as follows: A01: The warehouse is divided into several virtual functional areas according to the virtual partition ratio; among them, the virtual functional areas include high-frequency turnover areas, regular storage areas and idle buffer areas, and the storage locations in the virtual functional areas are all associated with virtual attribute parameters; The virtual partition ratio refers to the proportion of storage space allocated between the high-frequency turnover area, the regular storage area, and the idle buffer zone. The virtual partition ratio can be obtained through historical data statistics or set based on predicted product storage trends. The high-frequency turnover area is used to store high-turnover products, which have high inbound and outbound frequency and fast turnover speed, such as best-selling products, daily staples, and urgently needed production materials. The regular storage area is used to store medium-turnover products, which have moderate inbound and outbound frequency and stable inventory levels; these are neither best-selling nor long-term slow-moving goods. The idle buffer zone is used to store low-turnover products, which have low inbound and outbound frequency and slow turnover.

[0019] Virtual attribute parameters correspond one-to-one with storage locations in the virtual functional area, and these parameters adjust as the virtual functional area changes. Virtual attribute parameters form the data foundation for the automated operation of intelligent material handling equipment. During material handling operations, the equipment uses these parameters to locate storage locations, thereby achieving intelligent inbound and outbound processes.

[0020] It is worth noting that when dividing the warehouse into several virtual functional areas, only the regular areas are divided, while the original special areas are retained. The regular areas are used to store products stored at room temperature, and their environmental parameters are uniformly controlled under set conditions, generally at room temperature. The special areas are used to store products with special storage environment requirements, and their environmental parameters are uniformly controlled according to the product requirements.

[0021] If there are special storage needs that require fixed storage locations, several storage locations can be selected in the regular area and fixed. These fixed storage locations will not be considered when dividing the virtual functional areas. Once the special storage needs no longer exist, the fixed storage locations will be released and included in the virtual functional area division.

[0022] This invention sets virtual partition ratios based on commodity storage trends, logically partitions regular areas and associates them with flexibly changeable virtual attribute parameters, while reserving special areas according to storage needs and separating regular areas from special areas. This invention can dynamically adjust the storage capacity ratio of each virtual functional area without changing the physical layout of the warehouse, solving the problems of fixed physical partitions and uneven space utilization in traditional methods, while also taking into account the diverse storage needs of both regular and special goods.

[0023] A02: Retrieve the turnover attribute data of the products to be put into storage, and determine the target functional area to which the products to be put into storage are adapted based on the turnover attribute data; wherein, the turnover attribute data is used to identify the turnover type of the products to be put into storage. Turnover attribute data is used to analyze the compatibility between products to be received and various virtual functional areas, so as to match the most suitable virtual functional area for the products to be received. Turnover attribute data includes basic attribute data and turnover-related data. Basic attribute data includes product name, usage field, etc., while turnover-related data includes historical inbound and outbound frequency, historical turnover cycle, and average daily inbound and outbound quantity for the same period (the same period refers to the period corresponding to the set time period).

[0024] This invention determines the corresponding virtual functional area based on the turnover attribute data of products to be put into storage. It can comprehensively consider various characteristics to accurately match the corresponding virtual functional area for products to be put into storage, breaking the problem of partition matching deviation caused by a single evaluation standard. It ensures that the product circulation characteristics, storage requirements and virtual partition functional positioning are highly consistent, reducing the mismatch of goods from the source, ensuring the standardization of warehousing and storage, and allocating storage space resources according to the actual circulation pattern of products, optimizing the inbound and outbound operation flow, and further improving the overall efficiency of warehousing operations.

[0025] A03: Select the target storage location for the products to be stored from the target functional area.

[0026] This invention combines virtual functional area division, turnover attribute data matching with target functional areas, and target library determination to realize a complete product warehousing process. It relies on a dynamically adjustable virtual partition architecture to achieve flexible allocation of warehousing resources, and a multi-dimensional data model to ensure the accuracy of partition matching. Combined with a management mode that separates regular and special areas, it ensures product storage security. It not only overcomes the shortcomings of traditional fixed physical partitions, such as poor flexibility, excessive manual intervention, and low space utilization, but also allows for advance planning of partition ratios based on warehousing trends, proactively adapting to changes in warehousing operations. This comprehensively improves warehouse space utilization, inbound and outbound operation efficiency, storage security, and intelligent management level, and can effectively adapt to the multi-category, dynamic modern warehousing operation needs.

[0027] Traditional warehousing uses a physical zoning model, with fixed zoning boundaries and storage location assignments that cannot be flexibly adjusted to adapt to changes in the inbound structure of high, medium, and low-turnover products at different times. At the same time, manual zoning makes it difficult to accurately match the actual storage location requirements of different turnover types of products, which can easily lead to problems such as insufficient storage capacity in high-turnover product areas, low-turnover products occupying prime operating areas for extended periods, and an imbalance in the allocation of idle storage space resources. This not only results in low warehouse space utilization but also lengthens inbound and outbound operation lines, reduces overall turnover efficiency, and makes it impossible to make differentiated layouts for different operational stages such as peak, off-peak, and normal periods, making it difficult to adapt to dynamically changing needs for goods storage and turnover.

[0028] Please see Figure 2 The implementation process of dividing the warehouse into several virtual functional areas according to the virtual partition ratio in this invention is as follows: B01: Predict and obtain the product storage trend of the warehouse within a specified time period; where the product storage trend corresponds to product types including high-turnover products, medium-turnover products, and low-turnover products; Product storage trends correspond to product types categorized as high-turnover, medium-turnover, and low-turnover products. High-turnover products correspond to high turnover rates, low-turnover products to low turnover rates, and medium-turnover products to a turnover rate in between. The product storage trend forecast period can be set to one month or one week, and can also be divided into peak, off-peak, and normal periods based on historical storage data, forecasting the warehouse's product storage trends for each period.

[0029] Product storage trends can be predicted using time-series forecasting models, such as LSTM models. Historical data is used to train the LSTM model, which is then stored for later retrieval. The use of time-series forecasting models to predict future trends is widely available in existing solutions; the specific implementation process can be found in these solutions and will not be elaborated upon here.

[0030] B02: Transform product storage trends to obtain virtual partition ratios, and divide the storage locations in the warehouse into several virtual functional areas based on the virtual partition ratios.

[0031] Based on product storage trends, the required storage space for each product type within a set time period is statistically analyzed, and the ratio of the required storage space for each product type is used as the virtual partition ratio. Next, all available storage storage spaces in the regular area of ​​the warehouse are statistically analyzed, excluding storage storage spaces fixed according to special storage needs. The available storage storage spaces are then divided according to the virtual partition ratio to obtain a high-frequency turnover area, a regular storage area, and an idle buffer zone.

[0032] It is worth noting that when calculating the storage space demand for each product type within a set time period based on product storage trends, products that have not yet been shipped out at the start of the set time period should be considered. In other words, the storage space demand for each product type needs to be calculated by combining product storage trends and the already stored products. For example, if the predicted storage space demand for high-turnover products is A1 based on product storage trends, and the storage space occupied by high-turnover products in the warehouse is A2, then the storage space demand for high-turnover products within the set time period will be calculated as A1 + A2.

[0033] This invention first predicts the storage trends of high, medium, and low turnover products in the warehouse in different time periods. Combined with the characteristics of different warehouse operation stages, it accurately calculates the storage space demand for various products and uses this to calculate the virtual partition ratio. Available storage spaces are then divided proportionally to form various virtual functional areas. This invention can dynamically plan storage space resources based on the product turnover structure of different cycles and operational stages, ensuring that the size of the virtual partitions highly matches the actual storage needs of various product types. This fundamentally avoids mismatch and waste of storage space resources. Furthermore, it can complete partition reconstruction without altering the physical layout of the warehouse, effectively improving the utilization rate of warehouse space and the rationality of the partition layout. This ensures that products with different turnover characteristics can obtain suitable storage areas, optimizing the overall warehouse turnover efficiency.

[0034] The process of dividing storage locations in a warehouse into several virtual functional areas using a virtual partitioning ratio in this invention is as follows: C01: Calculate the turnover score of the storage locations in the warehouse; where the turnover score is used to evaluate the inbound and outbound efficiency of the storage locations. Turnover score is used to evaluate the inbound and outbound efficiency of storage locations. It can be calculated based on factors affecting the transportation efficiency of intelligent handling equipment, such as the handling distance between the storage location and the entrance / exit, and handling inflection points. For example, if the route distance of the shortest inbound / outbound route through the storage location is marked as J, and the number of handling inflection points in this shortest inbound / outbound route is marked as G, then the turnover score can be calculated using the formula... The turning point in transportation refers to the turning point. This is a preset scaling factor.

[0035] This invention calculates the turnover score of storage locations in the warehouse before assigning them to virtual functional areas, and evaluates the inbound and outbound efficiency of these locations based on the turnover score. A higher turnover score indicates higher inbound and outbound efficiency for the corresponding storage location, meaning that intelligent handling equipment can quickly process the products stored in the storage location. In other words, a higher turnover score increases the likelihood of the location being assigned to a high-frequency turnover area.

[0036] C02: Based on the virtual partition ratio and the turnover score of the storage location, the storage location is divided into several virtual functional areas.

[0037] When allocating storage locations based on the virtual partition ratio and storage location turnover score, the locations are divided sequentially into high-frequency turnover area, regular storage area, and idle buffer. The storage location sequence is obtained by sorting the turnover scores from high to low. The number of storage locations corresponding to the high-frequency turnover area is determined according to the virtual partition ratio. Storage locations corresponding to the number of storage locations are extracted from the storage location sequence and assigned to the high-frequency turnover area. This process is repeated to obtain the regular storage area and idle buffer.

[0038] It should be noted that when dividing storage locations based on virtual partition ratio and storage location turnover score, only the turnover score is considered. This may result in storage locations in the same virtual functional area not being adjacent. However, based on intelligent handling equipment, this will not affect the efficiency of product inbound and outbound operations.

[0039] Understandably, after the virtual functional areas are re-divided within a set time period, products that are not compatible with the virtual functional areas should be transferred to the corresponding virtual functional areas so that sufficient storage space can be freed up in the virtual functional areas to store products that will be stored in the future as well as those that have already been stored.

[0040] For example, taking a smart e-commerce warehouse as an example, the warehouse's basic information is as follows: the warehouse has a total of 1,000 regularly available storage locations, and the designated independent special storage area (constant temperature and explosion-proof storage area) is not included in the allocation.

[0041] Using a calendar month as a defined time period, an LSTM time series model is used to predict the current month's inventory trends, combined with existing warehouse inventory to determine storage space demand. It is predicted that next month's new high-turnover products (hot-selling daily necessities) will require 420 storage spaces (A1 = 420). Currently, high-turnover products in the warehouse already occupy 180 storage spaces (A2 = 180). The total storage space demand for high-turnover products is 420 + 180 = 600. Similarly, the total storage space demand for medium-turnover products is 160 + 40 = 200, and the total storage space demand for low-turnover products is 140 + 60 = 200. Based on this, the virtual partition ratio is 3:1:1. The turnover score of all storage spaces in the warehouse is calculated sequentially, and the storage spaces are sorted from highest to lowest to obtain a storage space sequence. The first 600 spaces in the sequence are designated as a high-frequency turnover area, the last 200 as an idle buffer zone, and the remaining storage spaces are designated as a regular storage area.

[0042] After dividing the warehouse into several virtual functional areas, virtual attribute parameters are set for the storage locations in each virtual functional area. The virtual attribute parameters include storage location ID, attribute label, storage constraints, load threshold, and environmental parameters. The storage location ID consists of the virtual functional area to which the storage location belongs and its number. The attribute label is used to identify the virtual functional area to which the storage location belongs. The storage constraints include rules such as the types of goods that can be stored in the storage location, prohibited mixed storage categories, stacking restrictions, and special protection requirements. The load threshold includes numerical indicators such as the rated load capacity limit of the storage location and its associated shelving, the maximum storage volume, and the number of stacking layers allowed.

[0043] Traditional warehousing relies on manual experience or simple rules to match products with storage zones and allocate warehouse locations. The evaluation dimensions are singular, and it is impossible to comprehensively combine multiple product attributes to complete accurate zoning and matching. The matching results are highly subjective and have large errors. At the same time, warehouse location allocation is mostly done randomly, which cannot make full use of the idle warehouse locations released after the previous goods are shipped out. This easily leads to scattered idle warehouse locations and insufficient resource utilization, which further reduces the overall storage efficiency and management intelligence level of the warehouse.

[0044] The implementation process of determining the target functional area to be adapted for the product to be put into the warehouse based on turnover attribute data in this invention is as follows: D01: Retrieve the partition adaptation model; the partition adaptation model is built based on artificial intelligence model training. The partition adaptation model establishes a mapping relationship between turnover attribute data and virtual functional areas. This model can be constructed using artificial intelligence models such as deep convolutional neural networks and backpropagation neural networks. A training sample set can be built using historical data, comprising several sample data points, each including product turnover attribute data and the corresponding virtual functional areas. The artificial intelligence model is trained using this training sample set, and the trained model is then labeled as the partition adaptation model. The data processing and training parameter settings during training can refer to existing solutions; the artificial intelligence model only needs a basic structure, and the specific training process will not be elaborated upon here.

[0045] D02: Input the turnover attribute data of the products to be put into the warehouse into the partition adaptation model to obtain the target functional area; where the target functional area is one of several virtual functional areas.

[0046] Please see Figure 3 The process for selecting the target storage location for the product to be stored from the target functional area in this invention is as follows: E01: Sort the products to be put into storage in the target functional area within the set period according to the storage time to obtain the product sequence; The time range of the set cycle is much shorter than the set period. The set period can be set to one month, while the set cycle can be set to one day. By setting the cycle, products awaiting warehousing are matched with target storage locations in batches, thereby achieving orderly warehousing. The warehousing time refers to the planned time for the products to be received. Target storage locations are assigned to products based on their warehousing time. In case of emergencies, the warehousing order of products can be adjusted by changing the warehousing time.

[0047] E02: Extract the products to be put into storage from the product sequence as target products in sequence; allocate storage locations for the target products in the target functional area as target storage locations.

[0048] When a product to be put into storage is identified as a target product, its corresponding storage time is extracted. After the storage time is extracted from the target functional area, an idle storage location is selected from the idle storage locations based on the quantity of the target product.

[0049] When allocating target storage locations for target products, this invention uses the product's entry time as the time base to retrieve available storage locations in the target functional area. These available storage locations include those vacated due to products leaving the warehouse before the entry time. This method of determining target storage locations can improve the utilization rate of storage locations.

[0050] For example, if the cycle is set to one day, tomorrow's product sequence includes three batches of products to be put into storage. Product B1 is a popular toiletries product (requiring 100 storage spaces), which corresponds to the high-frequency turnover area. Product B2 is home storage products, which corresponds to the regular storage area. Product B3 is seasonal slow-moving ornaments, which corresponds to the idle buffer zone.

[0051] According to the registered planned warehousing time, the order of priority is product B1, product B2, and product B3. When allocating target storage locations for product B1, it was found that 20 storage locations in the high-frequency turnover area were already vacant due to the previous day's goods being shipped out before its warehousing time. Combining this with the remaining vacant storage locations, a total of 130 vacant storage locations can be provided to meet the storage needs of product B1's 100 storage locations. From the remaining 130 vacant storage locations, 100 locations are selected according to their turnover scores from highest to lowest and allocated to product B1, thus completing the target storage location allocation for product B1. Following this logic, target storage locations for products B2 and product B3 are allocated sequentially.

[0052] This invention employs a partitioning adaptation model trained by an artificial intelligence model. Based on the mapping relationship between the turnover attribute data and virtual functional areas established by the model, partitioning matching is completed. It can achieve intelligent and accurate partitioning determination by comprehensively considering various product characteristics, thus eliminating matching biases caused by human experience. At the same time, goods are sorted based on the product's warehousing time, and target storage locations are allocated in combination with the dynamic vacancy status of storage locations. This makes full use of the storage locations vacated after the previous goods are shipped out and the storage location resources dynamically released during inventory checks, effectively reducing storage location idleness and waste. While improving the accuracy of partitioning matching, it significantly improves the overall utilization rate of storage locations.

[0053] When the storage capacity of the target storage partition corresponding to a product is insufficient, traditional warehousing either directly determines that the warehouse capacity is insufficient, resulting in interruption of inbound storage, or occupies other partition storage locations for a long time without restoring them. This can easily lead to continuous shortage of resources in high-priority turnover storage areas, the squeezing out of functions in low-priority storage areas, and long-term confusion in storage location ownership. This will disrupt the original partition layout and turnover order, and reduce the overall operational stability of warehousing.

[0054] In this invention, when the target functional area cannot meet the storage requirements of the target product, the target functional area is temporarily expanded. The implementation process is as follows: F01: Retrieve the expansion functional area associated with the target functional area; where the expansion functional area is a virtual functional area with a lower priority than the target functional area, and the priority of the virtual functional areas from high to low is high frequency turnover area, regular storage area and idle buffer. The virtual functional areas are prioritized from highest to lowest as follows: high-frequency turnover area, regular storage area, and idle buffer. The expansion functional areas corresponding to the high-frequency turnover area include both regular storage area and idle buffer. The expansion functional area corresponding to the regular storage area is the idle buffer, while the idle buffer does not have a corresponding expansion functional area. The expansion functional areas corresponding to the target functional area are pre-associated, so the associated expansion functional areas and their storage location parameters can be retrieved at any time.

[0055] F02: Determine whether the storage space shortage in the target functional area is less than the preset shortage threshold; if yes, temporarily expand the target functional area based on the expansion functional area; if no, determine that the storage space in the target functional area is insufficient.

[0056] When the target functional area cannot meet the storage requirements of the target product, it is determined whether the storage space shortage in the target functional area is less than a preset shortage threshold. If it is less than the preset shortage threshold, it indicates that the storage space shortage is not significant. In this case, the target functional area is temporarily expanded to complete the storage of the product to be received. In other words, when the high-frequency functional area cannot meet the storage requirements of the product to be received, a suitable storage space is selected from its associated expansion functional areas (including regular storage areas and idle buffers) for temporary storage of the product to be received. Here, the suitable storage space is still selected from the expansion functional areas according to the turnover score from high to low. It should be noted that when determining whether the storage requirements of the target product are met, not only the quantity storage requirements are considered, but also the load requirements, environmental requirements, etc.

[0057] For example, assuming the gap threshold is set to 30, the aforementioned product B2 requires 100 storage locations, but the available storage locations in the regular turnover area are 80. In this case, the storage location gap for product B2 is 20. Since the storage location gap is less than the gap threshold, temporary expansion is carried out.

[0058] The expansion function area of ​​the regular storage area is an idle buffer. From the idle storage locations in the idle buffer, 20 storage locations are selected according to the turnover score from high to low. By modifying the virtual attribute parameters of the 20 storage locations, they are assigned to the regular function area, thus completing the storage of product B2.

[0059] When the shortage of storage space exceeds the shortage threshold, it indicates that temporary expansion cannot solve the problem. At this time, it should be indicated that there are not enough available storage spaces in the target functional area (the regular functional area in this example), and the inbound of product B2 needs to be interrupted. At the same time, it can also be initiated to determine whether the virtual functional area needs to be adjusted.

[0060] It's important to note that temporary expansion refers to temporarily allocating storage locations from the expanded functional area to the target functional area. Once the products in these storage locations have been shipped out, they are returned to the original expanded functional area. For example, if the high-frequency functional area cannot meet storage requirements, some storage locations are selected from the regular storage area and allocated to the high-frequency functional area. After the products in these selected storage locations have been shipped out, they are returned to the regular storage area by modifying the virtual attribute parameters. The storage location shortage is calculated by the storage location demand of all unstored products in the corresponding product sequence of the target functional area and the remaining storage location in the target functional area.

[0061] This invention pre-matches corresponding expansion functional areas to each virtual functional area according to priority, clarifies the hierarchical expansion range, and sets a gap threshold as the basis for expansion judgment, initiating temporary expansion only when the gap is small. By temporarily allocating storage locations in low-priority partitions to target functional areas to replenish storage capacity, and automatically restoring the original partition attributes after the goods in the storage location have been removed, it can quickly replenish storage capacity and ensure continuous warehousing operations without changing the physical layout, while avoiding long-term occupation of storage locations that would cause partition function disorder. It takes into account both emergency storage needs and the original functional positioning of each virtual partition, realizing dynamic borrowing and cyclical reuse of storage location resources, further improving the flexibility of warehousing resource scheduling and overall operational stability.

[0062] Traditional warehousing involves directly implementing shelving operations after warehouse space allocation, lacking a standardized pre-emptive conflict verification process. It relies solely on manual experience to determine whether goods can be mixed, whether the environment is suitable, and whether the load-bearing capacity is compliant. This not only results in low efficiency and high subjectivity but also easily leads to problems such as illegal mixed storage of goods, incompatible storage environments, and overloading of shelves. This can easily cause product damage, create safety hazards, and delay the warehousing process due to subsequent rectification and rework. It is difficult to guarantee the standardization of warehousing and the safety of operations.

[0063] The implementation process of conflict verification before allocating target storage locations in this invention is as follows: G01: The storage location allocated to the products to be put into storage will be used as the basic storage location; G02: Determine if there is a storage conflict when the product to be put into storage is stored in the basic storage location; if yes, then redetermine the basic storage location; if no, then mark the basic storage location as the target storage location.

[0064] Storage conflicts include category-mixing conflicts, environmental parameter conflicts, and load-bearing conflicts. Category-mixing conflict verification checks whether there are any storage incompatibilities between the product to be stored and existing products in the surrounding storage area. For example, products with corrosive, volatile, odorous, flammable, or explosive properties must not be stored together with precision components, food, or ordinary daily necessities. Environmental parameter conflict verification verifies whether the actual environmental conditions of the storage area, such as temperature, humidity, ventilation, and lighting, match the storage requirements of the product to be stored, preventing products from becoming damp, deteriorating, aging, or failing due to substandard environmental conditions. Load-bearing conflict verification verifies whether the storage area meets the overall weight and volume requirements of the product to be stored, avoiding safety and storage problems such as overloading and deformation of shelves or exceeding product stacking limits.

[0065] For example, the basic storage location assigned to the aforementioned product B2 (ordinary household towels) is C012. The area surrounding this basic storage location C012 consists entirely of clothing and home textiles, with no flammable, explosive, corrosive, or odorous goods, thus avoiding any storage conflicts. The warehouse environment for towels requires normal temperature and ventilation, and the temperature, humidity, and ventilation conditions of this location meet the standards, eliminating environmental conflicts. The total weight, volume, and stacking number of the goods are all below the rated threshold of the shelving, eliminating load-bearing conflicts. After the storage conflict verification passes, the basic storage location is marked as the target storage location.

[0066] This invention adds a pre-emptive conflict verification process before formally allocating target storage locations. This process comprehensively checks three types of risks: mixed storage of product categories, environmental parameters, and load capacity. If a storage conflict is detected, the storage location is re-selected, and only locations without conflicts are confirmed as the final target storage locations. This solution can proactively avoid various storage risks, eliminating problems such as mixed storage of goods, incompatible environments, and overloaded shelves from the source, effectively ensuring product storage quality and warehousing operation safety. Simultaneously, by replacing manual judgment with a standardized process, it reduces human error, improves the compliance and reliability of storage location allocation, and ensures the smooth and orderly conduct of warehousing operations.

[0067] Traditional virtual zoning adjustment methods mostly rely on passive adjustments based on storage space shortages in a single period, without comprehensively analyzing long-term trends using multi-period data or considering the complementary relationship between storage capacity surpluses and shortages in different zones. This easily leads to an imbalance where some zones experience long-term storage capacity shortages while others remain idle. Frequent and repeated adjustments to zoning ratios not only increase the system's computational burden but also disrupt stable warehousing operations. It also fails to achieve coordinated allocation of storage space resources among various virtual functional areas, making it difficult for zoning layouts to adapt to the dynamic changes in warehousing operations in the long term.

[0068] The process for adjusting the virtual function area within a set time period in this invention is as follows: H01: Statistics on the storage space gap of virtual functional areas in several defined periods within a specified time period; wherein, the storage space gap is calculated based on the product sequence associated with the virtual functional area within the defined period; Each virtual functional area is associated with a product series within a given period. Based on the product series and the storage locations in the virtual functional areas, the storage location gap for each virtual functional area can be calculated. For example, if the high-frequency functional area has 100 idle storage locations within a given period, and the actual need for storage locations is calculated based on its associated product series, then the storage location gap is 20 locations.

[0069] H02: Determine whether the virtual partition ratio needs to be restructured and adjusted based on the complementarity of storage space gaps in several set periods; if yes, adjust the virtual partition ratio according to the storage space gaps; otherwise, do not adjust the virtual partition ratio.

[0070] The complementarity of storage space gaps refers to whether the storage space gaps between high-frequency turnover areas, regular storage areas, and idle buffer zones can be complementary. When the storage space gap in a certain virtual functional area is greater than the pre-gap threshold, and there are surplus storage spaces in other virtual functional areas, it can be understood that the storage space gaps are complementary.

[0071] Adjustments to virtual functional areas within a set time period can be made after the set period ends. This means that regardless of whether all products awaiting warehousing within the set period have been received, the inventory gap in virtual functional areas across several set periods within the set time period will be calculated, with the most recent set periods being counted by default. The virtual partition ratio should be recalculated at the end of each set time period. Therefore, adjustments to virtual functional areas within a set time period are generally not made if the current set time period is about to end. For example, if the set time period is one month and the set period is one day, and the time remaining before the end of the set time period for several consecutive set periods (a total of five days) is no more than five days, then the virtual functional areas can be adjusted after the set time period ends.

[0072] For example, referring to the previous example, there are 1000 storage locations in the warehouse. The original partition ratio is 3:1:1 for high-frequency turnover area: regular storage area: idle buffer, with corresponding storage locations of 600, 200, and 200 respectively. The time period is set to 1, and the cycle is set to 1 day.

[0073] The storage space shortage over five consecutive days is as follows: The high-frequency turnover area has an average daily shortage of 60 storage spaces (greater than the 30-space shortage threshold); the regular storage area has an average daily surplus of 30 storage spaces; and the idle buffer zone has an average daily surplus of 40 storage spaces. The storage space shortage complementarity analysis shows that the high-frequency turnover area has a long-term large shortage, while the regular storage area and the idle buffer zone have consistently large surpluses. This indicates a complementary nature to the storage space shortages, and the existing zoning ratio is no longer suitable for the current product structure. Therefore, it is determined that the virtual zoning ratio needs to be restructured. Based on this, the virtual zoning ratio is adjusted to (600+60):(200-30):(200-30). Virtual functional areas are then re-divided according to this ratio to ensure that the storage space shortage is filled. The surplus virtual functional areas are adjusted evenly by default.

[0074] In this example, if the high-frequency turnover area has a daily shortage of 35 storage spaces (greater than the shortage threshold of 30), the regular storage area has a daily surplus of 60 storage spaces, and the idle buffer has a daily surplus of 40 storage spaces, then the virtual partition ratio is (600+35):200:(200-35). Alternatively, the storage space shortage can be compensated by adjusting the proportion of the idle buffer.

[0075] This invention continuously analyzes the storage space shortage in each virtual functional area over multiple set periods and determines whether to restructure the virtual partition ratio based on the complementarity of storage space shortages in adjacent priority partitions. By combining long-term operational data to analyze the partition's operational status, this invention leverages the complementary characteristics of storage capacity surplus and shortage within each partition to comprehensively adjust the partition ratio, reducing unnecessary partition changes and maintaining overall warehouse layout stability. Simultaneously, it optimizes the redistribution of storage space resources across different virtual functional areas, resolving the issues of insufficient storage capacity in some partitions and idle resources in others from a long-term perspective, further improving the overall utilization rate of warehouse space and the rationality and stability of the partition layout.

[0076] Traditional warehousing relies on manual forklifts and human handling for goods entry and exit, resulting in low efficiency, high labor intensity, and susceptibility to human error, often leading to incorrect location selection or working in the wrong area. Furthermore, manual operations cannot be synchronized with dynamically changing virtual partitions and temporary expansion of storage spaces, making it difficult to adapt to scheduling modes with frequent changes in storage location attributes. This hinders the effective implementation of virtual partition management models, resulting in a low overall level of automation and intelligence in warehousing. The intelligent warehouse in this invention achieves intelligent product entry and exit through intelligent handling equipment.

[0077] The intelligent handling equipment in this invention is an automated logistics device deployed within a smart warehouse, relying on a warehouse management system and virtual partition data to achieve automatic operation. The main types include AGVs (Automated Guided Vehicles), stacker cranes, and shuttle vehicles. The intelligent handling equipment can read the virtual attribute parameters, location codes, and partition information corresponding to the storage location based on the inbound / outbound plan, automatically plan the travel path, and complete the shelving and handling of products from the inbound receiving area to the target storage location, as well as the picking and transfer from the storage location to the outbound area. The equipment requires no manual on-site operation throughout the entire process and can synchronously execute operations in conjunction with business logic such as dynamic adjustments to virtual partitions, temporary expansion of storage locations, and changes to storage location attributes, adapting to the entire inbound / outbound task under the warehouse virtual partitioning model.

[0078] After the target storage location is allocated, the intelligent handling equipment takes over the inbound and outbound operations. The intelligent handling equipment can directly read data such as the virtual attributes and partition affiliation of the storage location to automatically locate and transfer the goods, accurately matching the dynamic changes of functional virtual partitions and temporary expansion storage locations, and achieving seamless connection between partition scheduling and on-site handling operations. This not only completely reduces manual intervention, lowers labor costs and operational error rates, and significantly improves the efficiency of inbound and outbound operations, but also enables the entire virtual partition storage management solution to achieve intelligent operation of the entire process from logical partitioning and storage location allocation to on-site execution, giving full play to the technical advantages of dynamic partitioning and flexible inventory adjustment.

[0079] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments.

[0080] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any other combination thereof. When implemented using a software program, it can be implemented entirely or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0081] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A warehouse partitioned storage management method based on inbound product data, applied to intelligent warehouses, characterized in that: include: The warehouse is divided into several virtual functional areas according to the virtual partition ratio; among them, the virtual functional areas include high-frequency turnover areas, regular storage areas and idle buffer areas, and the storage locations in the virtual functional areas are all associated with virtual attribute parameters; Retrieve the turnover attribute data of the products to be put into storage, and determine the target functional area to which the products to be put into storage are adapted based on the turnover attribute data; wherein, the turnover attribute data is used to identify the turnover type of the products to be put into storage. Select the target storage location for the product to be stored from the target functional area.

2. The warehouse partitioned storage management method based on inbound product data according to claim 1, characterized in that, The warehouse is divided into several virtual functional areas based on the virtual partition ratio, including: Predict and obtain the product storage trend of the warehouse within a set time period; where the product storage trend corresponds to product types including high-turnover products, medium-turnover products, and low-turnover products; The product storage trend is converted to obtain the virtual partition ratio, and the storage locations in the warehouse are divided into several virtual functional areas based on the virtual partition ratio.

3. The warehouse partitioned storage management method based on inbound product data according to claim 2, characterized in that, The storage locations in the warehouse are divided into several virtual functional areas by using a virtual partitioning ratio, including: Calculate the turnover score of the storage locations in the warehouse; whereby the turnover score is used to evaluate the inbound and outbound efficiency of the storage locations. The storage locations are divided according to the virtual partition ratio and the turnover score of the storage locations, resulting in several virtual functional areas.

4. The warehouse partitioned storage management method based on inbound product data according to claim 1, characterized in that, Based on turnover attribute data, the target functional areas for the products to be received are determined, including: Retrieve the partition adaptation model; the partition adaptation model is built based on artificial intelligence model training. Input the turnover attribute data of the products to be put into the warehouse into the partition adaptation model to obtain the target functional area; where the target functional area is one of several virtual functional areas.

5. The warehouse partitioned storage management method based on inbound product data according to claim 2, characterized in that, Select the target storage location for the products to be received from the target functional area, including: The products to be received are sorted according to the receiving time within a set period, and a product sequence is obtained. Extract the products to be put into storage from the product sequence in sequence as target products; allocate storage locations for the target products in the target functional area as target storage locations.

6. The warehouse partitioned storage management method based on inbound product data according to claim 5, characterized in that, When the target functional area cannot meet the storage requirements of the target product, the target functional area is temporarily expanded, including: Retrieve the expansion functional area associated with the target functional area; among them, the expansion functional area is a virtual functional area with a lower priority than the target functional area, and the priority of the virtual functional areas from high to low is high frequency turnover area, regular storage area and idle buffer; Determine whether the storage space shortage in the target functional area is less than the preset shortage threshold; if yes, temporarily expand the target functional area based on the expansion functional area; if no, determine that the storage space in the target functional area is insufficient.

7. The warehouse partitioned storage management method based on inbound product data according to claim 5, characterized in that, Conflict checks are performed before allocating target storage locations, including: The storage location allocated to the products to be put into storage will be used as the base storage location; Determine if there is a storage conflict when the product to be put into storage is stored in the basic storage location; if yes, redetermine the basic storage location; otherwise, mark the basic storage location as the target storage location.

8. The warehouse partitioned storage management method based on inbound product data according to claim 5, characterized in that, Adjust the virtual function area within a set time period, including: The system calculates the storage space gap in virtual functional areas over several defined periods within a specified timeframe. The storage space gap is calculated based on the product sequence associated with the virtual functional areas within the defined periods. The decision on whether to restructure and adjust the virtual partition ratio is based on the complementarity of storage space gaps in several set periods; if yes, the virtual partition ratio is adjusted according to the storage space gaps; otherwise, the virtual partition ratio is not adjusted.

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