Storage location distribution method for goods warehousing

Abstract

The invention discloses a storage location distribution method for goods warehousing, and the method comprises the steps: dividing goods clusters according to the attribute information of goods; according to the goods cluster, in combination with the warehouse area attribute information, determining a warehouse area distribution result of the goods; and according to the warehouse area distribution result of the goods, in combination with a warehouse location distribution strategy, determining warehouse location coordinates of the goods based on the idle warehouse location information. According to the method, the goods are subjected to clustering analysis, accurate classification is realized according to goods attributes, a scientific basis is provided for warehouse area distribution, the management refinement degree is remarkably improved, and the workload and the error rate of manual classification are reduced. And different types of goods are allocated to the most suitable warehouse area in combination with warehouse area attributes, so that the warehouse layout is optimized, and space resources are utilized to the maximum extent. After the storage area is determined, based on the idle storage location information and the storage location distribution strategy, the storage location coordinates are accurately selected, the in-out storage path time is shortened, the operation efficiency is improved, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of logistics warehousing, and specifically to a storage location allocation method for warehousing goods. Background Art

[0002] In the logistics and warehousing industry, storage allocation is a key step in the process of goods entering the warehouse and putting them on the shelves. A reasonable storage allocation strategy can effectively improve the space utilization rate of the warehouse, reduce the time for goods to enter and exit the warehouse, reduce operating costs, and improve the overall efficiency of warehouse operations. With the continuous development of warehouse management technology, storage allocation strategies have gradually shifted from traditional experience-driven to data-driven intelligent decision-making.

[0003] Most existing allocation strategies only consider the single attribute of goods or warehouse areas, lacking comprehensive consideration of the attributes of goods and warehouse areas, resulting in unreasonable warehouse area allocation and affecting warehouse operation efficiency. For example, when the attributes of the goods themselves are not considered, but the warehouse area is considered to allocate the warehouse area within the scope of the idle warehouse area. This allocation will lead to higher warehouse space utilization, but due to the lack of consideration of the attributes of the goods themselves, it will lead to lower operation efficiency.

[0004] In addition, most existing allocation strategies only consider the warehouse allocation of goods, that is, allocating goods to different warehouse areas in the warehouse. However, this coarse-grained division has not been further refined to specific warehouse coordinates, resulting in the specific storage location of the goods in the warehouse area is still unclear. Due to unclear warehouse coordinates, the storage location of the goods in the warehouse may be unreasonable, resulting in too long in and out of the warehouse path, increasing transportation time and operating costs. In addition, due to unclear warehouse coordinates, operators may need to spend more time looking for suitable warehouse locations when putting goods on and off the shelves, reducing operational efficiency. Summary of the invention

[0005] The present invention provides a method for allocating storage locations for goods entering the warehouse, so as to solve the problems of unreasonable storage area allocation due to incomplete consideration of the properties of goods or storage areas, unreasonable storage locations and low operating efficiency due to unclear storage location coordinates.

[0006] The technical solution adopted by the present invention is: A method for allocating storage locations for goods entering a warehouse, comprising: Divide goods into clusters according to their attribute information; Determine the warehouse area allocation result of the goods according to the clustering of the goods and the warehouse area attribute information; According to the warehouse area allocation result of the goods, combined with the warehouse location allocation strategy, based on the free warehouse location information, the warehouse location coordinates of the goods are determined.

[0007] The storage location allocation method for goods warehousing described in the present invention also includes the following additional technical features: According to the product attribute information, the products are divided into clusters, specifically: Identify the goods according to their type. Perform cluster analysis based on the attribute information of multiple types of goods and divide the goods into clusters; According to the identifier and the corresponding relationship between the commodity attribute information and the commodity cluster, the corresponding relationship between the identifier of the commodity and the commodity cluster is obtained.

[0008] The specific cluster analysis method is as follows: If the data in the commodity attribute information is of a category type, feature engineering processing is performed to form a category feature group; If the data in the commodity attribute information is of numerical type, processing is performed to obtain a numerical feature group; According to the feature group obtained by processing the product attribute information, the distance between different product attribute information is calculated, and clustering is performed based on the distance to divide the products into clusters.

[0009] The product attribute information includes static attribute data and / or dynamic attribute data, wherein the static attribute data includes at least one of size, weight, and origin; and the dynamic attribute data includes at least one of sales volume within a period and turnover rate.

[0010] According to the clustering of goods and the warehouse attribute information, the warehouse allocation result of the goods is determined, specifically: Performing cluster analysis on the attribute information of the storage area to form storage location partitions; Analyze the feature group of the product cluster and the feature group of the storage location partition to obtain the corresponding relationship between the product cluster and the storage location partition; Among them, the analysis method includes at least one of exploratory mining and feature engineering clustering analysis, association rule analysis, and interpretability analysis.

[0011] The warehouse area attribute information includes at least one of location, lane width, shelf type, environmental conditions and the like.

[0012] According to the warehouse area allocation result of the goods, combined with the warehouse location allocation strategy, based on the free warehouse location information, the warehouse location coordinates of the goods are determined, specifically: According to the warehouse area allocation result of the goods, read the free warehouse location corresponding to the current warehouse area; Select the corresponding allocation strategy based on product clustering; According to the allocation strategy, the storage location coordinates are determined for the product from the free storage locations.

[0013] The allocation strategy includes at least one of random storage location allocation, exit proximity strategy, entrance proximity strategy, longest unused time strategy, low-level storage priority strategy, high-level storage priority strategy, and whole-stock priority strategy.

[0014] The present invention also provides a computer-readable storage medium, which is used to store computer instructions, and the computer instructions are used to enable a computer to execute the storage location allocation method for goods warehousing.

[0015] The present invention again provides an electronic device, comprising: Memory, for storing computer instructions; A processor is used to implement the storage location allocation method for goods warehousing when executing the computer instructions.

[0016] Due to the adoption of the above technical solution, the beneficial effects achieved by the present invention are as follows: 1. In the present invention, goods are clustered according to their attribute information. By using data mining or machine learning technology to perform cluster analysis on goods, goods with similar attributes (such as size, weight, sales volume, etc.) can be grouped into one category, thereby achieving accurate classification.

[0017] This classification method not only significantly improves the refinement of goods management, but also provides a scientific basis for subsequent warehouse allocation. In addition, by automatically clustering goods, warehouse managers can handle large quantities of goods more efficiently, significantly reduce the workload of manual classification, and effectively reduce the error rate. In this way, the entire warehouse management process becomes more intelligent and efficient, improving overall operational efficiency.

[0018] 2. In the present invention, the warehouse area allocation result of the goods is determined based on the clustering of goods and the warehouse area attribute information. By combining the clustering of goods with the warehouse area attributes (such as being close to the exit, having wide lanes, etc.), different types of goods can be allocated to the most suitable warehouse area to achieve a reasonable layout.

[0019] For example, fast-moving consumer goods can be assigned to an area that is close to the exit and convenient for operation, while slow-moving consumer goods can be placed in an area that is far away from the exit but has a larger storage capacity.

[0020] This layout not only improves the convenience and efficiency of warehouse operations, but also helps maximize the use of warehouse space resources by selecting appropriate storage areas based on the characteristics of the goods, avoiding space waste or congestion caused by unreasonable layout. Ultimately, this method can significantly optimize space utilization and improve overall warehouse operation efficiency.

[0021] 3. In the present invention, according to the warehouse area allocation result of the goods, combined with the warehouse location allocation strategy, based on the free warehouse location information, the warehouse location coordinates of the goods are determined. After determining the warehouse area to which the goods belong, combined with the warehouse location allocation strategy (such as the exit or entrance strategy, etc.) and the free warehouse location information, a specific warehouse location coordinate can be selected for each item to achieve precise positioning.

[0022] This not only clarifies the specific storage location of the goods, but also provides clear guidance for subsequent operations. Through reasonable allocation of storage locations, especially the strategy of being close to the exit or entrance, the time of the in-and-out path can be significantly shortened, thereby improving overall operational efficiency and reducing transportation costs.

[0023] In addition, clear storage location coordinates reduce the time operators spend searching for storage locations, increase the speed of loading and unloading, and thus improve the overall efficiency of warehousing operations. This method comprehensively optimizes storage location management, improves operational convenience, and significantly enhances the efficiency and economic benefits of warehouse operations.

[0024] 4. The storage location allocation method for goods warehousing in the present invention assists in goods warehousing decision-making, and realizes the automation and intelligence of the entire process from macro-division to micro-storage location selection, which greatly reduces the possibility of human intervention and improves the accuracy and consistency of decision-making.

[0025] In addition, this method can dynamically adjust the warehouse allocation strategy according to actual business needs to ensure that it is always in the optimal state and provide a reference for subsequent decision-making. This method not only effectively improves the space utilization and operational efficiency of the warehouse, but also reduces unnecessary path travel time and operating costs through accurate positioning of warehouse coordinates, achieving significant cost savings and efficiency improvements, thus showing excellent performance in reducing costs and increasing efficiency. This comprehensive method makes warehouse management more intelligent, efficient and sustainable. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 The figure is a flow chart of the method for allocating storage locations for goods warehousing according to one embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to more clearly illustrate the overall concept of the present invention, a detailed description is given below in an exemplary manner in conjunction with the accompanying drawings.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0029] like Figure 1 As shown, a method for allocating storage locations for goods entering a warehouse comprises: S100: Divide the goods into clusters according to the attribute information of the goods.

[0030] It should be noted that in warehouse management, there are many types of goods, each of which has different attribute information. It is necessary to collect various attribute information about goods from various data sources.

[0031] After collecting these data, data cleaning and preprocessing are required, including missing value filling, outlier processing, data standardization and other operations to ensure data quality and consistency. By using clustering algorithms to classify product attribute information, product clusters are formed.

[0032] It is understandable that the main purpose of clustering is to find goods with similar characteristics by analyzing the various attributes of goods and classify them into the same category. The benefit of doing so is that you can better understand the relationship between goods, optimize warehouse layout and operation processes, and improve overall operational efficiency.

[0033] Therefore, each product cluster contains multiple products with similar product attribute information. After clustering, the corresponding storage location allocation strategy can be formulated according to the characteristics of each product cluster. This classification method not only significantly improves the refinement of product management, but also provides a scientific basis for subsequent storage area allocation. By automatically clustering products, warehouse managers can handle large quantities of goods more efficiently, significantly reduce the workload of manual classification, and effectively reduce the error rate.

[0034] In general, product clustering can achieve refined management and scientific decision-making by comprehensively considering multiple attribute characteristics and grouping products with similar attributes into one category. This method not only improves the efficiency of warehouse management, but also optimizes space utilization and operational convenience, ultimately achieving the goal of reducing costs and increasing efficiency. By rationally applying the results of cluster analysis, the overall benefits of warehouse operations can be significantly improved.

[0035] S200: Determine the warehouse area allocation result of the goods according to the clustering of the goods and the warehouse area attribute information.

[0036] It is understandable that in order to optimize warehouse management, improve space utilization and operational efficiency, the warehouse can be divided into multiple warehouse areas with different attributes. Each warehouse area stores corresponding categories of goods according to its specific attributes, thereby achieving refined management and efficient operation.

[0037] For example, the following are several typical storage areas, including fast-moving consumer goods storage area, slow-moving consumer goods storage area, fragile goods storage area, temperature-controlled storage area, high-density storage area, etc.

[0038] Specifically, the FMCG storage area is located near the warehouse exit, which is convenient for quick access. The aisles are spacious, which is convenient for frequent in and out handling operations. Shelves are selected from low shelves or the bottom part of high-rise shelves, which are convenient for quick access. In terms of environment, ordinary storage conditions can be used, and no special temperature and humidity control is required.

[0039] The slow-moving goods storage area is far from the warehouse exit and is suitable for long-term storage. The aisles are of medium width and suitable for less handling operations. The shelves are high-level shelves or multi-layer shelves to maximize the use of space. The environment adopts ordinary storage conditions, but the needs of long-term storage need to be considered.

[0040] The fragile goods storage area is located near the dedicated operation area for special handling. The aisles are spacious and easy to operate to avoid collision damage. The shelves are protected to ensure the safety of the goods. The environment requires specific temperature and humidity control or other protective measures to ensure the quality of the goods.

[0041] The temperature-controlled storage area is usually located inside the warehouse, independent of other storage areas. The aisles are of medium width, suitable for special handling equipment. The shelves are ordinary shelves or special shelves, depending on specific needs. The environment needs to strictly control temperature and humidity, suitable for storing environmentally sensitive goods.

[0042] High-density storage areas are usually located deeper in the warehouse and are not frequently operated. The aisles are narrow and suitable for dense storage systems (such as automated high-bay warehouses). The shelves use high-level dense storage racks to maximize the use of vertical space. The environment uses ordinary storage conditions, but the safety of cargo stacking needs to be considered.

[0043] Different warehouse areas have different warehouse area attribute information and are suitable for storing different commodity clusters. For example, the fast-moving consumer goods warehouse area is suitable for storing fast-moving consumer goods (such as food, daily necessities, etc.), which are characterized by high sales, high turnover, and small packaging. The slow-moving consumer goods warehouse area is suitable for storing slow-moving consumer goods (such as furniture, home appliances, etc.), which are characterized by low sales, low turnover, and large packaging.

[0044] The fragile goods storage area is suitable for storing fragile goods (such as glass products, electronic products, etc.), with medium sales, moderate turnover, and the need for special packaging and protective measures. The temperature-controlled storage area is suitable for storing perishable goods (such as fresh food), chemicals, and medicines, which need to be stored in a specific environment to maintain quality and safety. The high-density storage area is suitable for storing bulk goods or seasonal goods with low frequency operations, with low turnover and large volume.

[0045] According to the clustering of goods and the attribute information of the warehouse area, the warehouse area allocation result of the goods is determined. By combining the clustering of goods with the attributes of the warehouse area (such as proximity to the exit, wide lanes, etc.), different types of goods can be allocated to the most suitable warehouse area to achieve a reasonable layout.

[0046] This layout method not only improves the convenience and efficiency of warehouse operations, but also selects appropriate storage areas according to the characteristics of the goods, which helps to maximize the use of warehouse space resources and avoid space waste or congestion caused by unreasonable layout. Ultimately, this method can significantly optimize space utilization and improve overall warehouse operation efficiency. Moreover, this method not only optimizes space utilization, but also improves operational convenience, ultimately achieving the goal of reducing costs and increasing efficiency.

[0047] S300: Determine the storage location coordinates of the product according to the storage area allocation result of the product and the storage location allocation strategy based on the free storage location information.

[0048] After completing the clustering of goods and the allocation of warehouse areas, the next step is to determine the specific warehouse location coordinates for each item. This process requires combining the warehouse location allocation strategy with the available free warehouse location information in the current warehouse to ensure that the goods can be stored efficiently and reasonably.

[0049] It is understandable that even in the same warehouse area, warehouse locations with different coordinates have their own unique attributes, which determine the types of goods it is suitable for storing and the way it is operated. Common warehouse location attributes include: The location number is a unique identifier for each location, for example, A3-1-5 indicates the location of row 1 and column 5 on the A3 shelf. Size and capacity: the physical dimensions of the storage location and the maximum number or volume of items it can accommodate; Load-bearing capacity, the maximum weight that the storage space can bear, affects the storage choice of heavy goods; Height: The height of the storage location affects whether it is suitable for storing goods on large or high-rise shelves; Environmental conditions: some storage locations may have specific temperature and humidity control or other special environmental requirements; Accessibility, the distance between the storage location and the main channel, affects the ease of operation of the handling equipment.

[0050] According to the different characteristics of storage locations, they can be divided into the following types: The low-level storage location is located at the bottom or lower level of the shelf; it is easy to operate and suitable for goods that are frequently in and out; it has a strong load-bearing capacity and is suitable for storing heavier goods.

[0051] High-level storage locations are located on the higher levels of the shelves; they utilize vertical space and are suitable for storing infrequently handled goods; they require specialized equipment such as forklifts or stackers to operate.

[0052] Dense storage locations use dense storage systems (such as automated high-bay warehouses); the spacing between locations is small to maximize space utilization; suitable for high-density storage, but with poor operational flexibility.

[0053] Special environment storage locations have specific temperature and humidity control or other special environmental requirements; they are suitable for storing environmentally sensitive goods; and they require special monitoring and maintenance equipment.

[0054] Protective storage locations provide additional protection to prevent damage to goods. They are suitable for storing fragile and vulnerable goods. They may be equipped with special shelf structures or protective devices.

[0055] Temporary storage locations are used to store goods for a short period of time, allowing for quick stocking and unstocking. They are located close to entrances or exits for easy operation. They are usually used to process urgent orders or for temporary storage.

[0056] Different types of storage locations have different storage location attributes and are suitable for storing goods of different clusters. For example, low-level storage locations are suitable for fast-moving consumer goods, perishable goods, and other goods that need to be quickly picked up and placed; heavier goods, such as home appliances and mechanical equipment. High-level storage locations are suitable for slow-moving consumer goods, seasonal goods, and other goods with low turnover rates; large, lightweight goods, such as furniture and plastic products.

[0057] Intensive storage locations are suitable for seasonal goods, bulk goods and other goods that do not require frequent operations; goods that do not need to be shipped out urgently, such as inventory spare parts and raw materials. Special environment locations are suitable for perishable goods (such as fresh food), medicines, chemicals, etc.; goods with strict requirements on temperature and humidity, such as electronic products and cosmetics.

[0058] Protective storage is suitable for fragile items such as glass products, ceramic products, precision instruments, and high-value goods such as jewelry and artworks. Temporary storage is suitable for goods in urgent orders and returns or exchanges that need to be processed quickly.

[0059] According to the characteristics of different storage locations, formulate corresponding storage location allocation strategies to ensure that goods can be stored efficiently and reasonably.

[0060] In general, by distinguishing the different storage locations in the warehouse area in detail and formulating a reasonable storage location allocation strategy based on the characteristics of the storage locations, the efficiency and accuracy of warehouse management can be significantly improved. Each storage location type has its own unique advantages and applicable scenarios. Proper use of these characteristics can not only optimize space utilization, but also improve operational convenience and overall operational efficiency. By continuously tuning model parameters and strategy configuration, the performance of the system can be continuously improved to ensure that warehouse operations are always kept in the best state.

[0061] As a preferred implementation of the present invention, the goods are divided into clusters according to the attribute information of the goods, specifically: Identify the goods according to their type. Perform cluster analysis based on the attribute information of multiple types of goods and divide the goods into clusters; According to the identifier and the corresponding relationship between the commodity attribute information and the commodity cluster, the corresponding relationship between the identifier of the commodity and the commodity cluster is obtained.

[0062] In this implementation, in order to achieve efficient warehouse management and optimize the operation process, different types of goods need to be identified, and the goods cluster to which they belong can be quickly obtained based on the corresponding relationship between the identification of the goods and the goods cluster.

[0063] First, each product needs to be uniquely identified so that it can be accurately identified and handled in the subsequent management process. Common identification methods include: SKU, a unique identifier for each product, usually composed of letters and numbers; Product category code, which is classified and coded according to the main characteristics or uses of the product, such as fast-moving consumer goods, slow-moving consumer goods, and perishable goods; Other identification information, such as supplier number, batch number, production date, etc.

[0064] A mapping table is generated based on the correspondence between the product attribute information and the product cluster. Then, the correspondence between the product identifier and the product cluster is obtained, and a table of correspondence between the product identifier and the product cluster is generated. This table can help quickly find the cluster to which each product belongs in the subsequent storage location allocation and management process.

[0065] Specifically, each type of goods in the warehouse has a unique code identification (hereinafter referred to as the goods ID, such as GBZ6000001). Each box of goods is uniquely identified by the goods ID plus the serial number (such as the label paper GBZ6000001-202406032780001 is affixed to each box of goods). The same goods ID can be put into the warehouse and put on the shelves for several boxes of goods every day (here the box refers to the SKU inventory unit).

[0066] When a new box of goods arrives at the warehouse shelf entrance via a transport vehicle and waits for storage and shelving, this method will first read the product ID number, and then query the product cluster number corresponding to the product ID number in the corresponding relationship table.

[0067] By distinguishing the types of goods in detail and performing cluster analysis, goods with similar attributes can be grouped together, thus achieving refined management and scientific decision-making. Establishing a correspondence between product identification and product clustering helps improve the accuracy and efficiency of warehouse management.

[0068] As a preferred embodiment of this implementation, the cluster analysis method is specifically as follows: If the data in the commodity attribute information is of a category type, feature engineering processing is performed to form a category feature group; If the data in the commodity attribute information is of numerical type, processing is performed to obtain a numerical feature group; According to the feature group obtained by processing the product attribute information, the distance between different product attribute information is calculated, and clustering is performed based on the distance to divide the products into clusters.

[0069] In order to achieve efficient product clustering, we first need to perform feature engineering on the product attribute information, and process the categorical and numerical attributes into feature groups. Then, we calculate the distances between different products based on these feature groups, and use these distances to perform cluster analysis.

[0070] It is understandable that the attribute information of goods generally includes categorical type and numerical type, among which numerical type attributes such as size, weight, monthly sales, etc. are generally represented by specific values. Categorical type attributes include origin, etc. According to the different types of goods attributes (categorical type and numerical type), feature engineering processing is performed separately.

[0071] For numeric attribute data, perform data cleaning, gap filling, normalization, conversion, and other operations, followed by data exploration and feature dimension reduction. For example, for numeric attributes (such as size, weight, monthly sales, etc.), the following processing is required: Standardization, converting numerical variables into a standard normal distribution with a mean of 0 and a standard deviation of 1; Normalization scales numerical variables to values ​​in the range [0, 1].

[0072] Specifically, assume that there are the following attribute data of numerical type:

[0073] It can be converted to a numerical feature using normalization: [[0., -0.70710678], [-1.22474487, -0.9486833], [0.20412415, -0.53452248], [1.02062073, -0.69006555]] For category attributes, the attribute characteristics of category types will also be cleaned, supplemented, binned, encoded, and converted. Feature engineering is performed using methods such as artificial expert experience + data exploration + feature encoding and then processing by numerical type to select the best variables or variable combinations to represent category attribute characteristics.

[0074] Specific encoding methods include One-Hot Encoding, which converts categorical variables into binary vector representations; Label Encoding, converting categorical variables into integer numbers; Binning Encoding divides continuous variables or ordered categorical variables into several intervals and assigns a number to each interval.

[0075] Specifically, assume that there are attribute data of the following categories:

[0076] It can be converted into a numerical feature using one-hot encoding: [[Product number, Supplier level_medium, Supplier level_low, Supplier level_high, Storage method_refrigerated, Storage method_normal temperature, Storage method_frozen] [0,GBZ6000001,0,0,1,1,0,0] [1, GBZ6000002, 1, 0, 0, 0, 1, 0] [2, GBZ6000003, 0, 0, 1, 0, 0, 1] [3,GBZ6000004,0,1,0,0,1,0]] When clustering, the input features are considered to be of two types: numerical features and categorical features. The distance between the numerical features of the samples is calculated. The clustering methods used include kprototypes, kmodes, kmeans, kmedoids, etc.

[0077] Among them, kprototypes is a hybrid method that combines the advantages of K-Means and K-Modes clustering algorithms, and is specifically used to process data sets that contain both numerical and categorical features. It can effectively perform cluster analysis on mixed data and is widely used in practical scenarios that require processing multiple types of data.

[0078] K-Modes is a clustering algorithm specifically designed for processing categorical data. Similar to the K-Means algorithm, K-Modes is also based on a partitioning method, but it uses a different distance metric and center point update strategy to process non-numeric (i.e. categorical) data. K-Modes is very useful in scenarios such as market segmentation and customer classification that require cluster analysis of categorical data.

[0079] K-Means is a widely used unsupervised learning clustering algorithm, which is mainly used to divide the data set into k clusters, so that the data points in each cluster are as similar as possible, while the data points between different clusters are as different as possible. The K-Means algorithm is simple and efficient, and is widely used in many fields such as market segmentation, image compression, document classification, etc.

[0080] K-Medoids is a clustering algorithm similar to K-Means, but it uses actual data points (called "medoids") as the centers of the clusters instead of using the mean of all the points within the cluster as K-Means does. K-Medoids is more robust to noise and outliers because it relies on actual data points instead of calculated means.

[0081] Specifically, the processed category feature groups and numerical feature groups are combined to form a complete feature matrix: [[0., -0.70710678, 0., 0., 1., 1., 0.] [-1.22474487, -0.9486833, 1., 0., 0., 1., 0.] [0.20412415, -0.53452248, 0., 0., 0., 0., 1.] [1.02062073, -0.69006555, 0., 1., 0., 1., 0.]] Next, the distances between different products are calculated based on the feature matrix, and clustering analysis is performed using a clustering algorithm. Common distance measurement methods include Euclidean distance, Hamming distance, etc.

[0082] Among them, Euclidean distance is a common distance measurement method used to measure the straight-line distance between two points in multidimensional space. It is widely used in various fields, such as machine learning, data mining, image processing, etc., especially in clustering algorithms (such as K-Means), classification algorithms (such as K-Nearest Neighbors, KNN) and dimensionality reduction techniques (such as PCA).

[0083] The Hamming distance is a distance metric used to measure the difference between two strings or vectors of equal length. It is particularly suitable for categorical data, especially binary data and character sequences. The Hamming distance is defined as the number of different characters in the same position of two strings.

[0084] Specifically, the K-Means algorithm is selected to perform cluster analysis.

[0085] The optimal number of clusters was determined by using methods such as the elbow method or silhouette coefficient, and three clusters were selected as the final number of clusters.

[0086] Input the processed feature data into the K-Means algorithm to obtain the cluster number to which each product belongs:

[0087] The final clustering results can help with more refined management and decision-making in warehouse management. For example, warehouse area allocation can allocate goods to the most suitable warehouse area according to the characteristics of different clusters; warehouse location allocation can formulate corresponding warehouse location allocation strategies based on the clustering results of goods to improve operational efficiency.

[0088] As another example of this implementation, the product attribute information includes static attribute data and / or dynamic attribute data, the static attribute data includes at least one of size, weight, and origin; the dynamic attribute data includes at least one of sales volume within a period and turnover rate.

[0089] It is understandable that in warehouse management and supply chain optimization, product attribute information is usually divided into static attribute data and dynamic attribute data. Static attribute data refers to those attributes that are relatively fixed and do not change frequently. They usually describe the basic physical characteristics or source information of the product.

[0090] Common static attribute data include: Dimensions: the length, width, and height of the item (in centimeters or inches). Dimensional information helps determine the appropriate storage space and handling equipment; Weight, the weight of the goods (units such as kilograms or pounds). Weight information affects the choice of handling method and storage location; Origin: where the product is produced or sourced. Origin information can be used to track supply chains, ensure compliance, and optimize procurement strategies; Outer packaging specifications, the size and type of the outer packaging of the goods (such as cartons, wooden boxes, etc.). This helps to arrange storage and transportation reasonably; Supplier number, which provides the supplier identifier of the goods for supply chain management and quality control; Shelf life is the expiration date of certain items, especially perishable items such as food and medicine. Shelf life information is critical to inventory management and sales strategies.

[0091] Dynamic attribute data refers to those attributes that change over time and reflect the current status of the product or its market performance. They usually describe the sales and inventory status of the product.

[0092] Common dynamic attribute data include: Sales volume within a certain period of time (such as monthly sales volume, quarterly sales volume). Sales volume data is an important basis for evaluating market demand and formulating replenishment plans; Turnover rate, inventory turnover rate, indicates the frequency of goods entering and leaving the warehouse within a certain period of time. A high turnover rate means that the goods are moving quickly, while a low turnover rate may require optimization of inventory management; Inventory quantity, the number of items currently in stock. Inventory quantity information is used to monitor inventory levels and avoid out-of-stock or overstocking; Cost value, the cost price or market value of goods. Cost value helps with financial management and pricing strategies; Order frequency, how often the item is ordered. Items with a high order frequency may require more frequent replenishment and better inventory management.

[0093] By combining static and dynamic attribute data, you can have a more comprehensive understanding of the characteristics of each product, and make effective management and decisions based on this. Using static and dynamic attribute data for cluster analysis, you can group products with similar characteristics into one category, thereby achieving refined management and scientific decision-making. According to the characteristics of different clusters, formulate reasonable storage location allocation strategies to improve operational efficiency. Through dynamic attribute data, formulate reasonable inventory management and replenishment strategies to avoid out-of-stock or backlogs.

[0094] As a preferred implementation of the present invention, based on the clustering of goods and combining the warehouse attribute information, the warehouse allocation result of the goods is determined, specifically: Performing cluster analysis on the attribute information of the storage area to form storage location partitions; Analyze the feature group of the product cluster and the feature group of the storage location partition to obtain the corresponding relationship between the product cluster and the storage location partition; Among them, the analysis method includes at least one of exploratory mining and feature engineering clustering analysis, association rule analysis, and interpretability analysis.

[0095] In order to achieve efficient warehouse management, the attribute information of the warehouse area can be clustered and the warehouse area with similar characteristics can be divided into different partitions. Then, the warehouse allocation strategy can be optimized by analyzing the correspondence between the commodity clusters and the warehouse location partitions.

[0096] First, it is necessary to collect various attribute information of the warehouse area, including at least one of the following information: location, lane width, shelf type, environmental conditions, etc.

[0097] These attributes can also be divided into static attributes and dynamic attributes. Static attributes include: location, the specific location of the storage area in the warehouse (such as close to the entrance, close to the exit, etc.). Lane width, the width of the lane in the storage area, which affects the convenience of handling equipment. Shelf type, the type of shelf used in the storage area (such as low-level shelves, high-level shelves, dense storage racks, etc.). Environmental conditions, whether there are special environmental controls (such as temperature and humidity control). Load-bearing capacity, the maximum load-bearing capacity of the storage area, which affects the storage selection of heavy goods.

[0098] Dynamic attributes include: utilization rate, the utilization rate of the current storage area (such as the proportion of occupied storage locations); operation frequency, the operation frequency within the storage area (such as the number of inbound and outbound operations); historical data, the usage of the storage area over a period of time (such as turnover rate, average inventory, etc.).

[0099] Specifically, assume that there are the following reservoir area attribute data:

[0100] In order to conduct cluster analysis, it is necessary to perform feature engineering on the reservoir area attribute data and convert categorical and numerical features into distance metrics suitable for clustering.

[0101] For categorical attributes (such as location, shelf type, environmental conditions), you can use One-Hot Encoding to convert them into numerical features: [[Storage area number, Lane width (m), Load capacity (kg), Utilization rate (%), Operation frequency (times / day), Location_middle area, Location_far from exit, Location_near exit, Location_near entrance, Shelf type_low shelf, Shelf type_high shelf, Shelf type_dense storage shelf, Environmental condition_refrigerated, Environmental condition_normal temperature] [0, LK001, 3.0, 500, 80, 100, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] [1, LK002, 2.0, 300, 50, 30, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0] [2, LK003, 2.5, 200, 70, 60, 60, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0] [3, LK004, 3.5, 500, 90, 120, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0]] For numerical attributes (such as lane width, load-bearing capacity, utilization rate, and operating frequency), standardization or normalization can be used to convert them into a distance metric suitable for clustering.

[0102] Use K-Means algorithm to perform cluster analysis on reservoir area attribute data: [[Warehouse area number, Location_Middle area, Location_Far from exit, Location_Near exit, Location_Near entrance, Shelf type_Low shelf, Shelf type_High shelf, Shelf type_Dense storage shelf, Environmental condition_Refrigerated, Environmental condition_Normal temperature, Aisle width (m), Load capacity (kg), Utilization rate (%), Operation frequency (times / day), Cluster] [0, LK001, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.707107, 1.414214, 1.414214, 0] [1, LK002, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, -1.414214, -1.414214, -1.414214, 1] [2, LK003, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, -0.707107, -1.414214, -0.707107, 2] [3, LK004, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.414214, 1.414214, 1.414214, 0]] Based on the clustering results, the reservoir area is divided into different partitions. For example:

[0103] Next, analyze the correspondence between product clustering and storage location partitioning. This can be achieved through the following analysis methods: Exploration mining and feature engineering cluster analysis: Combine the clustering results of the goods and the characteristics of the storage location partition to find the most appropriate matching relationship. For example: High-volume, small-sized packages (cluster 1): preferentially assigned to the storage partitions near the exit and with high frequency of operation (such as partition 0); Low sales volume and large packaging (Cluster 2): Prioritize allocation to storage partitions far from the exit and with low frequency of operation (such as partition 1); Requires special environmental control (Cluster 3): Prioritizes allocation to the middle area and storage location partitions that require special environmental control (such as partition 2).

[0104] Specifically, the output is: [[Product number, size (cm), weight (kg), monthly sales, inventory turnover rate, Cluster, Partition] [0, GBZ6000001, 3000, 2, 1000, 0.8, 0, 0] [1, GBZ6000002, 480000, 50, 50, 0.1, 1, 1] [2, GBZ6000003, 9000, 3, 200, 0.5, 2, 2] [3, GBZ6000004, 24000, 10, 150, 0.4, 0, 0]] Association rule analysis: Use association rule analysis (such as the Apriori algorithm) to find frequent patterns and association rules between product clustering and location partitioning. This can help discover potential optimization opportunities.

[0105] Interpretability Analysis: The clustering results are reduced in dimension through visualization tools (such as t-SNE and PCA), and the relationship between product clustering and storage location partitioning is displayed through charts to better understand their distribution and characteristics.

[0106] As a preferred implementation of the present invention, according to the warehouse area allocation result of the goods, combined with the warehouse location allocation strategy, based on the free warehouse location information, the warehouse location coordinates of the goods are determined, specifically: According to the warehouse area allocation result of the goods, read the free warehouse location corresponding to the current warehouse area; Select the corresponding allocation strategy based on product clustering; According to the allocation strategy, the storage location coordinates are determined for the goods from the free storage locations.

[0107] In order to efficiently manage and optimize warehousing operations, specific storage location coordinates can be systematically assigned to each item based on the item's storage area allocation results, clustering information, and available storage locations.

[0108] First, you need to obtain the free storage location information in each warehouse area. This can usually be achieved through a warehouse management system (WMS). Assume that there is a data structure to represent the free storage locations in each warehouse area.

[0109] For a product waiting to be put into storage and assigned a storage location, after determining the inventory partition, you also need to determine which specific inventory location to put it in, because there may be many inventory location units that meet the requirements of this category and this partition, so you need to choose to determine a more accurate unique inventory location coordinate.

[0110] When selecting a storage location, you can set a selection rule for each type of goods. The algorithm model will first read the free storage locations corresponding to the current storage location partition from the WMS system, and then select the allocation strategy.

[0111] The allocation strategy includes at least one of random storage location allocation, exit proximity strategy, entrance proximity strategy, longest unused time strategy, low-level storage priority strategy, high-level storage priority strategy, and whole-stock priority strategy.

[0112] Among them, random storage location allocation means randomly selecting a location in the available storage locations to store goods. It is suitable for goods that do not require high storage locations. When the warehouse space is sufficient and the goods liquidity is low, the operation process can be simplified.

[0113] The near-exit strategy prioritizes storing goods near the warehouse exit for quick picking and delivery. It is suitable for warehouses with high sales volume or goods that need to be shipped out frequently and can respond quickly to customer needs.

[0114] The near-entrance strategy prioritizes the storage of goods near the warehouse entrance to facilitate warehousing operations. It is suitable for goods that are frequently put into storage, and new goods need to be put into storage quickly to free up the unloading area.

[0115] The longest unused time strategy prioritizes the storage of goods in the least frequently used storage locations to balance the storage location utilization rate. It is suitable for goods with low inventory turnover and the warehouse needs to be cleaned and organized regularly.

[0116] The low-level storage strategy prioritizes the storage of goods in low-level storage locations to facilitate manual operation and the use of handling equipment. It is suitable for goods that need to be frequently in and out of the warehouse and is suitable for warehouses with manual operations.

[0117] The high-level storage strategy prioritizes the storage of goods in high-level warehouses to maximize the use of vertical space. It is suitable for large and heavy goods that need to be stored for a long time.

[0118] The priority storage strategy prioritizes the storage of full boxes or pallets of goods in designated storage locations, reducing the need for unpacking and repackaging operations. It is suitable for full boxes or pallets of goods shipped out of the warehouse, and the integrity of the original packaging needs to be maintained.

[0119] By rationally selecting and combining these strategies, warehouse operation efficiency can be effectively improved, inventory management can be optimized, and warehouse operations can always be kept in optimal condition.

[0120] After the accurate storage location selection is completed, this method will bind the final storage location allocation coordinates of the box of goods to the record system in the product ID serial number array and send it back to the WMS system. The WMS system receives the product ID serial number and the accurate storage location number and notifies the logistics vehicle to start the storage and shelving operation. The storage location allocation operation is now completed.

[0121] The present invention also provides a computer-readable storage medium, which is used to store computer instructions, and the computer instructions are used to enable a computer to execute the storage location allocation method for the warehousing of goods, thereby being able to achieve any effect of the storage location allocation method for the warehousing of goods, which will not be elaborated here.

[0122] The present invention also provides an electronic device, comprising: Memory, for storing computer instructions; The processor is used to implement the storage location allocation method for the warehousing of goods when executing the computer instructions, so it can achieve any effect of the storage location allocation method for the warehousing of goods, which will not be elaborated here.

[0123] Anything not described in the present invention can be achieved by adopting or drawing on existing technologies.

[0124] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0125] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for allocating storage locations for goods entering a warehouse, characterized in that: include: Divide goods into clusters according to their attribute information; Determine the warehouse area allocation result of the goods according to the clustering of the goods and the warehouse area attribute information; According to the warehouse area allocation result of the goods, combined with the warehouse location allocation strategy, based on the free warehouse location information, the warehouse location coordinates of the goods are determined.

2. The method for allocating storage locations for goods entering the warehouse according to claim 1, characterized in that: According to the product attribute information, the products are divided into clusters, specifically: Identify the goods according to their type. Perform cluster analysis based on the attribute information of multiple types of goods and divide the goods into clusters; According to the identifier and the corresponding relationship between the commodity attribute information and the commodity cluster, the corresponding relationship between the identifier of the commodity and the commodity cluster is obtained.

3. The method for allocating storage locations for goods entering the warehouse according to claim 2, characterized in that: The specific cluster analysis method is as follows: If the data in the commodity attribute information is of a category type, feature engineering processing is performed to form a category feature group; If the data in the commodity attribute information is of numerical type, processing is performed to obtain a numerical feature group; According to the feature group obtained by processing the product attribute information, the distance between different product attribute information is calculated, and clustering is performed based on the distance to divide the products into clusters.

4. The method for allocating storage locations for goods entering the warehouse according to claim 2, characterized in that: The product attribute information includes static attribute data and / or dynamic attribute data, wherein the static attribute data includes at least one of size, weight, and origin; and the dynamic attribute data includes at least one of sales volume within a period and turnover rate.

5. The method for allocating storage locations for goods entering the warehouse according to claim 1, characterized in that: According to the clustering of goods and the warehouse attribute information, the warehouse allocation result of the goods is determined, specifically: Performing cluster analysis on the attribute information of the storage area to form storage location partitions; Analyze the feature group of the product cluster and the feature group of the storage location partition to obtain the corresponding relationship between the product cluster and the storage location partition; Among them, the analysis method includes at least one of exploratory mining and feature engineering clustering analysis, association rule analysis, and interpretability analysis.

6. The method for allocating storage locations for goods entering the warehouse according to claim 5, characterized in that: The warehouse area attribute information includes at least one of location, lane width, shelf type, environmental conditions and the like.

7. The method for allocating storage locations for goods entering a warehouse according to claim 1, characterized in that: According to the warehouse area allocation result of the goods, combined with the warehouse location allocation strategy, based on the free warehouse location information, the warehouse location coordinates of the goods are determined, specifically: According to the warehouse area allocation result of the goods, read the free warehouse location corresponding to the current warehouse area; Select the corresponding allocation strategy based on product clustering; According to the allocation strategy, the storage location coordinates are determined for the product from the free storage locations.

8. The method for allocating storage locations for goods entering the warehouse according to claim 7, characterized in that: The allocation strategy includes at least one of random storage location allocation, exit proximity strategy, entrance proximity strategy, longest unused time strategy, low-level storage priority strategy, high-level storage priority strategy, and whole-stock priority strategy.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store computer instructions, and the computer instructions are used to enable a computer to execute the storage location allocation method for goods warehousing as described in any one of claims 1 to 8.

10. An electronic device, characterized in that: include: Memory, for storing computer instructions; A processor, configured to implement the method for allocating storage locations for warehousing goods as described in any one of claims 1 to 8 when executing the computer instructions.

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