Method, system and storage medium for determining storage capacity rate of warehouse

Abstract

The present application relates to the field of warehouse management technology, and provides a method, system and storage medium for determining the storage capacity rate of a warehouse. The method includes: receiving the expected arrival date of each product to the warehouse when laying out multiple products in the warehouse; determining the first date interval and the second date interval when the date interval corresponding to the current date and the expected arrival date meets the preset conditions; determining the first total area to be occupied by all products in stock, all products in transit and all resale products with an expected resale date in each first date interval; determining the date type of the date according to the resale interval of each date in the second date interval; determining the second total area occupied by the resale products in the second date interval according to the historical sales data of the same date type of each date in the preset time period before the current date; the LTC system determines the storage capacity rate of the warehouse according to the number of products to be laid out on the current date, the first total area and the second total area of ​​each product, so that the storage capacity rate calculation is more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of warehouse management, and in particular to a method, system and storage medium for determining the storage capacity rate of a warehouse. Background Art

[0002] Overseas customer demands of whole-machine modeling enterprises are relatively direct, with spot transactions accounting for the majority. However, the overseas distribution cycle is long, and often after the goods arrive at the warehouse, user demands or market conditions have changed. If the distribution quantity is small, the market will be missed; if the distribution quantity is large, warehouse resources will be wasted, making the warehouse capacity too high, and it may even cause warehouse explosion and product backlog.

[0003] At present, the coordination modes of distribution model and quantity with overseas warehouses are mainly: (1) Distribution based on inventory and warehouse area. The headquarters operation department sets up overseas subsidiaries around the world and plans to rent warehouses at all levels in advance. The management personnel of overseas subsidiaries distribute goods and replenish inventory based on the inventory and remaining warehouse area of ​​warehouses at all levels. (2) Distribution based on market forecasts. Distribution is carried out based on market forecasts and combined with the experience of managers. If the market forecast is good, the distribution volume is increased and new warehouses are rented.

[0004] However, the distribution and warehouse capacity ratio determination in the above manner do not take into account the impact of product turnover in different time periods on the warehouse capacity ratio, resulting in inaccurate calculation of the warehouse capacity ratio, which affects the distribution decisions for the warehouse. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method, system and storage medium for determining the storage capacity ratio of a warehouse, so as to solve the problem in the prior art that inaccurate calculation of the storage capacity of the warehouse affects the distribution decision for the warehouse.

[0006] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for determining the storage capacity rate of a warehouse, which is applied to a big data platform, wherein the warehouse is used to store products of at least one product line, including:

[0007] When multiple products of each product line need to be delivered to the warehouse on the current date, receive the estimated arrival date of each product at the warehouse sent by the LTC system;

[0008] When the date interval corresponding to the current date and the expected arrival date meets a preset condition, determining at least one first date interval and one second date interval according to the date interval;

[0009] Determine a first total area required to be occupied by all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within a date interval, and all products for sale in the warehouse whose estimated sale date is within each first date interval;

[0010] For each date in the second date interval, determine the transfer interval in which the date is located, and determine the date type of the date according to the transfer interval in which the date is located;

[0011] Determine the second total area that the resale products need to occupy in the second date interval according to the historical sales data of the same date type for each date in the preset time period before the current date;

[0012] The quantity to be distributed, the first total area and the second total area of ​​each product of each product line in the warehouse on the current date are sent to the LTC system to determine the warehouse capacity rate on the expected arrival date through the LTC system.

[0013] A second aspect of the present application provides a method for determining a storage capacity rate of a warehouse, which is applied to an LTC system, wherein the warehouse is used to store products of at least one product line, including:

[0014] When multiple products of each product line need to be delivered to the warehouse on the current date, the estimated arrival date of each product at the warehouse is determined based on the preset transportation parameters and the estimated departure date of each product;

[0015] Receive the quantity to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date sent by the big data platform;

[0016] Determine the total floor space required for all products to be laid out on the current date based on the quantity of each product of each product line to be laid out on the current date and the floor space occupied by each product model;

[0017] Determine the estimated storage area for each product line of the warehouse on the estimated arrival date based on the first total area, the second total area and the estimated total area;

[0018] Determine the estimated storage capacity utilization rate of each product line based on the estimated storage area of ​​each product line and the total floor space of the sub-warehouses corresponding to each product line under the warehouse;

[0019] Determine the estimated storage capacity utilization rate of the warehouse on the estimated arrival date based on the estimated storage capacity utilization rate of all product lines under the warehouse.

[0020] A third aspect of the present application provides a method for determining a storage capacity rate of a warehouse, where the warehouse is used to store products of at least one product line, and the method includes:

[0021] When multiple products of each product line need to be laid out to the warehouse on the current date, the LTC system determines the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product;

[0022] The big data platform receives the estimated arrival date of each product at the warehouse sent by the LTC system;

[0023] When the date interval corresponding to the current date and the expected arrival date meets the preset conditions, the big data platform determines at least one first date interval and one second date interval according to the date interval;

[0024] The big data platform determines the first total area required to be occupied by all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within a date interval, and all products for sale in the warehouse whose estimated sale date is within each first date interval;

[0025] For each date in the second date interval, the big data platform determines the transfer-cancellation interval in which the date is located, and determines the date type of the date according to the transfer-cancellation interval in which the date is located;

[0026] The big data platform determines the second total area required to be occupied by the resale products within the second date interval based on the historical sales data of the same date type for each date within a preset time period before the current date;

[0027] The big data platform sends the quantity to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system;

[0028] The LTC system determines the warehouse capacity rate on the expected arrival date based on the total quantity of goods to be distributed, the first total area, and the second total area.

[0029] In a fourth aspect, the present application provides a system for determining a storage capacity rate of a warehouse, where the warehouse is used to store products of at least one product line, and the system includes:

[0030] The LTC system is used to determine the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product when multiple products of each product line need to be placed in the warehouse on the current date; determine the warehouse capacity rate on the estimated arrival date based on the total quantity to be placed, the first total area and the second total area;

[0031] The big data platform is used to receive the estimated arrival date of each product at the warehouse sent by the LTC system; determine at least one first date interval and one second date interval according to the date interval when the date interval corresponding to the current date and the estimated arrival date meets the preset conditions; determine the first total area required to be occupied by all the products in stock in the warehouse on the current date, all the products in transit that arrive at the warehouse within the date interval, and all the resale products in the warehouse with the estimated resale date within each first date interval; for each date within the second date interval, determine the resale interval in which the date is located, and determine the date type of the date according to the resale interval in which the date is located; determine the second total area required to be occupied by the resale products in the second date interval according to the historical sales data of the same date type for each date within a preset time period before the current date; and send the number of products to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system.

[0032] A fifth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the above-mentioned method for determining the storage capacity ratio of a warehouse.

[0033] Through the above technical solution, the different product turnover volumes in different time periods are taken into consideration, so that the calculation of the warehouse capacity rate is more accurate, thereby being able to provide more accurate decision support for warehouse distribution, making the distribution of warehouses more reasonable.

[0034] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0036] Figure 1 A first flow chart of a method for determining a storage capacity ratio of a warehouse according to an embodiment of the present application is schematically shown;

[0037] Figure 2 A second flow chart of a method for determining a storage capacity ratio of a warehouse according to an embodiment of the present application is schematically shown;

[0038] Figure 3 A third flow chart of a method for determining a storage capacity ratio of a warehouse according to an embodiment of the present application is schematically shown;

[0039] Figure 4A schematic diagram of a system for determining a storage capacity ratio of a warehouse according to an embodiment of the present application is schematically shown;

[0040] Figure 5 The internal structure of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0042] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0043] Figure 1 The flowchart of the method for determining the storage capacity rate of a warehouse according to an embodiment of the present application is schematically shown. Figure 1 As shown, in one embodiment of the present application, a method for determining the storage capacity rate of a warehouse is provided. This embodiment mainly uses the method applied to a big data platform as an example, and includes the following steps:

[0044] Step 101: When multiple products of each product line need to be delivered to the warehouse on the current date, the estimated arrival date of each product at the warehouse is received from the LTC system.

[0045] A warehouse is used to store products of at least one product line. A warehouse can also be called a branch. A product line can produce multiple products. When multiple products of each product line need to be laid out to the warehouse on the current date, the LTC system can determine the estimated arrival date of each product at the warehouse. Specifically, the LTC system can determine the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product.

[0046] The LTC (LTC-Lead to Cash) system maintains basic information about warehouses, product lines, and individual models. The basic information about warehouses may include the country, province / state, city, branch code, branch name, branch address, lease start date, lease end date, and branch area of ​​the warehouse. The basic information about product lines may include the branch code, branch name, product line location code, product line name, and product line planning area of ​​the branch. The basic information about individual models may include the product line, product group, model, size, and floor space of the model or product.

[0047] In one embodiment, the preset transportation parameters include at least the booking period, domestic transportation period, international transportation period, subsidiary customs clearance period and certification period of each product. The preset transportation parameters can be maintained in the TMS system, and the LTC system can obtain the preset transportation parameters from the TMS system to calculate the estimated arrival date of each product at the warehouse. In the TMS system, the data associated with the booking period include the country of shipment, mode of transportation and loading method. The data associated with the domestic transportation period include the product line, domestic factory and port of departure of the corresponding product. The data associated with the international transportation period include the country of shipment, destination country, destination port, mode of transportation and loading method of the corresponding product.

[0048] The LTC system can determine the total transportation cycle required to transport each product from the preset factory address to the warehouse based on the preset transportation parameters of each product. Afterwards, the LTC system can determine the estimated arrival date of each product at the warehouse based on the estimated departure date of each product and the total transportation cycle. Specifically, the total transportation cycle can be added to each estimated departure date to obtain the estimated arrival date of each product.

[0049] After determining the expected arrival date of each product at the warehouse, the LTC system can send the expected arrival date of each product to the big data platform. The big data platform receives the expected arrival date of each product and can determine the date interval formed by the current date and the expected arrival date. The big data platform can determine whether the date interval meets the preset conditions. Among them, the preset condition can be that the number of dates in the date interval is greater than the preset number, or that the time span corresponding to the date interval is greater than the preset time span. Among them, the preset number can be 30. The preset time span can be set to 30 days. For example, if the number of dates in the date interval is greater than 30, or the time span corresponding to the date interval is greater than 30, it can be determined that the date interval corresponding to the current date and the expected arrival date meets the preset conditions. That is, at this time, the date difference between the current date and the expected arrival date is greater than 30 days.

[0050] Step 102: when the date interval corresponding to the current date and the expected arrival date meets a preset condition, determine at least one first date interval and one second date interval according to the date interval.

[0051] When the big data platform determines that the date interval meets the preset conditions, it can determine at least one first date interval and one second date interval according to the date interval. Among them, the first date interval can be divided based on the above-mentioned preset number or preset duration. The second date interval is an interval consisting of dates in the date interval except all the first date intervals. When dividing the date interval, the first date interval can be obtained by dividing according to the preset number or preset duration, and the remaining dates are determined as the second date interval. For example, if the date interval is 2024.6.13~2024.7.31, 2024.6.13~2024.7.13 can be used as a first date interval, and if the number of dates between 2024.7.13~2024.7.31 is less than 30 days, it can be used as the second date interval.

[0052] Step 103: Determine the first total area required to be occupied by all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within a date interval, and all products for sale in the warehouse with an estimated sale date within each first date interval.

[0053] The big data platform can determine the first total area required for all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within the date interval, and all products for transfer in the warehouse with an estimated transfer date within each first date interval. Products in transit refer to products that need to be delivered to the warehouse within the date interval in addition to the current delivery. Products for transfer refer to products that need to be transferred from the warehouse within the first date interval.

[0054] In an embodiment of the present application, determining the area occupied by all in-transit products that arrive at the warehouse within a date interval includes: determining a first in-transit area occupied by all in-transit products that arrive at the warehouse in each first date interval; determining an average daily incoming storage area of ​​the warehouse in a historical time period based on a total area occupied by all products that arrived at the warehouse in a historical time period before a current date; determining a second in-transit area occupied by all in-transit products that arrive at the warehouse in a second date interval based on the average daily incoming storage area and the number of dates included in the second date interval; and determining an area occupied by all in-transit products that arrive at the warehouse within the date interval based on all the first in-transit areas and the second in-transit areas.

[0055] The big data platform can determine the first in-transit area occupied by all in-transit products that arrive at the warehouse in each first date interval. In an embodiment of the present application, determining the first in-transit area occupied by all in-transit products that arrive at the warehouse in each first date interval includes: obtaining multiple orders that have been distributed, whose estimated arrival date is within each first date interval, whose business type is subsidiary distribution, and whose warehouse to be distributed is a warehouse; for each internal sales order included in each distributed order, obtaining the product type and product quantity of the distributed products included in the internal sales order, and the delivery type and execution status of the internal sales order; filtering out internal sales orders whose execution status is a preset status, and removing internal sales orders whose delivery type is port delivery from the filtered internal sales orders, so as to obtain the target internal sales order corresponding to each distributed order; determining the first in-transit area according to the quantity of each distributed product in all target internal sales orders and the corresponding single model occupied area.

[0056] The big data platform can obtain multiple orders for goods that are expected to arrive in the warehouse within each first date interval, and the business type is distribution to subsidiaries, and the warehouse where the product needs to be distributed is the warehouse. Among them, each distribution order includes multiple internal sales orders. For example, it may be a transfer or sale between various branches, or between the head office and various branches. Each internal sales order may include a model of product and its quantity, and may also include the delivery type and execution status of the model product. Among them, the delivery type may include port delivery, and may also include other possible delivery methods. Each internal sales order may correspond to an execution status. The execution status includes the status of the factory delivery posting, the status of the logistics customs clearance, and the status of the bill of lading date being entered.

[0057] For each internal sales order included in each distributed order, the big data platform can obtain the product type and product quantity of the distributed products included in the internal sales order, as well as the delivery type and execution status of the internal sales order. The big data platform can filter out the internal sales orders whose execution status is the preset status, and remove the internal sales orders whose delivery type is port delivery from the filtered internal sales orders to obtain the target internal sales order corresponding to each distributed order. The big data platform can determine the first in-transit area based on the quantity of each distributed product in all target internal sales orders and the corresponding single model occupied area. Among them, the single model occupied area can be obtained from the LTC system. Specifically, the big data platform can determine the first in-transit area as the sum of the products between the quantity of each distributed product and the corresponding single model occupied area.

[0058] In one embodiment, the LTC system data is taken, including distribution warehouse + business type (distribution by subsidiaries) + expected arrival date + sales order execution status (factory delivery has been posted, logistics has been declared, and bill of lading date has been entered) - model and quantity delivered at the port, and the area of ​​in-transit inventory is calculated in combination with the area occupied by the models being sold.

[0059] The big data platform can determine the average daily storage area of ​​the warehouse in the historical time period according to the total area occupied by all products that arrived at the warehouse in the historical time period before the current date. The historical time period can be configured according to demand, for example, the historical time period can be 3 months before the current date. The big data platform can determine the second in-transit area occupied by all in-transit products that arrived at the warehouse in the second date interval according to the average daily storage area and the number of dates included in the second date interval. Specifically, the big data platform can determine the second in-transit area as the product of the average daily storage area and the date data included in the second date interval.

[0060] In one embodiment, the second in-transit area may be determined according to the daily average SAP warehouse-in occupied area in the historical time period and the number of days in the second date interval.

[0061] The big data platform can determine the area occupied by all in-transit products that arrive at the warehouse within the date interval based on all the first in-transit areas and the second in-transit areas. Specifically, the sum of the areas between all the first in-transit areas and the second in-transit areas can be determined as the area occupied by all in-transit products that arrive at the warehouse within the date interval.

[0062] The big data platform can determine the area required to be occupied by all the resale products in the warehouse with an estimated resale date in each first date interval. In an embodiment of the present application, determining the area required to be occupied by all the resale products in the warehouse with an estimated resale date in each first date interval includes: obtaining external sales orders for products in stock in the warehouse, the external sales orders including the estimated resale date of the resale products; determining the external sales orders with an estimated resale date in each first date interval as target external sales orders; and determining the area required to be occupied by all the resale products in the warehouse with an estimated resale date in each first date interval based on the quantity of each resale product in all target external sales orders and the corresponding occupied area of ​​a single machine model.

[0063] The big data platform can obtain external sales orders for products in stock in the warehouse, and the external sales orders include the estimated resale date of the resale products. Among them, the external sales orders refer to business opportunity orders. The big data platform can determine the external sales orders with the estimated resale date in each first date interval as target external sales orders. The big data platform can determine the area required to be occupied by all resale products with the estimated resale date in the warehouse in each first date interval based on the quantity of each resale product in all target external sales orders and the corresponding single model occupied area. Specifically, the big data platform can determine the product of the quantity of each resale product and the corresponding single model occupied area as the area required to be occupied by all resale products with the estimated resale date in the warehouse in each first date interval.

[0064] In an embodiment of the present application, determining the area occupied by all products in stock in the warehouse on the current date includes: obtaining the product inventory model and product inventory quantity of each product in stock in the warehouse on the current date; obtaining the floor space occupied by a single model of each product in stock based on the storage location code of each product line, the product group code of each product in stock and the product inventory model of each product in stock; determining the area of ​​all products in stock in the warehouse based on all product inventory models corresponding to each product line, the product inventory quantity of each product inventory model and the floor space occupied by a single model of each product.

[0065] The big data platform can obtain the product inventory model and product inventory quantity of each product in stock in the warehouse on the current date. The big data platform can obtain the single model footprint of each product in stock based on the warehouse location code of each product line, the product group code of each product in stock, and the product inventory model of each product in stock. The big data platform can determine the area of ​​all products in stock in the warehouse based on all product inventory models corresponding to each product line, the product inventory quantity of each product inventory model, and the single model footprint of each product.

[0066] In one embodiment, the area of ​​all products in stock in the warehouse can be determined based on the factory where the products are located, the product inventory model in the SAP system at the current moment, the product quantity, and the floor space occupied by a single machine model.

[0067] The big data platform can determine the total area occupied by all the products in stock in the warehouse on the current date and all the products in transit that arrive at the warehouse within the date range, and can determine the difference between the total area and the area required to be occupied by all the resale products in the warehouse with an estimated resale date in each first date range as the first total area.

[0068] Step 104: for each date in the second date interval, determine the transfer-cancellation interval in which the date is located, and determine the date type of the date according to the transfer-cancellation interval in which the date is located.

[0069] For each date in the second date interval, the big data platform can determine the resale interval in which the date is located, and determine the date type of the date according to the resale interval in which the date is located. The resale interval can be determined based on the historical resale data of each product. For example, for a certain time period, if the resale of each product in a certain time interval is relatively frequent, stable, or infrequent, it can be used as a resale interval.

[0070] In an embodiment of the present application, the resale interval includes a high-frequency resale interval and a low-frequency resale interval, and the method also includes: for each historical time interval before the current date, obtaining the historical resale frequency of each product of each product line on each historical date in the historical time interval; determining the date on which the historical resale frequency of each historical date in the historical time interval is greater than or equal to a preset frequency as the target date; constructing a high-frequency resale interval for each product of each product line based on all target dates in the historical time interval; and constructing a low-frequency resale interval for each product of each product line based on dates other than all target dates in the historical time interval.

[0071] The resale interval includes a high-frequency resale interval and a low-frequency resale interval. The resale frequency of each date in the high-frequency resale interval is greater than or equal to the preset frequency. The resale frequency of each date in the low-frequency resale interval is less than the preset frequency. Before determining the resale interval in which each date in the second date interval is located, for each historical time interval before the current date, the big data platform can obtain the historical resale frequency of each product of each product line on each historical date in the historical time interval.

[0072] The big data platform may determine the date whose historical resale frequency for each historical date within the historical time interval is greater than or equal to the preset frequency as the target date. The big data platform may construct a high-frequency resale interval for each product of each product line based on all target dates within the historical time interval. The big data platform may construct a low-frequency resale interval for each product of each product line based on dates other than all target dates within the historical time interval. For example, if the resale is more concentrated at the end of the month, the high-frequency resale interval may include dates at the end of each month, such as the 21st to the 30th or 31st of each month, and the low-frequency resale interval may include dates other than the end of each month, such as the 1st to the 20th of each month.

[0073] When determining the resale interval in which each date in the second date interval is located, each date in the second date interval can be matched with the dates in the high-frequency resale interval and the low-frequency resale interval, so as to obtain the resale interval in which each date in the second date interval is located. Afterwards, the big data platform can determine the date type of the date according to the resale interval in which each date in the second date interval is located. Among them, the date type may include an upper-mid-ten-day type and a lower-ten-day type. For example, if the resale interval in which each date in the second date interval is located is a high-frequency resale interval, the date type of each date can be determined to be a lower-ten-day type. If the resale interval in which each date in the second date interval is located is a low-frequency resale interval, the date type of each date can be determined to be an upper-mid-ten-day type.

[0074] Step 105: Determine a second total area to be occupied by the resale products within a second date interval based on historical sales data of the same date type for each date within a preset time period before the current date.

[0075] The big data platform can determine the second total area that the resale products need to occupy in the second date interval based on the historical sales data of the same date type in the preset time period before the current date. The preset time period can be set according to actual needs, for example, the preset time period can be set to 3 months. The historical sales data can be the product model and product quantity sold.

[0076] Specifically, the average daily resale area occupied within the preset time period is determined based on the historical sales data of the date type belonging to each date within the preset time period before the current date and the number of dates of the same date type within the preset time period, and the area occupied by the same date type in the second date interval is determined based on the average daily resale area occupied and the number of dates of the same date type in the second date interval.

[0077] For the dates of the upper and middle ten-day type, the number of dates of the upper and middle ten-day type in the second date interval can be determined, the number of dates of the upper and middle ten-day type in the preset time period can be determined, the area occupied by all products sold on the dates of the upper and middle ten-day type in the preset time period can be determined, the ratio between the number of dates of the upper and middle ten-day type in the second date interval and the number of dates of the upper and middle ten-day type in the preset time period can be determined, and the product of the ratio and the above-mentioned area can be determined as the area occupied by the dates of the upper and middle ten-day type in the second date interval. Similarly, the area occupied by the dates of the lower ten-day type in the second date interval can be determined, thereby obtaining the second total area occupied by the resale products in the second date interval.

[0078] For example, if the current date is June 13, 2024, and the expected arrival date is July 31, 2024, June 13, 2024 to July 31, 2024 can be divided into June 13, 2024 to July 13, 2024, and July 14, 2024 to July 31, 2024. When determining the second total area occupied by the transferred products from July 14, 2024 to July 31, 2024, July 14, 2024 to July 31, 2024 can be divided into the non-month-end interval: July 14, 2024 to July 20, 2024, and the month-end interval: July 21, 2024 to July 31, 2024. For the non-month-end interval, a total of 7 days, the total area can be calculated based on the product models and quantities actually sold from April, May, and June 1 to 20, and the total area is calculated * (7 / (20*3)). For the month-end interval: July 21, 2024 to July 31, 2024, a total of 11 days, the total area can be calculated based on the product models and quantities actually sold from April, May, and June 21 to the end of the month, and the total area is calculated * (11 / (10+11+10)), so that the sum of the final data obtained for the month-end interval and the non-month-end interval is determined as the second total area occupied by the transferred products from July 14, 2024 to July 31, 2024. Among them, the actual sales are determined based on the sales orders that have been shipped with the business types of "Subsidiary Local" and "Subsidiary Export".

[0079] Step 106: Send the quantity to be distributed, the first total area, and the second total area of ​​each product of each product line of the warehouse on the current date to the LTC system, so as to determine the warehouse capacity rate on the expected arrival date through the LTC system.

[0080] The big data platform can send the quantity to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system. The LTC system can determine the storage capacity rate of the warehouse on the expected arrival date. Specifically, the LTC system can determine the estimated total floor space required for all products to be laid on the current date based on the quantity to be distributed of each product of each product line on the current date and the floor space of each product model. The LTC system can determine the estimated storage area for each product line in the warehouse on the expected arrival date based on the first total area, the second total area, and the estimated total floor space.

[0081] The LTC system can determine the estimated storage capacity utilization rate of each product line based on the estimated storage usage area of ​​each product line and the total floor space of the sub-warehouses corresponding to each product line under the warehouse. Specifically, the LTC system can determine the estimated storage capacity utilization rate of each product line as the ratio between the estimated storage usage area of ​​each product line and the total floor space of the sub-warehouses corresponding to each product line. The LTC system can determine the estimated storage capacity utilization rate of the warehouse on the estimated arrival date based on the estimated storage capacity utilization rates of all product lines under the warehouse. Specifically, the LTC system can determine the average of the estimated storage capacity utilization rates of all product lines as the estimated storage capacity utilization rate of the warehouse.

[0082] In one embodiment, for any product line, when the estimated inventory utilization rate of the product line is greater than the preset utilization rate, the LTC system may send an alarm prompt, and / or adjust the estimated departure date and the distribution quantity on the current date for multiple products of the product line.

[0083] Among them, the preset utilization rate can be set according to demand. For example, the preset utilization rate can be set to 0.8 or 80%. If the estimated storage capacity usage of the product line is greater than 0.8 or 80%, it can be determined that the sub-warehouse corresponding to the product line may be out of stock after this distribution, and the estimated departure date and distribution data of the products set during this distribution can be adjusted. If the estimated storage capacity usage of the product line is less than 0.8 or 80%, it can be determined that the sub-warehouse corresponding to the product line can be used normally after this distribution, and the possibility of out of stock is low. The estimated departure date and distribution data of the products set during this distribution do not need to be adjusted.

[0084] Through the above technical solution, when multiple products of each product line need to be laid in the warehouse on the current date, the estimated arrival date of each product at the warehouse sent by the LTC system is received; when the date interval corresponding to the current date and the estimated arrival date meets the preset conditions, at least one first date interval and a second date interval are determined according to the date interval; the first total area required to be occupied by all the products in stock in the warehouse on the current date, all the products in transit that arrive at the warehouse within the date interval, and all the transfer products in the warehouse with the estimated transfer date within each first date interval is determined; for each date in the second date interval, the transfer interval in which the date is located is determined, And determine the date type according to the resale range in which the date is located; determine the second total area that the resale products in the second date range need to occupy according to the historical sales data of the same date type for each date in the preset time period before the current date; send the number of products to be distributed, the first total area and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system, so as to determine the warehouse capacity rate on the expected arrival date through the LTC system, taking into account the different product resale volumes in different time periods, so that the warehouse capacity rate calculation is more accurate, thereby being able to provide more accurate decision support for warehouse distribution, making the distribution of warehouses more reasonable.

[0085] Figure 1 FIG. 1 is a flow chart of a method for determining the storage capacity rate of a warehouse in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0086] Figure 2 The flowchart of the method for determining the storage capacity rate of a warehouse according to an embodiment of the present application is schematically shown. Figure 2 As shown, in one embodiment of the present application, a method for determining the storage capacity rate of a warehouse is provided. This embodiment mainly uses the method applied to the LTC system as an example, and includes the following steps:

[0087] Step 201: When multiple products of each product line need to be delivered to the warehouse on the current date, the estimated arrival date of each product at the warehouse is determined based on the preset transportation parameters and the estimated departure date of each product.

[0088] The warehouse is used to store products of at least one product line. A product line can produce multiple products. When multiple products of each product line need to be delivered to the warehouse on the current date, the LTC (LTC-Lead to Cash) system can determine the estimated arrival date of each product at the warehouse. Specifically, the LTC system can determine the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product.

[0089] In an embodiment of the present application, determining the expected arrival date of each product at the warehouse based on the preset transportation parameters and the expected departure date of each product includes: for each product, determining the total transportation cycle required to transport each product from the preset factory address to the warehouse based on the preset transportation parameters of the product, wherein the preset transportation parameters include at least the booking cycle, domestic transportation cycle, international transportation cycle, subsidiary customs clearance cycle and certification cycle of the corresponding product; for each product, determining the expected arrival date of each product at the warehouse based on the expected departure date of the product and the total transportation cycle of the product.

[0090] The preset transportation parameters at least include the booking period, domestic transportation period, international transportation period, subsidiary customs clearance period and certification period of each product. The LTC system can determine the total transportation period required for each product to be transported from the preset factory address to the warehouse based on the preset transportation parameters of each product. Afterwards, the LTC system can determine the estimated arrival date of each product at the warehouse based on the estimated departure date of each product and the total transportation period. Specifically, the total transportation period can be added to each estimated departure date to obtain the estimated arrival date of each product.

[0091] After determining the expected arrival date of each product at the warehouse, the LTC system may send the expected arrival date of each product to the big data platform. The big data platform receives the expected arrival date of each product, and when the date interval corresponding to the current date and the expected arrival date meets the preset conditions, the big data platform determines at least one first date interval and a second date interval according to the date interval. The big data platform determines the first total area required to be occupied by all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within the date interval, and all resale products in the warehouse with an expected resale date within each first date interval. For each date in the second date interval, the big data platform determines the resale interval in which the date is located, and determines the date type of the date according to the resale interval in which the date is located. The big data platform determines the second total area required to be occupied by the resale products in the second date interval based on the historical sales data of the same date type for each date in the preset time period before the current date. The big data platform sends the number of products to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system.

[0092] Step 202: Receive the quantity to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date sent by the big data platform.

[0093] Step 203: Determine the estimated total floor space required for all products to be laid on the current date based on the quantity of each product of each product line to be laid on the current date and the floor space required for each product model.

[0094] Step 204: Determine the estimated storage area of ​​each product line of the warehouse on the estimated arrival date based on the first total area, the second total area and the estimated total area.

[0095] The LTC system can determine the warehouse capacity rate on the expected arrival date. Specifically, the LTC system can determine the estimated total floor space required for all products to be laid on the current date based on the number of products to be laid on each product line on the current date and the floor space of each product model. The LTC system can determine the estimated storage area for each product line of the warehouse on the expected arrival date based on the first total area, the second total area and the estimated total floor space.

[0096] Step 205: Determine the estimated storage capacity utilization rate of each product line based on the estimated storage usage area of ​​each product line and the total floor space of the sub-warehouses corresponding to each product line under the warehouse.

[0097] Step 206: Determine the estimated storage capacity utilization rate of the warehouse on the estimated arrival date based on the estimated storage capacity utilization rates of all product lines under the warehouse.

[0098] The LTC system can determine the estimated storage capacity utilization rate of each product line based on the estimated storage usage area of ​​each product line and the total floor space of the sub-warehouses corresponding to each product line under the warehouse. Specifically, the LTC system can determine the estimated storage capacity utilization rate of each product line as the ratio between the estimated storage usage area of ​​each product line and the total floor space of the sub-warehouses corresponding to each product line. The LTC system can determine the estimated storage capacity utilization rate of the warehouse on the estimated arrival date based on the estimated storage capacity utilization rates of all product lines under the warehouse. Specifically, the LTC system can determine the average of the estimated storage capacity utilization rates of all product lines as the estimated storage capacity utilization rate of the warehouse.

[0099] In an embodiment of the present application, the method also includes: for any product line, when the estimated inventory utilization rate of the product line is greater than the preset utilization rate, sending an alarm prompt, and / or adjusting the estimated departure date and the distribution quantity on the current date of multiple products of the product line.

[0100] For any product line, when the estimated storage capacity utilization rate of the product line is greater than the preset utilization rate, the LTC system can send an alarm prompt, and / or adjust the estimated departure date and the distribution quantity on the current date of multiple products in the product line.

[0101] Through the above technical solution, the estimated arrival date of each product at the warehouse is determined according to the preset transportation parameters and the estimated departure date of each product. The factors considered are more comprehensive, and the different product turnover volumes in different time periods are taken into account, so that the warehouse capacity rate calculation is more accurate, thereby being able to provide more accurate decision support for warehouse distribution, making the distribution of warehouses more reasonable.

[0102] Figure 3 The flowchart of the method for determining the storage capacity rate of a warehouse according to an embodiment of the present application is schematically shown. Figure 3 As shown, in one embodiment of the present application, a method for determining the storage capacity rate of a warehouse is provided, comprising the following steps:

[0103] Step 301: When multiple products of each product line need to be delivered to the warehouse on the current date, the LTC system determines the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product.

[0104] The warehouse is used to store products of at least one product line. One product line can produce multiple products. When multiple products of each product line need to be laid out to the warehouse on the current date, the LTC (LTC-Lead to Cash) system can determine the expected arrival date of each product at the warehouse. Specifically, the LTC system can determine the expected arrival date of each product at the warehouse based on the preset transportation parameters and expected departure date of each product. Among them, the preset transportation parameters include at least the booking cycle, domestic transportation cycle, international transportation cycle, subsidiary customs clearance cycle and certification cycle of each product. After determining the expected arrival date of each product at the warehouse, the LTC system can send the expected arrival date of each product to the big data platform.

[0105] Step 302: The big data platform receives the estimated arrival date of each product at the warehouse sent by the LTC system.

[0106] Step 303: When the date interval corresponding to the current date and the expected arrival date meets a preset condition, the big data platform determines at least one first date interval and one second date interval based on the date interval.

[0107] The big data platform receives the expected arrival date of each product and can determine the date interval formed by the current date and the expected arrival date. The big data platform can determine whether the date interval meets the preset condition. The preset condition can be that the number of dates in the date interval is greater than the preset number, or that the time span corresponding to the date interval is greater than the preset time span.

[0108] When determining that the date interval meets the preset conditions, the big data platform can determine at least one first date interval and one second date interval according to the date interval. The first date interval can be divided based on the above-mentioned preset number or preset duration. The second date interval is an interval consisting of dates in the date interval except all the first date intervals. When dividing the date interval, the first date interval can be obtained by dividing according to the preset number or preset duration, and the remaining dates are determined as the second date interval.

[0109] Step 304: The big data platform determines the first total area required to be occupied by all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within the date interval, and all products for sale in the warehouse with an estimated sale date within each first date interval.

[0110] The big data platform can determine the first total area required for all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within the date interval, and all products for transfer in the warehouse with an estimated transfer date within each first date interval. Products in transit refer to products that need to be delivered to the warehouse within the date interval in addition to the current delivery. Products for transfer refer to products that need to be transferred from the warehouse within the first date interval.

[0111] Step 305: For each date in the second date interval, the big data platform determines the transfer-cancellation interval in which the date is located, and determines the date type of the date according to the transfer-cancellation interval in which the date is located.

[0112] For each date in the second date interval, the big data platform can determine the resale interval in which the date is located, and determine the date type of the date according to the resale interval in which the date is located. The resale interval can be determined based on the historical resale data of each product. For example, for a certain time period, if the resale of each product in a certain time interval is more frequent, stable, or infrequent, it can be used as a resale interval. The date type may include an upper-mid-ten-day type and a lower-ten-day type. For example, if the resale interval in which each date in the second date interval is located is a high-frequency resale interval, the date type of each date can be determined to be a lower-ten-day type. If the resale interval in which each date in the second date interval is located is a low-frequency resale interval, the date type of each date can be determined to be an upper-mid-ten-day type.

[0113] Step 306: The big data platform determines the second total area required to be occupied by the resale products within the second date interval based on the historical sales data of the same date type for each date within a preset time period before the current date.

[0114] Step 307: The big data platform sends the quantity to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system.

[0115] The big data platform can determine the second total area that the resale products need to occupy in the second date interval based on the historical sales data of the same date type for each date in the preset time period before the current date. The preset time period can be set according to actual needs. For example, the preset time period can be set to 3 months. The historical sales data can be the product model and product quantity sold. The big data platform can send the number of products to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system.

[0116] Step 308: The LTC system determines the warehouse capacity rate on the estimated arrival date based on the total quantity of goods to be distributed, the first total area, and the second total area.

[0117] The LTC system can determine the warehouse capacity rate at the estimated arrival date based on the total number of goods to be distributed, the first total area, and the second total area. In one embodiment, for any product line, when the estimated storage capacity utilization rate of the product line is greater than the preset utilization rate, the LTC system can send an alarm prompt and / or adjust the estimated departure date and the distribution quantity of multiple products of the product line on the current date.

[0118] In an embodiment of the present application, a system for determining a storage capacity rate of a warehouse is provided, wherein the warehouse is used to store products of at least one product line, and the system includes:

[0119] The LTC system is used to determine the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product when multiple products of each product line need to be placed in the warehouse on the current date; determine the warehouse capacity rate on the estimated arrival date based on the total quantity to be placed, the first total area and the second total area;

[0120] The big data platform is used to receive the estimated arrival date of each product at the warehouse sent by the LTC system; determine at least one first date interval and one second date interval according to the date interval when the date interval corresponding to the current date and the estimated arrival date meets the preset conditions; determine the first total area required to be occupied by all the products in stock in the warehouse on the current date, all the products in transit that arrive at the warehouse within the date interval, and all the resale products in the warehouse with the estimated resale date within each first date interval; for each date within the second date interval, determine the resale interval in which the date is located, and determine the date type of the date according to the resale interval in which the date is located; determine the second total area required to be occupied by the resale products in the second date interval according to the historical sales data of the same date type for each date within a preset time period before the current date; and send the number of products to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system.

[0121] The warehouse is used to store products of at least one product line. A product line can produce multiple products. When multiple products of each product line need to be laid out to the warehouse on the current date, the LTC (LTC-Lead to Cash) system can determine the estimated arrival date of each product at the warehouse. Specifically, the LTC system can determine the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product. Among them, the preset transportation parameters include at least the booking cycle, domestic transportation cycle, international transportation cycle, subsidiary customs clearance cycle and certification cycle of each product.

[0122] After determining the estimated arrival date of each product at the warehouse, the LTC system can send the estimated arrival date of each product to the big data platform. The big data platform receives the estimated arrival date of each product and can determine the date interval formed by the current date and the estimated arrival date. The big data platform can determine whether the date interval meets the preset condition. The preset condition can be that the number of dates in the date interval is greater than the preset number, or that the time span corresponding to the date interval is greater than the preset time span.

[0123] When determining that the date interval meets the preset conditions, the big data platform can determine at least one first date interval and one second date interval according to the date interval. The first date interval can be divided based on the above-mentioned preset number or preset duration. The second date interval is an interval consisting of dates in the date interval except all the first date intervals. When dividing the date interval, the first date interval can be obtained by dividing according to the preset number or preset duration, and the remaining dates are determined as the second date interval.

[0124] The big data platform can determine the first total area required for all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within the date interval, and all products for transfer in the warehouse with an estimated transfer date within each first date interval. Products in transit refer to products that need to be delivered to the warehouse within the date interval in addition to the current delivery. Products for transfer refer to products that need to be transferred from the warehouse within the first date interval.

[0125] For each date in the second date interval, the big data platform can determine the resale interval in which the date is located, and determine the date type of the date according to the resale interval in which the date is located. The big data platform can determine the second total area to be occupied by the resale products in the second date interval based on the historical sales data of the same date type for each date in the preset time period before the current date. Among them, the preset time period can be set according to actual needs. For example, the preset time period can be set to 3 months. The historical sales data can be the product model and product quantity sold. The big data platform can send the number of products to be distributed, the first total area, and the second total area of ​​each product of each product line in the warehouse on the current date to the LTC system.

[0126] The LTC system can determine the warehouse capacity rate at the estimated arrival date based on the total quantity of goods to be distributed, the first total area, and the second total area. For any product line, if the estimated storage capacity utilization rate of the product line is greater than the preset utilization rate, the LTC system can send an alarm prompt and / or adjust the estimated departure date and the distribution quantity of multiple products of the product line on the current date.

[0127] In one embodiment, Figure 4 As shown, a schematic diagram of a system for determining the storage capacity ratio of a warehouse is provided.

[0128] Among them, the storage capacity calculation model is maintained in the LTC system. When determining the storage capacity rate of the warehouse, the storage capacity calculation model can obtain the booking cycle, domestic transportation cycle, international transportation cycle, unloading cycle, customs clearance cycle and certification cycle of the products in the TMS system through the interface service. The inventory on hand, stagnant inventory, products in transit and expected transfer are calculated through the big data platform. The storage capacity calculation model can obtain the inventory on hand, stagnant inventory, products in transit and expected transfer through the interface service. The human-computer interaction interface can display the storage capacity in levels according to the outlet group, outlet and product line. The storage capacity calculation model supports multiple versions and multiple languages, and can perform permission management, log management and calculation parameters. The storage capacity calculation model can determine the estimated storage capacity utilization rate of the warehouse based on the total storage area, total transit area, total sales forecast area, sales model area and warehouse area. If the estimated storage capacity utilization rate of the warehouse exceeds a certain range, the distribution quantity and the expected departure date of the product can be dynamically adjusted.

[0129] In one embodiment, a storage medium is provided, on which a program is stored, and when the program is executed by a processor, the method for determining the storage capacity ratio of a warehouse is implemented.

[0130] In one embodiment, a processor is provided, and the processor is used to run a program, wherein the program executes the above-mentioned method for determining the storage capacity ratio of a warehouse when it is run.

[0131] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data such as the storage capacity rate of the warehouse. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for determining the storage capacity rate of the warehouse is implemented.

[0132] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of a method for determining the storage capacity rate of a warehouse are implemented.

[0134] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the method steps for determining the storage capacity rate of a warehouse.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0141] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0142] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0143] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining the storage capacity rate of a warehouse, characterized in that: Applied to a big data platform, the warehouse is used to store products of at least one product line, and the method includes: When multiple products of each product line need to be delivered to the warehouse on the current date, receiving the estimated arrival date of each product at the warehouse sent by the LTC system; In the case where the date interval corresponding to the current date and the expected arrival date satisfies a preset condition, determining at least one first date interval and one second date interval according to the date interval; Determine a first total area required to be occupied by all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within the date interval, and all products in the warehouse whose estimated date of sale is within each first date interval; For each date in the second date interval, determining the transfer-cancellation interval in which the date is located, and determining the date type of the date according to the transfer-cancellation interval in which the date is located; Determine the second total area that the resale products need to occupy within the second date interval according to the historical sales data of the same date type for each date within a preset time period before the current date; Sending the quantity to be distributed of each product of each product line of the warehouse on the current date, the first total area, and the second total area to the LTC system, so as to determine the storage capacity rate of the warehouse on the expected arrival date through the LTC system; The transfer-sale interval includes a high-frequency transfer-sale interval and a low-frequency transfer-sale interval, and the transfer-sale interval is determined by: For each historical time interval before the current date, obtain the historical resale frequency of each product of each product line on each historical date within the historical time interval; Determine the date for which the historical transfer-sale frequency of each historical date in the historical time interval is greater than or equal to the preset frequency as the target date; Constructing a high-frequency resale range for each product in each product line based on all target dates within the historical time interval; A low-frequency resale interval for each product of each product line is constructed based on dates other than all target dates within the historical time interval.

2. The method for determining the storage capacity rate of a warehouse according to claim 1, characterized in that: Determining the area occupied by all in-transit products that arrive at the warehouse during the date interval includes: Determine a first in-transit area occupied by all in-transit products arriving at the warehouse during each first date interval; Determine the average daily storage area of ​​the warehouse in the historical time period according to the total area occupied by all products that arrived at the warehouse in the historical time period before the current date; Determine the second in-transit area occupied by all in-transit products arriving at the warehouse in the second date interval according to the daily average warehouse-in area and the number of dates included in the second date interval; The area occupied by all the in-transit products arriving at the warehouse within the date interval is determined according to the total first in-transit area and the second in-transit area.

3. The method for determining the storage capacity ratio of a warehouse according to claim 2, characterized in that: The determining of the first in-transit area occupied by all in-transit products arriving at the warehouse in each first date interval comprises: Get multiple orders that have been delivered to the warehouse whose estimated arrival date is within each first date interval, whose business type is subsidiary delivery, and whose warehouse to be delivered is the warehouse; For each internal sales order included in each distributed order, obtain the product type and product quantity of the distributed product included in the internal sales order, and the delivery type and execution status of the internal sales order; Filter out the internal sales orders whose execution status is the preset status, and remove the internal sales orders whose delivery type is port delivery from the filtered internal sales orders, so as to obtain the target internal sales order corresponding to each distributed order; The first in-transit area is determined according to the quantity of each distributed product in all target internal sales orders and the area occupied by the corresponding single model.

4. The method for determining the storage capacity ratio of a warehouse according to claim 1, characterized in that: Determining the area required for all the resale products in the warehouse with an estimated resale date within each first date interval includes: Obtaining an external sales order for the product in stock in the warehouse, wherein the external sales order includes an estimated transfer date for the transfer product; Determine the external sales orders with the expected transfer date in each first date interval as target external sales orders; The area required to be occupied by all the resale products with an estimated resale date in each first date interval in the warehouse is determined according to the quantity of each resale product in all the target external sales orders and the area occupied by the corresponding single machine model.

5. The method for determining the storage capacity ratio of a warehouse according to claim 1, characterized in that: Determining the area occupied by all products in stock in the warehouse on the current date includes: Obtain the product inventory model and product inventory quantity of each product in stock in the warehouse on the current date; Obtain the floor space occupied by a single machine of each product in stock based on the stock location code of each product line, the product group code of each product in stock, and the product inventory model of each product in stock; The area of ​​all the products in stock in the warehouse is determined based on all the product inventory models corresponding to each product line, the product inventory quantity of each product inventory model, and the floor space of a single machine of each product.

6. A method for determining the storage capacity rate of a warehouse, characterized in that: Applied to the LTC system, the warehouse is used to store products of at least one product line, and the method includes: When multiple products of each product line need to be delivered to the warehouse on the current date, an estimated arrival date of each product at the warehouse is determined based on preset transportation parameters and an estimated departure date of each product; Receive the quantity to be distributed, the first total area, and the second total area of ​​each product of each product line of the warehouse on the current date sent by the big data platform, wherein the first total area and the second total area are determined according to the method for determining the storage capacity rate of a warehouse according to any one of claims 1 to 5; Determine the estimated total floor space required for all products to be laid out on the current date based on the quantity of each product of each product line to be laid out on the current date and the floor space of each product model; Determine the estimated storage area of ​​each product line of the warehouse on the estimated arrival date according to the first total area, the second total area and the estimated total area; Determine the estimated storage capacity utilization rate of each product line based on the estimated storage usage area of ​​each product line and the total floor space of the sub-warehouses corresponding to each product line under the warehouse; The estimated storage capacity utilization rate of the warehouse on the estimated arrival date is determined based on the estimated storage capacity utilization rates of all product lines under the warehouse.

7. The method for determining the storage capacity ratio of a warehouse according to claim 6, characterized in that: Determining the estimated arrival date of each product at the warehouse according to the preset transportation parameters and the estimated departure date of each product includes: For each product, determine the total transportation period required for transporting each product from the preset factory address to the warehouse according to the preset transportation parameters of the product, wherein the preset transportation parameters at least include the booking period, domestic transportation period, international transportation period, subsidiary customs clearance period and certification period of the corresponding product; For each product, the estimated arrival date of each product at the warehouse is determined based on the estimated departure date of the product and the total transportation cycle of the product.

8. The method for determining the storage capacity ratio of a warehouse according to claim 6, characterized in that: The method further comprises: For any product line, when the estimated storage capacity utilization rate of the product line is greater than the preset utilization rate, an alarm prompt is sent, and / or the estimated departure date of multiple products of the product line and the distribution quantity on the current date are adjusted.

9. A method for determining the storage capacity rate of a warehouse, characterized in that: The warehouse is used to store products of at least one product line, and the method comprises: When multiple products of each product line need to be delivered to the warehouse on the current date, the LTC system determines the estimated arrival date of each product at the warehouse based on the preset transportation parameters and the estimated departure date of each product; The big data platform receives the estimated arrival date of each product at the warehouse sent by the LTC system; In the case where the date interval corresponding to the current date and the expected arrival date meets a preset condition, the big data platform determines at least one first date interval and one second date interval according to the date interval; The big data platform determines a first total area required to be occupied by all products in stock in the warehouse on the current date, all products in transit that arrive at the warehouse within the date interval, and all products in the warehouse with an estimated date of sale within each first date interval; For each date in the second date interval, the big data platform determines the resale interval in which the date is located, and determines the date type of the date according to the resale interval in which the date is located, wherein the resale interval includes a high-frequency resale interval and a low-frequency resale interval, and the resale interval is determined in the following manner: for each historical time interval before the current date, the historical resale frequency of each product of each product line on each historical date in the historical time interval is obtained; the date whose historical resale frequency of each historical date in the historical time interval is greater than or equal to a preset frequency is determined as the target date; a high-frequency resale interval for each product of each product line is constructed according to all target dates in the historical time interval; a low-frequency resale interval for each product of each product line is constructed according to dates other than all target dates in the historical time interval; The big data platform determines the second total area required to be occupied by the resale products within the second date interval according to the historical sales data of the same date type for each date within a preset time period before the current date; The big data platform sends the quantity of each product of each product line of the warehouse to be distributed on the current date, the first total area, and the second total area to the LTC system; The LTC system determines the storage capacity rate of the warehouse on the expected arrival date according to the total quantity of goods to be distributed, the first total area, and the second total area.

10. A system for determining the storage capacity of a warehouse, the warehouse being used to store products of at least one product line, characterized in that: The system comprises: The LTC system is used to determine the estimated arrival date of each product at the warehouse according to the preset transportation parameters and the estimated departure date of each product when multiple products of each product line need to be placed in the warehouse on the current date; and determine the storage capacity rate of the warehouse on the estimated arrival date according to the total quantity of goods to be placed, the first total area and the second total area; A big data platform is used to receive the estimated arrival date of each product at the warehouse sent by the LTC system; determine at least one first date interval and one second date interval according to the date interval when the date interval corresponding to the current date and the estimated arrival date meets the preset conditions; determine the first total area required to be occupied by all the products in stock in the warehouse on the current date, all the products in transit that arrive at the warehouse within the date interval, and all the diverted products in the warehouse whose estimated diverted date is within each first date interval; determine the diverted interval in which the date is located for each date within the second date interval, and determine the date type of the date according to the diverted interval in which the date is located; determine the diverted area in the second date interval according to the historical sales data of the same date type for each date within a preset time period before the current date. The second total area required for the sales products; the number of products to be distributed on each product line of the warehouse on the current date, the first total area and the second total area are sent to the LTC system, wherein the resale interval includes a high-frequency resale interval and a low-frequency resale interval, and the resale interval is determined in the following manner: for each historical time interval before the current date, the historical resale frequency of each product of each product line on each historical date in the historical time interval is obtained; the date on which the historical resale frequency of each historical date in the historical time interval is greater than or equal to the preset frequency is determined as the target date; a high-frequency resale interval for each product of each product line is constructed according to all target dates in the historical time interval; a low-frequency resale interval for each product of each product line is constructed according to dates other than all target dates in the historical time interval.

11. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing a machine to execute a method for determining a storage capacity ratio of a warehouse according to any one of claims 1 to 9.

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