A hot zone planning method and system based on simulated hot zones

Abstract

The present invention relates to a heat zone planning method and system based on simulated heat zones, and relates to the field of e-commerce warehousing technology. The method includes: obtaining all target picking batches and picking batch data corresponding to all target picking batches in a target warehouse within a set time; calculating a first time difference based on the picking batch data; the first time difference is the absolute value of the difference between the time when the target picking batch is executed in the target warehouse and the time when the target picking batch is executed in a preset warehouse; the preset warehouse is a warehouse provided with simulated heat zones, and the number of shelves in the simulated heat zones is equal to the length of the picking path; determining a maximum time difference based on multiple first time differences; determining the simulated heat zone of the preset warehouse corresponding to the maximum time difference; and adjusting the commodity heat zone in the target warehouse based on the simulated heat zone of the preset warehouse corresponding to the maximum time difference. The present invention provides a basis for commodity layout adjustment by setting simulated heat zones and calculating to determine the optimal heat zone.

Description

Technical Field

[0001] The present invention relates to the field of e-commerce warehousing technology, and in particular to a thermal zone planning method and system based on simulated thermal zones. Background Art

[0002] Heat maps highlight visitor-focused areas and their geographic locations. Due to their intuitive nature, they are widely used in fields such as web analytics and traffic analysis. Warehousing is also increasingly using heat maps to represent shelf heat within warehouses.

[0003] E-commerce warehousing differs significantly from traditional warehousing in that it focuses on fulfilling e-commerce orders and has stringent timelines. Optimizing hotspots within e-commerce warehousing is an effective means of improving fulfillment efficiency. However, for different merchants, how many SKUs (SKUs are the smallest physically indivisible stock-keeping units) should be allocated to hotspots? Should it be 50%, 30%, or even 20% of the total number of SKUs? Currently, there are no methods or tools on the market that can provide merchants with a basis for making such decisions. Summary of the Invention

[0004] The purpose of the present invention is to provide a heat zone planning method and system based on simulated heat zones, which determines the optimal heat zone by setting simulated heat zones for picking calculations, thereby providing a basis for adjusting commodity layout and saving manpower and material resources.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A thermal zone planning method based on simulating thermal zones, the thermal zone planning method comprising:

[0007] Obtain all target picking batches in the target warehouse within a set time and the picking batch data corresponding to all target picking batches; the picking batch data at least includes the picking path length in the target picking batch; the picking path length is the number of shelves between the picking starting point and the picking end point;

[0008] Calculating a first time difference based on the picking batch data; the first time difference is the absolute value of the difference between the time it takes to execute the target picking batch at the target warehouse and the time it takes to execute the target picking batch at a preset warehouse; the preset warehouse is a warehouse with a simulated hot zone, and the number of shelves in the simulated hot zone is equal to the length of the picking path;

[0009] determining a maximum time difference based on the plurality of first time differences;

[0010] Determining, based on the maximum time difference, a simulated hot zone of a preset warehouse corresponding to the maximum time difference;

[0011] The commodity hot zone in the target warehouse is adjusted according to the simulated hot zone of the preset warehouse corresponding to the maximum time difference.

[0012] Optionally, the picking batch data further includes the number of orders and picking time in the target picking batch;

[0013] Calculating the first time difference according to the picking batch data specifically includes:

[0014] Calculate the measured average order picking time based on the order picking time and the order quantity;

[0015] Get the average picking time for each simulated order;

[0016] Calculate the average time saved in the simulated hot zone picking process based on the measured average picking time and the simulated average picking time.

[0017] A first time difference is calculated according to the average picking time saved per order in the simulated hot zone and the number of orders covered by the simulated hot zone.

[0018] Optionally, the calculation process of the simulated average picking time per order specifically includes:

[0019] Determining a first target picking batch group based on the picking path lengths of the plurality of target picking batches and the simulated hot zone of the preset warehouse; the first target picking batch group includes a plurality of first target picking batches; the first target picking batches are target picking batches that can be picked within the simulated hot zone of the preset warehouse;

[0020] Calculating the total number of orders in the first target picking batch group;

[0021] Calculate the total time required to complete picking for the first target picking batch group;

[0022] The simulated average picking time per order is calculated based on the total number of orders and the total time.

[0023] Optionally, the calculation process of the simulated hot zone coverage order number specifically includes:

[0024] Calculate the number of SKUs in the simulated hot zone using the formula N = number of simulated hot zone shelves * (average number of bins per shelf * 0.7);

[0025] The number of orders covered in the simulated hot zone is determined based on the number of SKUs in the simulated hot zone and the TopN-SKU order coverage table; the TopN-SKU order coverage table is used to characterize the correspondence between the TopN occupancy rate and the number of covered orders in the simulated hot zone; the TopN occupancy rate is the ratio of the number of orders completed by N SKUs to the total number of orders; N SKUs are the top N SKUs in terms of popularity placed in the simulated hot zone.

[0026] Optionally, obtaining all target picking batches in the target warehouse within a set time specifically includes:

[0027] Get all initial picking batches in the target warehouse within the set time;

[0028] Determine a first benchmark order quantity based on the majority principle according to the order quantities of all the initial picking batches;

[0029] removing the initial picking batches that meet a first preset condition to determine a plurality of intermediate picking batches; the first preset condition being that the order quantity of the initial picking batch is not equal to the first benchmark order quantity;

[0030] Obtaining the picking time of the intermediate picking batch;

[0031] The intermediate picking batches that meet the second preset condition are removed to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the intermediate picking batches does not meet the set time requirement.

[0032] To achieve the above object, the present invention also provides the following technical solutions:

[0033] A thermal zone planning system based on simulated thermal zones, the thermal zone planning system comprising:

[0034] A picking batch data determination module is used to obtain all target picking batches in a target warehouse within a set time and the picking batch data corresponding to all target picking batches; the picking batch data at least includes the picking path length in the target picking batch; the picking path length is the number of shelves between the picking starting point and the picking end point;

[0035] a first time difference calculation module, configured to calculate a first time difference based on the picking batch data; the first time difference being the absolute value of the difference between the time it takes to execute the target picking batch at the target warehouse and the time it takes to execute the target picking batch at a preset warehouse; the preset warehouse being a warehouse with a simulated hot zone, and the number of shelves in the simulated hot zone being equal to the length of the picking path;

[0036] a maximum time difference calculation module, configured to determine a maximum time difference based on a plurality of said first time differences;

[0037] a simulated hot zone determining module, configured to determine, based on the maximum time difference, a simulated hot zone of a preset warehouse corresponding to the maximum time difference;

[0038] The hot zone adjustment module is used to adjust the commodity hot zone in the target warehouse according to the simulated hot zone of the preset warehouse corresponding to the maximum time difference.

[0039] Optionally, the picking batch data further includes the number of orders and picking time in the target picking batch;

[0040] The first time difference calculation module specifically includes:

[0041] A first time calculation submodule is configured to calculate the measured average picking time per order based on the picking time and the order quantity;

[0042] The second time calculation submodule is used to obtain the average picking time of a simulated order;

[0043] The average order-saving time calculation submodule is used to calculate the average order-saving time in the simulated hot zone based on the measured average order-picking time and the simulated average order-picking time;

[0044] The time saving calculation submodule is used to calculate the first time difference according to the average picking time saving of the simulated hot zone and the number of orders covered by the simulated hot zone.

[0045] Optionally, the second duration calculation submodule specifically includes:

[0046] a batch group determining unit, configured to determine a first target picking batch group based on the picking path lengths of the plurality of target picking batches and the simulated hot zone of the preset warehouse; the first target picking batch group includes a plurality of first target picking batches; the first target picking batches are target picking batches for which picking operations can be performed within the simulated hot zone of the preset warehouse;

[0047] a total order calculation unit, configured to calculate the total number of orders for the first target picking batch group;

[0048] A total time calculation unit, used to calculate the total time it takes to complete picking of the first target picking batch group;

[0049] The average order picking time calculation unit is used to calculate the simulated average order picking time according to the total number of orders and the total time.

[0050] Optionally, in terms of calculating the number of orders covered by the simulated hot zone, the time saving calculation submodule specifically includes:

[0051] SKU calculation unit, used to calculate the number of SKUs in the simulated hot zone according to the formula N = number of shelves in the simulated hot zone * (average number of bins per shelf * 0.7);

[0052] An order number calculation unit is used to determine the number of orders covered in the simulated hot zone based on the number of SKUs in the simulated hot zone and the TopN-SKU order coverage table; the TopN-SKU order coverage table is used to characterize the correspondence between the TopN occupancy rate and the number of covered orders in the simulated hot zone; the TopN occupancy rate is the ratio of the number of orders completed by N SKUs to the total number of orders; N SKUs are the top N SKUs in popularity placed in the simulated hot zone.

[0053] Optionally, in terms of obtaining all target picking batches in the target warehouse within a set time, the picking batch data determination module specifically includes:

[0054] The initial picking batch determination submodule is used to obtain all initial picking batches in the target warehouse within the set time;

[0055] A benchmark order determination submodule, configured to determine a first benchmark order quantity based on the majority principle according to the order quantities of all the initial picking batches;

[0056] an intermediate picking batch determination submodule, configured to remove the initial picking batch that meets a first preset condition to determine a plurality of intermediate picking batches; the first preset condition being that the order quantity of the initial picking batch is not equal to the first benchmark order quantity;

[0057] A picking time acquisition submodule is used to obtain the picking time of the intermediate picking batch;

[0058] The target picking batch determination submodule is used to remove the intermediate picking batches that meet the second preset condition to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the intermediate picking batches does not meet the set time requirement.

[0059] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0060] First, all target picking batches and their corresponding picking batch data are obtained from the target warehouse. The difference between the time it takes to execute a specific target picking batch in the target warehouse and the time it takes to execute the same target picking batch in the preset warehouse is calculated. The preset warehouse is a warehouse with a simulated hot zone, where the number of shelves in the simulated hot zone is equal to the length of the picking path. The maximum time difference is then determined based on the time differences calculated for different target picking batches. The corresponding simulated hot zone of the preset warehouse is then determined based on the maximum time difference. The product layout in the target warehouse is then adjusted based on the simulated hot zone of the preset warehouse.

[0061] The present invention determines the optimal hot zone by performing hot zone simulation picking based on the warehouse's picking batch data, and then optimizes the commodity layout, further improving the picking efficiency and saving manpower and material resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 Schematic diagram of the process of the thermal zone planning method based on thermal zone simulation of the present invention;

[0064] Figure 2 It is a structural diagram of the thermal zone planning system based on simulated thermal zones of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0066] The purpose of the present invention is to provide a hot zone planning method and system based on simulated hot zones. According to the merchant's order structure and the warehouse's existing picking batch data, by simulating hot zone picking, the optimal hot zone size recommendation for the merchant is automatically calculated, providing a quantifiable reference value for the warehouse's hot zone planning.

[0067] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] Example 1

[0069] In e-commerce warehouse planning, products with high sales volume or picking frequency are concentrated in a certain area so that most order picking can be completed in a smaller area. This area with high picking efficiency is called the hot zone.

[0070] like Figure 1 As shown, this embodiment provides a thermal zone planning method based on simulated thermal zones, including:

[0071] Step 100: Obtain all target picking batches in the target warehouse within a set time and the picking batch data corresponding to all target picking batches; the picking batch data at least includes the picking path length in the target picking batch; the picking path length is the number of shelves between the picking starting point and the picking end point. Specifically, step 100 includes:

[0072] Step 1001: Obtain all initial picking batches in the target warehouse within a set time.

[0073] Step 1002 determines a first benchmark order quantity based on the mode principle, based on the order quantities of all the initial picking batches. Specifically, the mode of the order quantities of the initial picking batches is generally the expected number of orders for a single picking batch set by the merchant. Picking batches smaller than this value are not useful as a reference. Therefore, it is necessary to determine the first benchmark order quantity, that is, the mode of all order quantities.

[0074] Step 1003 : Remove the initial picking batches that meet a first preset condition to determine a plurality of intermediate picking batches; the first preset condition is that the order quantity of the initial picking batch is not equal to the first benchmark order quantity.

[0075] Step 1004: Obtain the picking time of the intermediate picking batch.

[0076] Step 1005 , removing the intermediate picking batches that meet a second preset condition to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the intermediate picking batches does not meet the set time requirement.

[0077] Step 200 calculates a first time difference based on the picking batch data. This first time difference is the absolute value of the difference between the time it takes to execute the target picking batch at the target warehouse and the time it takes to execute the target picking batch at a pre-set warehouse. The pre-set warehouse is a warehouse with a simulated hot zone, and the number of shelves within the simulated hot zone is equal to the picking path length. During the algorithm iteration process, a hot zone is assumed to be an area of a certain size or containing a certain number of shelves. This hot zone is the simulated hot zone.

[0078] Specifically, the picking batch data also includes the number of orders and the picking time in the target picking batch; the step 200 specifically includes:

[0079] Step 2001: Calculate the actual average picking time per order based on the picking time and the order quantity.

[0080] Step 2002: Get the simulated average picking time. Step 2002 specifically includes:

[0081] (1) Determine a first target picking batch group based on the picking path lengths of the plurality of target picking batches and the simulated hot zone of the preset warehouse; the first target picking batch group includes a plurality of first target picking batches; the first target picking batch is a target picking batch that can be picked within the simulated hot zone of the preset warehouse.

[0082] (2) Calculate the total number of orders for the first target picking batch group.

[0083] (3) Calculate the total time required to complete picking for the first target picking batch group.

[0084] (4) Calculate the simulated average order picking time based on the total number of orders and the total time. Specifically, the simulated average order picking time = the total time to complete picking in the first target picking batch group / the total number of orders in the first target picking batch group.

[0085] Step 2003: Calculate the simulated hot zone average picking time saving based on the measured average picking time and the simulated average picking time. Specifically, simulated hot zone average picking time saving = measured average picking time - simulated average picking time.

[0086] Step 2004: Calculate the first time difference based on the average time saved per order in the simulated hot zone and the number of orders covered by the simulated hot zone. Specifically, the calculation process of the number of orders covered by the simulated hot zone includes:

[0087] The number of SKUs in the simulated hot zone is calculated according to the formula N = number of shelves in the simulated hot zone * (average number of bins on a single shelf * 0.7); the number of orders covered in the simulated hot zone is determined based on the number of SKUs in the simulated hot zone and the TopN-SKU order coverage table; the TopN-SKU order coverage table is used to characterize the correspondence between the TopN occupancy rate and the number of covered orders in the simulated hot zone; the TopN occupancy rate is the ratio of the number of orders completed by N SKUs to the total number of orders; N SKUs are the top N SKUs in popularity placed in the simulated hot zone.

[0088] Step 300: Determine a maximum time difference based on a plurality of said first time differences.

[0089] Step 400: Determine a simulated hot zone of a preset warehouse corresponding to the maximum time difference according to the maximum time difference.

[0090] Step 500 : adjusting the commodity hot zone in the target warehouse according to the simulated hot zone of the preset warehouse corresponding to the maximum time difference.

[0091] In a specific embodiment, the thermal zone planning method based on thermal zone simulation specifically includes:

[0092] First, all initial picking batches within 7 days of the target warehouse are obtained, as well as the picking path length, picking time and order quantity of all initial picking batches. All initial picking batches are cleaned, specifically: (1) Determine the mode of all order quantities and only retain the initial picking batches with order quantities equal to the mode. (2) Remove the picking batches with a picking time of less than 3 minutes or greater than 60 minutes. The remaining initial picking batches are the target picking batches obtained from the target warehouse; among them, the picking time of less than 3 minutes may be distorted data caused by employee misoperation, and the picking time of more than 60 minutes may be distorted data caused by employees eating. Through the above data cleaning steps, the data used in the hot zone simulation is more accurate and in line with reality.

[0093] To facilitate calculations, this embodiment defines a "walking index." Shelves in a warehouse are typically numbered. Depending on how these numbers are assigned by the system, pickers typically follow a specific order for picking from the shelves. Unlike shelf numbers, the walking index is a code for the picking sequence of all shelves in the warehouse. It typically starts at 1 and increases in integer units. The maximum walking index is generally equal to the total number of shelves in the warehouse. The picking path length is the difference between the walking index at the starting point and the walking index at the end of a picking batch, representing the length of the distance traveled by that batch within the warehouse.

[0094] Then, sort the picking path lengths of all picking batches from smallest to largest, starting with the target picking batch with the shortest picking path length. Calculate the difference between the time it takes to execute the target picking batch at the preset warehouse and the time it takes to execute the target picking batch at the target warehouse. The preset warehouse is a warehouse with a simulated hot zone, and the number of shelves in the simulated hot zone is equal to the picking path length. For example, if the picking path length of the shortest path is 10 shelves, the simulated hot zone in the preset warehouse is also 10 shelves. Calculate how much time can be saved compared to the current picking time when the simulated hot zone is 10 shelves.

[0095] Specifically, when the number of shelves in the simulated hot zone is 10, the picking path lengths of each picking batch in the existing data are used to determine the target picking batches whose picking path lengths do not exceed 10 shelves. In other words, all batches whose picking path lengths are less than or equal to the number of shelves in the simulated hot zone (10 shelves) are selected. These batches are the batches that can be completed within the simulated hot zone size. Note: The batch path length must be less than or equal to the simulated hot zone size; the specific starting and ending locations of the hot zone are not important.

[0096] The number of SKUs N that can be accommodated in a simulated hot zone is calculated using the formula N = number of simulated hot zone shelves * (average number of bins per shelf * 0.7). The *0.7 is calculated because hot zones often have high sales volumes and require more storage space. Therefore, the number of bins per shelf in a hot zone is set at 70% of the number of bins per shelf in the warehouse to reflect actual storage conditions.

[0097] According to the merchant order structure, the TopN-SKU order coverage table of the merchant is calculated. Specifically, for a merchant, when only the top N SKUs are selected, the number of orders that can be completed by these SKUs is calculated. Different N corresponds to different number of orders. Based on this correspondence, a To p The N-SKU order coverage table is shown below:

[0098] TopN SKU Covered order volume 1 303 2 1857 3 3385 4 3920 5 4398 6 4898

[0099] The TopN-SKU order coverage table is used to obtain the number of orders that the simulated hot zone can theoretically process when the hottest N SKUs are distributed in the simulated hot zone, that is, the number of orders covered by the simulated hot zone.

[0100] The actual average picking time per order is calculated according to the formula: actual average picking time per order = picking time per current batch / number of orders per current batch.

[0101] The simulated average picking time is calculated according to the formula: average picking time per order = total processing time for batches that can be completed within the simulated hot zone size / total number of orders that can be completed within the simulated hot zone size.

[0102] The simulated average picking time saving in hot zone is calculated according to the formula: simulated average picking time saving in hot zone = measured average picking time - simulated average picking time.

[0103] The time that can be saved when setting the current simulated hot zone compared to picking in the current target warehouse is calculated using the formula: First Time Difference = Average Picking Time Saved in Simulated Hot Zone * Number of Orders Covered by the Hot Zone.

[0104] Repeat the above calculation until the target picking batch with the longest picking path is calculated. Place the number of shelves in the simulated hot zones set in different preset warehouses and the first time difference corresponding to the picking in the simulated hot zones in the chart to obtain a query table. According to the query table, the maximum time difference and the simulated hot zone of the preset warehouse corresponding to the maximum time difference, that is, the optimal hot zone, are determined. Finally, according to the size of the optimal hot zone obtained above, the positions of the goods on the shelves of the target warehouse are adjusted so that the top N hottest goods are concentrated in the same channel or the same area. This allows staff to pick goods without having to run all over the warehouse, but can quickly complete the picking in a certain channel or several channels, greatly improving the picking efficiency.

[0105] Example 2

[0106] like Figure 2 As shown, this embodiment provides a thermal zone planning system based on thermal zone simulation, including:

[0107] The picking batch data determination module 101 is used to obtain all target picking batches in the target warehouse within a set time and the picking batch data corresponding to all target picking batches; the picking batch data at least includes the picking path length in the target picking batch; the picking path length is the number of shelves between the picking starting point and the picking end point.

[0108] Specifically, in terms of obtaining all target picking batches in the target warehouse within the set time, the picking batch data determination module 101 includes an initial picking batch determination submodule, a benchmark order determination submodule, an intermediate picking batch determination submodule, a picking time acquisition submodule and a target picking batch determination submodule.

[0109] Among them, the initial picking batch determination submodule is used to obtain all initial picking batches in the target warehouse within the set time. The benchmark order determination submodule is used to determine the first benchmark order quantity based on the majority principle according to the order quantity of all the initial picking batches. The intermediate picking batch determination submodule is used to remove the initial picking batches that meet the first preset condition to determine multiple intermediate picking batches; the first preset condition is that the order quantity of the initial picking batch is not equal to the first benchmark order quantity. The picking time acquisition submodule is used to obtain the picking time of the intermediate picking batch. The target picking batch determination submodule is used to remove the intermediate picking batches that meet the second preset condition to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the intermediate picking batch does not meet the set time requirement.

[0110] The first time difference calculation module 201 is used to calculate a first time difference based on the picking batch data; the first time difference is the absolute value of the difference between the time for executing the target picking batch in the target warehouse and the time for executing the target picking batch in the preset warehouse; the preset warehouse is a warehouse with a simulated hot zone, and the number of shelves in the simulated hot zone is equal to the length of the picking path.

[0111] The picking batch data also includes the number of orders and picking time in the target picking batch; the first time difference calculation module 201 includes a first time calculation submodule, a second time calculation submodule, an average time saving calculation submodule and a time saving calculation submodule.

[0112] The first duration calculation submodule is used to calculate the actual average picking time per order based on the picking time and the order quantity; the second duration calculation submodule is used to obtain the simulated average picking time per order; the average time saved calculation submodule is used to calculate the simulated average picking time saved in the hot zone based on the actual average picking time per order and the simulated average picking time; the time saved calculation submodule is used to calculate the first time difference based on the simulated average picking time saved in the hot zone and the number of orders covered by the simulated hot zone.

[0113] Preferably, the second duration calculation submodule specifically includes:

[0114] The batch group determination unit is used to determine a first target picking batch group based on the picking path lengths of multiple target picking batches and the simulated hot zone of the preset warehouse; the first target picking batch group includes multiple first target picking batches; the first target picking batch is a target picking batch that can be picked within the simulated hot zone of the preset warehouse.

[0115] A total order calculation unit is used to calculate the total number of orders of the first target picking batch group.

[0116] The total time calculation unit is used to calculate the total time required for the first target picking batch group to complete picking.

[0117] The average order picking time calculation unit is used to calculate the simulated average order picking time according to the total number of orders and the total time.

[0118] In a specific embodiment, in terms of the calculation of the number of orders covered by the simulated hot zone, the time saving calculation submodule includes a SKU calculation unit and a SKU calculation unit.

[0119] The SKU calculation unit is used to calculate the number of SKUs in the simulated hot zone according to the formula N = number of shelves in the simulated hot zone * (average number of bins per shelf * 0.7). The order number calculation unit is used to determine the number of orders covered by the simulated hot zone based on the number of SKUs in the simulated hot zone and the TopN-SKU order coverage table; the TopN-SKU order coverage table is used to represent the correspondence between the TopN occupancy rate and the number of covered orders in the simulated hot zone; the TopN occupancy rate is the ratio of the number of orders completed by N SKUs to the total number of orders; N SKUs are the top N SKUs placed in the simulated hot zone in terms of popularity.

[0120] a maximum time difference calculation module 301, configured to determine a maximum time difference based on a plurality of said first time differences;

[0121] The simulated hot zone determining module 401 is configured to determine, according to the maximum time difference, a simulated hot zone of a preset warehouse corresponding to the maximum time difference.

[0122] The hot zone adjustment module 501 is configured to adjust the commodity hot zone in the target warehouse according to the simulated hot zone of the preset warehouse corresponding to the maximum time difference.

[0123] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0124] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A thermal zone planning method based on thermal zone simulation, characterized in that: The thermal zone planning method comprises: Obtain all target picking batches in the target warehouse within a set time and the picking batch data corresponding to all target picking batches; the picking batch data at least includes the picking path length in the target picking batch; the picking path length is the number of shelves between the picking starting point and the picking end point; Calculating a first time difference based on the picking batch data; the first time difference is the absolute value of the difference between the time it takes to execute the target picking batch at the target warehouse and the time it takes to execute the target picking batch at a preset warehouse; the preset warehouse is a warehouse with a simulated hot zone, and the number of shelves in the simulated hot zone is equal to the length of the picking path; determining a maximum time difference based on the plurality of first time differences; Determining, based on the maximum time difference, a simulated hot zone of a preset warehouse corresponding to the maximum time difference; The commodity hot zone in the target warehouse is adjusted according to the simulated hot zone of the preset warehouse corresponding to the maximum time difference.

2. The thermal zone planning method based on thermal zone simulation according to claim 1, characterized in that: The picking batch data also includes the number of orders and picking time in the target picking batch; Calculating the first time difference according to the picking batch data specifically includes: Calculate the measured average picking time per order based on the picking time and the order quantity; Get the average picking time for each simulated order; Calculate the average time saved in the simulated hot zone picking process based on the measured average picking time and the simulated average picking time. A first time difference is calculated according to the average picking time saved per order in the simulated hot zone and the number of orders covered by the simulated hot zone.

3. The thermal zone planning method based on thermal zone simulation according to claim 2, characterized in that: The calculation process of the simulated average picking time includes: Determining a first target picking batch group based on the picking path lengths of the plurality of target picking batches and the simulated hot zone of the preset warehouse; the first target picking batch group includes a plurality of first target picking batches; the first target picking batches are target picking batches that can be picked within the simulated hot zone of the preset warehouse; Calculating the total number of orders in the first target picking batch group; Calculate the total time required to complete picking for the first target picking batch group; The simulated average picking time per order is calculated based on the total number of orders and the total time.

4. The thermal zone planning method based on thermal zone simulation according to claim 2, characterized in that: The calculation process of the simulated hot zone coverage order number specifically includes: Calculate the number of SKUs in the simulated hot zone using the formula N = number of simulated hot zone shelves * (average number of bins per shelf * 0.7); The number of orders covered in the simulated hot zone is determined based on the number of SKUs in the simulated hot zone and the TopN-SKU order coverage table; the TopN-SKU order coverage table is used to characterize the correspondence between the TopN occupancy rate and the number of covered orders in the simulated hot zone; the TopN occupancy rate is the ratio of the number of orders completed by N SKUs to the total number of orders; N SKUs are the top N SKUs in terms of popularity placed in the simulated hot zone.

5. The thermal zone planning method based on thermal zone simulation according to claim 1, characterized in that: The acquisition of all target picking batches in the target warehouse within the set time specifically includes: Get all initial picking batches in the target warehouse within the set time; Determine a first benchmark order quantity based on the majority principle according to the order quantities of all the initial picking batches; removing the initial picking batches that meet a first preset condition to determine a plurality of intermediate picking batches; the first preset condition being that the order quantity of the initial picking batch is not equal to the first benchmark order quantity; Obtaining the picking time of the intermediate picking batch; The intermediate picking batches that meet the second preset condition are removed to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the intermediate picking batches does not meet the set time requirement.

6. A thermal zone planning system based on simulated thermal zones, characterized in that: The thermal zone planning system includes: A picking batch data determination module is used to obtain all target picking batches in a target warehouse within a set time and the picking batch data corresponding to all target picking batches; the picking batch data at least includes the picking path length in the target picking batch; the picking path length is the number of shelves between the picking starting point and the picking end point; a first time difference calculation module, configured to calculate a first time difference based on the picking batch data; the first time difference being the absolute value of the difference between the time it takes to execute the target picking batch at the target warehouse and the time it takes to execute the target picking batch at a preset warehouse; the preset warehouse being a warehouse with a simulated hot zone, and the number of shelves in the simulated hot zone being equal to the length of the picking path; a maximum time difference calculation module, configured to determine a maximum time difference based on a plurality of said first time differences; a simulated hot zone determining module, configured to determine, based on the maximum time difference, a simulated hot zone of a preset warehouse corresponding to the maximum time difference; The hot zone adjustment module is used to adjust the commodity hot zone in the target warehouse according to the simulated hot zone of the preset warehouse corresponding to the maximum time difference.

7. The thermal zone planning system based on thermal zone simulation according to claim 6, characterized in that: The picking batch data also includes the number of orders and picking time in the target picking batch; The first time difference calculation module specifically includes: A first time calculation submodule is configured to calculate the measured average picking time per order based on the picking time and the order quantity; The second time calculation submodule is used to obtain the average picking time of a simulated order; The average order-saving time calculation submodule is used to calculate the average order-saving time in the simulated hot zone based on the measured average order-picking time and the simulated average order-picking time; The time saving calculation submodule is used to calculate the first time difference according to the average picking time saving of the simulated hot zone and the number of orders covered by the simulated hot zone.

8. The thermal zone planning system based on thermal zone simulation according to claim 7, characterized in that: The second duration calculation submodule specifically includes: a batch group determining unit, configured to determine a first target picking batch group based on the picking path lengths of the plurality of target picking batches and the simulated hot zone of the preset warehouse; the first target picking batch group includes a plurality of first target picking batches; the first target picking batches are target picking batches for which picking operations can be performed within the simulated hot zone of the preset warehouse; a total order calculation unit, configured to calculate the total number of orders for the first target picking batch group; A total time calculation unit, used to calculate the total time it takes to complete picking of the first target picking batch group; The average order picking time calculation unit is used to calculate the simulated average order picking time according to the total number of orders and the total time.

9. The thermal zone planning system based on thermal zone simulation according to claim 7, characterized in that: In terms of calculating the number of orders covered by the simulated hot zone, the time saving calculation submodule specifically includes: SKU calculation unit, used to calculate the number of SKUs in the simulated hot zone according to the formula N = number of shelves in the simulated hot zone * (average number of bins per shelf * 0.7); An order number calculation unit is used to determine the number of orders covered in the simulated hot zone based on the number of SKUs in the simulated hot zone and the TopN-SKU order coverage table; the TopN-SKU order coverage table is used to characterize the correspondence between the TopN occupancy rate and the number of covered orders in the simulated hot zone; the TopN occupancy rate is the ratio of the number of orders completed by N SKUs to the total number of orders; N SKUs are the top N SKUs in popularity placed in the simulated hot zone.

10. The thermal zone planning system based on thermal zone simulation according to claim 6, characterized in that: In terms of obtaining all target picking batches in the target warehouse within a set time, the picking batch data determination module specifically includes: The initial picking batch determination submodule is used to obtain all initial picking batches in the target warehouse within the set time; A benchmark order determination submodule, configured to determine a first benchmark order quantity based on the majority principle according to the order quantities of all the initial picking batches; an intermediate picking batch determination submodule, configured to remove the initial picking batch that meets a first preset condition to determine a plurality of intermediate picking batches; the first preset condition being that the order quantity of the initial picking batch is not equal to the first benchmark order quantity; A picking time acquisition submodule is used to obtain the picking time of the intermediate picking batch; The target picking batch determination submodule is used to remove the intermediate picking batches that meet the second preset condition to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the intermediate picking batches does not meet the set time requirement.

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