A hierarchical intelligent tuning method and system based on binary bit operations

Abstract

This invention provides a method and system for intelligent allocation of inventory based on binary bit operations, responding to selected goods and participating stores to perform allocation: The system first divides the store inventory into several base units; the remaining inventory is further divided into several binary units; all stores are sorted as recipients; each binary unit and base unit is prioritized to obtain a sender category consisting of several shipping units; candidate shipping units are matched for each recipient according to their target inventory deficit, from largest to smallest, based on the first bit operation; the matching status of each candidate shipping unit is calculated for each recipient based on the second bit operation, extracting the most matched and highest-priority shipping unit, and updating the sender category and recipient sorting. This solution, through splitting, classifying, and layering processing, enables efficient generation of inter-store allocation strategies targeting inventory allocation in complex scenarios, effectively reducing the residual code rate.

Description

Technical Field

[0001] This invention belongs to the field of code allocation technology for chain stores, and in particular relates to a method and system for intelligent code layering based on binary bit operations. Background Technology

[0002] For brick-and-mortar clothing and footwear stores, ensuring a full range of sizes is of paramount importance. When customers visit physical stores to buy clothing and footwear, they often expect to try on and select products in their correct size immediately. Frequent shortages of sizes significantly diminish the customer shopping experience. For stores, having a full range of sizes significantly increases sales opportunities. When customers can easily find the size that suits their needs, they are more likely to complete a purchase. Conversely, shortages directly lead to the loss of potential customers, especially during peak sales seasons, where unsold popular styles due to size shortages can severely impact a store's sales revenue.

[0003] For chain stores, the same product may have different sales performance across different stores. Over time, some stores may sell outdated sizes, while others may have stockpiled inventory. Considering the inventory pressure on stores with stockpiled stock, inter-store transfers are indispensable. The core idea behind inter-store transfers is to minimize outdated sizes (striving for the same product in the same store to be either completely out of stock or have all sizes available). Currently, although some transfer software exists, it suffers from two problems: firstly, it requires a large amount of computation and cannot make decisions in real time; secondly, it does not effectively solve the problem of allocating stock in full sizes. Summary of the Invention

[0004] The purpose of this invention is to provide a homogeneous code hierarchical intelligent tuning method and system based on binary bit operations to address the above-mentioned problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] A hierarchical intelligent allocation method based on binary bit manipulation, which responds to selected goods and several participating stores to perform allocation:

[0007] The inventory of each participating store is first divided according to a set baseline to obtain several baseline units;

[0008] The remaining inventory in each store is further divided into several binary units according to the binary system.

[0009] Sort all stores by target inventory deficit as recipients;

[0010] Priority classification of each binary unit and base unit yields a shipper classification consisting of several shipping units with different priorities;

[0011] Based on the recipient’s target inventory deficit from the largest to the smallest, the first operation sequentially matches and allocates candidate shipping units that satisfy the condition of consecutive / complete codes of the base unit after the recipient’s allocation.

[0012] Based on the second operation, the matching status of each candidate shipping unit is calculated for each recipient, the most matching and highest priority shipping unit is extracted, and the shipper classification and recipient sorting are updated.

[0013] In the above-mentioned intelligent tuning method based on binary bit operations, the first bit operation is an "OR" operation.

[0014] The second bit operation is either an AND operation or an XOR operation;

[0015] The base number for each size of each item in each store is set from 1 to 10.

[0016] In the above-mentioned intelligent adjustment method based on binary bit operations, each store is sorted according to the principle that the store with the greater the target inventory deficit is processed first.

[0017] The target inventory deficit for each store is the result of subtracting the actual inventory from the target inventory of the corresponding product in the store.

[0018] The target inventory is automatically calculated by the system based on one or more of the following store parameters: sales volume, store ranking, inventory balance, number of days on the market, and number of days off the market; or it can be manually designed by the user.

[0019] The target inventory shall not be less than the set base quantity of each size of the corresponding goods in the corresponding store.

[0020] In the above-mentioned intelligent adjustment method for full code layering based on binary bit operations, each store will be cut into a base unit, and the base unit is either the full code base or the residual code base.

[0021] "Full stock" refers to the number of items in stock for each size; "Damaged stock" refers to the number of items in stock for at least one size.

[0022] The binary unit mentioned above has a quantity of 0 or 1 corresponding to each size;

[0023] The number of binary units that can be divided for each item in each store is max(x1-y1, x2-y2, ..., xn-yn, 0), where x is the inventory quantity of the corresponding size of the corresponding item in the corresponding store, y is the set base number of the corresponding size of the corresponding item in the corresponding store, and n is the number of size types.

[0024] In the above-mentioned homogeneous code hierarchical intelligent tuning method based on binary bit operations, the priority classification of each binary unit and base unit specifically includes:

[0025] For goods with complete base unit codes, the remaining inventory is further divided into several binary units, which are then assigned to the first issuance priority.

[0026] For goods with residual codes in the base unit, the remaining inventory is further divided into several binary units, which are then assigned to the second issuance priority.

[0027] The base unit of the residual code of the unmarked store is assigned to the third priority.

[0028] In the above-mentioned intelligent adjustment method for hierarchical code based on binary bit operations, the priority classification of each binary unit and base unit also includes classifying the base unit of the code of the unmarked store as the fourth priority; and classifying the base unit of the residual code of the marked store as the fifth priority.

[0029] This method also includes performing binary conversion on each base unit to obtain a base binary unit; the binary conversion includes setting the quantity of sizes with a non-zero quantity to 1, and keeping the quantity of sizes with a zero quantity at 0.

[0030] The non-binary base unit in the shipper's classification is split into at least two binary shipping units. When the receiver to which the non-binary base unit belongs is successfully matched, the at least two binary shipping units obtained from the split will be removed from the shipper's classification.

[0031] The method for matching candidate shipping units to the recipient includes performing an OR operation on the base binary unit with shipping units of at least the first shipping priority, the second shipping priority, and the third shipping priority, and the result of the filtering operation is continuously set to the size range / all are 1 as candidate shipping units.

[0032] The method for extracting the best match from the candidate shipping units includes performing an AND / XOR operation on the base binary unit and its candidate shipping units, and extracting the result that continuously sets the size range / all are 0 / 1 and has the highest priority.

[0033] Updating the shipper classification and receiver sorting specifically includes removing successfully matched shipping units from the shipper classification and removing successfully matched receivers from the receiver sorting.

[0034] In the above-mentioned intelligent adjustment method based on binary bit operations, before the transfer, the stores with the complete code base number are sorted and removed from the receiving party.

[0035] If the termination conditions are not met after the first round of allocation, multiple rounds of allocation will be conducted.

[0036] In the above-mentioned intelligent adjustment method based on binary bit operation, this method also includes performing virtual transfer on the successfully matched receiver and delivery unit and determining whether the receiver after virtual transfer fully meets the requirements.

[0037] If not, the receiver after virtual allocation will be placed on the demand stack until the demand is fully met and then popped off the stack; when the receiver is fully coded, the demand is considered to be fully met.

[0038] Unsuccessful receivers are placed in the unsuccessful queue.

[0039] Unsuccessful queues and demand stacks are treated as new receiver queues for the next round of allocation, with the termination condition being that either the receiver queue or the shipper category is cleared.

[0040] In the above-mentioned intelligent dispatching method based on binary bit operations, in non-first round dispatching, the receiving queue and the dispatching unit are ORed one by one, and the dispatching unit that makes the result of the operation more than 1 is the second candidate dispatching unit of the corresponding receiving unit.

[0041] When a shipping unit is matched as the second candidate shipping unit for multiple recipients, a series of XOR operations are performed on the second candidate shipping unit and multiple recipients. If the results of the operations are all 0 or 1, it is considered that the second candidate shipping unit is matched with multiple recipients at the same time. The successfully matched shipping unit is removed from the shipping unit classification and the successfully matched recipient is removed from the recipient queue.

[0042] The number of matched receivers does not exceed the set value.

[0043] A hierarchical intelligent tuning system based on binary bit manipulation includes,

[0044] The cutting module is used to first cut the inventory of each participating store according to a set base number to obtain a number of base units, and to cut the remaining inventory of each store according to binary to obtain a number of binary units.

[0045] The hierarchical module is used to sort all stores by target inventory deficit and prioritize each binary unit and base unit to obtain a shipper classification consisting of several shipping units with different priorities.

[0046] The combination matching module is used to match candidate shipping units for the recipient according to the target inventory shortage from the largest to the smallest, based on the first operation, so that the base unit can be consecutively or completely aligned after allocation; based on the second operation, it calculates the matching status of each candidate shipping unit for each recipient, extracts the most matched and highest priority shipping unit, and updates the sender classification and recipient sorting.

[0047] The inventory adjustment module is used to perform allocations and update inventory data based on the matching results.

[0048] The advantages of this invention are: this solution, through splitting, classifying and layering, can achieve efficient generation of inter-store transfer strategies with complete codes as the goal in complex scenarios;

[0049] This solution is based on binary logic for inventory matching, which only requires the most basic computer operations. It is very simple for computers to process, so the computational load is low and the operation performance is very strong, allowing users to make quick decisions at any time.

[0050] The layered, binary matching method proposed in this solution can effectively reduce the residual code rate and provide chain stores with a high degree of code integrity guarantee. Attached Figure Description

[0051] Figure 1 This is a flowchart of the intelligent tuning method for homogeneous code hierarchical structure based on binary bit operations according to the present invention.

[0052] Figure 2 This is a flowchart illustrating the core allocation process of the intelligent allocation method for hierarchical binary code based on binary bit operations of the present invention.

[0053] Figure 3 This is an example of the removal process after the residual code base numbers of store A and store C are successfully matched in the binary bit-based hierarchical intelligent adjustment method of the present invention.

[0054] Figure 4 This is a block diagram of the module structure of the intelligent tuning system based on binary bit operations according to the present invention.

[0055] Attached diagram labels: Cutting module 1; Layering module 2; Combination matching module 3; Inventory adjustment module 4. Detailed Implementation

[0056] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0057] Example 1

[0058] like Figure 1 and Figure 2 As shown, this solution discloses a hierarchical intelligent allocation method based on binary bit operations. After the user selects the goods and participating stores and clicks "execute," the allocation begins based on the selected goods and several participating stores.

[0059] The inventory of each participating store is first divided into several base units according to the set base number.

[0060] Each store will be assigned a base number unit. Taking 5 participating stores as an example (in actual applications, there are usually more than 10 participating stores), each store will have a set base number for each size of each product. This base number can be set by the user or by the system based on certain rules, such as the store's sales ability for the corresponding product, historical sales performance, and the mainstream popularity of the size. The base number for each size of each product in each store can be 1 to 10, and for footwear and apparel, it is generally 1 to 3.

[0061] After selecting an item and executing the process, processing 5 stores will result in 5 base data units. Each base data unit contains the store identifier (store name or code, or other information that identifies the store) and the base data for each size of the corresponding item, similar to the format in Table 1 below. S represents the store identifier, and 34~40 are shoe sizes.

[0062] Table 1

[0063]

[0064] If the quantity of a certain size is not less than the corresponding preset quantity, then the value corresponding to that size in the preset quantity unit is the preset quantity; otherwise, the value corresponding to that size in the preset quantity unit is the actual quantity. For example, if a store's preset quantity for a certain women's shoe size 38 is 1, and the actual inventory is 2, then the value corresponding to size 38 in the preset quantity unit will be 1; if the actual inventory is 0, then the value corresponding to size 38 in the preset quantity unit will be 0.

[0065] Therefore, the base quantity for each store may be either the stock quantity for all sizes or the stock quantity for remaining sizes, depending on the actual situation. The stock quantity for all sizes means that there is at least one item of each size in stock for the corresponding product; the stock quantity for remaining sizes means that there is at least one size of the corresponding product that is out of stock.

[0066] Table 2

[0067]

[0068] In Table 2 above, row S1 is the base number of the residual code, and row S2 is the base number of the complete code.

[0069] The remaining inventory in each store is further divided into several binary units according to the binary system.

[0070] Binary units, with each size corresponding to either 0 or 1 pieces, similar to the format shown in Table 3 below:

[0071] Table 3

[0072]

[0073] The number of binary units that can be divided for each item in each store is max(x1-y1, x2-y2, ..., xn-yn, 0), where x is the inventory quantity of the corresponding size of the corresponding item in the corresponding store, y is the set base number of the corresponding size of the corresponding item in the corresponding store, and n is the number of size types.

[0074] If a store has no other inventory after its base unit is determined, no further binary units will be obtained for that store. If a store still has inventory after its base unit is determined, it will be further divided into binary units. The more remaining inventory a store has, the more binary units it will obtain. For example, if a store sets the base unit for all sizes of a certain shoe product to 1, the inventory for sizes 34-38 to be 3, and the inventory for sizes 39 and 40 to be 2, then after removing the base unit, the remaining inventory can be divided into two binary units, S1 and S2, similar to the form shown in Table 4 below:

[0075] Table 4

[0076]

[0077] Next, sort all stores by their target inventory deficit for the currently selected goods and then sort them as recipients.

[0078] Each store is prioritized based on the amount of target inventory shortfall, with the stores that have the most inventory shortfall being processed first. Stores with higher sales will have a higher target inventory set, but may have less inventory, resulting in a larger target inventory shortfall. Stores ranked from least to most will be placed last, while stores ranked from most to least will be placed first.

[0079] The target inventory deficit for each store is the result of subtracting the actual inventory from the target inventory for the corresponding item in that store. For example, if the target inventory for a certain item in a store is 20 and the actual inventory is 5, then the target inventory deficit is 15.

[0080] The target inventory is automatically calculated by the system based on one or more store parameters, including sales volume, store ranking, remaining inventory, number of days on the market, and number of days off the market, or it can be manually designed by the user. This is not the core of this solution, and its specifics are not limited or elaborated here.

[0081] Preferably, the target inventory is no less than the sum of the set base quantities for each size of the corresponding product. Furthermore, since the sales performance of each size of each product may vary, it is preferable that each size has a corresponding target inventory, and the target inventory for each store for the current product refers to the sum of the target inventories for all sizes of that product.

[0082] By prioritizing and classifying each binary unit and base unit, a shipper classification consisting of several shipping units with different priorities is obtained; the shipping units will be allocated to the receiver according to their priorities.

[0083] Priority classification of each binary unit and base unit specifically includes:

[0084] For goods with a complete base number code, the remaining inventory is further divided into several binary units, which are then assigned to the first issuance priority. This type is called a complete base number redundancy, such as... Figure 2 Line D2 in the shipper classification, Figure 2 In the example, the base value for each size in each store is 1.

[0085] For goods with residual codes in the base unit, the remaining inventory is further divided into several binary units, which are then assigned to the second issuance priority. This type is called residual code base redundancy, such as... Figure 2 C1 line in the shipper classification.

[0086] Unprotected residual code base units are classified as third transmission priority; these are called residual code bases, such as... Figure 2 C2 and E in the shipper classification. Before execution, users can manually set protection for stores. Protected stores will be marked, and their inventory data, regardless of whether it is complete or not, will not be retrieved. Therefore, they will not participate in the shipper classification and will not be matched with the receiver.

[0087] The first priority has the highest sending priority. When multiple shipping units are matched simultaneously after two bit operations, the shipping unit with the highest sending priority is selected.

[0088] It should be noted that this method focuses on parcel allocation. When put into use, it can be used alone or in combination with other methods, such as a decision-making algorithm that takes into account factors such as distance, number of parcels, and parcel limit to make the final allocation decision. The details will not be elaborated here.

[0089] Next, based on the recipient's target inventory deficit from largest to smallest, the OR operation sequentially matches the recipient's base unit with candidate shipping units that satisfy the condition of consecutive / complete base unit codes.

[0090] like Figure 2 In the table, the five stores A, B, C, D, and E are sorted by their target inventory deficit as A, B, C, E, and D. When the set base number is greater than 1, non-binary values ​​may appear in the base number cell. For example, if a store's set base number for a certain style of women's shoes in size 37 is 2, and its inventory is also 2, then the corresponding base number cell for that store might be as shown in Table 5.

[0091] Table 5

[0092]

[0093] To address this, this solution performs binary conversion on each base unit to obtain the base binary unit. The binary conversion process includes setting non-zero sizes to 1 and keeping zero sizes at 0. The results of the binary conversion in Table 5 are shown in Table 6 below.

[0094] Table 6

[0095]

[0096] Similarly, when non-binary values ​​appear in the base unit, the non-binary base unit in the shipper's classification is split into at least two binary shipping units. Taking Table 5 as an example, the split is as follows:

[0097] Table 7

[0098]

[0099] The base binary unit is ORed with the binary units of the first, second, and third priority shipments, respectively. The results of this filtering operation, either continuously setting a size range or all values ​​being 1, are selected as candidate shipment units. Continuously setting a size range of 1 indicates that after merging with the corresponding shipment unit, the recipient can have consecutive sizes within the set size range, such as... Figure 2 After merging A and C1, the result is 11110. All 1s indicate that after merging with the corresponding shipping unit, the recipient will have the complete code, such as... Figure 2 In the example, B and C2 are combined to form 11111. This means that after the OR operation, C1 is matched as a candidate shipping unit for store A, and C2 is matched as a candidate shipping unit for store B.

[0100] In practical applications, multiple shipping units may be able to meet the receiving store's requirements for complete / consecutive sizes. The logic behind this inventory optimization solution is that after the transfer, each store's inventory of related goods will either have complete / consecutive sizes or none at all. However, the result of the operation may be that some candidate shipping units are transferred, while others remain. For example, suppose... Figure 2 If A is 11010, then C1 cannot be exhausted after merging with it, so it is not the optimal choice.

[0101] To address this, this solution performs a further AND operation on the candidate shipping units selected based on the OR operation, extracting the result that continuously sets the size range / all values ​​to 0 and has the highest priority. Figure 2 The results of AND operations between A and C1, and between B and C2 are all 0. Therefore, C1 is considered the optimal choice for A, and C2 is considered the optimal choice for B.

[0102] The above methods can quickly and efficiently match the sender with the receiver, which can not only meet the receiver's code connection requirements, but also minimize the possibility of the sender having their code changed.

[0103] After the above matching is completed, C1 and C2 are exhausted and removed from the shipper category. Stores A and B are reassigned as receivers. Therefore, if the base units of stores A and B are involved in the shipper category, the base units of stores A and B are also removed from the shipper category. At the same time, stores A and B are removed from the receiver sorting. Store C is also removed from the receiver sorting because its base units have been reassigned.

[0104] Figure 3 An example of the removal process after the residual code base numbers of store A and store C are successfully matched is given.

[0105] Furthermore, since the purpose of this allocation is to allocate complete codes, stores with complete codes will be removed from the recipient's sorting list before the allocation is executed.

[0106] Furthermore, a single round of allocation is usually insufficient to meet the code requirements. Therefore, this solution involves determining the termination condition after the first round of allocation. If the condition is not met, a second round of allocation will proceed. The termination condition is that either the receiver's queue or the sender's category is cleared.

[0107] In the first round of allocation, the receiver queue is the receiver order. After the first round of allocation, a virtual allocation is performed on the successfully matched receivers and shipping units, and it is determined whether the receivers after the virtual allocation fully meet the requirements. If the receivers have all the required codes, it is considered that the requirements are fully met.

[0108] If not, this indicates that the sender is exhausted, but the receiver is not complete. In this case, the receiver after virtual allocation will be added to the demand stack as the new receiver to perform the next round of allocation until the demand is fully satisfied and then popped off the stack.

[0109] Recipients that fail to find a match are placed in the unsuccessful queue as recipients for the next round of allocation.

[0110] In non-first-round allocations, the receiver queue includes the demand stack and the unsuccessful queue.

[0111] In non-first rounds of allocation, each receiver in the receiver queue is ORed with each shipping unit. For example, if there are 8 receivers in the receiver queue and 10 shipping units in the shipping unit category, then each of the 8 receivers is ORed with one of the 10 shipping units.

[0112] The shipping units that result in a number of 1s after an operation that exceeds the number of 1s for the recipient will be considered as the second candidate shipping units for that recipient. That is, if a recipient performs an OR operation with a shipping unit, and the result of the operation has more 1s than the result of the operation for the recipient, it means that at least one size of the shipping unit can be allocated to the recipient, and these shipping units will be considered as the second candidate shipping units for that recipient.

[0113] When a shipping unit is matched as a second candidate shipping unit for multiple recipients, a series of XOR operations are performed on the second candidate shipping unit and each of the multiple recipients. If all the results are 0 or 1, the second candidate shipping unit is considered to have matched with multiple recipients simultaneously. When the number of recipients is even, all results of 0 indicate a successful match; when the number of recipients is odd, all results of 1 indicate a successful match. For example:

[0114] If a shipping unit E1 is 1111111, a receiver I1 is 1110000, and a receiver I2 is 0001111, then shipping unit E1 will be considered as the second candidate shipping unit for both receivers I1 and I2. XORing 1111111 with 1110000 will yield 0001111, and XORing 0001111 with 0001111 will yield 0000000. Therefore, E1 is considered to match I1 and I2. A portion of E1 will be allocated to I1, and another portion will be allocated to I2. Finally, E1 will be cleared.

[0115] If a shipping unit E1 is 1111111, a receiver I1 is 1111000, a receiver I2 is 0011111, and a receiver I3 is 1100111, then shipping unit E1 will be considered as the second candidate shipping unit for the aforementioned two receivers I1, I2, and I3. XORing 1111111 with 1111000 yields 0000111, then XORing 0000111 with 0011111 yields 0011000, and finally XORing 0011000 with 1100111 yields 1111111. Therefore, E1 is considered a match for I1, I2, and I3. A portion of E1 will be allocated to I1, a portion to I2, and the remainder to I3. In non-first rounds of allocation, the OR operation can be performed out of order, determining a corresponding second candidate shipping unit for each receiver. The XOR operation is performed according to the order of recipients or the order of shippers. The specific order of recipients is set by those skilled in the art or automatically generated by the system, with the recipient listed first in the order taking precedence. Shippers are classified according to priority, with the order within the same priority set by those skilled in the art or automatically generated by the system.

[0116] Each successful match removes the matched shipping unit from the shipper category. Similarly, if the base unit of the store corresponding to the successfully matched I1, I2, and I3 is still in the shipper category, it is removed from the shipper category. The successfully matched receiver is also removed from the receiver queue. If the successfully matched E1 is in the receiver queue, it is also removed from the receiver queue. When performing XOR matching on subsequent receivers and shipping units, the removed receivers and shipping units will no longer participate.

[0117] Preferably, the number of simultaneously matched recipients does not exceed a set value, typically set to 3, to prevent a single store from being assigned to too many stores at the same time. The number of simultaneously matched recipients can be limited to 2 in the second round, 3 in the third round, and so on, until the set value for the number of simultaneously matched recipients is reached or the termination condition is met.

[0118] This solution uses the most basic bit operations of computers to execute inter-store allocation decisions for the purpose of matching codes, and can achieve decisions with extremely low residual code rates at an extremely fast speed.

[0119] Example 2

[0120] This embodiment is similar to Embodiment 1, except that in this embodiment, candidate shipping units that meet the consecutive / complete size requirements of the base number units are matched according to the receiver's target inventory shortage quantity from most to least, based on an OR operation. Subsequently, a further XOR operation is performed on the candidate shipping units selected based on the OR operation to extract the result that continuously sets the size range / all values ​​are 1 and has the highest priority.

[0121] Example 3

[0122] This embodiment is similar to Embodiment 1 or Embodiment 2, except that the priority classification of each binary unit and base unit in this embodiment further includes classifying the base units of complete codes from unmarked stores as the fourth priority; and classifying the base units of residual codes from marked stores as the fifth priority. In each round of allocation, the principle of prioritizing matching the highest priority shipping unit is followed.

[0123] The fourth priority is issued, and the fifth priority is issued at least from the second round onwards. Preferably, the fourth priority is issued from the third round onwards, and the fifth priority is issued from the fourth round onwards.

[0124] Example 4

[0125] This embodiment provides a hierarchical intelligent tuning system based on binary bit operations, such as... Figure 4As shown, it includes a cutting module, which is used to first cut the inventory of each participating store according to a set base number to obtain a number of base number units, and to cut the remaining inventory of each store according to binary to obtain a number of binary units.

[0126] Layered module 1 is used to sort all stores by target inventory deficit and prioritize each binary unit and base unit to obtain a shipper classification consisting of several shipping units with different priorities.

[0127] The combination matching module 2 is used to match candidate delivery units for the recipient according to the target inventory shortage of the recipient, from the largest to the smallest, based on the first operation, so that the base unit can be consecutively coded / fully coded; based on the second operation, it calculates the matching status of each candidate delivery unit for each recipient, extracts the most matched and highest priority delivery unit, and updates the sender classification and recipient sorting.

[0128] Inventory adjustment module 3 is used to perform allocation and update inventory data based on the matching results.

[0129] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0130] Although this document frequently uses terms such as "cutting module 1," "layering module 2," "combination matching module 3," and "inventory adjustment module 4," the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of the invention; interpreting them as any additional limitation would contradict the spirit of the invention.

Claims

1. A hierarchical intelligent tuning method based on binary bit operations, characterized in that, This method responds to the selected goods and several participating stores to perform the transfer: The inventory of each participating store is first divided according to a set baseline to obtain several baseline units; The remaining inventory in each store is further divided into several binary units according to the binary system. Sort all stores by target inventory deficit as recipients; Priority classification of each binary unit and base unit yields a shipper classification consisting of several shipping units with different priorities; Based on the recipient's target inventory deficit, from largest to smallest, and using the "OR" operation, the recipient is matched with candidate shipping units that, after allocation, satisfy the condition of consecutive / complete numbers of base unit codes. Based on the AND or XOR operation, the matching status of each candidate shipment unit is calculated for each recipient. The shipment unit with the highest priority after the AND or XOR operation results in all 0 or 1 is extracted, and the shipment unit classification and recipient sorting are updated. Each store will be divided into a base number unit, and the base number unit is either a complete code base number or a residual code base number; "Full stock" refers to the number of items in stock for each size; "Damaged stock" refers to the number of items in stock for at least one size. The binary unit mentioned above has a quantity of 0 or 1 corresponding to each size; The number of binary units that can be divided for each item in each store is max(x1-y1, x2-y2, ..., xn-yn, 0), where x is the inventory quantity of the corresponding size of the corresponding item in the corresponding store, y is the set base number of the corresponding size of the corresponding item in the corresponding store, and n is the number of size types.

2. The homogeneous code hierarchical intelligent tuning method based on binary bit operations according to claim 1, characterized in that, The base number for each size of each item in each store is set from 1 to 10.

3. The homogeneous code hierarchical intelligent tuning method based on binary bit operations according to claim 1, characterized in that, Each store was sorted as the recipient based on the order in which the store with the largest outstanding target inventory was processed first. The target inventory deficit for each store is the result of subtracting the actual inventory from the target inventory of the corresponding product in the store. The target inventory is automatically calculated by the system based on one or more of the following store parameters: sales volume, store ranking, inventory balance, number of days on the market, and number of days off the market; or it can be manually designed by the user. The target inventory shall not be less than the set base quantity of each size of the corresponding goods in the corresponding store.

4. The homogeneous code hierarchical intelligent tuning method based on binary bit operations according to claim 1, characterized in that, Priority classification of each binary unit and base unit specifically includes: For goods with complete base unit codes, the remaining inventory is further divided into several binary units, which are then assigned to the first issuance priority. For goods with residual codes in the base unit, the remaining inventory is further divided into several binary units, which are then assigned to the second issuance priority. The base unit of the residual code of the unmarked store is assigned to the third priority.

5. The homogeneous code hierarchical intelligent tuning method based on binary bit operations according to claim 4, characterized in that, Priority classification of each binary unit and base unit also includes classifying the base unit of the complete code of the unmarked store as the fourth priority; and classifying the base unit of the residual code of the marked store as the fifth priority. This method also includes performing binary conversion on each base unit to obtain the base binary unit; The binary conversion process includes setting the quantity of sizes with a non-zero value to 1, and keeping the quantity of sizes with a zero value at 0. The non-binary base unit at the shipper's classification point is divided into at least two binary shipping units; The method for matching candidate shipping units to the recipient includes performing an OR operation on the base binary unit with shipping units of at least the first shipping priority, the second shipping priority, and the third shipping priority, and the result of the filtering operation is continuously set to the size range / all are 1 as candidate shipping units. The method for extracting the best match from the candidate shipping units includes performing an AND or XOR operation on the base binary unit and its candidate shipping units, and extracting the result that makes all the operation results in 0 or 1 and has the highest priority. Updating the shipper classification and receiver sorting specifically includes removing successfully matched shipping units from the shipper classification and removing successfully matched receivers from the receiver sorting.

6. The homogeneous code hierarchical intelligent tuning method based on binary bit operations according to claim 5, characterized in that, Before the transfer, stores with complete codes will be removed from the recipient's list. If the termination conditions are not met after the first round of allocation, multiple rounds of allocation will be conducted.

7. The homogeneous code hierarchical intelligent tuning method based on binary bit operations according to claim 6, characterized in that, This method also includes performing virtual transfer of successfully matched recipients and delivery units and determining whether the recipient after virtual transfer fully meets the requirements. If not, the receiver after virtual allocation will be placed on the demand stack until the demand is fully met and then popped off the stack; when the receiver is fully coded, the demand is considered to be fully met. Unsuccessful receivers are placed in the unsuccessful queue. Unsuccessful queues and demand stacks are treated as new receiver queues for the next round of allocation, with the termination condition being that either the receiver queue or the shipper category is cleared.

8. The homogeneous code hierarchical intelligent tuning method based on binary bit operations according to claim 7, characterized in that, In non-first round allocation, OR operation is performed on the receiving queue and the shipping unit one by one, and the shipping unit that makes the result of the operation more than 1 than the receiving unit is regarded as the second candidate shipping unit of the corresponding receiving unit. When a shipping unit is matched as the second candidate shipping unit for multiple recipients, a series of XOR operations are performed on the second candidate shipping unit and multiple recipients. If the results of the operations are all 0 or 1, it is considered that the second candidate shipping unit is matched with multiple recipients at the same time. The successfully matched shipping unit is removed from the shipping unit classification and the successfully matched recipient is removed from the recipient queue. The number of matched receivers does not exceed the set value.

9. A hierarchical intelligent tuning system based on binary bit operations, characterized in that, include, The cutting module is used to first cut the inventory of each participating store according to a set base number to obtain a number of base units, and to cut the remaining inventory of each store according to binary to obtain a number of binary units. Each store will be divided into a base number unit, and the base number unit is either a complete code base number or a residual code base number; "Full stock" refers to the number of items in stock for each size; "Damaged stock" refers to the number of items in stock for at least one size. The binary unit mentioned above has a quantity of 0 or 1 corresponding to each size; The number of binary units that can be cut for each item in each store is max(x1-y1, x2-y2, ..., xn-yn, 0), where x is the inventory quantity of the corresponding size of the corresponding item in the corresponding store, y is the set base number of the corresponding size of the corresponding item in the corresponding store, and n is the number of size types. The hierarchical module is used to sort all stores by target inventory deficit and prioritize each binary unit and base unit to obtain a shipper classification consisting of several shipping units with different priorities. The combination matching module is used to match candidate shipping units for the recipient based on the recipient's target inventory shortage, from most to least, using an "OR" operation, to ensure that the base unit has consecutive or complete codes after allocation. Based on the AND or XOR operation, the matching status of each candidate shipment unit is calculated for each recipient. The shipment unit with the highest priority after the AND or XOR operation results in all 0 or 1 is extracted, and the shipment unit classification and recipient sorting are updated. The inventory adjustment module is used to perform allocations and update inventory data based on the matching results.

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