Order allocation method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses an order allocation method and device, equipment and a storage medium. The method comprises the following steps: in response to a detected goods order, obtaining order item data in the goods order; based on the order item data, performing at least one fuzzy matching operation on at least one piece of pre-stored suit item data to obtain at least one piece of reference cargo allocation data; based on each piece of reference cargo allocation data, executing an order cargo allocation operation corresponding to the cargo order; wherein the fuzzy matching operation characterizes that the successfully matched suite article data is contained in the order article data, and the reference cargo allocation data comprises the bulk article data and / or the suite number corresponding to at least one piece of successfully matched suite article data; the bulk item data comprises item data which is not successfully matched with each set item data in the order item data. According to the embodiment of the invention, the distribution efficiency of the goods can be improved, and the consumption of the suit can also be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing logistics, and in particular to an order distribution method, device, equipment and storage medium. Background Art

[0002] At present, the volume of online sales orders is increasing, which brings huge challenges to warehousing logistics in terms of stocking, delivery and transportation. In particular, the volume of orders for goods sold in sets is increasing.

[0003] For orders of goods in set form, the current main method of communication is offline. Multiple goods in the order are packaged into sets in advance. After the order is sent to the warehouse management system, the order is matched to the corresponding set area to complete the order distribution process.

[0004] In the process of implementing the present invention, it is found that there are at least the following technical problems in the prior art:

[0005] In order to cope with different sales scenarios and the continuous innovation of various marketing plans, pre-packaged sets may no longer meet the product requirements in the product order, and manual operations such as disassembly and reassembly of the sets are required, which not only increases labor and time costs, but also reduces the order distribution efficiency. Summary of the invention

[0006] The embodiments of the present invention provide an order matching method, device, equipment and storage medium to solve the problem in the prior art that the assembled sets cannot accurately meet the product requirements in the product order, thereby improving the efficiency of product matching.

[0007] According to an embodiment of the present invention, a method for order distribution is provided, the method comprising:

[0008] In response to detecting a product order, acquiring order item data in the product order;

[0009] Based on the order item data, at least one fuzzy matching operation is performed on at least one pre-stored set item data to obtain at least one reference distribution data;

[0010] Based on each of the reference distribution data, executing the order distribution operation corresponding to the product order;

[0011] Among them, the fuzzy matching operation represents that the successfully matched set item data is included in the order item data, the reference distribution data contains loose item data and / or the set quantity corresponding to at least one successfully matched set item data, and the loose item data contains unsuccessfully matched item data in the order item data.

[0012] According to another embodiment of the present invention, an order matching device is provided, the device comprising:

[0013] An order item data acquisition module, configured to acquire order item data in the goods order in response to detecting the goods order;

[0014] A reference distribution data determination module is used to perform at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data to obtain at least one reference distribution data;

[0015] An order distribution operation execution module, used to execute the order distribution operation corresponding to the product order based on each of the reference distribution data;

[0016] Among them, the fuzzy matching operation represents that the successfully matched set item data is included in the order item data, the reference distribution data contains loose item data and / or the set quantity corresponding to at least one successfully matched set item data, and the loose item data contains unsuccessfully matched item data in the order item data.

[0017] According to another embodiment of the present invention, there is provided an electronic device, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the order distribution method described in any embodiment of the present invention.

[0021] According to another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the order matching method described in any embodiment of the present invention when executed.

[0022] The technical solution of the embodiment of the present invention is to obtain at least one reference distribution data by performing at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data in the goods order, and to perform the order distribution operation corresponding to the goods order based on each reference distribution data, wherein the fuzzy matching operation indicates that the successfully matched set item data is included in the order item data, and the fuzzy matching operation makes the number of reference distribution data finally obtained to be multiple, and the reference distribution data contains the number of sets corresponding to the loose item data and / or at least one successfully matched set item data, and the loose item data contains item data in the order item data that has not been successfully matched with each set item data, thereby solving the problem in the prior art that the assembled sets cannot accurately meet the goods requirements in the goods order, and improving the matching success rate between the order item data and the set item data, thereby improving the distribution efficiency of the goods, and increasing the consumption of the sets, and reducing the labor cost and time cost caused by operations such as disassembling and reassembling sets.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. 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 creative work.

[0025] Figure 1 A flow chart of an order matching method provided by one embodiment of the present invention;

[0026] Figure 2 A flowchart of a fuzzy matching method provided by one embodiment of the present invention;

[0027] Figure 3 A flowchart of another order matching method provided by an embodiment of the present invention;

[0028] Figure 4 A flowchart of another fuzzy matching method provided by one embodiment of the present invention;

[0029] Figure 5 A flowchart of another fuzzy matching method provided by one embodiment of the present invention;

[0030] Figure 6A flowchart of another order matching method provided by an embodiment of the present invention;

[0031] Figure 7 A flowchart of another order matching method provided by an embodiment of the present invention;

[0032] Figure 8 A schematic diagram of the structure of an order matching device provided by an embodiment of the present invention;

[0033] Fig. 9 The present invention is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] Figure 1 This is a flow chart of an order matching method provided by an embodiment of the present invention. This embodiment is applicable to matching goods for packaged goods orders. The method can be executed by an order matching device. The order matching device can be implemented in the form of hardware and / or software. The order matching device can be configured in a terminal device. Figure 1 As shown, the method includes:

[0037] S110 . In response to detecting a product order, acquiring order item data in the product order.

[0038] Exemplarily, a product order is used to describe an order for purchasing products generated by a user through a client. In a specific embodiment, before obtaining the order item data in the product order, when the order type corresponding to the product order meets the preset type range, the step of obtaining the order item data in the product order is performed.

[0039] Among them, illustratively, the preset type range includes beauty products, daily necessities and office supplies, etc., and the preset type range is not limited here.

[0040] On the basis of the above embodiment, specifically, when the order type corresponding to the product order does not meet the preset type range, the non-set picking process is executed.

[0041] The advantage of such a setting is that the order type of the goods order in the order distribution method is personalized, that is, the order distribution method provided by this implementation is executed only for the goods orders that meet the set picking format, so as to achieve the purpose of screening the goods orders and avoid the execution of the order distribution method provided by this implementation for the goods orders that do not meet the set picking format, thereby reducing the distribution efficiency of the goods orders.

[0042] Specifically, the order item data includes at least two order item categories and the order item quantities corresponding to each order item category. For example, the order item data M=2*a+3*b+4*c, where 2, 3 and 4 represent the order item quantities corresponding to order item category a, order item category b and order item category c, respectively.

[0043] S120: Based on the order item data, perform at least one fuzzy matching operation on at least one pre-stored package item data to obtain at least one reference distribution data.

[0044] Specifically, the package item data includes at least two package item categories and the quantity of package items corresponding to each package item category. For example, the package item data BOM1=1*a+3*b, where 1 and 3 represent the quantity of package items corresponding to package item category a and package item category b, respectively.

[0045] In this embodiment, the fuzzy matching operation indicates that the successfully matched set item data is included in the order item data. Specifically, the fuzzy matching operation is defined as follows: when the set item data is included in the order item data, it means that the set item data and the order item data are successfully matched; when the set item data is not included in the order item data, it means that the set item data and the order item data are not matched. For example, assuming that the set item data is BOM1=1*a+3*b, and the order item data is M=2*a+3*b+4*c, then the set item data and the order item data are successfully matched; assuming that the set item data is BOM1=1*a+4*b, and the order item data is M=2*a+3*b+4*c, then the set item data and the order item data are not matched.

[0046] In a specific embodiment, based on the order item data, at least one fuzzy matching operation is performed on at least one pre-stored package item data to obtain at least one reference distribution data, including: for each package item data, the package item data is used as the target package item data, and the target package item data and the order item data are used as the current package item data and the current item data to be matched, respectively; a fuzzy matching operation is performed on the current package item data and the current item data to be matched; when the matching result is a successful match, the next item data to be matched is determined based on the current package item data and the current item data to be matched, and the next item data to be matched is used as the current item data to be matched; the fuzzy matching operation is returned to perform a fuzzy matching operation on the current package item data and the current item data to be matched, until a preset end condition is met, and the reference distribution data corresponding to the package item data is determined based on the number of successful matches corresponding to the target package item data and the current item data to be matched.

[0047] In this embodiment, the fuzzy matching operation is defined as follows: if the current set item data is included in the current item data to be matched, it means that the current set item data is successfully matched with the current item data to be matched; if the current set item data is not included in the current item data to be matched, it means that the current set item data is unsuccessfully matched with the current item data to be matched.

[0048] Specifically, the next to-be-matched item data is the difference between the current to-be-matched item data and the current set item data. For example, the current set item data is BOM1=1*a+3*b, and the current to-be-matched item data is M=2*a+3*b+4*c, then the next to-be-matched item data is M'=a+4*c.

[0049] In this embodiment, the preset end condition includes at least one of the following: the matching result is a match failure, the current item data to be matched is empty, and the order item category in the current item data to be matched is one. Specifically, since each set item data contains at least two set item categories, if the current item data to be matched is empty or the order item category in the current item data to be matched is one, it means that even if the fuzzy matching operation is continued based on the current item data to be matched, no successful matching of the current set item data will be obtained, and the iteration end condition is met.

[0050] Figure 2 The following is a flow chart of a fuzzy matching method provided by an embodiment of the present invention. Specifically, Figure 2 The "BOM1 BOM2 BOM3 BOM4 BOM5" on the left side of the figure represent the five pre-stored set item data, and "M" represents the current item data to be matched. Select any set item data BOMx from each set item data, use the set item data BOMx as the target set item data aBOM, and use the order item data as the current item data M to be matched.

[0051] Perform a fuzzy matching operation on the current set item data aBOM and the current to-be-matched item data M. If the match is successful, perform the operation of M=M-aBOM, and determine whether M is empty or the order item category is one. If so, obtain the reference distribution data determined based on BOMx. If not, return to the step of performing a fuzzy matching operation on the current set item data aBOM and the current to-be-matched item data M. If the match fails, obtain the reference distribution data determined based on BOMx.

[0052] After obtaining the reference distribution data determined based on BOMx, the process returns to the step of selecting any BOMx of each set of item data, and finally obtains the reference distribution data determined by each set of item data.

[0053] For example, assuming M = 2*a+3*b+4*c+5*d, BOM1 = a+b+c+d, BOM2 = 2*a+3*b, BOM3 = b+2*c+2*d, BOM4 = 4*c+5*d, BOM5 = d+e, then the reference distribution data determined based on BOM1 is 2*BOM1+b+2*c+3*d, the reference distribution data determined based on BOM2 is BOM2+4*c+5*d, the reference distribution data determined based on BOM3 is 2*BOM3+2*a+b+d, the reference distribution data determined based on BOM4 is BOM4+2*a+3*b, and the reference distribution data determined based on BOM5 may be 2*a+3*b+4*c+5*d.

[0054] In this embodiment, the reference distribution data includes the loose item data and / or the set quantity corresponding to at least one successfully matched set item data, and the loose item data includes the unsuccessfully matched item data in the order item data.

[0055] In one embodiment, specifically, when the package item data of the package quantity is exactly the same as the order item data after being merged, the reference distribution data only includes the package quantity corresponding to the successfully matched package item data. In another embodiment, specifically, when the package item data is not included in the order item data, the reference distribution data only includes the loose item data, such as the reference distribution data determined based on BOM5 in the above example. In another embodiment, the reference distribution data includes the preset package identifiers and loose item data corresponding to each successfully matched package item data, such as the reference distribution data determined based on BOM1, BOM2, BOM3 and BOM4 in the above example.

[0056] S130: Based on each reference distribution data, execute the order distribution operation corresponding to the product order.

[0057] In an optional embodiment, based on each reference distribution data, an order distribution operation corresponding to the product order is executed, including: randomly selecting a reference distribution data from each reference distribution data as the target distribution data, and executing the order distribution operation corresponding to the product order based on the target distribution data.

[0058] In the related art, the order item data and each set item data are subjected to precise matching operation, and precise matching indicates that the order item data and the matched set item data are exactly the same. For example, assuming that the order item data M = 2*a+3*b, then the matched set item data BOM1 = 2*a+3*b, and accordingly, there is only one reference distribution data obtained through precise matching operation, and the reference distribution data only contains the preset set identifier BOM1 corresponding to the matched set item data.

[0059] The technical solution of this embodiment is to obtain at least one reference distribution data by performing at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data in the goods order, and to perform the order distribution operation corresponding to the goods order based on each reference distribution data, wherein the fuzzy matching operation indicates that the successfully matched set item data is included in the order item data, and the fuzzy matching operation makes the number of reference distribution data finally obtained to be multiple, and the reference distribution data contains the number of sets corresponding to the loose item data and / or at least one successfully matched set item data, and the loose item data contains item data in the order item data that has not been successfully matched with each set item data, thereby solving the problem in the prior art that the assembled sets cannot accurately meet the goods requirements in the goods order, and improving the matching success rate between the order item data and the set item data, thereby improving the goods distribution efficiency, and increasing the consumption of sets, and reducing the labor cost and time cost caused by operations such as disassembling and reassembling sets.

[0060] Figure 3 This is a flowchart of another order matching method provided by an embodiment of the present invention. This embodiment further refines the technical feature of "determining reference matching data corresponding to the target set item data based on the number of successful matches corresponding to the target set item data and the current item data to be matched" in the above embodiment. Figure 3 As shown, the method includes:

[0061] S210: In response to detecting a product order, obtaining order item data in the product order.

[0062] S220 . For each set of product data, use the set of product data as target set of product data, and use the target set of product data and the order product data as current set of product data and current to-be-matched product data, respectively.

[0063] S230: Perform a fuzzy matching operation on the current set item data and the current to-be-matched item data to obtain a matching result.

[0064] Specifically, the fuzzy matching operation is defined as follows: if the current set item data is included in the current item data to be matched, it means that the current set item data is successfully matched with the current item data to be matched; if the current set item data is not included in the current item data to be matched, it means that the current set item data is unsuccessfully matched with the current item data to be matched.

[0065] S240: Determine whether the matching result is a successful match. If yes, execute S250; if not, execute S280.

[0066] S250: Determine the next to-be-matched item data based on the current set item data and the current to-be-matched item data.

[0067] S260, determine whether the next to-be-matched item data is empty or the order item category is one, if yes, execute S291, if not, execute S270.

[0068] S270 , taking the next to-be-matched product data as the current to-be-matched product data, and returning to execute S230 .

[0069] S280, determining whether the number of failed matches of the target set product data is equal to the number threshold, if yes, executing S291, if no, executing S290.

[0070] Specifically, the number threshold is used to characterize the threshold of the number of matching failures corresponding to the target set item data. In a specific embodiment, different set item data respectively correspond to the same number thresholds, and the number threshold is the total number of sets corresponding to each set item data.

[0071] In another specific embodiment, different set item data respectively correspond to different number thresholds. If the fuzzy matching operations of each set item data are performed in sequence according to the set ranking results corresponding to each set item data, the number thresholds corresponding to different set item data are reduced in sequence. Exemplarily, assuming that the total number of sets of each set item data is N, the number thresholds corresponding to each set item data that is fuzzy matched in sequence are N, N-1, N-2, ..., 1. The set ranking results are explained in detail in the following steps.

[0072] S290 , based on the target set of product data, determine the next set of product data, and use the next set of product data as the current set of product data, and return to execute S230 .

[0073] In a specific embodiment, based on the target set of product data, determining the next set of product data includes: taking other set of product data except the target set of product data as reference set of product data; taking any reference set of product data in at least one reference set of product data on which a fuzzy matching operation is not performed as the next set of product data.

[0074] For example, assuming that each set of item data contains BOM1, BOM2, BOM3, BOM4, and BOM5, if the target set of item data is BOM2, then each reference set of item data contains BOM1, BOM3, BOM4, and BOM5. In the iteration process based on BOM2, if the match fails, the next set of item data will be BOM1, BOM3, BOM4, or BOM5. Assuming that BOM1 is used as the next set of item data, in the iteration process based on BOM1, if the match fails, the next set of item data will be BOM3, BOM4, or BOM5. Assuming that BOM3 is used as the next set of item data, in the iteration process based on BOM3, if the match fails, the next set of item data will be BOM4 or BOM5. Assuming that BOM4 is used as the next set of item data, in the iteration process based on BOM4, if the match fails, the next set of item data will be BOM5. In the iterative process based on BOM5, if the matching fails, S291 is executed.

[0075] Figure 4 A flowchart of another fuzzy matching method provided by an embodiment of the present invention is shown in FIG. Figure 4 Take the case where the number thresholds corresponding to the data of each set of items are the same as an example. Figure 4 The "BOM1BOM2 BOM3 BOM4 BOM5" on the left side represents each set of product data, and "M" represents the current product data to be matched. Each set of product data is used as the target set product data in turn. Specifically, the target set product data BOMx in each set of product data is selected, and the target set product data BOMx is used as the current set product data aBOM, and a fuzzy matching operation is performed on the current set product data aBOM and the current product data M to be matched. If the match is successful, the operation of M=M-aBOM is performed, and it is determined whether M is empty or the order product category is one. If so, the reference distribution data determined based on BOMx is obtained. If not, it returns to the step of performing a fuzzy matching operation on the current set product data aBOM and the current product data M to be matched.

[0076] If the match fails, determine whether the number of matching failures is equal to the total data volume N corresponding to each set item data. If so, obtain the reference distribution data determined based on BOMx. If not, take any reference set item data BOMy in the reference set item data that has not performed a fuzzy matching operation as the current set item data aBOM, and continue to execute the step of performing a fuzzy matching operation on the current set item data aBOM and the current item data M to be matched.

[0077] After obtaining the reference distribution data determined based on BOMx, the step of selecting the target package item data BOMx in each package item data is re-executed, and finally the reference distribution data determined by each package item data is obtained.

[0078] In this embodiment, each matching process based on each set of product data is similar, and the number threshold is the total number of sets corresponding to each set of product data. Specifically, in each matching process based on each set of product data, an iteration process needs to be performed based on each set of product data until all set of product data fail to match the current product data to be matched. Therefore, in this embodiment, assuming that the total number of sets corresponding to each set of product data is N, each set of product data needs to perform N fuzzy matching operations, and the number of executions of the fuzzy matching operation corresponding to the fuzzy matching process is N. N Second-rate.

[0079] In another specific embodiment, based on the target package item data, determining the next package item data includes: obtaining the package sorting results corresponding to each package item data, and based on the target package item data, obtaining the target sorting results from the package sorting results; and taking the package item data located after the current package item data in the target sorting results as the next package item data.

[0080] In a specific embodiment, obtaining the package sorting result corresponding to each package item data includes: randomly sorting each package item data to obtain the package sorting result.

[0081] In another specific embodiment, obtaining the package sorting result corresponding to each package item data includes: sorting each package item data based on the number of categories of the package goods in each package item data to obtain the package sorting result. In one embodiment, specifically, the sorting operation may be ascending sorting, that is, the package item data with the least number of categories is placed in front, and the package item data with the largest number of categories is placed in the back. In another embodiment, specifically, the sorting operation may be descending sorting, that is, the package item data with the largest number of categories is placed in front, and the package item data with the least number of categories is placed in the back.

[0082] On the basis of the above embodiments, specifically, based on the number of categories of the set product categories in each set product data, each set product data is sorted to obtain a set sorting result, including: based on the number of categories of the set product categories in each set product data, each set product data is sorted to obtain a reference sorting result; when there are at least two set product data with the same number of categories corresponding to them, the total set product quantities corresponding to at least two set product data with the same number of categories are obtained, and based on the total number of set products, at least two set product data with the same number of categories in the reference sorting result are sorted to obtain a set sorting result.

[0083] In one embodiment, specifically, the sorting operation is ascending sorting, that is, the set item data with the least number of categories is placed in front, and the set item data with the largest number of categories is placed in the back, and at the same time, the set item data with the least number of total set items is placed in front, and the set item data with the largest number of total set items is placed in the back. In another embodiment, specifically, the sorting operation can be descending sorting, that is, the set item data with the largest number of categories is placed in front, and the set item data with the least number of categories is placed in the back, and at the same time, the set item data with the largest number of total set items is placed in front, and the set item data with the smallest number of total set items is placed in the back.

[0084] The advantage of setting ascending sorting is that the package item data with the least number of categories and the least total number of package items has a higher probability of successfully matching with the order item data, thereby improving the efficiency of the matching process and further improving the efficiency of product distribution from the perspective of the matching process.

[0085] The advantage of setting descending sorting is that once the set item data with the largest number of categories and the largest total set item data can be successfully matched with the order item data, the order item categories and the order item quantities corresponding to the order item categories in the order item data will be offset more quickly, which can improve the efficiency of the matching process while reducing the number of sets in the reference distribution data, thereby improving the distribution efficiency of goods from both the matching process and the distribution process.

[0086] For example, assuming that the set sorting results corresponding to each set item data are BOM1, BOM2, BOM3, BOM4 and BOM5, and the target set item data is BOM2, then the target sorting results are BOM3, BOM4 and BOM5. In the iteration process based on BOM2, if the match fails, the next set item data is BOM3. In the iteration process based on BOM3, if the match fails, the next set item data is BOM4. In the iteration process based on BOM4, if the match fails, the next set item data is BOM5. In the iteration process based on BOM5, if the match fails, S291 is executed.

[0087] In this embodiment, the number threshold is the target set quantity corresponding to the target set item data determined based on the set sorting result. Specifically, the target set quantity is used to characterize the quantity of each set item data that is arranged after the target set item data in the set sorting result, including the target set item data itself. Exemplarily, assuming that the set sorting results are BOM1, BOM2, BOM3, BOM4 and BOM5, if the target set item data is BOM1, the target set quantity is 5, and if the target set item data is BOM4, the target set quantity is 2.

[0088] Figure 5 A flowchart of another fuzzy matching method provided by an embodiment of the present invention is shown in FIG. Figure 5 Take the example that the number thresholds corresponding to the data of each set of items are different. Figure 5 The "BOM1BOM2 BOM3 BOM4 BOM5" on the left side indicates the set sorting result corresponding to each set item data, and "M" indicates the current item data to be matched. Each set item data in the set sorting result is used as the target set item data in turn. Specifically, the target set item data BOMx in the set sorting result is selected, and the target set item data BOMx is used as the current set item data aBOM, and a fuzzy matching operation is performed on the current set item data aBOM and the current item data M to be matched. If the match is successful, the operation of M=M-aBOM is performed, and it is determined whether M is empty or the order item category is one. If so, the reference distribution data determined based on BOMx is obtained. If not, the step of performing a fuzzy matching operation on the current set item data aBOM and the current item data M to be matched is returned.

[0089] If the match fails, determine whether the number of matching failures is equal to the target set quantity Nx corresponding to the target set item data BOMx. If so, obtain the reference distribution data determined based on BOMx. If not, use the next set item data BOMy located after the current set item data in the set sorting result as the current set item data aBOM, and continue to execute the step of performing a fuzzy matching operation on the current set item data aBOM and the current item data M to be matched.

[0090] After obtaining the reference distribution data determined based on BOMx, the step of selecting the target package item data BOMx in the package sorting result is re-executed to finally obtain the reference distribution data determined by each package item data.

[0091] In this embodiment, the target set item data that is ranked later in the set sorting result has a smaller number threshold value. Assuming that the total number of sets corresponding to each set item data is N, in each matching process with each set item data as the main body, if each matching process is performed in sequence according to the set sorting result, the number threshold value corresponding to the target set item data decreases from N to 1, and the number of executions of the fuzzy matching operation corresponding to the fuzzy matching process is N! times.

[0092] S291: Determine reference distribution data corresponding to the target set item data based on the number of successful matches corresponding to at least one set item data that is successfully matched and the current item data to be matched.

[0093] In a specific embodiment, the number thresholds corresponding to the data of each set of items are the same. For example, assuming that M = 2*a+3*b+4*c+5*d, BOM1 = a+b+c+d, BOM2 = 2*a+3*b, BOM3 = b+2*c+2*d, BOM4 = 4*c+5*d, and BOM5 = d+e, the reference distribution data determined based on BOM1 is 2*BOM1+BOM3+d, the reference distribution data determined based on BOM2 is BOM2+BOM4, the reference distribution data determined based on BOM3 is 2*BOM3+2*a+b+d, the reference distribution data determined based on BOM4 is BOM4+BOM2, and the reference distribution data determined based on BOM5 may be the reference distribution data determined by BOM1, BOM2, BOM3 or BOM4.

[0094] In another specific embodiment, the number thresholds corresponding to the data of each set of items are different. For example, assuming that M = 2*a+3*b+4*c+5*d, BOM1 = a+b+c+d, BOM2 = 2*a+3*b, BOM3 = b+2*c+2*d, BOM4 = 4*c+5*d, BOM5 = d+e, and the set sorting results are BOM1, BOM2, BOM3, BOM4 and BOM5, then the reference distribution data determined based on BOM1 is 2*BOM1+BOM3+d, the reference distribution data determined based on BOM2 is BOM2+BOM4, the reference distribution data determined based on BOM3 is 2*BOM3+2*a+b+d, the reference distribution data determined based on BOM4 is BOM4+2*a+3*b, and the reference distribution data determined based on BOM5 is 2*a+3*b+4*c+5*d.

[0095] S292. Based on each reference distribution data, execute the order distribution operation corresponding to the product order.

[0096] The technical solution of this embodiment, when the matching result is a matching failure, determines the next set of item data based on the target set item data, and uses the next set of item data as the current set item data, and returns to execute the step of performing a fuzzy matching operation on the current set item data and the current item data to be matched, until the number of matching failures of the target set item data is equal to the number threshold, and then determines the reference distribution data corresponding to the target set item data based on the number of matching successes corresponding to at least one successfully matched set item data and the current item data to be matched, thereby solving the problem of too single successfully matched set item data in the reference distribution data, further improving the matching success rate between the order item data and the set item data, and further improving the consumption of the set and the distribution efficiency of the goods.

[0097] On the basis of the above embodiment, specifically, before performing at least one fuzzy matching operation on at least one pre-stored package item data based on the order item data to obtain at least one reference distribution data, the method also includes: performing a fuzzy matching operation on the order item data and at least one pre-stored package item data, and using at least one successfully matched package item data as the filtered package item data.

[0098] Among them, the fuzzy matching operation indicates that the successfully matched set item data is included in the order item data. Specifically, the set item data that failed to match may contain product categories that do not exist in the order item data. For example, assuming that the set item data is BOM1=1*a+4*b+c and the order item data is M=1*a+5*b, the product category c in the set item data does not exist in the order item data, so the set item data will fail to match the order item data. Specifically, the number of set items corresponding to at least one set item category in the set item data that failed to match may be greater than the number of order items corresponding to each order item category in the order item data. For example, assuming that the set item data is BOM1=1*a+4*b and the order item data is M=1*a+3*b, the number of product category b in the set item data is greater than the number of product category b in the order item data, so the set item data will fail to match the order item data.

[0099] For example, assuming M=2*a+3*b+4*c+5*d, BOM1=a+b+c+d, BOM2=2*a+3*b, BOM3=b+2*c+2*d, BOM4=4*c+5*d, BOM5=d+e, then the filtered package item data includes BOM1, BOM2, BOM3 and BOM4.

[0100] The advantage of such a setting is that the set item data that fails to be fuzzily matched with the order item data once cannot increase the consumption of the set, and even if the subsequent matching process is executed, the reference value of the reference distribution data obtained is not great. This embodiment screens the set item data through a fuzzy matching operation, while ensuring the matching success rate of each set item data after screening with the order item data, and reduces the total number of sets corresponding to each set item data in the subsequent matching process as much as possible, thereby further improving the efficiency of the matching process, and further improving the distribution efficiency of the goods.

[0101] On the basis of the above embodiment, specifically, the order item data includes bound item data and unbound item data. Accordingly, before performing at least one fuzzy matching operation on at least one pre-stored package item data based on the order item data to obtain at least one reference distribution data, the method also includes: performing an exact matching operation on the bound item data and at least one pre-stored package item data; adding the successfully matched package item data to the reference distribution data, and using the unbound item data as the filtered order item data.

[0102] Specifically, bound item data is used to represent item data in order item data that must be shipped out in the form of a set, and the bound item data contains at least two order item categories and the order item quantities corresponding to each order item category. Unbound item data is used to represent item data in order item data that can be shipped out in the form of a set or in the form of loose items, and the unbound item data contains at least two order item categories and the order item quantities corresponding to each order item category. For example, assuming that order item data M = 2*a+3*b+4*c, bound item data M1 = 2*a+2*b, and unbound item data M2 = b+4*c.

[0103] Specifically, exact matching is used to indicate that when the bound product data and the set product data are exactly the same, it means that the bound product data and the set product data are matched successfully; when the bound product data and the set product data are not exactly the same, it means that the bound product data and the set product data are matched unsuccessfully.

[0104] In this embodiment, specifically, the reference distribution data determined by each set of product data respectively includes the set product data that is accurately matched with the bound product data. For example, BOM1 = 2*a+2*b, BOM2 = b+5*c, BOM3 = b+3c, then the reference distribution data determined based on BOM1 is BOM1+BOM3+c, the reference distribution data determined based on BOM2 is BOM1+b+4*c, and the reference distribution data determined based on BOM3 is BOM1+BOM3+c.

[0105] The advantage of this setting is that it makes the order distribution method applicable to goods orders with limited bound item data, that is, the goods orders can be distributed in the form of pre-set bound item data, enriching the personalized customization function of the order distribution method and broadening the applicable scenarios of the order distribution method.

[0106] Figure 6 This is a flow chart of another order matching method provided by an embodiment of the present invention. This embodiment further refines the technical feature of "performing order matching operations corresponding to product orders based on each reference matching data" in the above embodiment. Figure 6 As shown, the method includes:

[0107] S310: In response to detecting a product order, obtaining order item data in the product order.

[0108] S320: Based on the order item data, perform at least one fuzzy matching operation on at least one pre-stored package item data to obtain at least one reference distribution data.

[0109] S330: Use reference distribution data including at least one successfully matched set item data and corresponding set quantity as first distribution data.

[0110] In this embodiment, the first distribution data includes the set quantity corresponding to at least one successfully matched set item data, or the first distribution data includes the set quantity corresponding to the loose item data and at least one successfully matched set item data.

[0111] S330: Obtain the quantity of package types of package product data corresponding to each first distribution data.

[0112] For example, assuming that the first distribution data A1 = 2*BOM3 + 2*BOM4, the number of package types corresponding to the first distribution data is 2. Assuming that the second distribution data A2 = BOM4 + 2*a + 3*b, the number of categories of loose goods corresponding to the second distribution data is 1.

[0113] S350: based on the quantity of each package type, obtain target distribution data from each first distribution data, and based on the target distribution data, perform an order distribution operation corresponding to the product order.

[0114] Specifically, based on the quantity of each package type, the target distribution data is obtained from each first distribution data, including: taking the first distribution data with the largest number of package types as the target distribution data. Taking the above example, the target distribution data is the first distribution data A1.

[0115] On the basis of the above embodiment, based on the quantity of each package type, the target distribution data is obtained from each first distribution data, including: when the number of first distribution data with the largest number of package types is at least two, the total package quantity corresponding to each first distribution data with the largest number of package types is obtained; based on each total package quantity, the target distribution data is obtained from each first distribution data with the largest number of package types.

[0116] Among them, for example, assuming that the first distribution data A1 = 2*BOM3+2*BOM4+b+d, the first distribution data A2 = BOM1+2*a+3*b, and the first distribution data A3 = BOM2+BOM5+3*a+b, then the first distribution data with the largest number of categories include the first distribution data A1 and the first distribution data A3, wherein the total number of packages corresponding to the first distribution data A1 is 4, and the total number of packages corresponding to the first distribution data A3 is 2, then the target distribution data is the first distribution data A1.

[0117] The advantage of this setting is that the number of set types in the set item data is used as the first sorting priority, and the total number of sets in the set item data is used as the second sorting priority. The target distribution data with the largest number of set types and the largest total number of sets can maximize the consumption of sets in the warehouse, thereby further reducing the labor cost and time cost caused by operations such as disassembling and reassembling sets.

[0118] The technical solution of this embodiment uses reference distribution data containing the set quantity corresponding to at least one successfully matched set item data as the first distribution data, and obtains the set type quantity of the set item data corresponding to each first distribution data, obtains the target distribution data from each first distribution data based on the quantity of each set type, and executes the order distribution operation corresponding to the product order based on the target distribution data, thereby solving the problem of selecting the target distribution data in the order distribution operation, so that the sets in the warehouse are hit and consumed to the greatest extent, and further reducing the labor cost and time cost caused by operations such as disassembling and reassembling sets.

[0119] Figure 7 This is a flow chart of another order matching method provided by an embodiment of the present invention. This embodiment further refines the technical feature of "performing order matching operations corresponding to product orders based on each reference matching data" in the above embodiment. Figure 7 As shown, the method includes:

[0120] S410: In response to detecting a product order, obtaining order item data in the product order.

[0121] S420: Based on the order item data, perform at least one fuzzy matching operation on at least one pre-stored package item data to obtain at least one reference distribution data.

[0122] S430: Use at least one reference distribution data including the data of the bulk items as the second distribution data.

[0123] In this embodiment, the second distribution data includes the loose item data, or the second distribution data includes the loose item data and the set quantity corresponding to at least one successfully matched set item data.

[0124] S440: Obtain the quantity of the category of the bulk goods corresponding to each bulk item data in each second distribution data.

[0125] In this embodiment, the bulk item data includes at least one bulk item category. For example, assuming that the second distribution data is B1=BOM3+2*a+b+d, the number of categories corresponding to the second distribution data is 3. assuming that the second distribution data is B2=BOM4+2*a+3*b, the number of categories corresponding to the second distribution data is 2.

[0126] S450: based on the quantity of each category, obtain target distribution data from each second distribution data, and based on the target distribution data, perform an order distribution operation corresponding to the product order.

[0127] Specifically, based on the quantity of each category, the target distribution data is obtained from each second distribution data, including: taking the second distribution data with the least quantity of categories as the target distribution data. Taking the above example, the target distribution data is the second distribution data B2.

[0128] On the basis of the above embodiment, specifically, based on the quantity of each category, the target distribution data is obtained from each second distribution data, including: when the number of second distribution data with the least number of categories is at least two, the total number of loose goods corresponding to each second distribution data with the least number of categories is obtained; based on the total number of loose goods, the target distribution data is obtained from each second distribution data with the least number of categories.

[0129] Among them, for example, assuming that the second distribution data B1 = 2*BOM3+2*a+b+d, the second distribution data B2 = BOM4+2*a+3*b, and the second distribution data B3 = BOM2+3*a+b, then the second distribution data with the least number of categories include the second distribution data B2 and the second distribution data B3, wherein the total number of loose parts items corresponding to the second distribution data B2 is 5, and the total number of loose parts items corresponding to the second distribution data B3 is 4, then the target distribution data is the second distribution data B3.

[0130] The advantage of this setting is that the number of categories in the loose item data is used as the first sorting priority, and the total number of loose items in the loose item data is used as the second sorting priority. The target distribution data with the least number of categories and the least total number of loose items can improve the picking efficiency in the subsequent distribution process, and further improve the distribution efficiency of goods from the perspective of the distribution process.

[0131] The technical solution of this embodiment, by using at least one reference distribution data containing loose item data as the second distribution data, and obtaining the category quantity of the loose goods category corresponding to each loose item data in each second distribution data, based on the quantity of each category, obtaining target distribution data from each second distribution data, and executing the order distribution operation corresponding to the goods order based on the target distribution data, solves the problem of selecting the target distribution data in the order distribution operation, can improve the picking efficiency in the subsequent distribution process, and further improves the distribution efficiency of the goods from the perspective of the distribution process.

[0132] On the basis of the above embodiment, specifically, the sorting priority of the number of package types of the set item data, the total number of sets of the set item data, the number of categories of the loose item data, and the total number of loose items of the loose item data in each reference distribution data decreases in sequence.

[0133] In another embodiment, specifically, the sorting priorities of the category quantity of the loose item data, the total loose item quantity of the loose item data, the set type quantity of the set item data, and the total set quantity of the set item data in each reference distribution data are reduced in sequence.

[0134] The advantage of this setting is that the target distribution data with the largest number of set types, the largest total number of sets, the smallest number of categories and the smallest total number of loose goods can not only increase the consumption of sets, but also improve the picking efficiency in the subsequent distribution process.

[0135] On the basis of the above embodiments, specifically, based on the target distribution data, an order distribution operation corresponding to the product order is executed, including: when the target distribution data contains the set quantities corresponding to the loose item data and at least one successfully matched set item data, obtaining the set positioning positions corresponding to each set item data in the target distribution data; based on each set positioning position, obtaining at least one loose item positioning position corresponding to each loose item category in the loose item data; based on the target distribution data, each set positioning position and each loose item positioning position, executing the order distribution operation corresponding to the product order.

[0136] Specifically, for each category of loose goods, when there are at least two loose parts positioning positions corresponding to the loose goods category, the picking distances corresponding to each loose parts positioning position and each set positioning position are obtained, and the loose parts positioning position with the shortest picking distance is used as the target positioning position corresponding to the loose goods category. Based on the target distribution data, each set positioning position and each target positioning position, the order distribution operation corresponding to the goods order is performed.

[0137] Among them, for example, assuming that there is one category of loose goods, the loose parts positioning positions corresponding to the loose goods category include three, namely p1, p2 and p3, and each set positioning position includes two, namely s1 and s2, then the number of each picking distance obtained is 6, namely L(p1,s1), L(p1,s2), L(p2,s1), L(p2,s2), L(p3,s1) and L(p3,s2), and the loose parts positioning position with the shortest picking distance is taken as the target positioning position corresponding to the loose goods category.

[0138] The advantage of this setting is that it can improve the picking efficiency in the subsequent distribution process, and further improve the distribution efficiency of goods from the perspective of the distribution process.

[0139] Figure 8 This is a schematic diagram of the structure of an order matching device provided by an embodiment of the present invention. Figure 8 As shown, the device includes: an order item data acquisition module 510, a reference distribution data determination module 520 and an order distribution operation execution module 530.

[0140] The order item data acquisition module 510 is used to acquire the order item data in the goods order in response to detecting the goods order;

[0141] A reference distribution data determination module 520 is used to perform at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data to obtain at least one reference distribution data;

[0142] An order distribution operation execution module 530 is used to execute an order distribution operation corresponding to a product order based on each reference distribution data;

[0143] Among them, the fuzzy matching operation indicates that the successfully matched set item data is included in the order item data, the reference distribution data contains the loose item data and / or the set quantity corresponding to at least one successfully matched set item data, and the loose item data contains the unsuccessfully matched item data in the order item data.

[0144] The technical solution of this embodiment is to obtain at least one reference distribution data by performing at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data in the goods order, and to perform the order distribution operation corresponding to the goods order based on each reference distribution data, wherein the fuzzy matching operation indicates that the successfully matched set item data is included in the order item data, and the fuzzy matching operation makes the number of reference distribution data finally obtained to be multiple, and the reference distribution data contains the number of sets corresponding to the loose item data and / or at least one successfully matched set item data, and the loose item data contains item data in the order item data that has not been successfully matched with each set item data, thereby solving the problem in the prior art that the assembled sets cannot accurately meet the goods requirements in the goods order, and improving the matching success rate between the order item data and the set item data, thereby improving the goods distribution efficiency, and increasing the consumption of sets, and reducing the labor cost and time cost caused by operations such as disassembling and reassembling sets.

[0145] Based on the above embodiment, specifically, the reference distribution data determination module 520 includes:

[0146] The current to-be-matched item data determining unit is used to, for each set item data, use the set item data as target set item data, and use the target set item data and the order item data as the current set item data and the current to-be-matched item data respectively;

[0147] A fuzzy matching operation execution unit, used to perform a fuzzy matching operation on the current set item data and the current to-be-matched item data;

[0148] The next to-be-matched item data determining unit is used to determine the next to-be-matched item data based on the current set item data and the current to-be-matched item data when the matching result is a successful match, and use the next to-be-matched item data as the current to-be-matched item data;

[0149] A reference distribution data determination unit is used to call the fuzzy matching operation execution unit until a preset end condition is met, and then determine the reference distribution data corresponding to the target set item data based on the number of successful matches corresponding to the target set item data and the current to-be-matched item data;

[0150] The preset end condition includes at least one of the following: the matching result is a matching failure, the current data of items to be matched is empty, and the order item category in the current data of items to be matched is one.

[0151] Based on the above embodiment, specifically, the reference distribution data determination unit includes:

[0152] The next set of product data determination subunit is used to determine the next set of product data based on the target set of product data when the matching result is a match failure, and use the next set of product data as the current set of product data, and call the fuzzy matching operation execution unit;

[0153] The reference distribution data determination subunit is used to determine the reference distribution data corresponding to the target set item data based on the number of successful matches corresponding to at least one successfully matched set item data and the current item data to be matched until the number of failed matches of the target set item data is equal to a number threshold.

[0154] Based on the above embodiment, specifically, the next set of product data determination subunit is specifically used for:

[0155] Using other set item data except the target set item data as reference set item data;

[0156] Taking any reference set of product data that has not been fuzzy matched in the at least one reference set of product data as the next set of product data;

[0157] Correspondingly, the number threshold is the total number of sets corresponding to the item data of each set.

[0158] Based on the above embodiment, specifically, the next set of product data determination subunit is specifically used for:

[0159] Obtaining the set sorting results corresponding to each set item data, and obtaining the target sorting results from the set sorting results based on the target set item data;

[0160] The package item data located after the current package item data in the target sorting result is used as the next package item data;

[0161] Correspondingly, the number threshold is the target set quantity corresponding to the target set item data determined based on the set sorting result.

[0162] On the basis of the above embodiment, specifically, the order item data includes bound item data and unbound item data, and accordingly, the device further includes:

[0163] An order item data screening module, configured to perform an accurate matching operation on the bound item data and at least one pre-stored set item data respectively before performing at least one fuzzy matching operation on at least one pre-stored set item data to obtain at least one reference distribution data based on the order item data;

[0164] The successfully matched set item data is added to the reference distribution data, and the unbound item data is used as the filtered order item data.

[0165] Based on the above embodiment, specifically, the order matching operation execution module 530 includes:

[0166] A package type quantity acquisition unit, used to use reference distribution data containing at least one package quantity corresponding to each successfully matched package item data as first distribution data, and acquire the package type quantity of each package item data corresponding to each first distribution data;

[0167] The first order distribution operation execution unit is used to obtain target distribution data from each first distribution data based on the quantity of each package type, and execute the order distribution operation corresponding to the product order based on the target distribution data.

[0168] Based on the above embodiment, specifically, the order matching operation execution module 530 includes:

[0169] A category quantity acquisition unit, used to use at least one reference distribution data containing bulk product data as second distribution data, and to acquire the category quantity of the bulk product category corresponding to each bulk product data in each second distribution data;

[0170] The second order distribution operation execution unit is used to obtain target distribution data from each second distribution data based on the quantity of each category, and execute the order distribution operation corresponding to the product order based on the target distribution data.

[0171] On the basis of the above embodiment, specifically, the second order matching operation execution unit or the second order matching operation execution unit is specifically used to:

[0172] When the target distribution data includes the quantity of sets corresponding to the individual item data and at least one successfully matched set item data, obtaining the set location corresponding to each set item data in the target distribution data;

[0173] Based on the positioning positions of each set, obtaining at least one loose parts positioning position corresponding to each loose parts product category in the loose parts data;

[0174] Based on the target distribution data, the positioning positions of each set and each individual part, the order distribution operation corresponding to the product order is executed.

[0175] The order matching device provided in the embodiment of the present invention can execute the order matching method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0176] Fig. 9A schematic diagram of the structure of an electronic device provided for one embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown in the embodiments of the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0177] like Fig. 9 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by the at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0178] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0179] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an order picking method.

[0180] In some embodiments, the order fulfillment method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the order fulfillment method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the order fulfillment method in any other appropriate manner (e.g., by means of firmware).

[0181] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0182] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute an order matching method, the method comprising:

[0183] In response to detecting a product order, obtaining order item data in the product order;

[0184] Based on the order item data, at least one fuzzy matching operation is performed on at least one pre-stored set item data to obtain at least one reference distribution data;

[0185] Based on each reference distribution data, execute the order distribution operation corresponding to the product order;

[0186] Among them, the fuzzy matching operation indicates that the successfully matched set item data is included in the order item data, the reference distribution data contains the loose item data and / or the set quantity corresponding to at least one successfully matched set item data, and the loose item data contains the unsuccessfully matched item data in the order item data.

[0187] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0189] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0190] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0191] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0192] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An order distribution method, characterized in that: include: In response to detecting a product order, acquiring order item data in the product order; Based on the order item data, at least one fuzzy matching operation is performed on at least one pre-stored set item data to obtain at least one reference distribution data; Based on each of the reference distribution data, executing the order distribution operation corresponding to the product order; Among them, the fuzzy matching operation represents that the successfully matched set item data is included in the order item data, the reference distribution data contains loose item data and / or the set quantity corresponding to at least one successfully matched set item data, and the loose item data contains unsuccessfully matched item data in the order item data.

2. The method according to claim 1, characterized in that The step of performing at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data to obtain at least one reference distribution data comprises: For each set of product data, the set of product data is used as the target set of product data, and the target set of product data and the order product data are used as the current set of product data and the current product data to be matched respectively; Perform a fuzzy matching operation on the current set item data and the current to-be-matched item data; If the matching result is successful, based on the current set item data and the current to-be-matched item data, determine the next to-be-matched item data, and use the next to-be-matched item data as the current to-be-matched item data; Return to perform a fuzzy matching operation on the current set item data and the current to-be-matched item data until a preset end condition is met, and determine the reference distribution data corresponding to the target set item data based on the number of successful matches corresponding to the target set item data and the current to-be-matched item data; The preset end condition includes at least one of the following: the matching result is a matching failure, the current data of items to be matched is empty, and the order item category in the current data of items to be matched is one.

3. The method according to claim 2, characterized in that The determining, based on the number of successful matches corresponding to the target set item data and the current to-be-matched item data, reference distribution data corresponding to the target set item data includes: If the matching result is a match failure, determining the next set of product data based on the target set of product data, and using the next set of product data as the current set of product data; Return to the step of performing a fuzzy matching operation on the current set item data and the current to-be-matched item data; Until the number of failed matches of the target set item data is equal to the number threshold, the reference distribution data corresponding to the target set item data is determined based on the number of successful matches corresponding to at least one successfully matched set item data and the current item data to be matched.

4. The method according to claim 3, characterized in that The step of determining the next set of product data based on the target set of product data includes: Using other package item data except the target package item data as reference package item data; Taking any reference set of product data that has not been fuzzy matched in the at least one reference set of product data as the next set of product data; Correspondingly, the number threshold is the total number of sets corresponding to each set of item data.

5. The method according to claim 3, characterized in that: The step of determining the next set of product data based on the target set of product data includes: Obtaining the package sorting results corresponding to each of the package item data, and obtaining a target sorting result from the package sorting results based on the target package item data; The package item data located after the current package item data in the target sorting result is used as the next package item data; Correspondingly, the number threshold is the target set quantity corresponding to the target set item data determined based on the set sorting result.

6. The method according to claim 1, characterized in that The order item data includes bound item data and unbound item data. Accordingly, before performing at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data to obtain at least one reference distribution data, the method further includes: Performing an accurate matching operation on the bound item data and at least one pre-stored set item data respectively; The successfully matched set item data is added to the reference distribution data, and the unbound item data is used as the filtered order item data.

7. The method according to claim 1, characterized in that The performing of the order distribution operation corresponding to the product order based on each of the reference distribution data includes: Using reference distribution data including at least one set quantity corresponding to each successfully matched set item data as first distribution data, and obtaining the set type quantity of each set item data corresponding to each of the first distribution data; Based on the quantity of each package type, target distribution data is obtained from each first distribution data, and based on the target distribution data, an order distribution operation corresponding to the product order is performed.

8. The method according to claim 1, characterized in that The performing of the order distribution operation corresponding to the product order based on each of the reference distribution data includes: Using at least one reference distribution data containing bulk product data as second distribution data, and obtaining the category quantity of the bulk product category corresponding to each bulk product data in each second distribution data; Based on the quantity of each category, target distribution data is obtained from each second distribution data, and based on the target distribution data, an order distribution operation corresponding to the product order is performed.

9. The method according to claim 7 or 8, characterized in that: The step of executing the order distribution operation corresponding to the product order based on the target distribution data includes: When the target distribution data includes the quantity of sets corresponding to the individual item data and at least one successfully matched set item data, obtaining the set location positions corresponding to each of the set item data in the target distribution data; Based on each of the set positioning positions, obtaining at least one loose parts positioning position corresponding to each loose parts product category in the loose parts data; Based on the target distribution data, the positioning positions of each set and the positioning positions of each individual component, an order distribution operation corresponding to the product order is performed.

10. An order matching device, characterized in that: include: An order item data acquisition module, configured to acquire order item data in the goods order in response to detecting the goods order; A reference distribution data determination module, configured to perform at least one fuzzy matching operation on at least one pre-stored set item data based on the order item data to obtain at least one reference distribution data; An order distribution operation execution module, used to execute the order distribution operation corresponding to the product order based on each of the reference distribution data; Among them, the fuzzy matching operation represents that the successfully matched set item data is included in the order item data, the reference distribution data contains loose item data and / or the set quantity corresponding to at least one successfully matched set item data, and the loose item data contains unsuccessfully matched item data in the order item data.

11. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the order distribution method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the order matching method according to any one of claims 1 to 9 when executed.

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