CN120806826A

CN120806826A - Store inventory allocation management method, store inventory allocation management system, intelligent terminal and storage medium

Info

Publication number: CN120806826A
Application number: CN202511309776.6A
Authority: CN
Inventors: 俞宏伟; 杨云兵; 陈嘉豪
Original assignee: Zhejiang Lemeng Information Technology Co ltd
Current assignee: Zhejiang Lemeng Information Technology Co ltd
Priority date: 2025-09-15
Filing date: 2025-09-15
Publication date: 2025-10-17

Abstract

The present application relates to a store inventory allocation management method, system, intelligent terminal and storage medium, and relates to the field of inventory management technology. The method includes: collecting store orders; determining the allocation mode of purchased goods based on the orders; when the allocation mode is a daily distribution mode, counting the orders to obtain the corresponding quantity to be purchased of the purchased goods; in response to the purchase completion operation of the purchased goods and the quantity to be purchased, obtaining the allocation algorithm of the purchased goods, the allocation algorithm includes an equal proportion allocation algorithm and a one-key allocation algorithm; calculating the allocation quantity of the purchased goods according to the allocation algorithm; generating a first allocation plan according to the purchased goods and the allocation quantity; when the allocation mode is a warehouse distribution mode, generating a second allocation plan according to the orders and the warehouse storage mode. The present application has the effect of improving the allocation management efficiency of store inventory.

Description

Store inventory allocation management method, store inventory allocation management system, intelligent terminal and storage medium

Technical Field

The application relates to the technical field of inventory management, in particular to a store inventory allocation management method, a store inventory allocation management system, an intelligent terminal and a storage medium.

Background

In the field of inventory allocation, efficient allocation of multi-store inventory directly affects enterprise operating costs and customer satisfaction.

The related art adopts a general distribution mode, which requires each store to report respective demands to a supplier. And the suppliers uniformly plan the transportation route, arrange vehicles and dispatch and distribute according to the demands of all stores.

Aiming at the related technology, the commodities in fresh and cooked food industries have high loss rate, so that inventory allocation is easy to make mistakes, and the allocation management efficiency is reduced.

Disclosure of Invention

In order to improve distribution management efficiency of store inventory, the application provides a store inventory distribution management method, a store inventory distribution management system, an intelligent terminal and a storage medium.

In a first aspect, the present application provides a store inventory allocation management method, which adopts the following technical scheme:

A store inventory allocation management method, comprising:

Collecting a stock order of a store;

Determining an allocation mode of purchased goods according to the order to be purchased;

under the condition that the distribution mode is a daily distribution mode, counting the order to be purchased to obtain the amount to be purchased corresponding to the purchased goods;

responding to the purchasing completion operation of the purchased goods and the quantity to be purchased, and acquiring a distribution algorithm of the purchased goods, wherein the distribution algorithm comprises an equal proportion distribution algorithm and a one-key distribution algorithm;

calculating the distribution quantity of the purchased goods according to the distribution algorithm;

generating a first distribution scheme according to the purchased goods and the distribution quantity;

and generating a second allocation scheme according to the order to be ordered and the warehouse storage mode under the condition that the allocation mode is the warehouse allocation mode.

By adopting the technical scheme, in the fresh mode, the to-be-purchased quantity is counted based on the to-be-purchased order, the distribution algorithm is called to generate the distribution scheme, the accurate supply of high-frequency goods and goods is ensured, and in the retail mode, the bin state generation scheme is directly combined, so that the calculation redundancy is reduced. The design remarkably improves the adaptability of the multi-mode scene, reduces the manual intervention cost, automatically generates a distribution scheme through an algorithm, avoids the risk of overdistribution or lack of stock, and integrally improves the response efficiency of the supply chain.

Optionally, calculating the difference between the to-be-purchased quantity and the metered quantity to obtain a distribution quantity difference;

calculating the difference between the batch configurable quantity and the batch allocated quantity to obtain a first cargo allocation difference;

Calculating the difference value between the stock quantity of the batch and the allocated quantity of the batch to obtain a second cargo allocation difference value;

Calculating the quotient of the purchase quantity of the target store and the difference value of the distribution quantity, and calculating the product of the quotient and the difference value of the first distribution quantity to obtain the batch distribution quantity of the target store, or calculating the quotient of the purchase quantity of the target store and the difference value of the distribution quantity, and calculating the product of the quotient and the difference value of the second distribution quantity to obtain the batch distribution quantity of the target store.

By adopting the technical scheme, the batch inventory is dynamically distributed according to the store purchasing demand proportion through the linkage of multidimensional parameters such as the difference of the distribution amount, the batch distributable/inventory difference and the like. The method is particularly suitable for a batch arrival scene of the pretty commodities, ensures that each store obtains reasonable quota according to actual demands, avoids the problem that stores monopolize stock or small stores are free from goods and can be matched, and remarkably improves the fairness of the supply chain and the satisfaction of the stores.

If the residual inventory is greater than or equal to the distribution quantity difference value, calculating the sum of the distribution quantity difference value and the distributed quantity of the batch to obtain the distribution quantity of the batch;

If the residual inventory is smaller than the distribution quantity difference value, calculating the sum of the residual inventory and the distributed quantity of the batch to obtain the distribution quantity of the batch.

By adopting the technical scheme, the full allocation or maximum inventory allocation strategy is automatically selected through direct comparison of inventory and inventory quantity difference values. The design greatly simplifies the operation flow, realizes second-level decision when the inventory is tension or the demand of replenishment is burst, avoids the delay of manual accounting, ensures the maximization of the utilization of inventory resources, and is particularly suitable for fresh and sales-promoting sensitive commodities with equal efficiency.

Optionally, acquiring store information of the store according to the category of the purchased commodity;

normalizing the store information to obtain normalized scores;

Weighting and calculating the normalized score to obtain the priority score of the store;

Descending order sorting is carried out on the priority scores to obtain score sorting;

and adjusting the distribution scheme of the purchased goods according to the score ordering.

By adopting the technical scheme, a scientific priority evaluation system is constructed by normalizing, weighting, scoring and sequencing store information. So that the high-value store can obtain the quota of the short-cut commodity preferentially, and the overall revenue potential is improved. The mechanism quantifies and blends business strategies into cargo allocation decisions, and reduces the probability of resource mismatch.

Optionally, acquiring a geographic location of the store;

clustering stores according to the geographic positions to obtain store clustering clusters;

Selecting the region of the store cluster by a frame to obtain a frame selected region;

Calculating the distance from the delivery point to the frame selection area to obtain a delivery distance;

And adjusting the distribution scheme of the store corresponding to the store cluster according to the distribution distance.

By adopting the technical scheme, the store clusters are generated through geographic position clustering, and the distribution scheme is dynamically adjusted by combining the distribution distance from the delivery point to the cluster area. The cross-region scattered distribution frequency is obviously reduced, the unit logistics cost is reduced, and the loading rate of the whole vehicle is improved. The method is particularly suitable for regional storage networks and realizes large-scale benefits.

Optionally, collecting a historical purchase record of the store;

establishing a commodity purchasing portrait of the store according to the historical purchasing record;

calculating the similarity between the commodity purchasing image and the order to be purchased;

If the similarity is smaller than a preset similarity threshold, performing accuracy judgment on the commodity purchasing image and the to-be-purchased order, and taking the one with larger accuracy of the commodity purchasing image and the to-be-purchased order as a correction parameter;

and if the similarity is greater than a preset similarity threshold, generating the distribution scheme according to the order to be delivered.

By adopting the technical scheme, the data source is intelligently selected by utilizing the similarity analysis of the historical purchasing portrait and the current order to be ordered, so that the cargo allocation scheme is ensured to respond to sudden demand change, and abnormal order interference long-term rules are avoided. The design gives the dynamic learning capability to the system, ensures stability, and simultaneously flexibly adapts to market fluctuation, and reduces the risk of concurrent sale and stock shortage.

Optionally, extracting the commodity category identification and the purchasing timestamp of the historical purchasing record;

Distributing dynamic attenuation weights to purchase records in different time periods based on the time difference between the current time and the purchase time stamp, wherein the larger the time difference is, the lower the weight is;

According to the dynamic attenuation weight and the commodity category identification, carrying out weighted calculation on the historical purchasing quantity of the similar commodity to generate a time-sensitive commodity purchasing image;

and replacing the commodity purchasing image with the time-sensitive commodity purchasing image.

By adopting the technical scheme, a time attenuation weight mechanism is introduced into the history purchasing record, so that the timeliness and decision accuracy of the commodity purchasing image are remarkably improved, more accurate commodity purchasing image can be generated, and the distortion probability of the commodity purchasing image is reduced.

In a second aspect, the present application provides a store inventory allocation management system, which adopts the following technical scheme:

A store inventory allocation management system, comprising:

The acquisition module is used for acquiring an order to be purchased, purchasing completion operation and a warehouse storage mode;

a memory for storing a program of the store inventory allocation management method;

and the processor is used for loading and executing programs in the memory by the processor and realizing the store inventory allocation management method.

In a third aspect, the present application provides an intelligent terminal, which adopts the following technical scheme:

an intelligent terminal comprising a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and executing the store inventory allocation management method of any of the above.

In a fourth aspect, the present application provides a computer storage medium capable of storing a corresponding program, which has a feature of facilitating improvement of distribution management efficiency of store inventory, and adopts the following technical scheme:

A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing any one of the store inventory allocation management methods described above.

In summary, the present application includes at least one of the following beneficial technical effects:

1. in the fresh mode, the quantity to be purchased is counted based on the order to be purchased, an allocation algorithm is called to generate a distribution scheme, accurate supply of high-frequency goods and goods is guaranteed, and in the retail mode, the calculation redundancy is reduced by directly combining with a warehouse state generation scheme. The design obviously improves the adaptability of the multi-mode scene, reduces the manual intervention cost, automatically generates a distribution scheme through an algorithm, avoids the risk of overdry or lack of stock, and integrally improves the response efficiency of a supply chain;

2. And dynamically distributing the batch inventory according to the store purchasing demand proportion by multi-dimensional parameter linkage such as the distribution quantity difference value, the batch configurable/inventory difference value and the like. The system is particularly suitable for a batch arrival scene of a pretty commodity, ensures that each store obtains reasonable quota according to actual demands, avoids the problem that stores monopolize stock or small stores are not available, and remarkably improves the fairness of a supply chain and the satisfaction of the stores;

3. Through direct comparison of the inventory and the inventory difference, full allocation or maximum inventory allocation strategies are automatically selected. The design greatly simplifies the operation flow, realizes second-level decision when the inventory is tension or the demand of replenishment is burst, avoids the delay of manual accounting, ensures the maximization of the utilization of inventory resources, and is particularly suitable for fresh and sales-promoting sensitive commodities with equal efficiency.

Drawings

Fig. 1 is a flowchart of a store inventory allocation management method according to an embodiment of the present application.

Fig. 2 is a flow chart of a first method for adjusting a cargo allocation scheme according to an embodiment of the present application.

Fig. 3is a flow chart of a second method for adjusting a cargo allocation scheme according to an embodiment of the present application.

Fig. 4 is a flow chart of a third method for adjusting a cargo allocation scheme according to an embodiment of the present application.

Fig. 5 is a flow chart of a fourth method for adjusting a cargo allocation scheme according to an embodiment of the present application.

Fig. 6 is a schematic structural diagram of a store inventory allocation management system according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to fig. 1 to 6 and the embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The embodiment of the application discloses a store inventory allocation management method. Referring to fig. 1, the method includes:

Step S101, collecting a stock order of a store.

The order to be placed is a request for goods from a store to a goods provider. The order for goods includes, but is not limited to, at least one of time for goods, type of goods, quantity of goods, weight of goods.

The order may be sent to the commodity supplier in real time by the store, or may be sent to the commodity supplier at regular time by the store.

Step S102, determining the distribution mode of the purchased goods according to the order.

The allocation modes include a daily allocation mode and a bin allocation mode. Daily distribution mode refers to a commodity supplier distributing commodity to a store every day. Daily dispensing mode is commonly used in fresh and cooked food and other goods. For example, commodity suppliers count and purchase commodity types and commodities required for each store every night, and distribute corresponding commodities to each store according to commodity types and commodities.

The warehouse allocation mode is a mode in which operations such as storage, transfer, and allocation of commodities are performed through a warehouse. The warehouse distribution mode needs to sort and distribute commodities according to distribution quantity. The bin dispensing mode is commonly used in retail products, snack products, and the like.

And step 103, under the condition that the distribution mode is a daily distribution mode, counting the order to be purchased to obtain the corresponding amount to be purchased of the purchased commodity.

The quantity to be purchased refers to daily quantity of purchased goods. The to-be-purchased quantity corresponds to the purchased goods one by one, and different purchased goods are provided with the corresponding to-be-purchased quantity.

Illustratively, for a target purchase item in the purchase items, the sub-quantities in each order to be purchased for the target purchase item are counted. Counting the sub-quantity to obtain the quantity to be purchased.

Step S104, responding to the purchasing completion operation of the purchased goods and the quantity to be purchased, and acquiring an allocation algorithm of the purchased goods, wherein the allocation algorithm comprises an equal proportion allocation algorithm and a one-key allocation algorithm.

The purchase completion operation indicates that purchase of the purchased commodity is completed. The purchase completion operation comprises a commodity type purchase completion operation and a commodity quantity purchase completion operation. The commodity category purchase completion operation indicates that the commodity supplier completes purchase of the commodity category corresponding to the purchased commodity. The commodity quantity purchasing completion operation indicates that the commodity provider completes purchasing of the commodity quantity corresponding to the purchased commodity.

In some embodiments, the equal-proportion distribution algorithm is specifically described as calculating the difference between the amount to be purchased and the metered amount to obtain a difference in the amount to be distributed. And calculating the difference between the batch dispensable quantity and the batch dispensable quantity to obtain a first goods dispensing quantity difference. And calculating the difference between the stock quantity of the batch and the allocated quantity of the batch to obtain a second distribution quantity difference. Calculating the quotient of the purchase quantity of the target store and the difference value of the distribution quantity, and calculating the product of the quotient and the first difference value of the distribution quantity to obtain the batch distribution quantity of the target store, or calculating the quotient of the purchase quantity of the target store and the difference value of the distribution quantity, and calculating the product of the quotient and the second difference value of the distribution quantity to obtain the batch distribution quantity of the target store.

For example, lot allocation amount=amount to be purchased/(amount to be purchased-dosed) × (lot configurable amount-lot allocated amount).

In some embodiments, the one-key distribution algorithm is specifically configured to calculate a difference between the to-be-purchased quantity and the dosed quantity to obtain a distribution quantity difference. If the residual inventory is greater than or equal to the distribution quantity difference, calculating the sum of the distribution quantity difference and the distributed quantity of the batch to obtain the distribution quantity of the batch. If the residual inventory is smaller than the distribution quantity difference value, calculating the sum of the residual inventory and the distributed quantity of the batch to obtain the distribution quantity of the batch.

For example, when the remaining inventory is greater than the difference between the to-be-purchased amount and the dosed amount, the lot inventory amount=to-be-purchased amount-dosed+lot-allocated amount. When the remaining inventory is less than the difference between the to-be-purchased and the dosed amount, the lot inventory = remaining inventory + lot dosed amount.

Step 105, calculating the distribution quantity of the purchased goods according to the distribution algorithm.

The distribution quantity refers to the quantity of purchased goods assigned to the store.

And S106, generating a first distribution scheme according to the purchased goods and the distribution quantity.

The first distribution scenario refers to a distribution scenario for a store in daily distribution mode. The first dispensing scenario is exemplified by store 1 requiring the dispensing of 20 items a and 5 items B, and store 2 requiring the dispensing of 10 items a and 3 items C.

And S107, generating a second allocation scheme according to the order to be ordered and the warehouse storage mode under the condition that the allocation mode is the warehouse allocation mode.

In one embodiment, in the case that the distribution mode is a warehouse distribution mode, the flow includes commodity purchasing and warehousing, commodity inventory visualization, store ordering, sorting and distribution according to the execution sequence.

In one embodiment, when the allocation mode is a warehouse allocation mode, the flow includes store ordering, summarizing store data into a purchase order, and receiving and collecting delivery to and from a warehouse in order of execution.

By adopting the technical scheme, under the fresh mode, the to-be-purchased quantity is counted based on the to-be-purchased order, and the distribution algorithm is called to generate the distribution scheme, so that the accurate supply of high-frequency goods and products is ensured. And the calculation redundancy is reduced by directly combining with a bin state generation scheme in a retail mode. The design remarkably improves the adaptability of the multi-mode scene, reduces the manual intervention cost, automatically generates a distribution scheme through an algorithm, avoids the risk of overdistribution or lack of stock, and integrally improves the response efficiency of the supply chain.

In the following embodiments, different levels of distribution priority may be provided to accommodate store demands based on the value of the store when distributing the store. Accordingly, an embodiment of the present application discloses a method for adjusting a cargo allocation scheme. Referring to fig. 2, the method includes:

step S201, acquiring store information of stores according to the types of purchased goods.

The store information includes at least one of a main product of the store, daily passenger flow volume, difference of purchased goods and main goods, daily net income, daily sales volume.

Wherein, the difference between the purchased commodity and the main commodity refers to the difference of commodity types. For example, if the purchased commodity is a vegetable, the main commodity is an aquatic product, a first number vector corresponding to the vegetable and a second number vector corresponding to the aquatic product are obtained. And taking the difference value of the first number vector and the second number vector as the difference between the purchased commodity and the main commodity.

And step S202, normalizing the store information to obtain normalized scores.

The normalization process is used to scale different kinds of store information so that the different store information falls into the same specific section. For example, the normalization process may use any one of linear normalization, standard deviation normalization, and logarithmic normalization.

And step 203, weighting and calculating the normalized score to obtain the priority score of the store.

The usage weight value may be preset in weight calculation of the normalized score. Illustratively, the weight value for the difference between purchased and main commodities is 60%, the weight value for the daily passenger flow is 20%, and the weight value for the daily net income is 20%.

And S204, descending order sorting is carried out on the priority scores to obtain score sorting.

Descending order of priority scores may quickly determine stores with higher priority scores, thereby providing limited priority distribution service to those stores.

Step S205, according to the score order, the distribution scheme of the purchased goods is adjusted.

Different purchased goods can provide different distribution schemes for the same store, because the same store has different selling effects on different goods when selling different goods.

Illustratively, the top n stores are ranked according to the score ranking, resulting in the target store. And determining a distribution scheme corresponding to the target store. The number of purchased items at the target store in the distribution scheme is increased. Further, for the target store, the number of the purchased goods increased may be determined according to the number of the target store in the score order, for example, if the target store is ranked first in the score order, the number of the purchased goods increased is 5% of the daily sales amount of the purchased goods by the target store.

In the following embodiment, when the goods are distributed to the store, the distribution scheme can be properly adjusted according to the geographic position of the store so as to improve the distribution efficiency of the purchased goods. The embodiment of the application discloses a second method for adjusting a cargo allocation scheme. Referring to fig. 3, the method includes:

Step S301, obtaining the geographic position of the store.

Illustratively, the geographic location is pre-stored in a database, so the geographic location of the store may be directly recalled via the database. For example, the database stores the correspondence between the store numbers and the geographic locations, and the geographic locations can be determined by the correspondence and the store numbers.

And S302, clustering the stores according to the geographic positions to obtain store clusters.

Clustering is a process of grouping geographically close stores together. The clustering method includes at least one of DBSCAN algorithm, K-Means algorithm and hierarchical clustering algorithm.

And step S303, selecting the region where the store cluster is located in a frame mode to obtain a frame selection region.

The box-selected area is the smallest circumscribed rectangle of the store cluster.

And step S304, calculating the distance from the delivery point to the box selection area to obtain the delivery distance.

The shipping point refers to the harvest location or purchase location of the purchased goods.

Optionally, calculating the shortest path from the shipping point to the framed area results in a shipping distance.

Optionally, the geometric center of the framed area is obtained. And calculating the path of the point of delivery in the framed area to obtain the delivery distance.

And step S305, adjusting the distribution scheme of the store corresponding to the store cluster according to the distribution distance.

Optionally, when the distribution distance is greater than a preset first distribution distance threshold, the number of purchased goods in the distribution scheme corresponding to the store in the store cluster is reduced. And when the distribution distance is smaller than a preset second distribution distance threshold value, increasing the quantity of purchased goods in the distribution scheme of the store corresponding to the store cluster.

The embodiment of the application discloses a third method for adjusting a cargo allocation scheme. Referring to fig. 4, the method includes:

step S401, collecting historical purchasing records of stores.

Historical purchase records refer to store purchases records over a historical period. The historical purchase record is used for storing at least one of the purchase commodity type, the purchase commodity quantity and the purchase time of the store.

Step S402, building commodity purchasing pictures of the store according to the historical purchasing records.

The merchandise purchase image is used to describe the purchase tendency of the store.

The method includes the steps of generating commodity purchasing features according to historical purchasing records, wherein the commodity purchasing features comprise commodity structural features, purchasing behavior features and purchasing price features, the commodity structural features are used for describing the composition of purchasing commodity types, the purchasing behavior features comprise purchasing frequency, purchasing period, order scale and seasonal index, and the purchasing price features are used for describing price trends of purchasing commodities in stores. And forming commodity purchasing images according to commodity purchasing characteristics.

Step S403, calculating the similarity between the commodity purchasing image and the order for commodity.

Illustratively, the order to be placed is converted into a feature vector. And calculating cosine similarity of the commodity purchasing image and the feature vector to obtain the similarity of the step.

And S404, if the similarity is smaller than a preset similarity threshold, performing accuracy judgment on the commodity purchase image and the order to be purchased, and taking the party with larger accuracy in the commodity purchase image and the order to be purchased as a correction parameter.

The similarity threshold is a preset experience value, and a technician can adjust the specific value of the similarity threshold according to actual requirements.

If the similarity is smaller than a preset similarity threshold, the fact that the commodity purchasing image and the order to be purchased have larger deviation is indicated, and the accuracy of one object is higher, so that the party with higher accuracy is determined to be the correction parameter.

Accuracy assessments are used to quantify the accuracy of merchandise purchase representations and order to be purchased.

Optionally, the accuracy assessment includes a data integrity assessment and a data consistency assessment. Illustratively, the data integrity evaluation includes a purchase image evaluation including checking a time period covered by the merchandise purchase image and a to-be-order evaluation including verifying the integrity of the items of the to-be-order, for example.

Illustratively, the data consistency assessment includes comparing the consistency of the merchandise purchase representation with a pre-set database and checking the consistency of the order to be purchased with the merchandise purchase representation.

If the correction parameter is the order, the commodity purchasing image is updated by using the correction parameter in step S405.

If the correction parameter is the order, it is indicated that the accuracy of the commodity purchase image is low, and the commodity purchase image needs to be updated by using the correction parameter.

Step S406, if the correction parameter is the commodity purchasing image, the commodity purchasing image is maintained.

If the correction parameter is the commodity purchasing image, it is indicated that the accuracy of the commodity purchasing image is high, and the commodity purchasing image is not required to be modified.

Step S407, if the similarity is greater than a preset similarity threshold, generating a distribution scheme according to the to-be-delivered order.

If the similarity is larger than a preset similarity threshold, the commodity purchasing image is close to the order to be purchased, and the commodity purchasing image and the order to be purchased are accurate and do not need to be modified.

The embodiment of the application discloses a method for adjusting a cargo allocation scheme. Referring to fig. 5, the method includes:

Step S501, extracting commodity category identification and purchase time stamp of the history purchase record.

The commodity category identification is used to uniquely identify the purchased commodity. Optionally, the commodity category identifier is a commodity classification code, for example, the fresh commodity category identifier is F001, and the commodity category identifier of the commodity is D002.

The purchase time stamp records the exact time that the purchase occurred.

Illustratively, the database is scanned and the commodity category identification and purchase timestamp fields of each record are parsed.

Step S502, dynamic attenuation weights are distributed to purchase records in different time periods based on the time difference between the current time and the purchase time stamp, wherein the larger the time difference is, the lower the weight is.

Alternatively, the calculation formula of the dynamic decay weight is 1/(1+0.01×time difference). Further, if the time difference is greater than a preset time threshold, the dynamic attenuation weight is set as a preset weight. For example, when the time difference is greater than 30 days, its corresponding dynamic decay weight is set to 0.05.

And step S503, carrying out weighted calculation on the historical purchase quantity of the similar commodities according to the dynamic attenuation weight and the commodity category identification, and generating a time-sensitive commodity purchase image.

Optionally, the similar commodities are combined according to the commodity category identification. And carrying out weighting operation on similar commodities according to the dynamic attenuation weight to obtain weighted purchase total. A time-sensitive commodity purchase image is generated based on the weighted purchase total.

Step S504, replacing the commodity purchasing image with the time-sensitive commodity purchasing image.

The time-sensitive commodity purchasing image is used to replace the commodity purchasing image, so that the time-sensitive commodity purchasing image is subject to subsequent procedures.

Based on the same inventive concept, an embodiment of the present application provides a store inventory allocation management system, including:

an acquisition module 601, configured to acquire a to-be-shipped order, a purchase completion operation, and a warehouse entry mode;

a memory 602 for storing a program of the store inventory allocation management method;

The processor 603, a program in a memory can be loaded and executed by the processor and implement the store inventory allocation management method.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.

Embodiments of the present application provide a computer-readable storage medium storing a computer program capable of being loaded by a processor and executing a store inventory allocation management method.

The computer storage medium includes, for example, a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

Based on the same inventive concept, an embodiment of the present application provides an intelligent terminal, including a memory and a processor, wherein the memory stores a computer program capable of being loaded by the processor and executing a store inventory allocation management method.

The foregoing description of the preferred embodiments of the application is not intended to limit the scope of the application in any way, including the abstract and drawings, in which case any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.

Claims

1. A store inventory allocation management method, the method comprising:

Collecting a stock order of a store;

2. The store inventory allocation management method according to claim 1, wherein the equal ratio allocation algorithm includes:

calculating the difference value between the to-be-purchased quantity and the metered quantity to obtain a distribution quantity difference value;

3. The store inventory allocation management method according to claim 1, wherein the one-key allocation algorithm includes:

4. The store inventory allocation management method according to claim 1, further comprising:

acquiring store information of the store according to the type of the purchased commodity;

normalizing the store information to obtain normalized scores;

5. The store inventory allocation management method according to claim 4, further comprising:

Acquiring the geographic position of the store;

calculating the distance from the delivery point to the frame selection area to obtain the delivery distance;

6. The store inventory allocation management method according to claim 4, further comprising:

Collecting historical purchasing records of the store;

7. The store inventory allocation management method according to claim 6, further comprising:

extracting commodity category identifiers and purchase time stamps of the historical purchase records;

8. A store inventory allocation management system for performing the store inventory allocation management method according to any one of claims 1 to 7, the system comprising:

9. An intelligent terminal comprising a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the store inventory allocation management method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the store inventory allocation management method according to any one of claims 1 to 7.