CN117670187A - Storage classification associated management system for intelligent logistics - Google Patents
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Abstract
The invention relates to the field of logistics management, and particularly discloses a storage classification associated management system for intelligent logistics, which comprises a storage product information collection module, a product association mining module, a storage goods position management module and a display module; according to the invention, the storage product information collection module is used for collecting product attribute information and product order information, the similarity between products is calculated according to the product attribute information, the product relevance is mined according to the similarity between the products, the product demand degree prediction model is constructed by utilizing the product order information, the goods space layout is adjusted according to the product demand degree, the storage manager is facilitated to better understand the product and order data, the relevance between the products is mined, the goods space management is optimized, the inventory efficiency is improved, the cost is reduced, the intelligence and the accuracy of decision making are improved, and the efficiency of overall logistics management can be improved.
Description
Technical Field
The invention belongs to the technical field of logistics product management, and particularly relates to a storage classification associated management system for intelligent logistics.
Background
The warehouse network management of warehouse classification is widely applied in a large network supermarket, and the large network supermarket has the characteristics of multiple product types, one single product for multiple products, multiple warehouse storage and the like, so that the warehouse layout among multiple warehouses needs to be effectively managed in the operation process. The existing food orders in the large-scale network supermarket have the difficult problem of splitting, and because the influence of the attribute of the product is not considered, the lack of pertinence during the splitting of the orders influences the quality and the delivery efficiency of goods, so that the freight cost is increased; and because the season changes, the condition that partial products are in diapause, such as the refrigerated food in summer and outdoor products are in smooth sale, and the diapause in winter, the demand of customers for the products is predicted, so that the goods space of goods is convenient to manage in time, and in order to solve the problems, a technical scheme is provided.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a warehouse classification associated management system for intelligent logistics, which is used for collecting product attribute information and product order information through a warehouse product information collecting module, calculating the similarity between products according to the product attribute information, mining the product association through the similarity between the products, constructing a product demand degree prediction model by utilizing the product order information, adjusting the goods space layout according to the product demand degree, helping warehouse managers to better understand the product and order data, mining the association between the products, optimizing the goods space management, improving the inventory efficiency, reducing the cost, increasing the intelligence and the accuracy of decision and improving the efficiency of the whole logistics management, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the storage classification associated management system for intelligent logistics comprises a storage product information collection module, a product association mining module, a storage goods space management module and a display module, wherein the storage product information collection module is used for collecting product attribute information and product order information, and the product order information comprises the number of purchased customers, the number of customers accessing a commodity interface, sales volume, order volume, click rate, conversion rate and order withdrawal number; the storage goods space management module is used for constructing a product demand degree prediction model, adjusting the goods space layout according to the product demand degree, and constructing the demand degree prediction model through sales volume, order volume, click rate, conversion rate and withdrawal volume, wherein the formula of the product demand degree prediction model is as follows:
wherein: c (C) X To the extent of product demand, K XO K is the sales of the product XG For sales of products, K XT K is the order quantity of the product XR For click rate of product, K XE K is the conversion of the product XL The number of the return sheets is the product.
As a further scheme of the invention, the conversion rate of the product is obtained by purchasing the number of customers and accessing the commodity interface, and the calculation formula of the conversion rate of the product is as follows:
conversion rate of the product= (number of customers purchased/number of customers accessing the merchandise interface) x 100%.
As a further scheme of the invention, each module has the following functions:
the warehouse product information collection module is used for collecting product attribute information and product order information, wherein the product attribute information comprises the category, size, weight, production place, price, inventory and sales of the product, and the product order information comprises the number of purchased customers, the number of customers accessing a commodity interface, sales, order quantity, click rate, conversion rate and order withdrawal number;
the product relevance mining module is used for calculating the similarity between products according to the product attribute information and mining the product relevance through the similarity between the products;
the warehouse goods space management module is used for constructing a product demand degree prediction model and adjusting goods space layout according to the product demand degree;
the display module is used for displaying the product attribute information, the product order information, the product demand degree and the warehouse goods position layout.
As a further scheme of the invention, the warehouse product information collection module is connected with the product relevance mining module, the product relevance mining module is connected with the warehouse goods space management module, the warehouse goods space management module is connected with the display module, the product relevance mining module comprises a product similarity calculation unit and a relevance mining unit, and the product similarity calculation unit is connected with the relevance mining unit; the warehouse goods space management module comprises a product demand prediction unit and a goods space management unit, and the product demand prediction unit is connected with the goods space management unit.
As a further scheme of the present invention, the product similarity calculation unit uses a vector space model to represent product attribute information as a vector according to product attribute information, and calculates similarity between products by using cosine similarity, wherein a calculation formula of the similarity between products is:
wherein: CS (A, B) is the similarity between products, A is the vector representation of A products, and B is the vector representation of B products.
As a further aspect of the present invention, the step of representing product attribute information as a vector using a vector space model includes:
step one, information processing: cleaning the product attribute information, including removing special characters, punctuation marks and numbers, and dividing the cleaned product attribute information into words through blank spaces;
step two, constructing an information corpus: the processed product attribute information is formed into an information corpus, and each product attribute information corresponds to one product attribute;
step three, weight calculation: the weight of the word in the product attribute information is calculated, and the formula for calculating the weight of the word in the product attribute information is as follows:
wherein: q is the weight of the word T in the product attribute information, C T For the number of times word T appears in the product attribute information, Z d For the total number of all words T in the product attribute information, D is the total number of the product attribute information, D T A product attribute information number for containing the word T;
step four, constructing a product attribute information vector: each piece of product attribute information is expressed as a vector, the element of the vector is a word, and the value of the vector is the weight of the word in the product attribute information;
step five, vector normalization: the product attribute information vector is divided by its modulo length to ensure that the product attribute information vector has a unit length.
As a further scheme of the invention, the relevance mining unit mines the relevance of the products according to the similarity values among the products, and when the similarity values among the products are more than 0 and less than 1, the vector similarity among the products is high, which means that the relevance of the products is high; when the similarity value between the products is more than-1 and less than or equal to 0, the vector similarity between the products is low, which means that the relevance of the products is low.
As a further scheme of the invention, the order structure analysis unit places the products with high product relevance in the same delivery package according to the output of the relevance mining unit, sorts the similarity of the products with low relevance, and delivers the products with low product relevance respectively.
As a further aspect of the present invention, the goods space management unit divides the products into hot-sell products, normal products and stagnant-sell products according to the degree of demand of the products, and adjusts the goods space layout,
when 70 percent < the product demand degree is less than or equal to 100 percent, the product belongs to a hot-sell product;
when the product demand degree is less than or equal to 70 percent in the range of 40 percent, the product belongs to common products;
when the product demand degree is less than or equal to 0 and less than or equal to 40 percent, the product belongs to a diapause product.
The invention relates to a technical effect and advantages of a warehouse classification associated management system for intelligent logistics:
1. according to the invention, the product order information is utilized to construct a product demand degree prediction model, and according to the product demand degree, the method is beneficial to ensuring that the hot-sold products can be placed at a position which is easier to access, so that the logistics efficiency is improved, the problem of excessive or insufficient inventory is reduced, and through real-time demand prediction, a warehouse manager can better meet the demands of customers, and the risk of the products in stagnation is reduced;
2. according to the invention, through digging the relevance among products, the goods space management is optimized, the inventory efficiency is improved, the cost is reduced, the intelligence and the accuracy of the decision are increased, and the efficiency of the overall logistics management can be improved;
3. according to the invention, through the warehouse product information collection module, the system can acquire extensive product attribute information and order information, so that the characteristics, order conditions and data interacted with customers of the products can be better known, a comprehensive product knowledge base can be formed, and a solid foundation is provided for better warehouse management.
Drawings
Fig. 1 is a schematic structural diagram of a warehouse classification association management system for intelligent logistics.
Detailed Description
The technical solutions of the present embodiment will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is apparent that the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The storage classification associated management system for intelligent logistics comprises a storage product information collection module, a product association mining module, a storage goods space management module and a display module, wherein the storage product information collection module is used for collecting product attribute information and product order information, and the product order information comprises the number of purchased customers, the number of customers accessing a commodity interface, sales volume, order volume, click rate, conversion rate and order withdrawal number; the storage goods space management module is used for constructing a product demand degree prediction model, adjusting the goods space layout according to the product demand degree, and constructing the demand degree prediction model through sales volume, order volume, click rate, conversion rate and withdrawal volume, wherein the formula of the product demand degree prediction model is as follows:
wherein: c (C) X To the extent of product demand, K XO K is the sales of the product XG For sales of products, K XT K is the order quantity of the product XR For click rate of product, K XE K is the conversion of the product XL The number of the return sheets is the product.
The sales amount refers to the number of products sold in a certain time, is used for measuring the popularity and the demand degree of the products, and the order amount refers to the number of products purchased by a customer when the customer places an order, and can be aimed at a single product or can be a combination of a plurality of products.
Example 1
When the sales volume of frozen foods in winter season is 1000 orders, the sales volume is 500000 yuan, the order volume is 2000 pieces, the click rate is 0.2, the conversion rate is 0.1, the quantity of withdrawal is 100 pieces, and the method is obtained through a formula of a product demand degree prediction model:
the demand degree of the frozen food in winter season is 0.11 according to the formula of the product demand degree prediction model, and the frozen food in winter season belongs to a diapause product.
Example 2
When the sales volume of the juice in one quarter in summer is 10000 orders, the sales volume is 300000 yuan, the order volume is 15000 pieces, the click rate is 0.6, the conversion rate is 0.7, the number of return orders is 500, and the method is obtained through a formula of a product demand degree prediction model:
and obtaining the demand degree of the juice in the first quarter in summer to be 0.77 according to a formula of the product demand degree prediction model, wherein the juice in the first quarter in summer belongs to a hot sale product.
Example 3
When the information collection module of the warehouse product obtains that the sales volume of fruits in winter in one quarter is 20000 orders, the sales volume is 400000 yuan, the order volume is 22000 pieces, the click rate is 0.4, the conversion rate is 0.45, the number of returned orders is 200, and the information collection module is obtained through a formula of a product demand degree prediction model:
the formula of the product demand degree prediction model is used for obtaining that the demand degree of fruits in one quarter in winter is 0.51, and the fruits in one quarter in winter belong to common products.
Example 4
When the sales volume of the annual bread is 96000 items, the sales volume is 5000000 yuan, the order volume is 100000 items, the click rate is 0.64, the conversion rate is 0.51, the return number is 15000, and the annual bread is obtained through a formula of a product demand degree prediction model:
the demand degree of the whole-year bread is 0.80 according to the formula of the product demand degree prediction model, and the whole-year bread is obtained and belongs to a hot-sell product.
In the embodiment of the invention, the conversion rate of the product is obtained by purchasing the number of customers and accessing the commodity interface, and the calculation formula of the conversion rate of the product is as follows:
conversion rate of the product= (number of customers purchased/number of customers accessing the merchandise interface) x 100%.
The conversion rate of the product can be calculated to help merchants know the attractiveness of the commodity to customers, so that commodity display and marketing strategies are optimized to improve sales and customer satisfaction.
In the embodiment of the invention, each module has the following functions:
the warehouse product information collection module is used for collecting product attribute information and product order information, wherein the product attribute information comprises the category, size, weight, production place, price, inventory and sales of the product, and the product order information comprises the number of purchased customers, the number of customers accessing a commodity interface, sales, order quantity, click rate, conversion rate and order withdrawal number;
the product relevance mining module is used for calculating the similarity between products according to the product attribute information and mining the product relevance through the similarity between the products;
the warehouse goods space management module is used for constructing a product demand degree prediction model and adjusting goods space layout according to the product demand degree;
the display module is used for displaying the product attribute information, the product order information, the product demand degree and the warehouse goods position layout.
In the embodiment of the invention, a storage product information collection module is connected with a product relevance mining module, the product relevance mining module is connected with a storage goods space management module, the storage goods space management module is connected with a display module, the product relevance mining module comprises a product similarity calculation unit and a relevance mining unit, and the product similarity calculation unit is connected with the relevance mining unit; the warehouse goods space management module comprises a product demand prediction unit and a goods space management unit, and the product demand prediction unit is connected with the goods space management unit.
In the embodiment of the invention, the product similarity calculation unit uses a vector space model to represent the product attribute information as a vector according to the product attribute information, and calculates the similarity between products by using cosine similarity, wherein the calculation formula of the similarity between the products is as follows:
wherein: CS (A, B) is the similarity between products, A is the vector representation of A products, and B is the vector representation of B products.
The similarity between the products can be calculated by using the cosine similarity to help determine the similarity degree between the products, and the similar products can be identified by comparing the multidimensional attribute vectors of the products, so that the relevance between the products can be better known.
In the embodiment of the invention, the step of using the vector space model to represent the product attribute information as a vector comprises the following steps:
step one, information processing: cleaning the product attribute information, including removing special characters, punctuation marks and numbers, and dividing the cleaned product attribute information into words through blank spaces;
step two, constructing an information corpus: the processed product attribute information is formed into an information corpus, and each product attribute information corresponds to one product attribute;
step three, weight calculation: the weight of the word in the product attribute information is calculated, and the formula for calculating the weight of the word in the product attribute information is as follows:
wherein: q is the weight of the word T in the product attribute information, C T For the number of times word T appears in the product attribute information, Z d For the total number of all words T in the product attribute information, D is the total number of the product attribute information, D T A product attribute information number for containing the word T;
step four, constructing a product attribute information vector: each piece of product attribute information is expressed as a vector, the element of the vector is a word, and the value of the vector is the weight of the word in the product attribute information;
step five, vector normalization: the product attribute information vector is divided by its modulo length to ensure that the product attribute information vector has a unit length.
By decomposing the product attribute information into words and calculating the weight of each word, the multidimensional attribute information can be effectively represented as a vector; the information processing step allows special characters, punctuation marks and numbers to be removed, so that text information is better extracted, noise is reduced, and the quality of vectors is improved; the construction of the information corpus is beneficial to organizing and managing the product attribute information, and each product attribute information corresponds to one vector, so that the subsequent analysis and comparison are convenient; the importance of each word in describing the product attribute can be better captured by calculating the weight of the word in the product attribute information, so that important features in the vector are weighted higher; helping to take into account the importance of a word in the whole corpus and helping to capture key features; vector normalization ensures that all product attribute information vectors have a unit length, helping to eliminate differences in vector length and thus better perform similarity comparisons. Representing product attribute information as vectors facilitates better utilization of textual information in analyzing and processing product data, improves efficiency of information extraction and analysis, and better understanding of similarities and associations between products, facilitating promotion of product recommendations, market segments, and user personalized experience.
In the embodiment of the invention, the relevance mining unit mines the relevance of the products according to the similarity values among the products, and when the similarity values among the products are more than 0 and less than 1, the vector similarity among the products is high, which means that the relevance of the products is high; when the similarity value between the products is more than-1 and less than or equal to 0, the vector similarity between the products is low, which means that the relevance of the products is low.
In the embodiment of the invention, the order structure analysis unit places the products with high product relevance in the same delivery package according to the output of the relevance mining unit, sorts the similarity of the products with low relevance, and delivers the products with low product relevance respectively.
In the embodiment of the invention, the goods space management unit divides the products into hot-sell products, common products and diapause products according to the product demand degree, adjusts the goods space layout,
when 70 percent < the product demand degree is less than or equal to 100 percent, the product belongs to a hot-sell product;
when the product demand degree is less than or equal to 70 percent in the range of 40 percent, the product belongs to common products;
when the product demand degree is less than or equal to 0 and less than or equal to 40 percent, the product belongs to a diapause product.
According to the embodiment of the invention, the product attribute information and the product order information are collected through the warehouse product information collection module, wherein the product attribute information comprises the category, the size, the weight, the production place, the price, the inventory and the sales volume of products, the number of customers who visit a commodity interface, the sales volume, the order volume, the click rate, the conversion rate and the average purchasing period of customers, so that the characteristics of the products, the order condition and the data interacted with the customers can be better known, a comprehensive product knowledge base can be formed, and a solid foundation is provided for better warehouse management; according to the product attribute information, calculating the similarity between products, and decomposing the product attribute information into words and calculating the weight of each word, the multidimensional attribute information can be effectively represented as a vector; the information processing step allows special characters, punctuation marks and numbers to be removed, so that text information is better extracted, noise is reduced, and the quality of vectors is improved; the construction of the information corpus is beneficial to organizing and managing the product attribute information, and each product attribute information corresponds to one vector, so that the subsequent analysis and comparison are convenient; the importance of each word in describing the product attribute can be better captured by calculating the weight of the word in the product attribute information, so that important features in the vector are weighted higher; helping to take into account the importance of a word in the whole corpus and helping to capture key features; vector normalization ensures that all product attribute information vectors have a unit length, helping to eliminate differences in vector length and thus better perform similarity comparisons. Representing product attribute information as vectors facilitates better utilization of textual information in analyzing and processing product data, improves efficiency of information extraction and analysis, and better understanding of similarities and associations between products; the product order information is utilized to construct a product demand degree prediction model, the goods space layout is adjusted according to the product demand degree, the situation that hot-selling products can be placed at a position which is easier to access is facilitated, logistics efficiency is improved, the problem of excessive or insufficient inventory is reduced, through real-time demand prediction, a warehouse manager can better meet customer demands, risks of the stagnant products are reduced, the warehouse manager is facilitated to better understand products and order data, relevance among the products is mined, goods space management is optimized, inventory efficiency is improved, cost is reduced, intelligence and accuracy of decision making are improved, and efficiency of overall logistics management can be improved.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The storage classification associated management system for intelligent logistics comprises a storage product information collection module, a product association mining module, a storage goods position management module and a display module, and is characterized in that the storage product information collection module is used for collecting product attribute information and product order information, and the product order information comprises the number of purchased customers, the number of customers accessing a commodity interface, the sales volume, the order volume, the click rate, the conversion rate and the order withdrawal number; the storage goods space management module is used for constructing a product demand degree prediction model, adjusting the goods space layout according to the product demand degree, and constructing the demand degree prediction model through sales volume, order volume, click rate, conversion rate and withdrawal volume, wherein the formula of the product demand degree prediction model is as follows:
wherein: c (C) X To the extent of product demand, K XO K is the sales of the product XG For sales of products, K XT K is the order quantity of the product XR For click rate of product, K XE K is the conversion of the product XL The number of the return sheets is the product.
2. The warehousing classification association management system for intelligent logistics according to claim 1, wherein,
the warehouse product information collection module is used for collecting product attribute information and product order information, wherein the product attribute information comprises the category, size, weight, production place, price, inventory and sales of the product, and the product order information comprises the number of purchased customers, the number of customers accessing a commodity interface, sales, order quantity, click rate, conversion rate and order withdrawal number;
the product relevance mining module is used for calculating the similarity between products according to the product attribute information and mining the product relevance through the similarity between the products;
the warehouse goods space management module is used for constructing a product demand degree prediction model and adjusting goods space layout according to the product demand degree;
the display module is used for displaying the product attribute information, the product order information, the product demand degree and the warehouse goods position layout.
3. The warehouse classification associated management system for intelligent logistics according to claim 1, wherein the warehouse product information collection module is connected with the product association mining module, the product association mining module is connected with the warehouse goods location management module, the warehouse goods location management module is connected with the display module, the product association mining module comprises a product similarity calculation unit and an association mining unit, and the product similarity calculation unit is connected with the association mining unit; the warehouse goods space management module comprises a product demand prediction unit and a goods space management unit, and the product demand prediction unit is connected with the goods space management unit.
4. The warehouse classification association management system for intelligent logistics according to claim 3, wherein the product similarity calculation unit uses a vector space model to represent the product attribute information as a vector according to the product attribute information, calculates the similarity between products by using cosine similarity, and calculates the similarity between the products by using the calculation formula:
wherein: CS (A, B) is the similarity between products, A is the vector representation of A products, and B is the vector representation of B products.
5. The warehouse classification associated management system for intelligent logistics of claim 4, wherein the step of using the vector space model to represent the product attribute information as a vector comprises:
step one, information processing: cleaning the product attribute information, including removing special characters, punctuation marks and numbers, and dividing the cleaned product attribute information into words through blank spaces;
step two, constructing an information corpus: the processed product attribute information is formed into an information corpus, and each product attribute information corresponds to one product attribute;
step three, weight calculation: the weight of the word in the product attribute information is calculated, and the formula for calculating the weight of the word in the product attribute information is as follows:
wherein: q is the weight of the word T in the product attribute information, C T For the number of times word T appears in the product attribute information, Z d For the total number of all words T in the product attribute information, D is the total number of the product attribute information, D T A product attribute information number for containing the word T;
step four, constructing a product attribute information vector: each piece of product attribute information is expressed as a vector, the element of the vector is a word, and the value of the vector is the weight of the word in the product attribute information;
step five, vector normalization: the product attribute information vector is divided by its modulo length to ensure that the product attribute information vector has a unit length.
6. The warehouse classification association management system for intelligent logistics according to claim 3, wherein the association mining unit is used for mining product association according to similarity values among products, and when the similarity values among the products are more than 0 and less than 1, vector similarity among the products is high, and the vector similarity among the products is high, so that the product association is high; when the similarity value between the products is more than-1 and less than or equal to 0, the vector similarity between the products is low, which means that the relevance of the products is low.
7. A warehouse classification associated management system for intelligent logistics as claimed in claim 3, wherein the inventory management unit classifies the products into hot-sell products, normal products and stagnant products according to the degree of demand of the products, and adjusts the inventory layout,
when 70 percent < the product demand degree is less than or equal to 100 percent, the product belongs to a hot-sell product;
when the product demand degree is less than or equal to 70 percent in the range of 40 percent, the product belongs to common products;
when the product demand degree is less than or equal to 0 and less than or equal to 40 percent, the product belongs to a diapause product.
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| CN107545381A (en) * | 2016-06-24 | 2018-01-05 | 北京京东尚科信息技术有限公司 | Manage the method and warehouse management system of warehouse inventory |
| CN109886478A (en) * | 2019-01-29 | 2019-06-14 | 东南大学 | A kind of slotting optimization method of finished wine automatic stereowarehouse |
| CN110766359A (en) * | 2019-04-10 | 2020-02-07 | 韩领有限公司 | System and method for machine learning assisted inventory placement |
| CN116342026A (en) * | 2023-01-18 | 2023-06-27 | 工业互联网创新中心（上海）有限公司 | Information processing system of logistics storage goods shelf based on industrial Internet |
| CN116934220A (en) * | 2023-07-05 | 2023-10-24 | 中博信息技术研究院有限公司 | Intelligent warehouse layout method based on intelligent data analysis and algorithm optimization |
| CN116796910A (en) * | 2023-08-21 | 2023-09-22 | 青岛中德智能技术研究院 | Order batch optimization method based on goods allocation strategy |
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