CN114022167A - New product price elastic calculation method and device based on industrial internet identification codes - Google Patents
New product price elastic calculation method and device based on industrial internet identification codes Download PDFInfo
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Abstract
The embodiment of the invention provides a new product price elastic calculation method and device based on industrial internet identification codes. The method comprises the following steps: acquiring industrial internet identification codes of a plurality of sold commodities and new commodities in the same industry; filtering the sold commodities to obtain a plurality of sold commodities to be processed; screening the sold commodities to be processed to obtain a plurality of standby sold commodities; obtaining a first price elasticity average value and a first price elasticity binary number according to the price elasticity of all the standby sold commodities; obtaining the total number of categories and the similarity among the categories of the standby sold commodities and the category with the shortest average value of the distance between the category and the industrial internet identification code of the new commodity by utilizing a community detection algorithm; and determining the price elasticity of the new product according to the condition that the similarity among the categories is smaller than a preset threshold value. The price elasticity of the new product obtained by calculation is more reasonable, and the new product can be rapidly and automatically priced according to the price elasticity of the new product.
Description
Technical Field
The invention relates to the technical field of commodity pricing, in particular to a method and a device for flexibly calculating the price of a new commodity based on industrial internet identification codes.
Background
Commodity pricing is an important link of commodity sales, and directly influences the rationality of commodity price and the final value of commodities. Therefore, before or during the sale of the commodity, the price of the commodity is adjusted according to the market condition, the cost of the commodity, holidays and other factors. The pricing models which are commonly used at present and have good use effects are all elastic pricing strategies based on price elasticity. The calculation of price elasticity in the elastic pricing is an indispensable link. And the calculation of price elasticity relies on a historical sales record of a sufficient number of individual items. For a new product, because the new product is not sold on the market, no historical sales record is available, the price elasticity cannot be calculated, automatic pricing cannot be carried out, only professional persons with pricing experience of the same type of product can carry out pricing, the workload is large, the professional persons are lack, and the pricing cost is high.
Disclosure of Invention
The invention aims to solve the technical problem of providing a new product price elastic calculation method and a device based on industrial internet identification codes, and solves the problems of high cost and large workload of the conventional commodity pricing.
In order to solve the technical problems, the technical scheme of the invention is as follows:
according to one aspect of the invention, a new product price elasticity calculation method based on industrial internet identification codes is provided, and comprises the following steps:
acquiring industrial internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and price elasticity of the industrial internet identification codes, and industrial internet identification codes of new commodities, wherein the industrial internet identification codes comprise basic classification codes and attribute codes;
filtering the sold commodities of which the sales number does not accord with a preset sales threshold value to obtain a plurality of sold commodities to be processed;
screening the sold commodities to be processed according to the basic classification codes and the attribute codes of the new commodities to obtain a plurality of standby sold commodities;
obtaining a first price elasticity average value and a first price elasticity binary number according to the price elasticity of all the standby sold commodities;
obtaining the total number of categories and the similarity among the categories of the standby sold commodities and the category with the shortest average value of the distance between the category and the industrial internet identification code of the new commodity by utilizing a community detection algorithm;
if the similarity among the categories is smaller than a preset threshold value, determining a first price elastic average value or a first price elastic binary number as the price elasticity of the new product; otherwise, determining the price elasticity average value or the price elasticity binary number of the standby sold commodity as the price elasticity of the new commodity in the category with the shortest average value of the industrial Internet identification codes of the new commodity.
Further, the preset sales threshold is:
plus or minus 2-fold variance of a gaussian distribution of sales quantities of a plurality of sold goods.
Further, according to the basic classification code and the attribute code of the new product, screening the sold commodities to be processed to obtain a plurality of standby sold commodities, including:
using each bit in the basic classification code and the attribute code of the sold commodity to be processed as the characteristic, searching the commodity in the same class with the new commodity in the sold commodity to be processed by using a clustering algorithm, and using the commodity as the sold commodity of the same class;
and determining M commodities with the shortest Euclidean distance to the new commodity in the similar sold commodities as standby sold commodities, wherein M is a preset value.
Further, the N commodities with the shortest euclidean distance to the new commodity in the similar sold commodities are determined as spare sold commodities, and the formula of the euclidean distance is as follows:
wherein x and y represent new products and similar sold products respectively, and n is the digit number of the basic classification code and the attribute code.
Further, after filtering out the sold commodities of which the sales quantity does not meet the preset sales threshold, the method further comprises the following steps:
and filtering the sold commodities of which the sales quantity accords with the abnormal value to obtain a plurality of sold commodities to be processed, wherein the abnormal value is at least one of sales outliers, holiday sales records and promotion period records.
In a second aspect of the present invention, there is provided a new product price elasticity calculation device based on industrial internet identification coding, including:
the acquisition module is used for acquiring industrial Internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and price elasticity of the industrial Internet identification codes and industrial Internet identification codes of new commodities, wherein the industrial Internet identification codes comprise basic classification codes and attribute codes;
the filtering module is used for filtering the sold commodities of which the sales quantity does not accord with the preset sales threshold value to obtain a plurality of sold commodities to be processed;
the screening module is used for screening the sold commodities to be processed according to the basic classification codes and the attribute codes of the new commodities to obtain a plurality of standby sold commodities;
the calculation module is used for obtaining a first price elasticity average value and a first price elasticity binary number according to the price elasticity of all standby sold commodities;
the detection module is used for obtaining the total number of the categories of the standby sold commodities, the similarity among the categories and the category with the shortest average value of the distance between the category and the industrial internet identification code of the new commodity by utilizing a community detection algorithm;
the determining module is used for determining the first price elastic average value or the first price elastic binary number as the new price elasticity if the similarity between the categories is smaller than a preset threshold; otherwise, determining the price elasticity average value or the price elasticity binary number of the standby sold commodity as the price elasticity of the new commodity in the category with the shortest average value of the industrial Internet identification codes of the new commodity.
Further, the preset sales threshold is:
plus or minus 2-fold variance of a gaussian distribution of sales quantities of a plurality of sold goods.
Further, the screening module is specifically configured to:
using each bit in the basic classification code and the attribute code of the sold commodity to be processed as the characteristic, searching the commodity in the same class with the new commodity in the sold commodity to be processed by using a clustering algorithm, and using the commodity as the sold commodity of the same class;
and determining M commodities with the shortest Euclidean distance to the new commodity in the similar sold commodities as standby sold commodities, wherein M is a preset value.
Further, the formula of the euclidean distance is:
wherein x and y represent new products and similar sold products respectively, and n is the digit number of the basic classification code and the attribute code.
Further, the filtering module is specifically further configured to:
and filtering the sold commodities of which the sales quantity accords with the abnormal value to obtain a plurality of sold commodities to be processed, wherein the abnormal value is at least one of sales outliers, holiday sales records and promotion period records.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, based on the industrial Internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and price elasticity of the sold commodities and the industrial Internet identification codes of the new commodities, a commodity set similar to the new commodities can be searched more accurately by using a community detection algorithm, the price elasticity of the new commodities obtained through calculation is more reasonable, meanwhile, according to the price elasticity of the new commodities, the new commodities can be priced quickly and automatically, cost of enterprises is saved, and meanwhile the commodity value is maximized.
Drawings
FIG. 1 is a step diagram of a new product price elasticity calculation method based on industrial Internet identification coding according to the present invention;
fig. 2 is a device connection diagram of the new product price elasticity calculation device based on the industrial internet identification code of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides a new product price elasticity calculation method based on an industrial internet identification code, including:
s1, acquiring industrial Internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and price elasticity of the commodities, and industrial Internet identification codes of new commodities, wherein the industrial Internet identification codes comprise basic classification codes and attribute codes;
s2, filtering the sold commodities of which the sales number does not accord with the preset sales threshold value to obtain a plurality of sold commodities to be processed;
s3, screening the sold commodities to be processed according to the basic classification codes and the attribute codes of the new commodities to obtain a plurality of standby sold commodities;
s4, obtaining a first price elasticity average value and a first price elasticity binary number according to the price elasticity of all the standby sold commodities;
s5, obtaining the total number of the categories of the standby sold commodities, the similarity among the categories and the category with the shortest average value of the distance between the industrial internet identification codes of the new commodities by utilizing a community detection algorithm;
s6, if the similarity among the categories is smaller than a preset threshold value, determining the first price elastic average value or the first price elastic binary number as the new price elasticity; otherwise, determining the price elasticity average value or the price elasticity binary number of the standby sold commodity as the price elasticity of the new commodity in the category with the shortest average value of the industrial Internet identification codes of the new commodity.
The invention can more accurately search a commodity set similar to the new commodity by utilizing a community detection algorithm based on the industrial Internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and the price elasticity and the industrial Internet identification code of the new commodity, so that the price elasticity of the new commodity obtained by calculation is more reasonable, and meanwhile, according to the price elasticity of the new commodity, the rapid and automatic pricing of the new commodity can be realized, the cost of an enterprise is saved, and the commodity value is maximized.
The industrial internet identification code is characterized in that an industrial internet secondary node issues an identification prefix to an enterprise, after the enterprise obtains the enterprise prefix, the enterprise self-defines the identification code, and the identification code can be in batches or in one object and one code, for example, the following is a complete industrial internet coding structure:
86.121.MA006KNB0/JGP.04010502.2536.01.20200308123156Y77987U, wherein,
86: country code, China
121: trade codes, meat products and by-product processing in the subsidiary agricultural food processing industry
MA006KNB 0: enterprise code
JGP: processed food
04010502: basic classification code for processed food, jerky
2536: dried beef product code
01: branch of south China
20200308123156Y77987U production serial number, 14-bit time stamp + N-bit custom serial number
MA006KNB0 "issued by the secondary node; and the subsequent self-defining by enterprises (different industries have different coding specifications, but all industries can mutually recognize) and finally forming a complete industrial Internet identification code.
After the new product generates a new industrial internet identification code, the new product can be externally released along with a corresponding product, and can also be obtained by scanning a two-dimensional code or butting with an enterprise-level industrial internet platform and the like.
In an optional embodiment of the present invention, the preset sales threshold in step S2 is:
plus or minus 2 times sigma (variance of gaussian distribution) of the gaussian distribution of the sales volume of the plurality of sold commodities.
The upper and lower boundaries of the sales record number of the commodity with more accurate elasticity judged according to experience can be selected according to specific conditions and user requirements, so that the method is more accurate and improves the adaptability.
In an optional embodiment of the present invention, in step S3, the method for screening the sold commodities to be processed according to the basic classification code and the attribute code of the new commodity includes:
using each bit in the basic classification code and the attribute code of the sold commodity to be processed as the characteristic, searching the commodity in the same class with the new commodity in the sold commodity to be processed by using a clustering algorithm, and using the commodity as the sold commodity of the same class;
and determining M commodities with the shortest Euclidean distance to the new commodity in the similar sold commodities as standby sold commodities, wherein M is a preset value.
Wherein, the formula of the Euclidean distance is as follows:
wherein x represents the basic classification code or the attribute code of a new product, y represents the basic classification code or the sum attribute code of the similar sold products, and n is the number of digits of the basic classification code and the attribute code. If x is the basic classification code of the new product, y is the basic classification code of the same kind of sold product, if x is the attribute code of the new product, y is the attribute code of the same kind of sold product, and if the basic classification code of the new product is 2536, x is 2536.
In an optional embodiment of the present invention, after the step S2 filters out the sold commodities whose sales number does not meet the preset sales threshold, the method further includes:
and filtering the sold commodities of which the sales quantity accords with the abnormal value to obtain a plurality of sold commodities to be processed, wherein the abnormal value is at least one of sales outliers, holiday sales records and promotion period records.
According to the user requirements and the accuracy of the final result, at least one or more of the sales outliers, the holiday sales records and the promotion period records can be selected as abnormal values to screen the sold commodities, and the accuracy of the finally obtained new product price elasticity is improved.
As shown in fig. 2, an embodiment of the present invention provides a new product price elasticity calculation apparatus based on industrial internet identification code, including:
the acquisition module is used for acquiring industrial Internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and price elasticity of the industrial Internet identification codes and industrial Internet identification codes of new commodities, wherein the industrial Internet identification codes comprise basic classification codes and attribute codes;
the filtering module is used for filtering the sold commodities of which the sales quantity does not accord with the preset sales threshold value to obtain a plurality of sold commodities to be processed;
the screening module is used for screening the sold commodities to be processed according to the basic classification codes and the attribute codes of the new commodities to obtain a plurality of standby sold commodities;
the calculation module is used for obtaining a first price elasticity average value and a first price elasticity binary number according to the price elasticity of all standby sold commodities;
the detection module is used for obtaining the total number of the categories of the standby sold commodities, the similarity among the categories and the category with the shortest average value of the distance between the category and the industrial internet identification code of the new commodity by utilizing a community detection algorithm;
the determining module is used for determining the first price elastic average value or the first price elastic binary number as the new price elasticity if the similarity between the categories is smaller than a preset threshold; otherwise, determining the price elasticity average value or the price elasticity binary number of the standby sold commodity as the price elasticity of the new commodity in the category with the shortest average value of the industrial Internet identification codes of the new commodity.
The invention can more accurately search a commodity set similar to the new commodity by utilizing a community detection algorithm based on the industrial Internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and the price elasticity and the industrial Internet identification code of the new commodity, so that the price elasticity of the new commodity obtained by calculation is more reasonable, and meanwhile, according to the price elasticity of the new commodity, the rapid and automatic pricing of the new commodity can be realized, the cost of an enterprise is saved, and the commodity value is maximized.
In an optional embodiment of the present invention, the preset sales threshold is:
plus or minus 2 times sigma (variance of gaussian distribution) of the gaussian distribution of the sales volume of the plurality of sold commodities.
The upper and lower boundaries of the sales record number of the commodity with more accurate elasticity judged according to experience can be selected according to specific conditions and user requirements, so that the method is more accurate and improves the adaptability.
In an optional embodiment of the present invention, the screening module is specifically configured to:
using each bit in the basic classification code and the attribute code of the sold commodity to be processed as the characteristic, searching the commodity in the same class with the new commodity in the sold commodity to be processed by using a clustering algorithm, and using the commodity as the sold commodity of the same class;
and determining M commodities with the shortest Euclidean distance to the new commodity in the similar sold commodities as standby sold commodities, wherein M is a preset value.
In an optional embodiment of the present invention, the formula of the euclidean distance is:
wherein x represents the basic classification code or the attribute code of a new product, y represents the basic classification code or the sum attribute code of the similar sold products, and n is the number of digits of the basic classification code and the attribute code.
In an optional embodiment of the present invention, the filtering module is further specifically configured to:
and filtering the sold commodities of which the sales quantity accords with the abnormal value to obtain a plurality of sold commodities to be processed, wherein the abnormal value is at least one of sales outliers, holiday sales records and promotion period records.
According to the user requirements and the accuracy of the final result, at least one or more of the sales outliers, the holiday sales records and the promotion period records can be selected as abnormal values to screen the sold commodities, and the accuracy of the finally obtained new product price elasticity is improved.
It should be noted that the apparatus is an apparatus corresponding to the method described in fig. 1, and all the implementations of the illustrated method are applicable to the embodiment of the apparatus, and the same technical effects can be achieved.
The embodiment of the invention provides a new product price elasticity calculation method based on industrial internet identification codes, which comprises the following specific working procedures:
industrial internet identification codes of all commodities in the industry in recent two years are collected. For example, the liquor industry: and collecting all the industrial internet identification codes of the white spirit with the sales records in the last two years.
And screening sold commodities which are similar to the Internet identification code basic classification code and the attribute code of the new commodity to be priced, and counting sales records of the selected sold commodities in the last two years. The following method can be used for selecting the commodities with similar basic classification codes and attribute codes:
(1) taking each bit of the basic classification code and the attribute code as the characteristics of the basic classification code and the attribute code, and searching for sold commodities in the same class with the new commodity through clustering;
(2) and (3) selecting the M sold commodities with the shortest Euclidean distance to the new product in the step (1), or specifying a plurality of characteristics with the largest influence on the price, and calculating the M sold commodities with the shortest Euclidean distance to the new product as standby commodities.
Sold goods with sales records outliers are filtered out. Sales record outliers such as: sales outliers, holiday sales records, known promotional period records, etc., which can affect the accuracy of subsequent results.
And calculating the price elasticity value of the sold commodity and the statistical values of the maximum and minimum value, the mean value, the variance, the quantile and the like of the price elasticity.
The price elasticity value can be calculated by the following method: demand elasticity (price elasticity value) is the percentage of demand change divided by the percentage of price change, or sales is a1 price + a2 competitor price + a3 stock + … …, and a1 can be used as price elasticity.
Using a community detection algorithm, such as: fast Newman, tag propagation algorithm, etc., detects the total number of categories (community number) of the goods in the sold goods and the similarity between the categories. If the similarity between the categories is smaller than a certain threshold value (measured by tests), the average value or the binary number of the price elasticity is used as the price elasticity of the new product; otherwise, searching the community with the shortest average value of the Internet identification coding distance of the new product by using methods such as Euclidean distance, Mahalanobis distance, Manhattan distance, cosine distance or correlation coefficient, and taking the price elasticity average value or binary number as the price elasticity of the new product.
The total number of the categories of the sold commodities is the extracted characteristics (such as color, price, shape, manufacturer and the like) of the sold commodities, and the price elasticity value can be used as one-dimensional characteristics of the sold commodities, is not necessary, and needs to be obtained according to the analysis of actual data to obtain a determined characteristic value.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A new product price elastic calculation method based on industrial Internet identification codes is characterized by comprising the following steps:
acquiring industrial internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and price elasticity of the industrial internet identification codes, and industrial internet identification codes of new commodities, wherein the industrial internet identification codes comprise basic classification codes and attribute codes;
filtering the sold commodities of which the sales number does not accord with a preset sales threshold value to obtain a plurality of sold commodities to be processed;
screening the sold commodities to be processed according to the basic classification codes and the attribute codes of the new commodities to obtain a plurality of standby sold commodities;
obtaining a first price elasticity average value and a first price elasticity binary number according to the price elasticity of all the standby sold commodities;
obtaining the total number of categories and the similarity among the categories of the standby sold commodities and the category with the shortest average value of the distance between the category and the industrial internet identification code of the new commodity by utilizing a community detection algorithm;
if the similarity among the categories is smaller than a preset threshold value, determining a first price elastic average value or a first price elastic binary number as the price elasticity of the new product; otherwise, determining the price elasticity average value or the price elasticity binary number of the standby sold commodity as the price elasticity of the new commodity in the category with the shortest average value of the industrial Internet identification codes of the new commodity.
2. The method for flexibly calculating the price of the new product based on the industrial internet identification code as claimed in claim 1, wherein the preset sales threshold is as follows:
plus or minus 2-fold variance of a gaussian distribution of sales quantities of a plurality of sold goods.
3. The method as claimed in claim 2, wherein the step of screening the sold commodities to be processed according to the basic classification code and the attribute code of the new commodity to obtain a plurality of spare sold commodities comprises:
using each bit in the basic classification code and the attribute code of the sold commodity to be processed as the characteristic, searching the commodity in the same class with the new commodity in the sold commodity to be processed by using a clustering algorithm, and using the commodity as the sold commodity of the same class;
and determining M commodities with the shortest Euclidean distance to the new commodity in the similar sold commodities as standby sold commodities, wherein M is a preset value.
4. The method of claim 3, wherein the N commodities with the shortest Euclidean distance to the new commodity in the similar sold commodities are determined as spare sold commodities, and the formula of the Euclidean distance is as follows:
wherein x represents the basic classification code or the attribute code of a new product, y represents the basic classification code or the sum attribute code of the similar sold products, and n is the number of digits of the basic classification code and the attribute code.
5. The method for flexibly calculating the price of a new product based on the industrial internet identity code as claimed in claim 4, wherein after filtering out the sold commodities whose sales quantity does not meet the preset sales threshold, the method further comprises:
and filtering the sold commodities of which the sales quantity accords with the abnormal value to obtain a plurality of sold commodities to be processed, wherein the abnormal value is at least one of sales outliers, holiday sales records and promotion period records.
6. A new product price elasticity computing device based on industry internet identification code, characterized by comprising:
the acquisition module is used for acquiring industrial Internet identification codes of a plurality of sold commodities in the same industry, the corresponding sales quantity and price elasticity of the industrial Internet identification codes and industrial Internet identification codes of new commodities, wherein the industrial Internet identification codes comprise basic classification codes and attribute codes;
the filtering module is used for filtering the sold commodities of which the sales quantity does not accord with the preset sales threshold value to obtain a plurality of sold commodities to be processed;
the screening module is used for screening the sold commodities to be processed according to the basic classification codes and the attribute codes of the new commodities to obtain a plurality of standby sold commodities;
the calculation module is used for obtaining a first price elasticity average value and a first price elasticity binary number according to the price elasticity of all standby sold commodities;
the detection module is used for obtaining the total number of the categories of the standby sold commodities, the similarity among the categories and the category with the shortest average value of the distance between the category and the industrial internet identification code of the new commodity by utilizing a community detection algorithm;
the determining module is used for determining the first price elastic average value or the first price elastic binary number as the new price elasticity if the similarity between the categories is smaller than a preset threshold; otherwise, determining the price elasticity average value or the price elasticity binary number of the standby sold commodity as the price elasticity of the new commodity in the category with the shortest average value of the industrial Internet identification codes of the new commodity.
7. The device for calculating the price elasticity of the new product based on the industrial internet identity code as claimed in claim 6, wherein the preset sales threshold is:
plus or minus 2-fold variance of a gaussian distribution of sales quantities of a plurality of sold goods.
8. The device for calculating the price elasticity of new products based on industrial internet identification codes as claimed in claim 7, wherein the screening module is specifically configured to:
using each bit in the basic classification code and the attribute code of the sold commodity to be processed as the characteristic, searching the commodity in the same class with the new commodity in the sold commodity to be processed by using a clustering algorithm, and using the commodity as the sold commodity of the same class;
and determining M commodities with the shortest Euclidean distance to the new commodity in the similar sold commodities as standby sold commodities, wherein M is a preset value.
9. The device for calculating the price elasticity of new products based on industrial internet identity codes as claimed in claim 8, wherein the formula of the euclidean distance is as follows:
wherein x represents the basic classification code or the attribute code of a new product, y represents the basic classification code or the sum attribute code of the similar sold products, and n is the number of digits of the basic classification code and the attribute code.
10. The device for calculating the price elasticity of new products based on industrial internet identification codes as claimed in claim 9, wherein the filtering module is further configured to:
and filtering the sold commodities of which the sales quantity accords with the abnormal value to obtain a plurality of sold commodities to be processed, wherein the abnormal value is at least one of sales outliers, holiday sales records and promotion period records.
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