CN112308624A - Internet-based online commodity sales prediction method and equipment - Google Patents
Internet-based online commodity sales prediction method and equipment Download PDFInfo
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- CN112308624A CN112308624A CN202011241302.XA CN202011241302A CN112308624A CN 112308624 A CN112308624 A CN 112308624A CN 202011241302 A CN202011241302 A CN 202011241302A CN 112308624 A CN112308624 A CN 112308624A
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Abstract
The invention discloses an online commodity sales forecasting method based on the Internet, relating to the technical field of data processing; the method comprises the following steps: s1, obtaining historical data of the commodity sales of the stores in a preset time period as modeling data; s2, training the linear relation between the modeling data characteristics and the predicted sales amount later to obtain the linear coefficient and constant of the linear relation; s3, quoting a linear function according to the parameters obtained after model training, so as to obtain future prediction of commodity sales of the corresponding store; the invention has the beneficial effects that: the sales volume of each commodity in the store can be predicted, and the risk of raw material waste in the Internet cake industry is reduced.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an internet-based online commodity sales prediction method and device.
Background
In the internet cake e-commerce industry, the raw material stock flow of cake bread is roughly as follows: according to past sales experiences, each store goes to corresponding suppliers to stock in advance according to own experiences of store owners. This "just-in-time" approach does not provide the accuracy and rationality of stock keeping, and in real-life situations, it is very easy to waste raw materials or to reduce the sales due to insufficient raw materials due to uncertainty of sales.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an online commodity sales prediction method based on the Internet, which can predict the sales of each commodity in a store and reduce the risk of raw material waste in the Internet cake industry.
The technical scheme adopted by the invention for solving the technical problems is as follows: in an internet-based method for predicting the sale of goods online, the improvement comprising the steps of:
s1, obtaining historical data of the commodity sales of the stores in a preset time period as modeling data;
s2, training the linear relation between the modeling data characteristics and the predicted sales amount later to obtain the linear coefficient and constant of the linear relation;
and S3, quoting a linear function according to the parameters obtained after the model training, so as to obtain the future prediction of the corresponding store commodity sales.
Further, the step S1 includes the following steps:
s11, acquiring a commodity sales prediction date, taking a time period before the commodity sales prediction date as a preset time period, acquiring commodity sales volume, time dimension characteristics and commodity portrait characteristics of each shop every day in the preset time period, and taking the time dimension characteristics and the commodity portrait characteristics as modeling data characteristics.
Further, the step S2 includes the following steps:
s21, training the linear relation according to a linear regression algorithm, and assuming that an estimation equation is as follows:
Y＝b+wX
wherein Y is the sales volume of each commodity in each shop, X is the modeling data characteristic, w is a linear coefficient, and b is a constant;
s22, constructing a Mean Square Error (MSE):
wherein the content of the first and second substances,
for model predictionRecord of the ith store sales in the store's historical sales, yiRecording the commodity sales of the ith store in the historical sales data, wherein i is more than or equal to 1 and less than or equal to n, and n is the recorded number of the historical sales data;
s23, minimizing the Mean Square Error (MSE) through a gradient descent model optimization algorithm to obtain a calculation formula of linear coefficients and constants:
wherein, wjLinear coefficient obtained for the jth gradient descent, alpha is learning coefficient of gradient descent, bjIs a constant obtained by the gradient descent of the jth time.
Further, the step S2 further includes the following steps:
s24, obtaining a judgment coefficient of the linear relation, judging the fitting effect of the linear relation according to the judgment coefficient, wherein the Mean Square Error (MSE) is the judgment coefficient, and the smaller the error is, the better the fitting effect is.
Further, in step S24, the acquiring the determination coefficient of the linear relationship includes the following steps:
s241, calculating the sum of the squares of the historical sales data:
s242, calculating a regression square sum:
coefficient of determination
Further, in step S11, the time period before the predicted date of commodity sales is set as follows.
Further, in step S11, the time dimension characteristics include, but are not limited to, month, quarter, and whether it is a holiday.
Further, in step S11, the characteristics of the merchandise representation include, but are not limited to, weight, type, and material.
In another aspect, the present invention provides an internet-based online commodity sales prediction apparatus, the improvement comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the internet-based online commodity sales prediction method of any one of claims 1-6 according to instructions in the program code.
The invention has the beneficial effects that: according to the online commodity sales prediction method based on the Internet, the sales of each commodity in each store in a future period of time can be predicted through a linear regression technology and a modeling technology, and the risk of raw material waste in the Internet cake industry is reduced.
Drawings
Fig. 1 is a flow chart of an internet-based online commodity sales prediction method according to the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The conception, the specific structure, and the technical effects produced by the present invention will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the features, and the effects of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and those skilled in the art can obtain other embodiments without inventive effort based on the embodiments of the present invention, and all embodiments are within the protection scope of the present invention. In addition, all the connection/connection relations referred to in the patent do not mean that the components are directly connected, but mean that a better connection structure can be formed by adding or reducing connection auxiliary components according to specific implementation conditions. All technical characteristics in the invention can be interactively combined on the premise of not conflicting with each other.
Referring to fig. 1, the present invention discloses an internet-based online commodity sales prediction method, by which online commodity sales are predicted to avoid wasting raw materials, and in this embodiment, the method includes the following steps:
s1, obtaining historical data of the commodity sales of the stores in a preset time period as modeling data; in this embodiment, the step S1 includes the following steps:
s11, acquiring a commodity sales prediction date, taking a time period before the commodity sales prediction date as a preset time period, acquiring commodity sales volume, time dimension characteristics and commodity portrait characteristics of each shop every day in the preset time period, and taking the time dimension characteristics and the commodity portrait characteristics as modeling data characteristics.
In step S11, the time dimension characteristics include, but are not limited to, month, quarter, and whether it is a holiday; the merchandise representation features include, but are not limited to, weight, type, and material.
S2, training the linear relation between the modeling data characteristics and the predicted sales amount later to obtain the linear coefficient and constant of the linear relation;
in this embodiment, the step S2 includes the following steps:
s21, training the linear relation according to a linear regression algorithm, and assuming that an estimation equation is as follows:
Y＝b+wX
wherein Y is the sales volume of each commodity in each shop, X is the modeling data characteristic, w is a linear coefficient, and b is a constant;
s22, constructing a Mean Square Error (MSE):
wherein the content of the first and second substances,
predicting the ith store sales record, y, in store historical sales for the modeliRecording the commodity sales of the ith store in the historical sales data, wherein i is more than or equal to 1 and less than or equal to n, and n is the recorded number of the historical sales data;
s23, minimizing the Mean Square Error (MSE) through a gradient descent model optimization algorithm to obtain a calculation formula of linear coefficients and constants:
wherein, wjLinear coefficient obtained for the jth gradient descent, alpha is learning coefficient of gradient descent, bjIs a constant obtained by the gradient descent of the jth time.
S24, obtaining a judgment coefficient of the linear relation, judging the fitting effect of the linear relation according to the judgment coefficient, wherein the Mean Square Error (MSE) is the judgment coefficient, and the smaller the error is, the better the fitting effect is.
In the above embodiment, the step S24 of obtaining the determination coefficient of the linear relationship includes the following steps:
s241, calculating the sum of the squares of the historical sales data:
s242, calculating a regression square sum:
coefficient of determination
And S3, quoting a linear function according to the parameters obtained after the model training, so as to obtain the future prediction of the corresponding store commodity sales.
The invention also provides an online commodity sales predicting device based on the Internet, which comprises a processor and a memory; the memory is used for storing program codes and transmitting the program codes to the processor; the processor is used for executing the internet-based online commodity sales amount prediction method according to instructions in the program codes.
According to the online commodity sales prediction method based on the Internet, the sales of each commodity in each store in a future period of time can be predicted through a linear regression technology and a modeling technology, and the risk of raw material waste in the Internet cake industry is reduced.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An internet-based online commodity sales prediction method is characterized by comprising the following steps:
s1, obtaining historical data of the commodity sales of the stores in a preset time period as modeling data;
s2, training the linear relation between the modeling data characteristics and the predicted sales amount later to obtain the linear coefficient and constant of the linear relation;
and S3, quoting a linear function according to the parameters obtained after the model training, so as to obtain the future prediction of the corresponding store commodity sales.
2. The internet-based online commodity sales predicting method according to claim 1, wherein the step S1 comprises the steps of:
s11, acquiring a commodity sales prediction date, taking a time period before the commodity sales prediction date as a preset time period, acquiring commodity sales volume, time dimension characteristics and commodity portrait characteristics of each shop every day in the preset time period, and taking the time dimension characteristics and the commodity portrait characteristics as modeling data characteristics.
3. The internet-based online commodity sales predicting method according to claim 2, wherein the step S2 comprises the steps of:
s21, training the linear relation according to a linear regression algorithm, and assuming that an estimation equation is as follows:
Y＝b+wX
wherein Y is sales volume of each commodity in each shop, X is modeling data characteristic (i.e. time dimension characteristic and commodity portrait characteristic), w is linear coefficient, and b is constant (obtained by training);
s22, constructing a Mean Square Error (MSE):
wherein the content of the first and second substances,
predicting the ith store sales record, y, in store historical sales for the modeliRecording the commodity sales of the ith store in the historical sales data, wherein i is more than or equal to 1 and less than or equal to n, and n is the recorded number of the historical sales data;
s23, minimizing the Mean Square Error (MSE) through a gradient descent model optimization algorithm to obtain a calculation formula of linear coefficients and constants:
wherein, wjLinear coefficient obtained for the jth gradient descent, alpha is learning coefficient of gradient descent, bjIs a constant obtained by the gradient descent of the jth time.
4. The internet-based online commodity sales predicting method according to claim 3, wherein the step S2 further comprises the steps of:
s24, obtaining a judgment coefficient of the linear relation, judging the fitting effect of the linear relation according to the judgment coefficient, wherein the Mean Square Error (MSE) is the judgment coefficient, and the smaller the error is, the better the fitting effect is.
5. The internet-based online commodity sales predicting method according to claim 4, wherein the step S24 of obtaining the linear relationship determination coefficient includes the steps of:
s241, calculating the sum of the squares of the historical sales data:
s242, calculating a regression square sum:
coefficient of determination
6. The internet-based online commodity sales predicting method according to claim 2, wherein in step S11, the time period before the commodity sales predicting date is commodity sales data of the previous 5 days.
7. The internet-based online commodity sales predicting method of claim 2, wherein in step S11, the time dimension characteristics include, but are not limited to, month, quarter, and holiday.
8. The method as claimed in claim 2, wherein in step S11, the commodity image features include but are not limited to weight, type and material.
9. An internet-based online commodity sales predicting apparatus, characterized in that the apparatus comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the internet-based online commodity sales prediction method of any one of claims 1-8 according to instructions in the program code.
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