Data diversity and task diversity-based tire enterprise sales forecasting method

A prediction method and a variety of technologies, applied in data processing applications, electrical digital data processing, special data processing applications, etc., can solve problems such as large differences and huge impacts, and achieve improved accuracy, benefit accuracy, and improve accuracy Effect

Active Publication Date: 2017-12-26
FUDAN UNIV
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AI Technical Summary

Problems solved by technology

For the retail and wholesale market of tires, the demand estimation of primary dealers is greatly affected by market changes, and there is a large difference between the demand estimated based on experience and the real order. The sales plan accuracy rate of the tire retail and wholesale market is less than 50%. Reasonable product inventory is required

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  • Data diversity and task diversity-based tire enterprise sales forecasting method
  • Data diversity and task diversity-based tire enterprise sales forecasting method
  • Data diversity and task diversity-based tire enterprise sales forecasting method

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Embodiment Construction

[0050] The present invention will be described in further detail below in conjunction with the examples.

[0051] (1) System design and data preprocessing: According to the dimension that needs to be predicted, the sales forecasting system first extracts the sales data under this dimension from the database, and then sorts it according to time. In order to ensure the rationality of the prediction, the sales data after the time when the user requests the parameters is directly deleted. If there is a discontinuous gap in the sales data before this time due to data loss or other reasons, it will be ignored directly and handed over to the follow-up logic. to process.

[0052] There are two ways to load external data sources. One is to construct a training set or test set based on the loaded sales data, and query the external data of the corresponding time from the database each time. The advantage of this method is that the logic is clear and easy to implement, and the disadvanta...

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Abstract

The invention belongs to the technical field of industrial big data application, and specifically discloses a data diversity and task diversity-based tire enterprise sales forecasting method. The method comprises five parts of: (1) theme feature extraction: extracting features of semi-structured data with respect to the tire enterprise through an LDA model; (2) structure feature extraction: mapping enterprises in the tire industry into a heterogeneous network according to a related relation and extracting structure features; (3) network structure extension: aiming at the problem of network sparsity, fusing the theme features and the structure features, extending a relationship map among the enterprises and finding potential association factors; (4) solving defects of a high-dimensional sample space by using a multi-task learning method of an LASSO method; and (5) factor feature extraction and time sequence analysis: effectively integrating and utilizing a plurality of data sets (tire demand plans, main engine plant product sales, tire sales and delivery data and the like) related to sales, and carrying out experimental data verification to improve the tire sales forecasting correctness.

Description

technical field [0001] The invention belongs to the technical field of industrial big data application, and in particular relates to a tire enterprise sales prediction method. Background technique [0002] With the deep integration of informatization and industrialization, information technology has penetrated into all links of the industrial chain of industrial enterprises, barcodes, two-dimensional codes, RFID, industrial sensors, industrial automatic control systems, industrial Internet of Things, ERP, CAD / CAM / CAE / Technologies such as CAI have been widely used in industrial enterprises, especially the application of new generation information technologies such as the Internet, mobile Internet, and Internet of Things in the industrial field, and the data owned by industrial enterprises is also increasingly abundant. The amount of data generated, collected and processed by industrial equipment is far greater than the data generated by computers and humans in enterprises. I...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q10/06
CPCG06Q10/06375G06F16/2462G06F16/2465
Inventor 李敏波廖倡许晓彬
Owner FUDAN UNIV
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