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Parcel quantity prediction method and device, equipment and storage medium

A forecasting method and forecasting value technology, which is applied in forecasting, data processing applications, calculations, etc., can solve problems such as inaccurate forecasted parts and unfavorable business development, and achieve the effect of improving accuracy and improving work efficiency

Pending Publication Date: 2020-12-11
SHANGHAI DONGPU INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] For the forecasting of the number of pieces in the logistics field, the number of pieces always changes with time. At present, the industry mainly uses manual forecasting or rough methods to predict the delivery volume of express delivery, which is quite different from the actual number of pieces, and the predicted number of pieces is inaccurate. , is not conducive to the company's business development

Method used

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  • Parcel quantity prediction method and device, equipment and storage medium
  • Parcel quantity prediction method and device, equipment and storage medium
  • Parcel quantity prediction method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] Please refer to figure 1 , the piece quantity prediction method in the present embodiment, comprises:

[0050] Step S1: Obtain the historical data of piece quantity, preprocess the historical data, and select a weekly target data set for at least one historical period.

[0051] In this embodiment, the piece quantity historical data refers to the piece quantity data stored in the logistics industry, and may also be the piece quantity data in the logistics industry within a certain period of time published by a statistical agency. The number of pieces includes the number of incoming pieces, and it can also include the amount of outgoing pieces. In the database, whether it is online or offline, information on the number of dispatches and receipts will be stored. Such information may include, but is not limited to: the type and time of the quantity. Time can be stored by day, or by week, or by the specific time entered into the system.

[0052] Preprocessing the acquire...

Embodiment 2

[0088] The present invention also provides a piece quantity forecasting device, see image 3 , the device consists of:

[0089] Data preprocessing module 1, used to obtain the historical data of piece quantity, carry out preprocessing to historical data, select the weekly target data set of at least one historical period;

[0090] Trend elimination module 2, used to adjust the weekly target data set and eliminate the weekly trend:

[0091] The stationarity detection module 3 is used to check the stationarity of data for the weekly target data set that eliminates the trend of change, so as to obtain a stable weekly target data set;

[0092] The model creation module 4 is used to create a double exponential smoothing model to predict the piece quantity based on the stable weekly target data set, and output the weekly piece quantity forecast value;

[0093] The model verification module 5 is used to compare the predicted value of the weekly piece quantity with the actual value ...

Embodiment 3

[0096] The second embodiment described above describes the piece quantity prediction device of the present invention in detail from the perspective of modularized functional entities, and the following describes the piece quantity prediction device of the present invention in detail from the perspective of hardware processing.

[0097] Please see Figure 4 , the quantity forecasting device 500 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 510 (for example, one or more processors) and memory 520, One or more storage media 530 (such as one or more mass storage devices) storing application programs 533 or data 532 . Wherein, the memory 520 and the storage medium 530 may be temporary storage or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction op...

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PUM

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Abstract

The invention discloses a parcel quantity prediction method, device and equipment and a storage medium, and aims to solve the problems that due to the fact that the parcel quantity of express parcelsis predicted by mainly adopting a manual prediction or rough method in the current logistics industry, the difference between the parcel quantity and the actual parcel quantity is large, and the parcel quantity prediction is inaccurate. According to the invention, the method comprises the steps: processing the historical data of the parcel quantity to obtain the test data suitable for creating thedouble-exponential smoothing model; performing short-term prediction on the parcel quantity through employing the double-exponential smoothing model, thereby improving the accuracy of parcel quantityprediction, providing a powerful data basis for orderly development of logistics work, and improving the working efficiency of logistics enterprises.

Description

technical field [0001] The invention belongs to the technical field of business volume forecasting, and in particular relates to a business volume forecasting method, device, equipment and storage medium. Background technique [0002] Forecasting is the core application of big data, and big data forecasting extends the traditional meaning of forecasting to "on-the-spot measurement". The advantage of big data prediction is that it transforms a very difficult prediction problem into a relatively simple description problem, which is beyond the reach of traditional small data sets. From the perspective of forecasting, the results of big data forecasting are not only simple and objective conclusions for dealing with real business, but also can be used to help enterprises make business decisions. The collected data can also be planned to guide the development of greater consumption power . [0003] Time series data mining takes the data formed by the state of things at different...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/08
CPCG06Q10/04G06Q10/08
Inventor 夏扬陈玉芬李斯
Owner SHANGHAI DONGPU INFORMATION TECH CO LTD