CN112183827A - Method, device, equipment and storage medium for predicting express monthly pickup quantity - Google Patents
Method, device, equipment and storage medium for predicting express monthly pickup quantity Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for predicting a monthly express pickup quantity, which aim at the problem that the predicted pickup quantity is inaccurate due to the fact that the current logistics industry mainly adopts an artificial prediction or rough method to predict the monthly express pickup quantity and the difference between the predicted pickup quantity and the actual quantity is large.
Description
Technical Field
The invention belongs to the technical field of traffic prediction, and particularly relates to a prediction method, a prediction device, prediction equipment and a storage medium for express monthly parcel pickup quantity.
Background
Prediction is the most core application of big data, and big data prediction expands traditional meaning prediction to 'present measurement'. The advantage of big data prediction is that it transforms a very difficult prediction problem into a relatively simple description problem that is not at all achievable with traditional small data sets. From the perspective of prediction, the result obtained by big data prediction not only can obtain a simple and objective conclusion of processing the actual business, but also can be used for helping enterprise operation decision, and the collected data can be planned to guide the development of larger consumption power.
The time series data mining takes data formed by the states of objects at different moments as research objects, and reveals the development and change rules of the objects by analyzing and researching the characteristics of the time series data, so as to be used for guiding activities of people such as society, economy, military affairs, life and the like. Time series mining has great significance for the development of human society, science and technology and economy, and is gradually becoming one of the research hotspots of data mining.
With the rapid development of the logistics industry, the management and control of the traffic volume (express volume) are related to whether the business of the logistics company can be normally performed. Therefore, it is important to predict the amount of the parts.
For the problem of quantity prediction in the logistics field, the quantity always changes along with time, and currently, the industry mainly adopts manual prediction or rough methods to predict the quantity of collected goods (such as the quantity of collected goods in the month) in express delivery, which has a large difference from the actual quantity, and the predicted quantity is inaccurate, so that the business development of companies is not facilitated.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a storage medium for predicting a monthly express pickup quantity, and the accuracy of quantity prediction is improved.
In order to solve the problems, the technical scheme of the invention is as follows:
a prediction method for a monthly express pickup quantity comprises the following steps:
step S1: acquiring historical data of the quantity of the collected parts, preprocessing the historical data, and selecting a monthly target data set of at least one historical period;
step S2: establishing a part prediction model based on linear regression and wavelet analysis, and inputting a monthly target data set into the part prediction model to obtain parameters of the part prediction model;
step S3: and predicting the monthly acquisition quantity of the next period by adopting the quantity prediction model based on the parameters and the monthly target data set, and outputting a predicted value.
According to an embodiment of the present invention, the step S1 further includes:
cleaning historical data, and replacing null data and abnormal data;
dividing historical data according to months, and calculating the total quantity of packages in each month to obtain a plurality of values;
and grouping the plurality of numerical values, and taking 12 numerical values as a group to obtain the monthly target data set.
According to an embodiment of the present invention, the step S2 further includes:
the calculation formula of the component prediction model based on linear regression and wavelet analysis is as follows:
wherein y represents a predicted value of the model output, x represents a time series, a, b, c, ω,
Are all model parameters.
According to an embodiment of the present invention, the step S2 further includes:
the month target data set comprises data of a plurality of historical periods, one historical period is one year, and the total quantity of the packages in each month is calculated according to the year to obtain a plurality of groups of data;
respectively inputting multiple groups of data into the component prediction model to obtain multiple groups of model parameters;
and carrying out additional weight on multiple groups of model parameters, and integrating the multiple groups of model parameters into one group of model parameters as fixed parameters of the part prediction model.
According to an embodiment of the present invention, the step S3 further includes:
and inputting the monthly target data set of a historical period into a component quantity prediction model based on the fixed parameters, and outputting a monthly component quantity prediction value of the next period.
According to an embodiment of the present invention, after step S3, the method further includes: comparing the predicted value output by the component prediction model with the actual value of the component, and calculating an error; and adjusting parameters of the model prediction based on the error.
A prediction device for collecting quantity of express monthly goods comprises:
the data preprocessing module is used for acquiring historical data of the acquisition quantity, preprocessing the historical data and selecting a monthly target data set of at least one historical period;
the model creating module is used for creating a part prediction model based on linear regression and wavelet analysis, inputting the monthly target data set into the part prediction model and obtaining parameters of the part prediction model;
and the component prediction module is used for predicting the monthly acquisition component of the next period by adopting the component prediction model based on the parameters and the monthly target data set and outputting a predicted value.
According to an embodiment of the invention, the prediction device for collecting the quantity of the express delivery month further comprises a model checking module, which is used for comparing the size of a predicted value output by the quantity prediction model with the size of an actual value of the quantity of the express delivery month and calculating an error; and adjusting parameters of the model prediction based on the error.
A prediction device for a monthly parcel pickup amount in express delivery comprises:
a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor calls the instructions in the memory to enable the prediction device of the express monthly offer amount to execute the prediction method of the express monthly offer amount in one embodiment of the invention.
A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing a method for predicting an express monthly offer amount according to an embodiment of the present invention.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:
the method for predicting the monthly express item-receiving quantity in the embodiment of the invention aims at the problem that the predicted item-receiving quantity is inaccurate due to the fact that the current logistics industry mainly adopts manual prediction or a rough method to predict the monthly express item-receiving quantity and the difference between the predicted item-receiving quantity and the actual quantity is large, a monthly target data set is obtained by processing historical data of the item-receiving quantity, prediction is carried out by using a quantity prediction model based on linear regression and wavelet analysis, the accuracy of monthly item-receiving quantity prediction is improved, a powerful data basis is provided for orderly development of logistics work, and therefore the work efficiency of logistics enterprises is improved.
Drawings
Fig. 1 is a flow chart of a prediction method of an express monthly pickup quantity in an embodiment of the present invention;
fig. 2 is a block diagram of a device for predicting an express monthly pickup amount according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a device for predicting an express monthly parcel pickup amount according to an embodiment of the present invention.
Detailed Description
The method, the apparatus, the device and the storage medium for predicting the amount of the express monthly parcel proposed by the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims.
Example one
Referring to fig. 1, the method for predicting the package pickup amount of the express delivery month in the embodiment includes:
step S1: acquiring historical data of the acquisition quantity, preprocessing the historical data, and selecting a monthly target data set of at least one historical period.
In this embodiment, the historical data of the quantity refers to the quantity data stored in the logistics industry, or may be the quantity data in the logistics industry in a certain period of time published by a certain statistical institution. The quantity includes a pulling quantity, which may be a sending quantity, and the pulling quantity is taken as an example in this embodiment. In the database, the information of the number of the parts is stored whether the parts are on-line or off-line. The information may include, but is not limited to: type, time, number of pieces. The time may be stored by day, by week, or by the specific time entered into the system.
Preprocessing the acquired historical data, comprising: cleaning historical data, and replacing null data and abnormal data; dividing historical data according to months, and calculating the total quantity of packages in each month to obtain a plurality of values; and (4) grouping a plurality of values, and taking 12 values as a group to obtain a monthly target data set.
And cleaning the historical data, removing unnecessary information in the acquired historical data and replacing abnormal data. Often, some irregularities need to be filtered out before statistical analysis of the data is performed to ensure the accuracy of the analysis. Data cleansing is a process that reduces data errors and inconsistencies, primarily detecting and deleting or correcting irregular data.
In this embodiment, since the prediction is mainly performed for the monthly component amount, the single sign information and the address information included in the history data can be removed. In the historical data, null data or data with numerical abnormality (such as non-numerical representation) may occur, and the null data or the data with numerical abnormality is replaced by adjacent data.
Specifically, the historical data includes the quantity of the collected parts, the quantity of the collected parts (with an order or without an order) information of each website can be called from the database according to different service scenes, the quantity of the collected parts of a certain website is taken as test data, the date of the historical data is 2011/01/01-2017/12/31, and the obtained historical data can be shown in the following table after being cleaned.
The above table lists the part quantity collection data for 1 month in the historical data. According to the format of the table, the historical data from 2011 to 2017 are processed.
Then, dividing the historical data of the quantity of the 7 years according to months, and calculating the total quantity of the components in each month to obtain a plurality of values; and (4) grouping a plurality of values, and taking 12 values as a group to obtain a monthly target data set. That is, the number of the collecting items of each month from 2011 to 2017 is calculated, and the corresponding value is 12 months in a year. Selecting the numerical values of the package quantities in the months from 2011 to 2016, grouping the numerical values by years to obtain six groups of data as a month target data set. And the rest 2017 months contain component data as subsequent model verification data.
Step S2: and (3) creating a component prediction model based on linear regression and wavelet analysis, and inputting the monthly target data set into the component prediction model to obtain parameters of the component prediction model.
In statistics, linear regression (linear regression) is a regression analysis that models the relationship between one or more independent and dependent variables using a least squares function called a linear regression equation. Such a function is a linear combination of one or more model parameters called regression coefficients. The case of only one independent variable is called simple regression, and the case of more than one independent variable is called multiple regression.
In linear regression, data is modeled using a linear prediction function, and unknown model parameters are also estimated from the data. These models are called linear models. The most common linear regression modeling is that the conditional mean of y given the value of x is an affine function of x. Less generally, the linear regression model may be a median or some other linear function representation of the quantile of the conditional distribution of y given x as x. Like all forms of regression analysis, linear regression also focuses on the conditional probability distribution of y given the value of x, rather than the joint probability distribution of x and y (the field of multivariate analysis).
Linear regression was the first type of rigorous study in regression analysis and is widely used in practical applications. This is because a model that depends linearly on its unknown parameters is easier to fit than a model that depends non-linearly on its location parameters, and the resulting estimated statistical properties are also easier to determine.
Linear regression models are often fitted with a least squares approximation, but they may also be fitted with other methods, such as minimizing the "fit defect" in some other specification (such as minimum absolute error regression), or minimizing the penalty of a least squares loss function in bridge regression. Instead, least squares approximation can be used to fit those models that are non-linear.
The embodiment adopts a linear regression and wavelet analysis method to create a component prediction model. The basic idea is to fit a waveform curve, find a rule therein, and perform the next prediction. The calculation formula of the component prediction model is as follows:
wherein y represents a predicted value of the model output, x represents a time series, a, b, c, ω,
Are all model parameters.
The historical data of the collected quantities from 2011 to 2016 are formed into six groups of data, and the data are respectively input into the quantity prediction model to obtain six groups of model parameters
Additional weighting of the six sets of model parameters, e.g.
The sum of the weights of the sets of model parameters is 1. In weighting the model parameters, the weight assignment may be performed based on the model parameter group obtained from the history data closer to the next cycle (i.e., 2017) and the weighting is larger.
Then, the six sets of model parameters with the weights added are integrated into a set of model parameters. The set of model parameters is used as fixed parameters of the quantity prediction model, and the set of model parameters is used for predicting the quantity of the monthly items.
Step S3: and predicting the monthly acquisition component of the next period by adopting a component prediction model based on the model parameters and the monthly target data set, and outputting a predicted value.
According to the calculation formula of the component prediction model:
at a, b, c, omega,
Under the condition that the parameters of the model are known, corresponding wave curves can be drawn. Selecting the component collecting value in month 1 of 2011-2016, performing linear regression on the component collecting value to obtain a regression curve, and predicting the component collecting value in month 1 of 2017. And then, obtaining a predicted value of the monthly collecting quantity of the month 2 to 12 in 2017 according to the predicted value of the month 1 in 2017 and the previously determined model parameters.
In order to verify the accuracy of the quantity prediction model, the predicted value of the quantity collected in the month of 1-12 months in 2017 output by the model is compared with the actual value of the quantity collected in the historical month of 1-12 months in 2017, and an error is calculated. If the error is large, the parameters of the model can be predicted according to the error adjusting component.
The forecasting method for the monthly item picking quantity in the express delivery can achieve the forecasting accuracy of more than 95%, greatly improves the forecasting accuracy of the monthly item picking quantity compared with the traditional method (the forecasting accuracy can only achieve 80%), provides a powerful data base for the orderly development of logistics work, and accordingly improves the work efficiency of logistics enterprises.
Example two
The invention also provides a device for predicting the quantity of the collected express monthly goods, and referring to fig. 2, the device comprises:
the data preprocessing module 1 is used for acquiring historical data of the acquisition quantity, preprocessing the historical data and selecting a monthly target data set of at least one historical period;
the model creating module 2 is used for creating a component prediction model based on linear regression and wavelet analysis, inputting the monthly target data set into the component prediction model and obtaining parameters of the component prediction model;
and the component prediction module 3 is used for predicting the monthly acquisition component of the next period by adopting a component prediction model based on the parameters and the monthly target data set and outputting a predicted value.
The model checking module 4 is used for comparing the predicted value output by the component prediction model with the actual value of the component and calculating an error; and adjusting parameters of the model prediction based on the error.
The specific contents and implementation methods of the data preprocessing module 1, the model creating module 2, the component predicting module 3, and the model checking module 4 are all as described in the first embodiment, and are not described herein again.
EXAMPLE III
The second embodiment of the present invention describes the prediction apparatus for collecting quantity of express months in detail from the perspective of the modular functional entity, and the following describes the prediction apparatus for collecting quantity of express months in detail from the perspective of hardware processing.
Referring to fig. 3, the prediction device 500 for the express monthly package size may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, one or more storage media 530 (e.g., one or more mass storage devices) storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations in the prediction device 500 for an express monthly offer amount.
Further, the processor 510 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the device 500 for predicting the amount of the express monthly offer.
The express monthly offer prediction device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows service, Vista, and the like.
Those skilled in the art will appreciate that the configuration of the device for predicting the express monthly claim quantity shown in fig. 3 does not constitute a limitation of the device for predicting the express monthly claim quantity, and may include more or fewer components than those shown, some components in combination, or a different arrangement of components.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the method for predicting an express monthly offer amount according to the first embodiment.
The modules in the second embodiment, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be substantially or partially implemented in software, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and devices may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, it is still within the scope of the present invention if they fall within the scope of the claims of the present invention and their equivalents.
Claims (10)
1. A prediction method for a monthly express pickup quantity is characterized by comprising the following steps:
step S1: acquiring historical data of the quantity of the collected parts, preprocessing the historical data, and selecting a monthly target data set of at least one historical period;
step S2: establishing a part prediction model based on linear regression and wavelet analysis, and inputting a monthly target data set into the part prediction model to obtain parameters of the part prediction model;
step S3: and predicting the monthly acquisition quantity of the next period by adopting the quantity prediction model based on the parameters and the monthly target data set, and outputting a predicted value.
2. The method for predicting the express monthly parcel pickup quantity according to claim 1, wherein the step S1 further comprises:
cleaning historical data, and replacing null data and abnormal data;
dividing historical data according to months, and calculating the total quantity of packages in each month to obtain a plurality of values;
and grouping the plurality of numerical values, and taking 12 numerical values as a group to obtain the monthly target data set.
3. The method for predicting the express monthly parcel pickup quantity according to claim 1, wherein the step S2 further comprises:
the calculation formula of the component prediction model based on linear regression and wavelet analysis is as follows:
wherein y represents a predicted value of the model output, x represents a time series, a, b, c, ω,
Are all model parameters.
4. The method for predicting the express monthly parcel pickup quantity according to claim 3, wherein the step S2 further comprises:
the month target data set comprises data of a plurality of historical periods, one historical period is one year, and the total quantity of the packages in each month is calculated according to the year to obtain a plurality of groups of data;
respectively inputting multiple groups of data into the component prediction model to obtain multiple groups of model parameters;
and carrying out additional weight on multiple groups of model parameters, and integrating the multiple groups of model parameters into one group of model parameters as fixed parameters of the part prediction model.
5. The method for predicting the express monthly parcel pickup quantity according to claim 4, wherein the step S3 further comprises:
and inputting the monthly target data set of a historical period into a component quantity prediction model based on the fixed parameters, and outputting a monthly component quantity prediction value of the next period.
6. The method for predicting the express monthly parcel pickup quantity according to claim 1, wherein after the step S3, the method further comprises: comparing the predicted value output by the component prediction model with the actual value of the component, and calculating an error; and adjusting parameters of the model prediction based on the error.
7. A prediction device for collecting quantity of express monthly goods is characterized by comprising:
the data preprocessing module is used for acquiring historical data of the acquisition quantity, preprocessing the historical data and selecting a monthly target data set of at least one historical period;
the model creating module is used for creating a part prediction model based on linear regression and wavelet analysis, inputting the monthly target data set into the part prediction model and obtaining parameters of the part prediction model;
and the component prediction module is used for predicting the monthly acquisition component of the next period by adopting the component prediction model based on the parameters and the monthly target data set and outputting a predicted value.
8. The express monthly parcel quantity predicting device according to claim 7, further comprising a model checking module for comparing the predicted value output by the parcel predicting model with the actual value of the parcel quantity and calculating an error; and adjusting parameters of the model prediction based on the error.
9. A prediction device for collecting quantity of express monthly goods is characterized by comprising:
a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the device for predicting an express monthly claim quantity to perform the method for predicting an express monthly claim quantity according to any one of claims 1 to 6.
10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for predicting an express monthly offer amount according to any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112734340A (en) * | 2021-01-21 | 2021-04-30 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for screening prediction indexes of express delivery quantity |
| CN113191537A (en) * | 2021-04-15 | 2021-07-30 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for predicting express package data |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112734340A (en) * | 2021-01-21 | 2021-04-30 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for screening prediction indexes of express delivery quantity |
| CN112734340B (en) * | 2021-01-21 | 2023-09-01 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for screening prediction index of express delivery quantity |
| CN113191537A (en) * | 2021-04-15 | 2021-07-30 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for predicting express package data |
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