CN112734340A - Method, device, equipment and storage medium for screening prediction indexes of express delivery quantity - Google Patents
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Abstract
A prediction index screening method, a device, equipment and a storage medium for predicting the quantity of delivered items are provided, wherein the method comprises the following steps: acquiring historical data of the quantity, preprocessing the historical data and screening out target quantity data information; designing a prediction index for predicting the delivery quantity according to the target quantity data information; respectively establishing a regression analysis prediction model between each prediction index and the component index; and respectively predicting the quantity of the express delivery by adopting various regression analysis prediction models, calculating error information of the quantity prediction result of each regression analysis prediction model according to the actual quantity data information, and screening a prediction index contained in the regression analysis prediction model with the minimum error as a prediction index for predicting the quantity of the express delivery. By designing various prediction indexes, creating a regression analysis prediction model between the prediction indexes and the express delivery quantity and reasonably screening out the optimal prediction indexes, the accuracy of express delivery quantity prediction can be improved according to the screened prediction indexes.
Description
Technical Field
The invention relates to the technical field of express delivery, in particular to a method, a device, equipment and a storage medium for screening prediction indexes for predicting the quantity of express delivery.
Background
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 quantity of the parts; however, the prediction of the amount of the delivered items is influenced by various factors, and if the prediction index for predicting the amount of the delivered items cannot be accurately provided, the purpose of accurately predicting the amount of the delivered items cannot be achieved.
Disclosure of Invention
In order to solve the problem of how to screen the prediction index influencing the predicted express quantity in the technical field of express delivery, the application provides a method, a device, equipment and a storage medium for screening the prediction index for predicting the express quantity, and the prediction accuracy of the express quantity is improved by screening the optimal prediction index for predicting the express quantity.
The technical scheme of the invention is as follows:
the invention provides a prediction index screening method for predicting the quantity of delivered goods, which comprises the following steps:
acquiring historical data of the quantity, preprocessing the historical data and screening out target quantity data information;
designing a prediction index for predicting the delivery quantity according to the target quantity data information;
respectively establishing a regression analysis prediction model between each prediction index and the component index;
and respectively predicting the quantity of the express delivery by adopting various regression analysis prediction models, calculating error information of the quantity prediction result of each regression analysis prediction model according to the actual quantity data information, and screening a prediction index contained in the regression analysis prediction model with the minimum error as a prediction index for predicting the quantity of the express delivery.
Further preferably, the prediction index includes: recent factors, contemporaneous factors, periodic factors, holiday factors, electricity quotient factors, month end factors, temporary holiday factors, weather factors and economic factors.
Further preferably, the establishing of the regression analysis prediction model between each prediction index and the component index specifically includes the steps of:
screening independent variables for predicting the quantity of the parts from all the prediction indexes;
and taking the component as a dependent variable, and respectively establishing a regression analysis prediction model between each prediction index and the component index according to the screened independent variable.
Further preferably, the screening out the independent variables for predicting the quantity of the component from the prediction indexes specifically includes the steps of:
calculating the correlation among the prediction indexes;
screening out indexes with relatively small absolute values of correlation coefficients among the indexes and relatively large correlation coefficients of the indexes and the parts;
fitting the relationship between the screened indexes and the component quantity, and establishing a regression model;
and screening the indexes with the P value less than 0.05 or less than 0.01 in the regression model as the independent variables of the predicted quantity.
Further preferably, the calculating the correlation between the prediction indexes specifically includes calculating a pearson correlation coefficient between the prediction indexes.
Further preferably, the preprocessing the historical data includes: cleaning historical data, replacing null data and processing abnormal data.
The invention also provides a prediction index screening device for predicting the quantity of the express delivery, which comprises the following components:
the data processing module is used for acquiring historical data of the quantity, preprocessing the historical data and screening out target quantity data information;
the prediction index design module is used for designing a prediction index for predicting the delivery quantity according to the target quantity data information;
the regression model establishing module is used for respectively establishing regression analysis prediction models between the prediction indexes and the component indexes;
and the prediction index screening module is used for respectively predicting the quantity of the express delivery by adopting various regression analysis prediction models, calculating error information of the quantity prediction result of each regression analysis prediction model according to the actual quantity data information, and screening the prediction index contained in the regression analysis prediction model with the minimum error as the prediction index for predicting the quantity of the express delivery.
Further preferably, the regression model creation module comprises a screening unit and a creation unit;
the screening unit is used for screening independent variables for predicting the quantity of the parts from all the prediction indexes;
the creating unit is used for taking the component as a dependent variable and respectively establishing a regression analysis prediction model between each prediction index and the component index according to the screened independent variable.
The invention also provides a prediction index screening device for predicting the delivery volume, which comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the prediction index screening method.
The present invention also provides a computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the above-mentioned prediction index screening method.
According to the prediction index screening method, apparatus, terminal device and storage medium of the above embodiments, by designing various prediction indexes, establishing a regression analysis prediction model between each prediction index and a quantity index, and obtaining a prediction instruction included in an optimal regression analysis prediction model by screening as a prediction index for predicting a quantity of a delivered item, compared with the prior art of blindly selecting the prediction indexes, the method for screening the prediction indexes can reasonably screen the optimal prediction indexes, therefore, the accuracy of the express delivery quantity prediction can be improved according to the screened prediction indexes, further, the express delivery quantity can be accurately predicted, a powerful data basis can be provided for the orderly development of logistics work, for example, the preparation of workers and vehicles can be made in advance based on the predicted quantity, so that the effects of reducing cost and loss are achieved.
Drawings
FIG. 1 is a flow chart of a predictive index screening method;
FIG. 2 is a flow chart of an index screening method;
FIG. 3 is a schematic diagram of a prediction index screening apparatus;
fig. 4 is a schematic diagram of a prediction index screening apparatus.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings.
The first embodiment is as follows:
the embodiment provides a method for screening a prediction index for predicting an amount of a delivered item, and a flowchart of the method is shown in fig. 1, and specifically includes the following steps.
S100: and acquiring historical data of the quantity, preprocessing the historical data and screening target quantity data information.
S200: and designing a prediction index for predicting the delivery quantity according to the target quantity data information.
S300: and respectively establishing a regression analysis prediction model between each prediction index and the component index.
S400: and respectively predicting the quantity of the express delivery by adopting various regression analysis prediction models, calculating error information of the quantity prediction result of each regression branch board prediction model according to the actual quantity data information, and screening a prediction index contained in the regression analysis prediction model with the minimum error as a prediction index for predicting the quantity of the express delivery.
The following specifically describes the above steps S100 to S400.
In step S100, historical data of the quantity is obtained, the historical data is preprocessed, and target quantity data information is screened, where the historical data of the quantity refers to quantity data stored in the logistics industry, and may also be quantity data in the logistics industry in a certain period of time published by a certain statistical institution. The delivery amount includes an addressee amount, may also include a delivery amount, and may also include an addressee amount and a delivery amount. In the database, the information of the dispatch amount and the receiving amount is stored no matter on-line or off-line. The information may include, but is not limited to: type of piece, time. 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, replacing null data and processing abnormal data; 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 the present embodiment, since the prediction is mainly performed for the quantity, the single number information and the address information included in the history data can be eliminated. 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 receiving amount and/or the sending amount, the receiving amount (with an order or without an order) and the sending amount information of each website can be called from the database according to different service scenes, the receiving amount of a certain website is taken as test data, the date of the historical data is the collecting amount in the period from 2017 to 2020, and the obtained historical data can be shown in the following table 1 after being cleaned.
TABLE 1
| Date of collection | Amount of received data |
| 2017/1/1 | XXXXXX |
| 2017/1/2 | XXXXXX |
| ... | XXXXXX |
| 2020/12/31 | XXXXXX |
The abnormal data may be processed by a deletion method, a substitution method (substitution of a continuous variable mean, substitution of a discrete variable with a mode and a median), or an interpolation method (regression interpolation, multiple interpolation), and the abnormal value may be changed into a missing value first and then the missing value may be supplemented. In practical applications, the abnormal value processing is generally classified as NA missing value processing or data trimming.
In step S200, a prediction index for predicting the delivery quantity is designed according to the target quantity data information; specifically, a plurality of prediction indexes are designed, preferably, the prediction indexes include: the method comprises the following steps of selecting a plurality of prediction indexes in recent factors, contemporaneous factors, periodic factors, holiday factors, electricity quotient factors, month end factors, temporary holiday factors, weather factors and economic factors, and performing index smoothing on the prediction indexes by adopting index smoothing of different times, so that the processed indexes are more stable and have smaller fluctuation.
Further, the recent factors comprise recent piece quantity, a recent piece quantity smooth value and a recent week average value, the contemporaneous factors comprise an contemporaneous piece quantity index in the historical year, and the period factors comprise piece quantity of the same day in the historical week, piece quantity of the same day in the historical month, piece quantity of the same day in the historical quarter and piece quantity of the same day in the historical year.
The following description will take an example in which an index smoothing method is used to design a prediction index of an express delivery amount.
Assuming that the component collecting quantity of the t day is xt, the component collecting quantity of the t-1 day is xt-1, and so on, designing a prediction index:
the factors include the following:
1) recent quantity: xt-1, xt-2, …, xt-14 represent the first 1, 2, …, 14 day pieces, respectively.
2) The recent piece quantity is smooth: on average, approximately 3 days, approximately 5 days, approximately 7 days, approximately 10 days, and approximately 14 days. Wherein (xt-1+ xt-2+ xt-3)/3 represents the average of nearly 3 days, and so on
3) Average in recent weeks: the average is approximately 2-8 days, and the average is approximately 7-14 days.
Synchronization factors include the following:
the contemporaneous factors include an indication of the number of contemporaneous components in the historical year, for example, a last year contemporaneous indicator such as today lxt of the last year is calculated. The current year synchronization indexes of all indexes in recent factors can be calculated by only changing the current year acquisition quantity into the last year acquisition quantity in the same time period.
The period factors include the following:
the cycle factors include the quantity of the same day in the historical week, the quantity of the same day in the historical month, the quantity of the same day in the historical quarter, and the quantity of the same day in the historical year, for example, the quantity of the same day in the last week, the same day in the last month, the same day in the last quarter, and the same day in the last year.
Holiday factors include the following:
the express package is influenced by production and life and has holidays such as Yuan Dan, Qingming, Dragon boat festival, mid-autumn, national day, spring festival and the like. The corresponding date is respectively represented by the indexes of Yuan Dan, Qingming, Dragon boat festival, mid-autumn, national day, spring festival and the like by using a variable of 0-1. Holidays in the time representation may also be called.
The e-commerce factors include the following:
express packages are influenced by the electric commerce, and the electric commerce sections comprise six-one-eight, double-nine, double-eleven, double-twelve, annual commodity section and the like. The corresponding date is respectively represented by indexes such as six-eight, double nine, double eleven, double twelve, annual freight section and the like by using a variable 0-1. The e-commerce holidays in the time representation may also be called.
The end-of-month factors include the following:
the express delivery volume is influenced by an operation target, a client sales target and activities, and the situation that the express delivery volume is increased suddenly at the end of a month can occur, and the variable of 0-1 is used for expressing the daily use of 26-31 at the end of the month.
The temporary holiday includes the following:
under the influence of policy factors, temporary holidays can appear in part of regions, such as Beijing national celebration reading type, Shanghai Advance meeting and the like, and are represented by variables of 0-1.
Weather factors include the following:
the express delivery quantity can be influenced by weather factors, so that the warehousing quantity lags behind the actual quantity.
The economic environment includes the following:
e-commerce shopping is influenced by economic environment, and then the quantity of express delivery is influenced, for example, the quantity of express delivery is reduced rapidly in 2 months in 2020 due to new crown epidemic situation.
In step S300, a regression analysis prediction model between each prediction index and the part index is respectively established, which specifically includes the steps of:
s301: and screening out independent variables for predicting the quantity of the part from the prediction indexes.
Through the above design process of the prediction index, various prediction indexes can be designed, for example: the method comprises the following steps that various prediction indexes in recent factors, contemporaneous factors, periodic factors, holiday factors, electricity and commercial factors, month and end factors, temporary holiday factors, weather factors and economic factors are selected, although each prediction index is closely related to the quantity of the delivered goods, all prediction indexes cannot be used for predicting the quantity of the goods, and therefore the method is not only inconvenient to use, but also can influence the prediction accuracy, and therefore, how to select the appropriate prediction index is of great importance.
In this step, the independent variables for predicting the quantity are screened out through the following steps, and a specific flow chart of the screening is shown in fig. 2:
s3011: and calculating the correlation among the prediction indexes.
Specifically, a Pearson correlation coefficient (Pearson product-moment correlation coefficient, abbreviated as PPMCC or PCCs) between the two variables X and Y is calculated, and in statistics, the Pearson correlation coefficient (Pearson product-moment correlation coefficient) is used to measure the correlation (linear correlation) between the two variables X and Y, and the value of the correlation coefficient is between-1 and 1. Specifically, if the pearson correlation coefficient is greater than 0, it indicates that the two variables are positively correlated, i.e., the larger the value of one variable is, the larger the value of the other variable is; if the Pearson correlation coefficient is less than 0, the two variables are in negative correlation, namely the larger the value of one variable is, the smaller the value of the other variable is; the larger the approximate absolute value of the pearson correlation coefficient is, the stronger the correlation between the two variables is; if the pearson correlation coefficient is equal to 0, this indicates that there is not a linear correlation between the two variables, but there may be other ways of correlation.
In step S3011, pairwise correlation or non-correlation between the two prediction indexes is calculated, and a pearson correlation coefficient between the prediction indexes is calculated to obtain a correlation between the two prediction indexes.
S3012: and screening out the indexes with relatively small absolute values of the correlation coefficients among the indexes and relatively large correlation coefficients of the indexes and the parts.
Because the correlation values of each scene are different, some are more common and some are less common, the correlation values can be selected according to specific practice, for example, a relatively larger value can be selected, and also a relatively smaller value can be selected, for example, the correlation values are all more large, one correlation value is 0.9, and the other correlation value is 0.95, then 0.95 is selected.
S3013: and fitting the relationship between the screened indexes and the component quantity to establish a regression model.
That is, the indexes selected in step S3012 are fitted to the component according to the corresponding relationship to establish a regression model.
S3014: and screening the indexes with the P value less than 0.05 or less than 0.01 in the regression model as the independent variables of the predicted quantity.
Not only can the coefficient of each variable be fitted through fitting the regression model, but also the P value of each independent variable can be fitted, and indexes with the P value smaller than 0.05 or the P value smaller than 0.01 are screened out in step S3014 to serve as the independent variables of the final predicted quantity.
S302: and taking the component as a dependent variable, and respectively establishing a regression analysis prediction model between each prediction index and the component index according to the screened independent variable.
For example, a linear regression analysis prediction model is established for each prediction index by the prediction index screened in step S301.
In step S400, the quantity of the express delivery is predicted by using each regression analysis prediction model, and the error information of the quantity prediction result of each regression analysis prediction model is calculated according to the actual quantity data information, and the prediction index included in the regression analysis prediction model with the smallest error is selected as the prediction index for predicting the quantity of the express delivery.
Further, calculating error information of the part prediction result of each regression analysis prediction model according to the actual part data information, wherein the error information specifically comprises any one or more of calculated part prediction error value, part prediction error rate, model error, measurement error, truncation error and rounding error; the calculation process of the component prediction error value and the component prediction error rate is as follows:
prediction error value formula: (A-E)/E, the excess is positive and the deficiency is negative, wherein A represents the measured value and E represents the normal value.
The prediction error rate calculation method comprises the following steps:
a is the first measurement, b is the second measurement, c is the third measurement, d is the fourth measurement, e is the fifth measurement
(a + b + c + d + e)/5 as an average value
Average/100 is a percentage of the average.
The above model errors are: in the process of establishing the mathematical model, complicated phenomena are required to be abstracted and summarized into the mathematical model, the influence of some secondary factors is usually omitted, and the problem is simplified. Therefore, the mathematical model and the practical problem have certain errors, and the errors are called model errors.
The above measurement errors are: the data used in the modeling and detailed calculation processes are often obtained by observation and measurement, and due to the limitation of precision, the data are generally approximate, i.e., have errors, which are called measurement errors.
The truncation error described above refers to: because the actual operation can only complete finite term or finite step operation, the operation which needs to be carried out by finite or infinite process is limited, and the infinite process is cut off, so the generated error becomes a cut-off error.
The rounding error mentioned above means: in the numerical calculation, due to the limitation of the calculation tool, some numbers are often rounded, only the first few digits are kept as the approximate value of the number, and the error caused by rounding becomes the rounding error.
Through any one or more errors of the above calculation, the regression analysis prediction model with the smallest error and the prediction index corresponding to the regression analysis prediction model are screened, for example, the regression analysis prediction model corresponding to the quantity prediction error value and the quantity prediction error rate and the prediction index of the holiday factor corresponding to the regression analysis prediction model can be screened according to the quantity prediction error value and the quantity prediction error rate, and for example, the regression analysis prediction model corresponding to the model error and the prediction index of the recent factor corresponding to the regression analysis prediction model can be screened according to the model error, so that in practical application, the optimal regression analysis prediction model can be screened according to the designed target error, and the optimal prediction index for predicting the express delivery quantity is further screened, so that the prediction accuracy of the quantity is improved through the screened prediction indexes.
Through the screening method of the prediction indexes provided by the embodiment, by designing various prediction indexes, establishing the regression analysis prediction model between each prediction index and the quantity index, and screening to obtain the prediction instructions contained in the optimal regression analysis prediction model as the prediction indexes for predicting the quantity of the express delivery, compared with the existing blind selection of the prediction indexes, the screening method of the prediction indexes provided by the embodiment can reasonably screen the optimal prediction indexes, so that the accuracy of the prediction of the quantity of the express delivery can be improved according to the screened prediction indexes, further, the accurate prediction of the quantity of the express delivery can provide a powerful data basis for the orderly development of logistics work, for example, the preparation of workers and vehicles can be made in advance based on the predicted quantity of the express delivery, and the effects of reducing the cost and reducing the loss are achieved.
Example two:
based on the first embodiment, the first embodiment provides a prediction index screening apparatus for predicting an amount of a courier, and a schematic diagram of the apparatus is shown in fig. 3, and specifically includes a data processing module 100, a prediction index designing module 200, a regression model creating module 300, and a prediction index screening module 400.
Specifically, the data processing module 100 is configured to obtain historical data of the quantity, pre-process the historical data, and screen out target quantity data information. The historical data of the quantity refers to the quantity data stored in the logistics industry, and can also be the quantity data in the logistics industry within a certain period of time published by a certain statistical institution. The delivery amount includes an addressee amount, may also include a delivery amount, and may also include an addressee amount and a delivery amount.
Preprocessing the acquired historical data, comprising: cleaning historical data, replacing null data and processing abnormal data; 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 the present embodiment, since the prediction is mainly performed for the quantity, the single number information and the address information included in the history data can be eliminated. 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.
The abnormal data may be processed by a deletion method, a substitution method (substitution of a continuous variable mean, substitution of a discrete variable with a mode and a median), or an interpolation method (regression interpolation, multiple interpolation), and the abnormal value may be changed into a missing value first and then the missing value may be supplemented.
The prediction index design module 200 is configured to design a prediction index for predicting the delivery volume according to the target volume data information. Wherein the prediction index comprises: recent factors, contemporaneous factors, periodic factors, holiday factors, electricity merchant factors, end of month factors, temporary holiday factors, weather factors, and economic factors. For the description of recent factors, contemporaneous factors, periodic factors, holiday factors, electricity business factors, month and end factors, temporary holiday factors, weather factors and economic factors, reference is made to the first embodiment, which is not repeated herein.
The regression model creation module 300 is used for respectively establishing regression analysis prediction models between the prediction indexes and the component indexes.
Specifically, the regression model creating module 300 includes a screening unit 301 and a creating unit 302, where the screening unit 301 is configured to screen out an independent variable used for predicting the component from each prediction index, and the creating unit 302 is configured to use the component as a dependent variable and respectively establish a regression analysis prediction model between each prediction index and the component index according to the screened independent variable.
Preferably, the screening unit 301 further includes a calculating unit 3011, a first screening unit 3012, a fitting unit 3013 and a second screening unit 3014; wherein, the calculating unit 301 is used for calculating the correlation between each prediction index; the first screening unit 3012 is configured to screen out an index in which an absolute value of a correlation coefficient between indexes is relatively small and a correlation coefficient between the index and a component is relatively large; the fitting unit 3013 is configured to fit a relationship between the screened indexes and the component quantities, and establish a regression model; and the second screening unit is used for screening out indexes with the P value smaller than 0.05 or smaller than 0.01 in the regression model as the independent variables of the predicted quantity.
The prediction index screening module 400 is configured to respectively perform quantity prediction by using various regression analysis prediction models, calculate error information of a quantity prediction result of each regression board prediction model according to actual quantity data information, and screen a prediction index included in the regression analysis prediction model with the smallest error as a prediction index for predicting the quantity of delivered items.
Further, calculating error information of the part prediction result of each regression analysis prediction model according to the actual part data information, wherein the error information specifically comprises any one or more of calculated part prediction error value, part prediction error rate, model error, measurement error, truncation error and rounding error; for the description of the component prediction error value, the component prediction error rate, the model error, the measurement error, the truncation error, and the rounding error, reference is made to the first embodiment, which is not repeated herein.
The prediction index screening module 400 screens the regression analysis prediction model with the smallest error and the prediction index corresponding to the regression analysis prediction model through any one or more errors calculated, for example, the regression analysis prediction model corresponding to the error value of the piece quantity prediction and the error rate of the piece quantity prediction and the prediction index of the holiday factor corresponding to the regression analysis prediction model can be screened according to the error value of the piece quantity prediction and the error rate of the piece quantity prediction, and for example, the regression analysis prediction model corresponding to the error value of the model and the prediction index of the recent factor corresponding to the regression analysis prediction model can be screened according to the error value of the model, so that in practical application, the best regression analysis prediction model can be screened according to the designed target error, the best prediction index for predicting the express piece quantity can be screened, and the accuracy rate of the piece quantity prediction can be improved through the screened prediction indexes.
Through the prediction index screening device provided by the embodiment, various prediction indexes are designed, the regression analysis prediction model between each prediction index and the quantity index is established, and the optimal prediction instructions contained in the regression analysis prediction model are obtained through screening to serve as the prediction indexes for predicting the quantity of the express delivery, compared with the existing blind selection of the prediction indexes, the prediction index screening method provided by the application can reasonably screen the optimal prediction indexes, so that the accuracy of express delivery quantity prediction can be improved according to the screened prediction indexes, further, the express delivery quantity can be accurately predicted, a powerful data basis can be provided for the orderly development of logistics work, for example, the preparation of workers and vehicles can be made in advance based on the predicted quantity, and the effects of reducing cost and reducing loss are achieved.
Example three:
based on the first embodiment and the second embodiment, the present embodiment provides a prediction index screening device for predicting an amount of a courier, a schematic diagram of the device is shown in fig. 4, and the device 500 may be a tablet computer, a notebook computer, or a desktop computer. The device 500 may also be referred to by other names such as portable terminal, laptop terminal, desktop terminal, and the like.
In general, the device 500 includes a processor 5001 and a memory 5002, and the processor 5001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 5001 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 5001 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state.
In some embodiments, the processor 5001 may be integrated with a GPU (Graphics Processing Unit) which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 3001 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
Therefore, the device 500 of the present application, which executes the component prediction method provided in the first embodiment through at least one instruction, at least one program, a code set, or an instruction set, has the following advantages: compared with the conventional blind selection of the prediction indexes, the prediction index screening method provided by the application can reasonably screen the optimal prediction indexes, so that the accuracy of the prediction of the express delivery quantity can be improved according to the screened prediction indexes, further, the express delivery quantity can be accurately predicted, a powerful data basis can be provided for the orderly development of logistics work, for example, the preparation of workers and vehicles can be made in advance based on the predicted quantity, and the effects of reducing the cost and reducing the loss are achieved.
In some embodiments, the apparatus 500 may further optionally include: a peripheral interface 5003 and at least one peripheral. The processor 5001, memory 5002, and peripheral interface 5003 may be connected thereto by buses or signal lines. Various peripheral devices may be connected to the peripheral interface 5003 via a bus, signal line, or circuit board.
Specifically, in this embodiment, in order to implement the method for screening a prediction index, the corresponding peripheral device includes a database 5004, further, the processor 5001 may obtain the historical data information of the quantity of the dispatch through the database 5004, and the processor 5001 performs processing according to the historical data information of the quantity of the dispatch to obtain the data information of the target quantity of the dispatch, designs a prediction index according to the data information of the target quantity of the dispatch, establishes a regression analysis prediction model, and screens an optimal prediction index for predicting the quantity of the dispatch.
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 prediction index screening method of the first embodiment.
The system of the second embodiment, if implemented in the form of a software functional module and sold or used as a standalone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, 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.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (10)
1. A prediction index screening method for predicting the quantity of delivered items is characterized by comprising the following steps:
acquiring historical data of the quantity, preprocessing the historical data and screening out target quantity data information;
designing a prediction index for predicting the delivery quantity according to the target quantity data information;
respectively establishing a regression analysis prediction model between each prediction index and the component index;
and respectively predicting the quantity of the express delivery by adopting various regression analysis prediction models, calculating error information of the quantity prediction result of each regression analysis prediction model according to the actual quantity data information, and screening a prediction index contained in the regression analysis prediction model with the minimum error as a prediction index for predicting the quantity of the express delivery.
2. The predictive index screening method according to claim 1, wherein the predictive index includes: recent factors, contemporaneous factors, periodic factors, holiday factors, electricity quotient factors, month end factors, temporary holiday factors, weather factors and economic factors.
3. The method for screening prediction indexes according to claim 2, wherein the establishing of the regression analysis prediction model between each prediction index and the component index specifically comprises the steps of:
screening independent variables for predicting the quantity of the parts from all the prediction indexes;
and taking the component as a dependent variable, and respectively establishing a regression analysis prediction model between each prediction index and the component index according to the screened independent variable.
4. The predictive index screening method according to claim 3, wherein the screening of the independent variables for the predicted component amount from each predictive index specifically includes the steps of:
calculating the correlation among the prediction indexes;
screening out indexes with relatively small absolute values of correlation coefficients among the indexes and relatively large correlation coefficients of the indexes and the parts;
fitting the relationship between the screened indexes and the component quantity, and establishing a regression model;
and screening the indexes with the P value less than 0.05 or less than 0.01 in the regression model as the independent variables of the predicted quantity.
5. The method of claim 4, wherein the calculating the correlation between the predictors includes calculating a Pearson correlation coefficient between the predictors.
6. The predictor screening method of claim 1, wherein the preprocessing the historical data comprises: cleaning historical data, replacing null data and processing abnormal data.
7. A prediction index screening apparatus for predicting an amount of an express delivery, comprising:
the data processing module is used for acquiring historical data of the quantity, preprocessing the historical data and screening out target quantity data information;
the prediction index design module is used for designing a prediction index for predicting the delivery quantity according to the target quantity data information;
the regression model establishing module is used for respectively establishing regression analysis prediction models between the prediction indexes and the component indexes;
and the prediction index screening module is used for respectively predicting the quantity of the express delivery by adopting various regression analysis prediction models, calculating error information of the quantity prediction result of each regression analysis prediction model according to the actual quantity data information, and screening the prediction index contained in the regression analysis prediction model with the minimum error as the prediction index for predicting the quantity of the express delivery.
8. The prediction index screening apparatus according to claim 7, wherein the regression model creates a model including a screening unit and a creating unit;
the screening unit is used for screening independent variables for predicting the quantity of the parts from all the prediction indexes;
the creating unit is used for taking the component as a dependent variable and respectively establishing a regression analysis prediction model between each prediction index and the component index according to the screened independent variable.
9. A prediction index screening apparatus for predicting an amount of a courier, comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the prediction index screening method according to any one of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the prediction index screening method according to any one of claims 1 to 6.
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