CN112070292A - Method, device, equipment and storage medium for predicting quantity of components - Google Patents
Method, device, equipment and storage medium for predicting quantity of components Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for predicting quantity, aiming at the problems that the quantity of an express delivery is predicted mainly by adopting a manual prediction or rough method in the current logistics industry, the difference between the quantity of the express delivery and the actual quantity is large, and the predicted quantity is inaccurate, the historical data of the quantity is processed to obtain test data suitable for creating a double-index smooth model, the quantity is predicted in a short term by utilizing the double-index smooth model, the accuracy of quantity prediction is improved, a powerful data basis is provided for the orderly development of logistics work, and therefore the work efficiency of logistics enterprises is improved.
Description
Technical Field
The present invention belongs to the technical field of traffic prediction, and in particular, to a method, an apparatus, a device, and a storage medium for predicting traffic.
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 a manual prediction or rough method to predict the delivery quantity of express delivery, which has a large difference with the actual quantity, the predicted quantity is inaccurate, and 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 quantity, which adopt a time series prediction model to improve the accuracy of quantity prediction.
In order to solve the problems, the technical scheme of the invention is as follows:
a method of component prediction, comprising:
step S1: acquiring historical data of the quantity, preprocessing the historical data, and selecting a weekly target data set of at least one historical period;
step S2: adjusting a weekly target data set to eliminate a weekly variation trend;
step S3: carrying out data stability verification on the week target data set with the change trend eliminated to obtain a stable week target data set;
step S4: and based on the stable weekly target data set, creating a bi-exponential smoothing model to predict the parts, and outputting a predicted value of the weekly parts.
According to an embodiment of the present invention, the step S1 further includes:
cleaning historical data, and replacing null data and abnormal data;
analyzing the weekly variation trend of the historical data to obtain a weekly variation data set;
and performing data smoothing treatment on the weekly change data set to obtain a weekly target data set.
According to an embodiment of the present invention, the step S2 further includes:
and adjusting data in the weekly target data set by adopting the following calculation formula to eliminate the weekly variation trend:
wherein, ai,jIs the dose value on day j in week i, i is a positive integer greater than 1, j is 1,2,3,4,5,6, 7; a isi-1,jIs the dose value on day j in week i-1; si-1Is the sum of the dose values for 7 days on week i-1.
According to an embodiment of the present invention, the step S3 further includes:
detecting data stationarity of a week target data set by adopting a time sequence diagram or an autocorrelation diagram;
and adjusting the unstable data to obtain a stable weekly target data set.
According to an embodiment of the present invention, the step S4 further includes:
the bi-exponential smoothing model is:
Yt+T＝at+bt·T
wherein,
is a first exponential smoothing value of the t period;
respectively the secondary exponential smoothing values of the t period and the t-1 period; a is a smoothing coefficient; y ist+TIs the predicted value of T + T period, and T is the number of periods moving backwards from T period.
According to an embodiment of the present invention, after step S4, the method further includes: comparing the predicted value of the weekly component quantity with the actual value of the component quantity, and calculating an error; and adjusting parameters of the dual-exponential smoothing model according to the error.
A quantity prediction apparatus comprising:
the data preprocessing module is used for acquiring historical data of the quantity, preprocessing the historical data and selecting a weekly target data set of at least one historical period;
the trend elimination module is used for adjusting the weekly target data set and eliminating the weekly variation trend:
the stability detection module is used for carrying out data stability verification on the weekly target data set with the change trend eliminated to obtain a stable weekly target data set;
and the model creating module is used for creating a bi-exponential smoothing model to predict the parts based on the stable weekly target data set and outputting the predicted values of the weekly parts.
According to an embodiment of the present invention, the component prediction apparatus further includes: the model checking module is used for comparing the predicted value of the weekly component with the actual value of the component and calculating an error; and adjusting parameters of the dual-exponential smoothing model according to the error.
A quantity prediction apparatus 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 calls the instructions in the memory to cause the component prediction apparatus to perform a component prediction method in an embodiment of the present invention.
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a component prediction method in an embodiment of the invention.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:
aiming at the problems that the difference between the current logistics industry and the actual quantity is large and the predicted quantity is inaccurate when the quantity of the express is predicted mainly by adopting a manual prediction or rough method, the quantity prediction method in one embodiment of the invention obtains test data suitable for creating a double-index smooth model by processing historical data of the quantity, performs short-term prediction on the quantity by using the double-index smooth model, improves the accuracy of quantity prediction, provides a powerful data base for the orderly development of logistics work, and thus improves the working efficiency of logistics enterprises.
Drawings
FIG. 1 is a flow diagram of a component prediction method in an embodiment of the present invention;
FIG. 2 is a graph illustrating a trend of elimination cycle variation according to an embodiment of the present invention;
FIG. 3 is a block diagram of a component prediction apparatus according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a component prediction apparatus according to an embodiment of the present invention.
Detailed Description
A method, an apparatus, a device and a storage medium for predicting a component according to the present invention will be 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 a component in the present embodiment includes:
step S1: and acquiring historical data of the quantity, preprocessing the historical data, and selecting a weekly 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 of delivery includes the quantity of receipt and may also include the quantity of delivery. 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, and replacing null data and abnormal data; analyzing the weekly variation trend of the historical data to obtain a weekly variation data set; and performing data smoothing treatment on the weekly change data set to obtain a weekly 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 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 received amount and/or the delivered amount, and the received amount (with an order or without an order) and the delivered amount information of each network point can be called from the database according to different service scenarios, the received amount of a certain network point is taken as test data, the date of the historical data is 2017/07/01-2017/07/30, and the obtained historical data after data cleaning can be shown as the following table 1.
When smoothing the historical data, it is necessary to analyze the change trend of the historical data to obtain a data set with a change trend, such as a weekly change data set with a weekly change trend.
The weekly variation data set is smoothed, so that the influence of statistical errors on the part prediction result can be reduced, and a weekly target data set is obtained. The following method can be adopted for smoothing the data:
weighted moving average method
The basic principle of the weighted moving average method is as follows: the weighted value of the central data in the average interval is the largest, and the weighted value of the data far away from the center is smaller. The weight coefficient can adopt a least square principle to enable the smoothed data to approach the original data with a minimum mean square error.
smooth function smoothing method
Calling a function: z ═ smooth (Y, span, method), where Z denotes the smoothed data vector, Y denotes the original data vector being smoothed, span denotes the number of smoothing points, and method denotes the smoothing method (including moving average, lowess linear weighted smoothing, loss second weighted smoothing, etc.).
The two methods for smoothing data are described above, but the smoothing of data is not limited to these two methods, and other methods, such as smoothing data by using a gaussian function or SG filtering, may also be used, and will not be described herein.
Step S2: and adjusting the weekly target data set to eliminate the weekly variation trend.
Specifically, the weekly target data sets are arranged in time series and plotted as a curve, such as curve a (a curve formed by connecting dots) in fig. 2. To improve the accuracy of the data, the values of the quantities are normalized. As can be seen from curve a, the weekly trend is a significant downward trend from Monday (2017/7/4) to Sunday (2017/7/10). This downward trend is eliminated for the weekly target data set, and the curve a is made as gentle as possible, as shown by the curve b (a curve formed by connecting square points) in fig. 2. The curve b has a significantly reduced tendency to change as compared with the curve a, and exhibits a smooth characteristic as a whole.
The method for eliminating the weekly variation trend in the embodiment comprises the following steps: and adjusting the weekly target data set through the following calculation formula to enable the data to tend to be stable.
Wherein, ai,jIs the dose value on day j in week i, i is a positive integer greater than 1, j is 1,2,3,4,5,6, 7; a isi-1,jIs the dose value on day j in week i-1; si-1Is the sum of the dose values for 7 days on week i-1. And adjusting the numerical values in the weekly target data set of the piece quantity one by one according to the calculation formula. The resulting weekly target data set is transformed into a curve as shown by curve b in fig. 2.
Step S3: and carrying out data stability verification on the week target data set with the change trend eliminated to obtain a stable week target data set.
This embodiment provides two methods for detecting data stationarity, which are timing diagram detection and autocorrelation diagram detection.
The detection of the timing diagram follows the principle that the mean value and the variance of a steady time sequence are constants, the steady sequence shows the characteristic of random fluctuation near a certain constant value in the timing diagram, and the fluctuation range is limited without obvious trend or periodicity. If a sequence exhibits a pronounced trend or periodicity in the timing diagram, it is said that the sequence is not a smooth sequence.
The autocorrelation coefficient p of the stationary time series is rapidly decayed to zero with the increase of the delay period number k in the autocorrelation graph detection. The decay rate of the autocorrelation coefficients of non-stationary sequences to zero is generally relatively slow. Typical autocorrelation plots for non-stationary sequences: obvious triangular symmetry is shown on an autocorrelation graph; on the zero axis side, the characteristic features of a monotonous trend sequence or obvious sine wave law are provided.
According to the principle of the time sequence diagram and the autocorrelation diagram, the time sequence diagram detection or the autocorrelation diagram detection is carried out on the week target data set, and the data stability is judged. If an unstable sequence is found, the unstable sequence is adjusted (for example, a linear growth trend, a new stable (trend-elimination) time sequence can be formed by first-order difference) until the sequence in the week target data set tends to be stable.
Step S4: and based on the stable weekly target data set, creating a bi-exponential smoothing model to predict the parts, and outputting a predicted value of the weekly parts.
The exponential smoothing method is a method for performing time series prediction on univariate data, and comprises primary exponential smoothing, secondary exponential smoothing and the like. The formula of the linear quadratic exponential smoothing method is as follows:
in the formula:
respectively the secondary exponential smoothing values of the t period and the t-1 period; a is a smoothing coefficient. In that
And
under known conditions, the prediction model of the quadratic exponential smoothing method (i.e. the bi-exponential smoothing model) is:
Yt+T＝at+bt·T
in the formula: y ist+TIs the predicted value of T + T period, and T is the number of periods moving backwards from T period.
And creating a double-exponential smoothing model according to the formula, and respectively carrying out logarithm taking, exponential taking, square taking, difference taking and integral taking on the week target data set to obtain five curves serving as the data basis of the double-exponential smoothing model.
Then, a set of values of model parameters including the historical data duration, the model prediction duration and the data starting point are selected, for example, the historical data duration is 7 days, the model prediction duration is 1 day and the data starting point is a data value of 1 day per month in the week target data set. And writing the parameters into a double-exponential smoothing model, and predicting the quantity of the parts to obtain a predicted value of the quantity of the parts.
In order to make the obtained prediction result more accurate, different model parameters can be adopted to carry out multiple component predictions. The following model parameters for the part prediction may be taken: and (3) writing the parameters into a double-index sliding model to predict the quantity of the components, so as to obtain another quantity predicted value.
Thus, a plurality of predicted values of the quantity of components can be obtained, each predicted value of the quantity of components is compared with the actual value of the quantity of components, and errors are calculated; and adjusting parameters of the dual-exponential smoothing model according to the error.
The method for predicting the quantity of the express delivery in the embodiment aims at the problems that the quantity of the express delivery is predicted mainly by adopting a manual prediction or rough method in the current logistics industry, the difference between the quantity of the express delivery and the actual quantity is large, and the predicted quantity of the express delivery is inaccurate, historical data of the quantity is processed to obtain test data suitable for creating a double-index smooth model, the quantity is predicted in a short term by using the double-index smooth model, the accuracy of 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.
Example two
The present invention also provides a component prediction apparatus, referring to fig. 3, the apparatus including:
the data preprocessing module 1 is used for acquiring historical data of the quantity, preprocessing the historical data and selecting a weekly target data set of at least one historical period;
the trend elimination module 2 is used for adjusting the weekly target data set and eliminating the weekly variation trend:
the stability detection module 3 is used for carrying out data stability verification on the weekly target data set with the change trend eliminated to obtain a stable weekly target data set;
the model creating module 4 is used for creating a bi-exponential smoothing model to predict the parts based on the stable weekly target data set and outputting predicted values of the weekly parts;
the model checking module 5 is used for comparing the predicted value of the weekly component with the actual value of the component and calculating an error; and adjusting parameters of the dual-exponential smoothing model according to the error.
The specific contents and implementation methods of the data preprocessing module 1, the trend eliminating module 2, the stationarity detecting module 3, the model creating module 4, and the model checking module 5 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 component prediction apparatus in detail from the perspective of the modular functional entity, and the following describes the component prediction apparatus in detail from the perspective of hardware processing.
Referring to fig. 4, the component prediction apparatus 500 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 in the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations for the component prediction apparatus 500.
Further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the component prediction apparatus 500.
The component prediction apparatus 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 server, Vista, and the like.
Those skilled in the art will appreciate that the configuration of the component prediction device illustrated in fig. 4 does not constitute a limitation of the component prediction device and may include more or fewer components than those illustrated, or some components may be combined, 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 has stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the component prediction method of 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 method for predicting a quantity, comprising:
step S1: acquiring historical data of the quantity, preprocessing the historical data, and selecting a weekly target data set of at least one historical period;
step S2: adjusting a weekly target data set to eliminate a weekly variation trend;
step S3: carrying out data stability verification on the week target data set with the change trend eliminated to obtain a stable week target data set;
step S4: and based on the stable weekly target data set, creating a bi-exponential smoothing model to predict the parts, and outputting a predicted value of the weekly parts.
2. The component prediction method according to claim 1, wherein the step S1 further includes:
cleaning historical data, and replacing null data and abnormal data;
analyzing the weekly variation trend of the historical data to obtain a weekly variation data set;
and performing data smoothing treatment on the weekly change data set to obtain a weekly target data set.
3. The component prediction method according to claim 2, wherein the step S2 further includes:
and adjusting data in the weekly target data set by adopting the following calculation formula to eliminate the weekly variation trend:
wherein, ai，jIs the dose value on day j in week i, i is a positive integer greater than 1, j is 1,2,3,4,5,6, 7; a isi-1，jIs the dose value on day j in week i-1; si-17 days in i-1 weekThe sum of the component values.
4. The component prediction method according to claim 1, wherein the step S3 further includes:
detecting data stationarity of a week target data set by adopting a time sequence diagram or an autocorrelation diagram;
and adjusting the unstable data to obtain a stable weekly target data set.
5. The component prediction method according to claim 1, wherein the step S4 further includes:
the bi-exponential smoothing model is:
Yt+T＝at+bt.T
wherein S ist (1)Is a first exponential smoothing value of the t period;
respectively the secondary exponential smoothing values of the t period and the t-1 period; a is a smoothing coefficient; y ist+TIs the predicted value of T + T period, and T is the number of periods moving backwards from T period.
6. The component prediction method according to claim 1, wherein said step S4 is further followed by: comparing the predicted value of the weekly component quantity with the actual value of the component quantity, and calculating an error; and adjusting parameters of the dual-exponential smoothing model according to the error.
7. A quantity prediction apparatus, comprising:
the data preprocessing module is used for acquiring historical data of the quantity, preprocessing the historical data and selecting a weekly target data set of at least one historical period;
the trend elimination module is used for adjusting the weekly target data set and eliminating the weekly variation trend:
the stability detection module is used for carrying out data stability verification on the weekly target data set with the change trend eliminated to obtain a stable weekly target data set;
and the model creating module is used for creating a bi-exponential smoothing model to predict the parts based on the stable weekly target data set and outputting the predicted values of the weekly parts.
8. The quantity prediction device of claim 7, further comprising: the model checking module is used for comparing the predicted value of the weekly component with the actual value of the component and calculating an error; and adjusting parameters of the dual-exponential smoothing model according to the error.
9. A quantity prediction apparatus, 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 component prediction device to perform the component prediction method of any of claims 1-6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for component prediction according to any one of claims 1 to 6.
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| CN112686433B (en) * | 2020-12-21 | 2023-07-28 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for predicting express quantity |
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