CN110110932A - Order forecast method and device, logistics system and computer-readable medium - Google Patents
Order forecast method and device, logistics system and computer-readable medium Download PDFInfo
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Abstract
The present invention provides a kind of order forecast method and device, logistics system and computer-readable medium.This method comprises: the History Order data of acquisition route, the History Order data include that order generates time and order volume；The History Order data are integrated according to the order generation time；From the N days order volumes of History Order data cutout through integrating as timing list entries, and using the N+1 days order volumes as the label of the timing list entries, mobile data intercepts window and obtains multiple training samples, and the training sample includes timing list entries and label；Use the full Connection Neural Network model of the multiple training sample training；The order volume of testing time is predicted using housebroken full Connection Neural Network model.
Description
Technical field
The invention mainly relates to Intelligent logistics field more particularly to a kind of order forecast method and device, logistics system and
Computer-readable medium.
Background technique
Intelligent logistics transport field is the crossing domain of artificial intelligence technology and logistics field, it is intended to utilize artificial intelligence skill
Intelligent algorithm in art substitutes artificial method to solve the FAQs in logistics field, such as order forecasting problem, path
Planning problem, road junction Plan Problem, vehicle dispatching problem, Warehouse Location problem etc..Wherein order forecasting problem again can basis
Predetermined period is divided into a day degree, weekly, monthly, annual order forecasting etc..It, can be further according to delivery in order forecasting problem
Ground and place of acceptance are divided into each route order forecasting problem, are divided into each vehicle order forecasting problem and more complicated according to vehicle
Order forecasting problem etc. of each vehicle route on the multicycle.
Intelligent logistics transport field solves the problems, such as the scheme of order forecasting, usually empirically formula, is gone through by analysis
History order data, vehicle factor yield, transport power data, market situation etc. carry out linear fit to History Order data, and are solved
Order forecasting problem certainly is mostly monthly or more macrocyclic prediction.This scheme has the following disadvantages:
1, the prediction mode precision of prediction of linear fit is low, response speed is slow, is unable to satisfy the prediction of daily Logistic Scheduling
Demand.
2, higher to the skill requirement of prognosticator, prediction effect is related to the experience height of people, it is difficult to meet intelligent object
Flow the needs of transport field Continuous optimization algorithm.
Summary of the invention
The technical problem to be solved in the present invention is to provide order forecast methods and dress based on full Connection Neural Network model
It sets, logistics system and computer-readable medium.
In order to solve the above technical problems, an aspect of of the present present invention provides a kind of order forecast method, this method comprises: adopting
Collect the History Order data of route, the History Order data include that order generates time and order volume；It is raw according to the order
The History Order data are integrated at the time；From the N days order volumes of History Order data cutout through integrating as when
Sequence list entries, and using the N+1 days order volumes as the label of the timing list entries, it is more that mobile data intercepts window acquisition
A training sample, the training sample include timing list entries and label；Use the full connection of the multiple training sample training
Neural network model；The order volume of testing time is predicted using housebroken full Connection Neural Network model.
In one embodiment of this invention, the History Order data for acquiring a plurality of route calculate in a plurality of route every two
The similarity of route History Order data merges the two lines road that similarity is greater than preset value.
In one embodiment of this invention, using manhatton distance similarity algorithm, Euclidean distance similarity algorithm, cosine
Similarity algorithm or Pearson's similarity algorithm calculate the similarity of every two lines road History Order data in a plurality of route.
In one embodiment of this invention, according to the order generate the time number of weeks to the History Order data into
Row integration.
In one embodiment of this invention, the History Order data that the step-length of mobile data interception window is Dan Tian.
In one embodiment of this invention, the History Order data for acquiring route include: to obtain the corresponding original of History Order
Beginning data；Initial data is pre-processed, the order for obtaining History Order data generates time and order volume.
In one embodiment of this invention, the pretreatment includes at least one in outlier processing or missing values processing
Kind.
It in one embodiment of this invention, further include the online data for obtaining order, simultaneously based on online data training
Update the full Connection Neural Network model.
Another aspect provides a kind of order forecasting device, which includes: acquisition unit, acquires route
History Order data, the History Order data include that order generates time and order volume；Integral unit, it is raw according to the order
The History Order data are integrated at the time；Training sample acquiring unit, from the History Order data cutout N through integrating
It order volume moves number as timing list entries, and using the N+1 days order volumes as the label of the timing list entries
Multiple training samples are obtained according to interception window, the training sample includes timing list entries and label；Training unit uses institute
State the full Connection Neural Network model of multiple training sample training；Predicting unit uses housebroken full Connection Neural Network model
The order volume of testing time is predicted.
In one embodiment of this invention, the acquisition unit acquires the History Order data of a plurality of route, calculates a plurality of
The similarity of every two lines road History Order data in route merges the two lines road that similarity is greater than preset value.
In one embodiment of this invention, the acquisition unit uses manhatton distance similarity algorithm, Euclidean distance phase
Every two lines road History Order data in a plurality of route are calculated like degree algorithm, cosine similarity algorithm or Pearson's similarity algorithm
Similarity.
In one embodiment of this invention, the integral unit is gone through according to the number of weeks that the order generates the time to described
History order data is integrated.
In one embodiment of this invention, the History Order data that the step-length of mobile data interception window is Dan Tian.
In one embodiment of this invention, the History Order data of the acquisition unit acquisition route include: acquisition history
The corresponding initial data of order；Initial data is pre-processed, the order for obtaining History Order data generates time and order
Amount.
In one embodiment of this invention, the pretreatment includes at least one in outlier processing or missing values processing
Kind.
In one embodiment of this invention, the training unit obtains the online data of order, is based on the online data
It trains and updates the full Connection Neural Network model.
It is yet another aspect of the present invention to provide a kind of logistics systems, comprising: memory can be executed for storing by processor
Instruction；Processor realizes method as described above for executing described instruction.
Another aspect of the invention provides a kind of computer-readable medium, is stored thereon with computer instruction, wherein when
When computer instruction is executed by processor, method as described above is executed.
Compared with prior art, the present invention integrates track data according to the similitude that order changes, and utilizes depth
Full Connection Neural Network model in study is fitted logistics order history order data, when can predict one section following
The interior possible order volume occurred has general so that subsequent logistic optmum algorithm can achieve the optimal value of objective function
Property is strong, timeliness is strong, accuracy is high, intelligentized advantage.
Detailed description of the invention
For the above objects, features and advantages of the present invention can be clearer and more comprehensible, below in conjunction with attached drawing to tool of the invention
Body embodiment elaborates, in which:
Fig. 1 is the exemplary process diagram of the order forecast method of one embodiment of the invention；
Fig. 2 is that the mobile data interception window of one embodiment of the invention obtains the schematic diagram of training sample；
Fig. 3 is the structural schematic diagram of full Connection Neural Network model；
Fig. 4 is the exemplary patterns of Sigmoid function；
Fig. 5 is the order volume in one embodiment of the invention using housebroken full Connection Neural Network model to the testing time
The exemplary process diagram predicted；
Fig. 6 is the order forecasting comparative result figure of one embodiment of the invention；
Fig. 7 is the structural block diagram of the order forecasting device of one embodiment of the invention；
Fig. 8 is the block diagram of the logistics system of one embodiment of the invention.
Specific embodiment
For the above objects, features and advantages of the present invention can be clearer and more comprehensible, below in conjunction with attached drawing to tool of the invention
Body embodiment elaborates.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, but the present invention can be with
It is different from other way described herein using other and implements, therefore the present invention is by the limit of following public specific embodiment
System.
As shown in the application and claims, unless context clearly prompts exceptional situation, " one ", "one", " one
The words such as kind " and/or "the" not refer in particular to odd number, may also comprise plural number.It is, in general, that term " includes " only prompts to wrap with "comprising"
Include clearly identify the step of and element, and these steps and element do not constitute one it is exclusive enumerate, method or apparatus
The step of may also including other or element.
Fig. 1 is the exemplary process diagram of the order forecast method of one embodiment of the invention.Refering to what is shown in Fig. 1, the order
Prediction technique the following steps are included:
Step 110, the History Order data of route are acquired.
The History Order data include corresponding to the order generation time of a certain route and order volume.Route can be by delivering
Ground and place of acceptance determine that the place of departure and place of acceptance can issue city and purpose city, such as Shanghai-Shanghai line respectively
Road, Shanghai-Suzhou route etc..Place of departure and place of acceptance are also possible to province, county-level city, area, storage code etc..For example, table one
It show by the obtained History Order data of Shanghai-Shanghai route.
Table one
* in table one is any number for indicating order volume.The order generation time is not limited to the record form in table one, also
It may include year, month, day, hour, min, second and what day information.Since the purpose of the invention is to according to History Order
Data predict the order volume of the following some day, that is, carry out day degree order forecasting, therefore order in History Order data
Single generation time should at least generate using day as the period.In some embodiments, order generates the time also in History Order data
It can be generated using hour as the period.
In this step, the History Order data that can acquire a plurality of route simultaneously calculate every two lines in a plurality of route
The similarity of road History Order data merges the two lines road that similarity is greater than preset value, so as to increase subsequent carry out entirely
The scale of the training dataset of Connection Neural Network model.Wherein, every two lines road History Order data in a plurality of route are calculated
The method of similarity can include but is not limited to manhatton distance similarity algorithm, Euclidean distance similarity algorithm, cosine phase
Like degree algorithm or Pearson's similarity algorithm.For example, by similarity calculation the result shows that, Shanghai-Shanghai route and Shanghai-
The similarity of Suzhou route is higher, is greater than preset value, then merges the two lines road, i.e., will correspond to the history on the two lines road
Order data is combined the History Order data as same route (such as being denoted as A route).Obviously, produced by after merging
A route in include all History Order data in former Shanghai-Shanghai route and Shanghai-Suzhou route, adopted by A route
The data volume of History Order data obtained from collection is equal to History Order number in former Shanghai-Shanghai route and Shanghai-Suzhou route
According to the sum of data volume.
It is understood that can also there is the similarity between the History Order data more than two lines road to be both greater than
Preset value, then two lines road should be more than by merging.
In some embodiments, the History Order data for acquiring route may include: that acquisition History Order is corresponding original
Data；Initial data is pre-processed, the order for obtaining History Order data generates time and order volume.In some embodiments
In, pretreatment includes at least one of outlier processing or missing values processing.
Outlier processing, which can be, carries out inspection verification, excluding outlier, to keep away to the corresponding initial data of History Order
Exempting from exceptional value influences the accuracy of subsequent prediction.Missing values processing can be, and examine to the corresponding initial data of History Order
It checks pair, corresponding missing values is filled up, to improve the accuracy of order forecasting.
Step 120, History Order data are integrated according to the order generation time.
The purpose of this step is by integrating to History Order data, after being suitable for these History Order data
The training and test of continuous full Connection Neural Network model.For example, generating the sequence of time according to order to arrange History Order
Data.
It, can be in some way for data set made of being merged as the History Order data of more similar a plurality of route
The History Order data for corresponding to a plurality of route are arranged, for example, being arranged according to the sequence that order generates the time.
In some embodiments, History Order data are integrated according to the number of weeks that order generates the time.Firstly, will
Each order generates the time and arranges according to the number of weeks corresponding to it, as shown in Table 2.
Table two:
| Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
| Day1 | Day2 | Day3 | Day4 | Day5 | Day6 | Day7 |
| Day8 | Day9 | Day10 | Day11 | Day12 | Day13 | Day14 |
| Day15 | Day16 | Day17 | Day18 | Day19 | Day20 | Day21 |
Table two illustratively lists the History Order data of the limited quantity arranged according to number of weeks.At each
It is number of days or the date that corresponding order generates the time in column corresponding to number of weeks.Again by all order generate the time according to
Number of weeks generates different data sequences, as shown in Table 3.
Table three:
| Sequence 1 | Day1 | Day8 | Day15 | …… | Day n |
| Sequence 2 | Day2 | Day9 | Day16 | …… | Day n+1 |
| Sequence 3 | Day3 | Day10 | Day17 | …… | Day n+2 |
| Sequence 4 | Day4 | Day11 | Day18 | …… | Day n+3 |
| Sequence 5 | Day5 | Day12 | Day19 | …… | Day n+4 |
| Sequence 6 | Day6 | Day13 | Day20 | …… | Day n+5 |
| Sequence 7 | Day7 | Day14 | Day21 | …… | Day n+6 |
Sequence 1 in table three separately includes the order generation time for all corresponding to Monday to Sunday into sequence 7.
According still further to the content of table three, History Order data corresponding to the time are generated to each order according to the sequence of sequence from 1 to 7
It is arranged line by line.It is assumed that each sequence includes that m order generates the time, then it is right the order in sequence 1 to be generated time institute
The History Order data arrangement answered is D1 to Dm, and the order in sequence 2 generates History Order data arrangement corresponding to the time and is
Dm+1 to D2m, and so on, it is Dm+6 to D7m that the order in sequence 7, which generates History Order data arrangement corresponding to the time,.
As shown in Table 4.
Table four:
| Sequence 1 | D1 | D2 | D3 | …… | Dm |
| Sequence 2 | Dm+1 | Dm+2 | Dm+3 | …… | D2m |
| Sequence 3 | D2m+1 | D2m+2 | D2m+3 | …… | D3m |
| Sequence 4 | D3m+1 | D3m+2 | D3m+3 | …… | D4m |
| Sequence 5 | D4m+1 | D4m+2 | D4m+3 | …… | D5m |
| Sequence 6 | D5m+1 | D5m+2 | D5m+3 | …… | D6m |
| Sequence 7 | D6m+1 | D6m+2 | D6m+3 | …… | D7m |
It is from table four when full Connection Neural Network model is trained or is tested using these History Order data
These History Order data are arranged according to sequence line by line, as shown in Table 5 for for full Connection Neural Network model training or
The data set of test:
Table five:
| D1 | D2 | D3 | …… | D15 | D16 | D17 | D18 | D19 | …… |
In the data set, D1 indicates the route in the order volume on Day1 (Monday), and D2 indicates the route in Day8 (star
Phase one) order volume, and so on, the history that sequence 2 is only until the last one History Order data Dm of sequence 1 is ordered
Forms data.
Step 130, from the N days order volumes of History Order data cutout through integrating as timing list entries, and with N
Label of+1 day order volume as the timing list entries, mobile data intercept window and obtain multiple training samples.The training
Sample includes timing list entries and label.
Fig. 2 is that the mobile data interception window of one embodiment of the invention obtains the schematic diagram of training sample.Institute is determined first
The number of days N to be intercepted.In the embodiment shown in Figure 2, N=15.Refering to what is shown in Fig. 2, the length of data cutout window is N+1=
16, which includes totally 16 days History Order data, i.e. order volume from D1 to D16.Wherein, the order volume conduct of D1 to D15
Timing list entries, i.e., timing list entries 1 as shown in Figure 2, label of the D16 as the timing list entries, i.e., such as Fig. 2
Shown in label 1.Timing list entries 1 and label 1 form a training sample.
In the embodiment shown in Figure 2, the History Order data that the step-length of mobile data interception window is Dan Tian.Such as figure
Shown in 2, after determining timing list entries 1 and label 1.Data cutout window is moved backward 1 day, to obtain timing
List entries 2 and label 2.It wherein include the order volume of D2 to D16 in timing list entries 2, label 2 is the order volume of D17.When
Sequence list entries 2 and label 2 form second training sample.Obviously, in timing list entries 1 and timing list entries 2
Order volume data overlap.According to the method for mobile data shown in Fig. 2 interception window, multiple timing can be obtained
List entries and label.The quantity of the timing list entries and label is related with the big smallest number of total data set.
It is understood that in other examples, the step-length that mobile data intercepts window can be greater than one day.In order to
All data in data set are made full use of, the step-length of mobile data interception window should be less than N, each in data set to guarantee
A data may be used to timing list entries.When the step-length is single day, prediction effect is best.
Step 140, using the full Connection Neural Network model of multiple training samples training.
Fig. 3 is the structural schematic diagram of full Connection Neural Network model.Refering to what is shown in Fig. 3, full Connection Neural Network (Fully-
Connected Networks) it include input layer 310, hidden layer 320 and output layer 330.It wherein at least include one layer of hidden layer
320.In the embodiment shown in fig. 3, which includes three layers of hidden layer 320.A circle in Fig. 3 is enclosed and is indicated
One neuron.Operation about full Connection Neural Network model includes two parts: linear segment and non-linear partial.Wherein,
Linear segment is responsible for linear transformation, and non-linear partial is applied to nonlinear transformation.The function representation of full Connection Neural Network model
As shown in following formula:
z(l)=W(l)*a(l-1)+b(l)
a(l)=f (z(l))
Wherein, z(l)For l layers of output, a(l-1)For l layers of input, W(l)For the parameter matrix of linear segment, b(l)For
Model bias term, f (z(l)) it is nonlinear activation function.
For l layers in neural network, input variable is upper one layer of output variable, i.e.,The output vector of linear segmentIt can be by as follows
Formula acquires:
z(l)=W(l)*a(l-1)+b(1)
Wherein, W(1)For the parameter matrix of linear segment, matrix size m*n.B=[b0,b1,…bm]TFor model biasing
?.
The non-linear partial of full Connection Neural Network model is made of activation primitive, is provided for deep learning model non-linear
Mapping relations.Activation primitive can be such as, but not limited to Sigmoid, Relu, tanh, softmax etc..It is activated with Sigmoid
For function.Fig. 4 is the exemplary patterns of Sigmoid function, refering to what is shown in Fig. 4, Sigmoid activation primitive such as S type curve.Its
Functional form is as shown in following formula:
A certain number of training samples are obtained using mobile data interception window as shown in Figure 2, by these training samples
As the input of full Connection Neural Network model, which is trained.For example, by taking embodiment shown in Fig. 2 as an example, it is each
A training sample all include have N days order volume timing list entries and the order volume with the N+1 days label.By this
A little training samples are all input in full Connection Neural Network model, using multiple timing list entries to full Connection Neural Network mould
Type is trained, will be as corresponding to the N+1 days estimated order volumes of timing list entries and the timing list entries
Label is compared, and error between the two is made to reach minimum, then training terminates.
It is understood that corresponding to different routes, training sample is also different.Therefore, for different routes
For, the parameters in housebroken full Connection Neural Network model are also different.
In some embodiments, further include the online data for obtaining order, based on online data training and update full connection
Neural network model.By the full Connection Neural Network model for learning to update previous off-line training again and completing to online data,
Full Connection Neural Network model dynamic can be made to update, improve the accuracy of prediction.
Step 150, the order volume of testing time is predicted using housebroken full Connection Neural Network model.
Fig. 5 is the order volume in one embodiment of the invention using housebroken full Connection Neural Network model to the testing time
The exemplary process diagram predicted.Refering to what is shown in Fig. 5, the pre- flow gauge the following steps are included:
Step 510, route j N days History Order data before the testing time are collected.Here testing time is user
Want some day of prediction order volume.
Step 520, first N days History Order data are integrated, generates order forecasting input set.It is carried out in this step
The method of integration can be according to step 120 shown in Fig. 1.
Step 530, it is loaded into the housebroken full Connection Neural Network model parameter of corresponding line.
Step 540, order forecasting input set is input in full Connection Neural Network model.
Step 550, the prediction order volume of testing time is exported.
Pre- flow gauge shown in fig. 5 can be the step of circulation, complete to route j the testing time order
After the prediction of amount, and then route j+1 can be predicted in the order volume of testing time.Wherein, for different routes
The testing time for the order volume predicted can be different.
It should be noted that when being integrated according to number of weeks to History Order data, and from the data after the integration
It extracts training sample to be trained full Connection Neural Network model, the full Connection Neural Network model is to the following week
Order volume prediction have the effect of it is best.
Illustrate the effect of order forecast method of the invention below by a specific embodiment.The embodiment is collected
Route is Shanghai-Jiangsu route.Wherein, the order generation time of the history day degree order data in training data is from 2013
On December 31, on January 8, to 2015 in January, totally 1065 effective samples；History day degree order data in test data
It is totally 320 effective samples from December 31,1 day to 2016 January in 2016 that order, which generates the time,.To the training number of the route
Multiple training samples are obtained according to the pretreatment carried out as shown at step 120, and by step 130.With these training samples to complete
Connection Neural Network model is trained, and obtains the model parameter for corresponding to the route.Further according to pre- flow gauge shown in fig. 5,
The data of every day in test data are predicted.
Fig. 6 is the order forecasting comparative result figure of one embodiment of the invention.It is above-mentioned Shanghai-Jiangsu route shown in Fig. 6
Order forecasting comparative result figure.Horizontal axis is number of days in figure, is shared 320 days, the longitudinal axis is order volume.Order corresponding to every day
The actual value of amount is as shown in dotted line, and predicted value is by shown in solid.As it can be seen that predicted value and actual value have for the embodiment
There is the preferable goodness of fit.
It is understood that the embodiment of the present invention is to carry out by taking route as an example to order volume corresponding to certain route
Prediction.Thought according to the present invention can also acquire History Order data for different vehicles, transport power type etc., and be directed to
Order volume corresponding to these types is predicted.
Order forecast method according to the present invention can be spent on the period in day predicts the following order volume.Its advantage exists
In:
(1) versatile: order forecast method of the invention can use before any Logistic Scheduling algorithm, be next
Logistic Scheduling in a working day or even certain following a period of time provides prediction and instructs, more efficient carry out stream line optimization,
Transport capacity dispatching and haul-cycle time optimization.
(2) timeliness is strong, and accuracy is high: the present invention uses intelligent algorithm, realize intelligently to historical data into
Row is analyzed and is predicted the following order volume, and prediction timeliness is strong, accuracy is high, can be realized the high frequency prediction of day degree, and
And there is very high prediction accuracy.
(3) intelligent: the present invention can intelligently realize self-teaching, optimization by intelligent algorithm, and can be certainly
It moves according to the logistics conditions such as every stream line, vehicle, transport power type, adaptively self-optimization, there is very high degree of intelligence.
Fig. 7 is the structural block diagram of the order forecasting device of one embodiment of the invention.Refering to what is shown in Fig. 7, the device includes adopting
Collect unit 710, integral unit 720, training sample acquiring unit 730, training unit 740 and predicting unit 750.
Wherein, acquisition unit 710 is used to acquire the History Order data of route, which includes that order generates
Time and order volume.In some embodiments, acquisition unit 710 acquires the History Order data of a plurality of route, calculates a plurality of line
The similarity of every two lines road History Order data in road merges the two lines road that similarity is greater than preset value.Acquisition unit 710
Using manhatton distance similarity algorithm, Euclidean distance similarity algorithm, cosine similarity algorithm or Pearson's similarity algorithm
Calculate the similarity of every two lines road History Order data in a plurality of route.Specific method can refer to step 110 shown in FIG. 1
And its description.
In some embodiments, the History Order data of acquisition unit acquisition route may include: to obtain History Order pair
The initial data answered；Initial data is pre-processed, the order for obtaining History Order data generates time and order volume.One
In a little embodiments, pretreatment may include at least one of outlier processing or missing values processing.
Outlier processing, which can be, carries out inspection verification, excluding outlier, to keep away to the corresponding initial data of History Order
Exempting from exceptional value influences the accuracy of subsequent prediction.Missing values processing can be, and examine to the corresponding initial data of History Order
It checks pair, corresponding missing values is filled up, to improve the accuracy of order forecasting.
Integral unit 720 is used to integrate History Order data according to the order generation time.In some embodiments,
Integral unit 720 integrates History Order data according to the number of weeks that order generates the time.Specific integration method can be with
With reference to step 120 shown in FIG. 1 and its description.
Training sample acquiring unit 730 is used for from the N days order volumes of History Order data cutout through integrating as timing
List entries, and using the N+1 days order volumes as the label of the timing list entries, it is multiple that mobile data intercepts window acquisition
Training sample, the training sample include timing list entries and label.In some embodiments, the step of mobile data interception window
The History Order data of a length of Dan Tian.Specific training sample acquisition methods can refer to step 130 shown in FIG. 1 and its explanation
Content.
Training unit 740 is used to connect entirely using multiple training samples training as acquired in training sample acquiring unit 730
Connect neural network model.Specific training method can refer to step 140 shown in FIG. 1 and its description.
In some embodiments, training unit obtains the online data of order, described in online data training and updating
Full Connection Neural Network model.Pass through the full Connection Neural Network for learning to update previous off-line training again and completing to online data
Model can be such that full Connection Neural Network model dynamic updates, improve the accuracy of prediction.
Predicting unit 750 is used to carry out using order volume of the housebroken full Connection Neural Network model to the testing time pre-
It surveys.Specific prediction technique can refer to step 150 shown in FIG. 1 and its description.
Technical solution of the present invention also proposed a kind of logistics system, including memory and processor.Wherein, memory is used
In the instruction that storage can be executed by processor；Processor is for executing the instruction to realize that order shown in the embodiment of the present invention is pre-
Survey method.Fig. 8 is the block diagram of the logistics system of one embodiment of the invention.Refering to what is shown in Fig. 8, the logistics system may include inside
Communication bus 801, processor 802, read-only memory (ROM) 803, random access memory (RAM) 804, communication port 805,
And hard disk 806.The data communication inside logistics system between various components may be implemented in internal communication bus 801.Processor
802 can be judged and be issued prompt.In some embodiments, processor 802 can be made of one or more processors.
Communication port 805 may be implemented logistics system and external module, such as client etc., between carry out data communication.
In some embodiments, logistics system may be coupled to network by communication port 805, and sends and connect from network
By information and data.The connection can be wired connection, be wirelessly connected, can be realized data transmission and/or it is received it is any its
He communicates to connect, and/or any combination of these connections.Wired connection may include such as cable, optical cable, telephone wire or its
Any combination.Wireless connection may include such as bluetoothTMLink, Wi-FiTMLink, WiMaxTMLink, WLAN link, ZigBee chain
Road, mobile network's link (for example, 3G, 4G, 5G etc.) etc. or combinations thereof.In some embodiments, communication port 805 can be packet
Include Standardization Communication port, RS232, RS485 etc..
Memory in logistics system may include various forms of program storage units and data storage element, such as
Hard disk 806, read-only memory 803 and random access memory 804, can store computer disposal and/or communication use it is each
Possible program instruction performed by kind data file and processor 802.Processor 802 in logistics system executes these
Instruct the major part to realize the method for prediction order of the invention.The result of the processing of processor 802 passes through communication port 805
It is transmitted to client, and can be shown on a user interface.
Order forecast method of the invention can be stored in computer-readable medium in the form of computer program code,
The computer program code is executed by processor to realize that this method wraps function to be achieved.
The some aspects of order forecast method of the invention can completely by hardware execute, can completely by software (including
Firmware, resident software, microcode etc.) it executes, can also be executed by combination of hardware.Hardware above or software are referred to alternatively as
" data block ", " module ", " engine ", " unit ", " component " or " system ".Processor can be one or more dedicated integrated electricity
Road (ASIC), digital signal processor (DSP), digital signal processing device (DAPD), programmable logic device (PLD), scene
Programmable gate array (FPGA), processor, controller, microcontroller, microprocessor or a combination thereof.In addition, the application's is each
Aspect may show as the computer product being located in one or more computer-readable mediums, which includes computer-readable
Program coding.For example, computer-readable medium may include, but be not limited to, magnetic storage device is (for example, hard disk, floppy disk, magnetic
Band ...), CD (for example, compact disk CD, digital versatile disc DVD ...), smart card and flash memory device (for example, card,
Stick, key drive ...).
Computer-readable medium may include the propagation data signal containing computer program code in one, such as in base
Take or as carrier wave a part.There are many transmitting signal possibility form of expression, including electromagnetic form, light form etc.,
Or suitable combining form.Computer-readable medium can be any computer-readable in addition to computer readable storage medium
Medium, the medium can realize communication, propagation or transmission for making by being connected to an instruction execution system, device or equipment
Program.Program coding on computer-readable medium can be propagated by any suitable medium, including nothing
The combination of line electricity, cable, fiber optic cables, radiofrequency signal or similar mediums or any of above medium.
In addition, except clearly stating in non-claimed, the sequence of herein described processing element and sequence, digital alphabet
Using or other titles use, be not intended to limit the sequence of the application process and method.Although by each in above-mentioned disclosure
Kind of example discuss it is some it is now recognized that useful inventive embodiments, but it is to be understood that, such details only plays explanation
Purpose, appended claims are not limited in the embodiment disclosed, on the contrary, claim is intended to cover and all meets the application
The amendment and equivalent combinations of embodiment spirit and scope.For example, although system component described above can be set by hardware
It is standby to realize, but can also be only achieved by the solution of software, such as pacify on existing server or mobile device
Fill described system.
Similarly, it is noted that in order to simplify herein disclosed statement, to help real to one or more application
Apply the understanding of example, above in the description of the embodiment of the present application, sometimes by various features merger to one embodiment, attached drawing or
In descriptions thereof.But this disclosure method is not meant to mention in aspect ratio claim required for the application object
And feature it is more.In fact, the feature of embodiment will be less than whole features of the single embodiment of above-mentioned disclosure.
The number of description ingredient, number of attributes is used in some embodiments, it should be appreciated that such to be used for embodiment
The number of description has used qualifier " about ", " approximation " or " generally " to modify in some instances.Unless in addition saying
It is bright, " about ", " approximation " or " generally " show the variation that the number allows to have ± 20%.Correspondingly, in some embodiments
In, numerical parameter used in description and claims is approximation, approximation feature according to needed for separate embodiment
It can change.In some embodiments, numerical parameter is considered as defined significant digit and using the reservation of general digit
Method.Although the Numerical Range and parameter in some embodiments of the application for confirming its range range are approximation, specific real
It applies in example, being set in for such numerical value is reported as precisely as possible in feasible region.
Although the present invention is described with reference to current specific embodiment, those of ordinary skill in the art
It should be appreciated that above embodiment is intended merely to illustrate the present invention, can also be done in the case where no disengaging spirit of that invention
Various equivalent change or replacement out, therefore, as long as to the variation of above-described embodiment, change in spirit of the invention
Type will all be fallen in the range of following claims.
Claims (10)
1. a kind of order forecast method, this method comprises:
The History Order data of route are acquired, the History Order data include that order generates time and order volume；
The History Order data are integrated according to the order generation time；
From the N days order volumes of History Order data cutout through integrating as timing list entries, and with the N+1 days order volumes
As the label of the timing list entries, mobile data intercepts window and obtains multiple training samples, when the training sample includes
Sequence list entries and label；
Use the full Connection Neural Network model of the multiple training sample training；
The order volume of testing time is predicted using housebroken full Connection Neural Network model.
2. order forecast method as described in claim 1, which is characterized in that acquire the History Order data of a plurality of route, count
The similarity of every two lines road History Order data in a plurality of route is calculated, the two lines road that similarity is greater than preset value is merged.
3. order forecast method as claimed in claim 2, which is characterized in that using manhatton distance similarity algorithm, European
Distance conformability degree algorithm, cosine similarity algorithm or Pearson's similarity algorithm calculate every two lines road history in a plurality of route and order
The similarity of forms data.
4. order forecast method as described in claim 1, which is characterized in that generate the number of weeks pair of time according to the order
The History Order data are integrated.
5. order forecast method as described in claim 1, which is characterized in that the step-length that mobile data intercepts window is Dan Tian's
History Order data.
6. order forecast method as described in claim 1, which is characterized in that the History Order data for acquiring route include:
Obtain the corresponding initial data of History Order；
Initial data is pre-processed, the order for obtaining History Order data generates time and order volume.
7. order forecast method as claimed in claim 6, which is characterized in that the pretreatment includes outlier processing or missing
At least one of value processing.
8. a kind of order forecasting device, the device include:
Acquisition unit, acquires the History Order data of route, and the History Order data include that order generates time and order volume；
Integral unit integrates the History Order data according to the order generation time；
Training sample acquiring unit, from the N days order volumes of History Order data cutout through integrating as timing list entries, and
Using the N+1 days order volumes as the label of the timing list entries, mobile data intercepted window and obtains multiple training samples, institute
Stating training sample includes timing list entries and label；
Training unit uses the full Connection Neural Network model of the multiple training sample training；
Predicting unit predicts the order volume of testing time using housebroken full Connection Neural Network model.
9. a kind of logistics system, comprising:
Memory, for storing the instruction that can be executed by processor；
Processor, for executing described instruction to realize the method according to claim 1 to 7.
10. a kind of computer-readable medium, is stored thereon with computer instruction, wherein when computer instruction is executed by processor
When, execute the method according to claim 1 to 7.
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Application publication date: 20190809 |