CN110084438A - Prediction technique and device, the logistics system and computer-readable medium of order - Google Patents
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Abstract
The present invention relates to a kind of prediction technique of order and devices, logistics system and computer-readable medium.This method includes extracting more History Orders, and every History Order has at least one order parameter, and the order parameter includes the order time;The time series of the History Order is obtained according to the order parameter;Using the time series of the History Order and associated external parameter as sample, training convolutional neural networks model;And the order volume using housebroken convolutional neural networks model prediction target time section.Order forecast method and device of the invention has that accuracy is high, timeliness is good and intelligentized advantage.
Description
Technical field
The present invention relates to a kind of Intelligent logistics transport field more particularly to a kind of methods of prediction logistics quantity on order distribution
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 supply demand problem, path
Planning problem, road junction Plan Problem, vehicle dispatching problem, Warehouse Location problem etc..Wherein supply demand problem again can basis
Predictive content and predetermined period are divided into order forecasting, transport power prediction, storage prediction, the prediction of day degree, monthly prediction, annual prediction
Deng.In order forecasting problem, further problem can be refined according to place of departure, place of acceptance, type of merchandise etc..
Intelligent logistics transport field solves the problems, such as the scheme of order forecasting, usually manually divides History Order data
Analysis, according to the data such as quantity on order, commodity output and sales volume, storage situation, in conjunction with external data (such as weather relevant to commodity
Situation, economic conditions, staple commodities price etc.), fit a series of experiences that can substantially describe quantity on order changing rule
Formula, and the quantity on order for predicting following a period of time.This scheme has the following disadvantages:
1, the process of artificial garbled data and design experiences formula is slow and inefficient, the experience height phase of prediction effect and people
It closes, it is difficult to meet the needs that Intelligent logistics transport field Continuous optimizations algorithm, predicted in real time;
2, empirical equation is generally fairly simple, is fitted with linear model mostly, it is difficult to be described between a variety of data
The relationship and feature of existing complexity.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of order forecast method based on convolutional neural networks model,
The order volume of target time section can be accurately predicted in real time.
The present invention is including following to solve above-mentioned technical problem and the technical solution adopted is that a kind of prediction technique of order
Step: more History Orders are extracted, every History Order has at least one order parameter, when the order parameter includes order
Between;The time series of the History Order is obtained according to the order parameter;By the time series of the History Order and pass
The external parameter of connection is as sample, training convolutional neural networks model;And it is pre- using housebroken convolutional neural networks model
Survey the order volume of target time section.
Optionally, the step of obtaining the time series of the History Order according to the order parameter includes: according to
The order time obtains the time series of a History Order.
Optionally, the step of obtaining the time series of the History Order according to the order parameter includes: according to
One or more order parameters except the order time classify the more History Orders;It is obtained according to the order time every
The time series of class History Order.
Optionally, the order parameter further include: departure place, destination, merchandise classification, order type or combinations thereof.
Optionally, the external parameter includes weather, economic data or combinations thereof.
It optionally, further include by the variation characteristic of the time series of the History Order including in the sample.
Optionally, the convolutional neural networks model includes multiple layers and convolution kernel, the quantity of the multiple layer and described
The size of convolution kernel is associated with the quantity of the data characteristics of the History Order and/or the sample.
Optionally, further include the sample is divided into training set, verifying collection and test set, wherein using the training set come
The training convolutional neural networks model, and accuracy of the convolutional neural networks model on verifying collection is observed, when described
When accuracy on verifying collection reaches highest, deconditioning, and it is related to network model on the test set using the convolution
Performance carry out Evaluated effect.
Optionally, every to generate new History Order, with regard to extract real-time History Order.
The present invention also provides a kind of devices for predicting order, comprising: extraction unit, for extracting more History Orders, often
History Order has at least one order parameter, and the order parameter includes the order time;Acquiring unit, for according to
Order parameter obtains the time series of the History Order;Construction unit, for by the time series of the History Order and
Associated external parameter is as sample, training convolutional neural networks model;And predicting unit, for using housebroken convolution
The order volume of Neural Network model predictive target time section.
Optionally, the acquiring unit obtains the time series of a History Order according to the order time.
Optionally, the acquiring unit obtains the time series as follows: except the order time
One or more order parameters the more History Orders are classified;Every class History Order is obtained according to the order time
Time series.
Optionally, the order parameter further include: departure place, destination, merchandise classification, order type or combinations thereof.
Optionally, the external parameter includes weather, economic data or combinations thereof.
Optionally, the construction unit is also used to the variation characteristic of the time series of the History Order include described
In sample.
Optionally, the convolutional neural networks model includes multiple layers and convolution kernel, the quantity of the multiple layer and described
The size of convolution kernel is associated with the quantity of the data characteristics of the History Order and/or the sample.
Optionally, the construction unit is also used to for the sample being divided into training set, verifying collection and test set, wherein utilizing
The training set trains the convolutional neural networks model, and observes standard of the convolutional neural networks model on verifying collection
Exactness, when the accuracy on the verifying collection reaches highest, deconditioning, and it is related to network model in institute using the convolution
The performance stated on test set carrys out Evaluated effect.
Optionally, every to generate new History Order, the extraction unit is with regard to extract real-time History Order.
The technical solution that the present invention uses to solve above-mentioned technical problem further includes a kind of device for predicting order, packet
It includes: memory, for storing the instruction that can be executed by processor;Processor, for executing described instruction to realize as described above
Method.
The technical solution that the present invention uses to solve above-mentioned technical problem further includes that one kind is stored with computer program generation
The computer-readable medium of code, the computer program code realize method as described above when executed by the processor.
The present invention due to using the technology described above, is allowed to compared with prior art, have the following obvious advantages:
1, accuracy is high.Letter relevant to the following order volume is extracted from a variety of data using convolutional neural networks model
Breath, and predicted according to order volume of these information to target time section, more information are considered than traditional mode, from
And keep the accuracy of prediction result higher;
2, timeliness is good.Trained convolutional neural networks model is put into cloud, and is connect with database, it can be real-time
Newest data are obtained, prediction result is updated;
3, intelligent.Convolutional neural networks model can carry out on-line study according to newest data, continually strengthen itself
Predictive ability, have the characteristics that intelligentized.
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 the exemplary structure signal of the convolutional neural networks model in the order forecast method of one embodiment of the invention
Figure;
Fig. 3 is the schematic diagram that order forecast method according to an embodiment of the invention carries out order volume prediction;
Fig. 4 is the prediction result of order forecast method according to an embodiment of the invention;
Fig. 5 is the performing environment schematic diagram of the order forecast method of one embodiment of the invention;
Fig. 6 is the block diagram of the device of the prediction order of one embodiment of the invention.
Fig. 7 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, more History Orders are extracted.Each pen all has at least one order ginseng in the more History Orders
Number, and the order parameter includes at least the order time.
History Order can be the order generated within any a period of time before being predicted, can be by
It is measured according to the periods such as day, the moon or year are unit.It in an embodiment of the present invention, is measurement unit come to the present invention according to the moon
Order forecast method be illustrated.In other examples, order forecast method of the invention can also be according to day or year
Etc. units measure.
In some embodiments, order parameter can also include but is not limited in addition to the order time: departure place, purpose
Ground, merchandise classification, order type or any combination thereof.User oneself can define the concrete meaning of each order parameter.Such as
The order time can indicate the time that order generates, or indicate the practical outbound time of cargo.Departure place can indicate to produce
The address of manufacturer, or indicate the warehouse location of cargo.Merchandise classification can be the qualitative classification according to commodity, such as vapour
For garage's industry, merchandise classification can be body style, such as two-box automobile, three-box car, sport car, station wagon, open car, SUV.It orders
Single type can be divided into such as general type or urgent type according to emergency, can also be divided under such as phone according to lower single mode
List, network ordering, mobile platform place an order.
Step 120, the time series of History Order is obtained according to order parameter.In this step, expect that obtaining history orders
Single time series can be the mass data list arranged sequentially in time, information included in the data list all with
The order time is corresponding.
When the order parameter in step 110 is the order time, this step further includes obtaining one according to the order time to go through
The time series of history order.The time series of History Order obtained can be as shown in Table 1:
Table one:
Order collection number | The order time | Order volume |
1 | In January, 2018 | N1 |
2 | 2 months 2018 | N2 |
3 | In March, 2018 | N3 |
4 | In April, 2018 | N4 |
5 | In May, 2018 | N5 |
When containing multiple order times in the time series of History Order shown in table one and corresponding to each order
Between order volume.In order forecast method of the invention, by each order time and its history of corresponding order volume composition
The time series of order is as sample, for inputting convolutional neural networks model and being trained to the model, for not
The order volume for carrying out a period of time (target time section) is predicted.Therefore, further comprise in Table 1 order collection number, for pair
Each group of order time and its corresponding order volume are numbered.The content filled in table one is example, is not used in limitation
Content in the table.
When the order parameter in step 110 further includes some other parameter in addition to the order time, this step further includes
More History Orders are classified according to one or more order parameters except the order time, and every class is obtained according to the order time
The time series of History Order.The time series of History Order obtained can be as shown in Table 2.
Table two:
Order collection number | The order time | Order volume | Departure place | Destination | Merchandise classification | Order type |
1 | In January, 2018 | N1 | * | * | * | * |
2 | 2 months 2018 | N2 | * | * | * | * |
3 | In March, 2018 | N3 | * | * | * | * |
4 | In April, 2018 | N4 | * | * | * | * |
5 | In May, 2018 | N5 | * | * | * | * |
In table two, * indicates arbitrary value, is not used in the content limited in the table.As shown in Table 2, order parameter is removed and is ordered
It further include departure place, destination, merchandise classification and order type except single time.For example, in this step, joining in order
In number, classified according to merchandise classification to more History Orders, such as merchandise classification includes A class, B class and C class.Then correspond to
These three types of History Orders can obtain the time series of every class History Order, as shown in three A of table to three C of table according to the order time.
Three A of table:
Order collection number | The order time | Order volume | Merchandise classification |
11 | In January, 2018 | AN1 | A |
12 | 2 months 2018 | AN2 | A |
13 | In March, 2018 | AN3 | A |
14 | In April, 2018 | AN4 | A |
15 | In May, 2018 | AN5 | A |
Three B of table:
Order collection number | The order time | Order volume | Merchandise classification |
21 | In January, 2018 | BN1 | B |
22 | 2 months 2018 | BN2 | B |
23 | In March, 2018 | BN3 | B |
24 | In April, 2018 | BN4 | B |
25 | In May, 2018 | BN5 | B |
Three C of table:
Difference is numbered in order collection of three A of table into three C of table, indicates that the time series of every class History Order is all used as difference
Sample recycle trained convolutional neural networks for inputting convolutional neural networks model and being trained to the model
Model predicts the order volume of target time section.
It is understood that the time series of History Order shown in table one and table two is merely illustrative, those skilled in the art
Member can set the content to be included in order parameter according to actual needs.Wherein, table one show the time of History Order
The content that sequence at least to be included, i.e. order time and its corresponding order volume.
In specific implementation, the time series that possibly can not directly obtain History Order shown in table one and table two, can only
Obtain the initial data such as the corresponding complete vehicle logistics order time series data of History Order, inventory's sequence data.Therefore, Ke Yitong
It crosses certain algorithm to handle acquired initial data, for example, data cleansing, excluding outlier etc., therefrom extract
The time series of History Order required for the present invention.
Step 130, using the time series of History Order and associated external parameter as sample, training convolutional nerve net
Network model.
Fig. 2 is the exemplary structure signal of the convolutional neural networks model in the order forecast method of one embodiment of the invention
Figure.Convolutional neural networks (Convolutional Neural Network, CNN) model includes multiple layers and convolution kernel, this is more
A layer of quantity and the size of convolution kernel are associated with the quantity of the data characteristics of History Order and/or sample.Refering to what is shown in Fig. 2,
It may include input layer 220, multiple hidden layers 240 and output layer 250 in multiple layers of convolutional neural networks.Multiple hidden layers
240 may include one or more convolutional layers, the linear elementary layer (ReLU layers) of one or more amendments, one or more ponds
Layer, one or more be fully connected layer, or the like, or combinations thereof.
For illustrative purposes, multiple exemplary hidden layers 240 of CNN model are shown comprising convolutional layer 240-1,
Pond layer 240-2 and it is fully connected a layer 240-N.Convolutional layer 240-1 may include multiple convolution kernels (such as A, B, C and D).Chi Hua
Layer 240-2 can be using the output of convolutional layer 240-1 as input.Pond layer 240-2 may include multiple pond nodes (such as E,
F, G and H).Multiple pond node can be used to sample the output of convolutional layer 240-1, and therefore can reduce meter
The computation burden of the data processing of calculation machine and increase data processing speed.Being fully connected a layer 240-N may include multiple nerves
First (such as O, P, M and N).Multiple neuron may be coupled to multiple nodes from such as previous layer of pond layer.
Fig. 3 is the schematic diagram that order forecast method according to an embodiment of the invention carries out order volume prediction.With reference to Fig. 3 institute
Show, in the method, using the time series of History Order as the sample 311 for being input to the convolutional neural networks.In these samples
Before sheet 311 is input to convolutional neural networks, needs to carry out these samples 311 data normalization processing 320, be conducive to mention in this way
Rise the operational efficiency and learning performance of algorithm.
In some embodiments, be input in the sample of convolutional neural networks model further include History Order time series
Variation characteristic.The variation characteristic can indicate within certain a period of time, and order volume changed with time rule, such as in 1 year
In, the changing rule of order volume every month;Again for example in 1 year, the change of goods orders amounts every month of certain a kind of merchandise classification
Law etc..For the embodiment of the present invention to how to obtain the variation characteristic with no restrictions, those skilled in the art can be according to reality
It needs, the variation characteristic of any order parameter and combinations thereof is calculated using any way.
In practical applications, some associated external parameters can generate important influence to the order volume of logistics order.This
A little associated external parameters can include but is not limited to weather, economic data etc. or combinations thereof.For example, fine within certain a period of time
Bright weather is more, and order volume increases, and bad weather is more, and order volume is reduced;Economic data is preferable within certain a period of time, order
Amount increases, and economic data is poor, and order volume is reduced.Therefore, by the time series of these associated external parameters and History Order
Together as the sample of input convolutional neural networks model.In the order using the convolutional neural networks model to target time section
When amount is predicted, so that it may in conjunction with factors such as weather forecast and/or Economic Analysis Reports, the more accurate prediction object time
The order volume of section.In these embodiments, can will parameter related with time series, such as M~N-1 months orders
The input sample as convolutional layer 331 such as amount, weather condition, economic conditions, and by the other parameters 312 unrelated with time series
As the input sample for being fully connected layer 332 as composed by several layers neural network hidden layer.Respectively by the output of convolutional layer 331
With input of the output as next layer of neural network hidden layer 340 for being fully connected layer 332, and by the neural network 340 output
Prediction result 350.It is merely illustrative shown in Fig. 3, it is not limited to the actual quantity of each layer of neural network.
In some embodiments, sample obtained in this step 130 can be divided into training set, verifying collection and test
Collection.Wherein using training set come training convolutional neural networks model, and observe standard of the convolutional neural networks model on verifying collection
Exactness.When the accuracy on verifying collection reaches highest, deconditioning, and using convolutional neural networks model on test set
Performance carrys out Evaluated effect.For example, table four is enter into the sample instantiation of the convolutional layer of convolutional neural networks model.
Table four:
In the example shown in table four, be input to convolutional neural networks model convolutional layer sample include by the order time,
The time series and respective orders time corresponding external parameter for the History Order that merchandise classification obtains.The wherein order time
From the M month to the N-1 month, total N-M months order volumes are input quantities.The order volume of the N month was used as according to the M month to the N-1 month
The output quantity that predicts of order volume.An example of the weather as external parameter, also corresponds respectively to these order times.Root
Classify according to merchandise classification and weather to these History Orders, obtains the time series of every class History Order, be formed more
A sample comprising input-output.
In some embodiments, the sample in table four is divided into three parts, it is assumed that the sample size in every portion is s.The
Portion is used as training set, is used for training convolutional neural networks model.It include order volume, the commodity class in the t middle of the month in the training set
The data such as other and weather.Assuming that using the order volume in the t middle of the month, merchandise classification and weather as input, t+1 months order volumes
As output.Merchandise classification corresponding to t+1 months order volumes and weather are known.By training, keep the convolution refreshing
Output through network model is equal to t+1 months, certain a kind of merchandise classification, the order volume under certain weather condition.
BP algorithm can be used during being trained to convolutional neural networks model.Wherein, by training error come
The input weight and biasing of each interlayer are gradually adjusted, the algorithm of the process foundation of this adjustment may include gradient descent method and most
Small square law.Gradient descent method is used in a preferred embodiment of the invention.
Second part of sample is as verifying collection, before training process terminates, the sample that verifying is concentrated is input to trained
Convolutional neural networks model in.Since the sample data of verifying collection is different from the data in training set, it is passed through by observation
The output result of trained convolutional neural networks model can assess the accuracy of the housebroken convolutional neural networks model, when
When accuracy on verifying collection reaches highest, deconditioning.In another embodiment, an accuracy desired value E can be preset, if
Accuracy desired value E has not yet been reached in the accuracy of the housebroken convolutional neural networks model, then continues with training set pair
The convolutional neural networks model is trained, if having reached accuracy desired value E, deconditioning.It is understood that
When accuracy desired value E has not yet been reached in the accuracy of the housebroken convolutional neural networks model, training can also be updated
The sample of concentration, such as increase some new samples and enter in training set, continue to train the convolution using the training set of the update
Neural network model.
Third part sample is input to trained convolutional neural networks mould as test set, by the sample data in test set
In type.Assuming that the sample data in test set includes the data such as order volume, merchandise classification and the weather in the c middle of the month.Pass through training
The output obtained of good convolutional neural networks model is c+1 months certain a kind of merchandise classification, under certain weather condition
Order volume.Certain a kind of merchandise classification, the order volume under certain weather condition are known within c+1 months.Compare convolution mind
Output and actual order volume through network model, can assess the performance of the convolutional neural networks model.
Above-mentioned example is not limited to division mode and quantity to sample.For example, in some embodiments, Ke Yixuan
It selects the 50% of sample total and is used as training set, verifying collects and the quantity of test set is all the 25% of sample total.Other real
It applies in example, can be used to be input to the content that the sample in convolutional neural networks model is included determine according to actual needs.
Step 140, using the order volume of housebroken convolutional neural networks model prediction target time section.
Still shown in the table four for sample, in this embodiment, target time section is the n-th moon, the n-th moon
Order volume not yet generate, particular number is unknown.When the convolutional neural networks model is by after training, it can use the warp
Trained convolutional neural networks model predicts the order volume of the n-th moon.Due to removing the order time in the exemplary sample
It further include outside merchandise classification and weather, therefore when being predicted using the housebroken convolutional neural networks model, Ke Yigen
According to the information of weather forecast, the order volume of the n-th moon a kind of merchandise classification is predicted.
The present invention using convolutional neural networks model can from a variety of sample datas, such as the time, place, merchandise classification,
Information relevant to the following order volume variation is extracted in the various data such as weather, economy, and using these information to order volume
It is predicted.
Compared with the traditional way, order forecast method of the invention can preferably be intended by convolutional neural networks model
Close nonlinear relationship between data;Convolutional neural networks model can from data self-aid learning data characteristics, do not need too
More artificial designs solves the problems, such as the experience that prediction effect depends on people.In addition to this, convolutional neural networks can also be very
Feature possessed by the data of good fitting a period of time sequence learns variation rule of the logistics order volume within phase of history period
Rule.
In some embodiments, when generating new History Order, order forecast method of the invention can be with extract real-time
New History Order is added in the set of all History Orders by the History Order.Convolutional Neural net in these embodiments
Network model can update the data of input sample in real time, and carry out on-line study, make its own predictive ability constantly quilt
Strengthen, has the characteristics that intelligentized.
Fig. 4 is the prediction result of order forecast method according to an embodiment of the invention.Refering to what is shown in Fig. 4, with line is used
The result A of property approximating method is compared, and utilizes the predictablity rate and stabilization of order forecast method result B obtained of the invention
Property is obviously improved.
Fig. 5 is the performing environment schematic diagram of the order forecast method of one embodiment of the invention.Refering to what is shown in Fig. 5, the execution
Environment is mainly by line server 510, firewall 520, router 530 and user 540.Wherein, user 540 illustratively includes
User 541 and user 542, the example are not limited to the quantity and distribution of user.By order forecasting side according to the invention
In method trained convolutional neural networks model is put into line server 510, and be stored with History Order, order parameter
Database is connected.Line server 510 is by the logistics order data in reading data in real-time library, automatically by convolutional Neural
Network model carries out data processing and order volume prediction, has good timeliness.In some embodiments, line server
510 can realize in cloud platform.Only as an example, cloud platform may include private clound, public cloud, mixed cloud, community cloud, distribution
Cloud, interconnection cloud, multiple cloud etc. or any combination thereof.
Firewall 520 keeps apart line server 510 and public access net.Router 530 is used to interconnect in Multi net voting
Establish flexible connection in environment, and by from line server 510 data and information be supplied to user 540.
Fig. 6 be one embodiment of the invention prediction order device 600 block diagram, device 600 include extraction unit 601,
Acquiring unit 602, construction unit 603 and predicting unit 604.Extraction unit 601 is for extracting more History Orders, every history
Order has at least one order parameter, and order parameter includes the order time.Acquiring unit 602 according to order parameter for obtaining
The time series of History Order.Construction unit 603 be used for using the time series of History Order and associated external parameter as
Sample, training convolutional neural networks model.Predicting unit 604 is used to use housebroken convolutional neural networks model prediction target
The order volume of period.
In one embodiment of this invention, acquiring unit 602 obtains the time sequence of a History Order according to the order time
Column.Optionally, acquiring unit 602 obtains time series as follows: being ordered according to the one or more except the order time
One-parameter classifies more History Orders;The time series of every class History Order is obtained according to the order time.
In one embodiment of this invention, the order parameter further include: departure place, destination, merchandise classification, order class
Type or combinations thereof.In one embodiment of this invention, external parameter includes weather, economic data or combinations thereof.
In one embodiment of this invention, construction unit 603 is also used to the variation characteristic of the time series of History Order
Including in the sample.
In one embodiment of this invention, convolutional neural networks model includes multiple layers and convolution kernel, the multiple layer
Quantity and the size of convolution kernel are associated with the quantity of the data characteristics of History Order and/or sample.
In one embodiment of this invention, construction unit 603 is also used to for sample being divided into training set, verifying collection and test
Collection, wherein using training set come training convolutional neural networks model, and observe standard of the convolutional neural networks model on verifying collection
Exactness, when the accuracy on verifying collection reaches highest, deconditioning, and it is related to network model on test set using convolution
Performance carrys out Evaluated effect.
In one embodiment of this invention, every to generate new History Order, extraction unit 601 is ordered with regard to extract real-time history
It is single.
Technical solution of the present invention further includes a kind of logistics system, which includes memory and processor.The system can
To be included in line server 510 shown in fig. 5, it is also possible to autonomous system.
Fig. 7 is the block diagram of the logistics system of one embodiment of the invention.Refering to what is shown in Fig. 7, the logistics system may include interior
Portion's communication bus 701, processor 702, read-only memory (ROM) 703, random access memory (RAM) 704, communication port
705 and hard disk 707.The data communication between internal system various components may be implemented in internal communication bus 701.Processor
702 can be judged and be issued prompt.In some embodiments, processor 702 can be made of one or more processors.
System and external module may be implemented in communication port 705, such as data communication is carried out between user 540 shown in Fig. 6.
In some embodiments, system may be coupled to network by communication port 705, and letter is sent and received from network
Breath and data.The connection can be wired connection, be wirelessly connected, can be realized data transmission and/or received any other are logical
Any combination of letter connection, and/or these connections.Wired connection may include such as cable, optical cable, telephone wire or it is any
Combination.Wireless connection may include such as bluetoothTMLink, Wi-FiTMLink, WiMaxTMLink, WLAN link, ZigBee link,
Mobile network's link (for example, 3G, 4G, 5G etc.) etc. or combinations thereof.In some embodiments, communication port 705 can be including
Standardization Communication port, RS232, RS485 etc..
Memory in system may include various forms of program storage units and data storage element, such as hard disk
707, read-only memory 703 and random access memory 704 can store computer disposal and/or various numbers that communication uses
According to possible program instruction performed by file and processor 702.Processor 702 in system executes these instructions with reality
The major part of the method for existing prediction order of the invention.The result of the processing of processor 702 is transmitted to user by communication port 705
540, and show 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 this method 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 prediction technique of order, comprising the following steps:
More History Orders are extracted, every History Order has at least one order parameter, when the order parameter includes order
Between;
The time series of the History Order is obtained according to the order parameter;
Using the time series of the History Order and associated external parameter as sample, training convolutional neural networks model;
And
Use the order volume of housebroken convolutional neural networks model prediction target time section.
2. the method as described in claim 1, which is characterized in that obtain the time of the History Order according to the order parameter
The step of sequence includes:
The time series of a History Order is obtained according to the order time.
3. the method as described in claim 1, which is characterized in that obtain the time of the History Order according to the order parameter
The step of sequence includes:
The more History Orders are classified according to one or more order parameters except the order time;
The time series of every class History Order is obtained according to the order time.
4. the method as described in claim 1, which is characterized in that the order parameter further include: departure place, destination, commodity
Classification, order type or combinations thereof.
5. the method as described in claim 1, which is characterized in that the external parameter includes weather, economic data or combinations thereof.
6. the method as described in claim 1, which is characterized in that further include that the variation of the time series of the History Order is special
Sign includes in the sample.
7. the method as described in claim 1, which is characterized in that the convolutional neural networks model includes multiple layers and convolution
The size of core, the quantity of the multiple layer and the convolution kernel and the data characteristics of the History Order and/or the sample
Quantity association.
8. a kind of device for predicting order, comprising:
Extraction unit, for extracting more History Orders, every History Order has at least one order parameter, the order ginseng
Number includes the order time;
Acquiring unit, for obtaining the time series of the History Order according to the order parameter;
Construction unit, for using the time series of the History Order and associated external parameter as sample, training convolutional
Neural network model;And
Predicting unit, for using the order volume of housebroken convolutional neural networks model prediction target time section.
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 for being stored with computer program code, the computer program code is by processor
The method according to claim 1 to 7 is realized when execution.
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