CN107038503A - A kind of Demand Forecast method and system of shared equipment - Google Patents
A kind of Demand Forecast method and system of shared equipment Download PDFInfo
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- CN107038503A CN107038503A CN201710253210.5A CN201710253210A CN107038503A CN 107038503 A CN107038503 A CN 107038503A CN 201710253210 A CN201710253210 A CN 201710253210A CN 107038503 A CN107038503 A CN 107038503A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Abstract
The invention discloses a kind of Demand Forecast method and system of shared equipment, wherein this method includes：Gather the location data of target area within a predetermined period of time；Location data is inputted to the pretreatment layer of the neural network model pre-established, the corresponding sample data in target area is obtained to handle each location data according to preprocessing rule；The hidden layer that each sample data is inputted into neural network model is to carry out sample training；When getting prediction instruction, receive current position determination data and input into the neural network model after trained to be predicted the outcome.This method sets up neural network model using neural network algorithm, the sample data largely obtained is trained by deep learning, so as to which after input current position determination data, predicting the outcome for the corresponding demand in target area can be obtained, so as to realize real-time estimate.In addition, after location data is got, can be pre-processed to location data, therefore, it is possible to improve the accuracy predicted the outcome.
Description
Technical field
The present invention relates to technical field of data processing, the Demand Forecast method of more particularly to a kind of shared equipment and it is
System.
Background technology
The life of people for convenience, occurs in that much shared equipment, for example, shares bicycle, or shared automobile etc..It is shared
Bicycle refers to that enterprise cooperates with government, is carried in campus, subway station, bus station, residential block, shopping centre, common service area etc.
It is provided from bicycle shared service of driving a vehicle.A few days ago, various types of shared bicycles sweep the country rapidly various regions, have even extended to sea
Outside.
However, people occur in that many problems during shared bicycle is used, one of them is exactly to cycle difficult ask
Topic, many users really can not find car around when needing to hire a car, and when need not sometimes hiring a car around have a large amount of spare time
Put car, on the one hand, this, using great inconvenience is brought, on the other hand, also brings very big loss of income to user to enterprise.
As can be seen here, how the demand of the regional shared equipment of look-ahead one, so as to improve shared equipment
Utilization rate be those skilled in the art ground urgently to be resolved hurrily problem.
The content of the invention
It is an object of the invention to provide a kind of Demand Forecast method and system of shared equipment, for look-ahead one
The demand of the shared equipment in area, so as to improve the utilization rate of shared equipment.
In order to solve the above technical problems, the present invention provides a kind of Demand Forecast method of shared equipment, including：
Gather the location data of target area within a predetermined period of time；Wherein, the location data include positional information, when
Between information and shared equipment quantity；
The location data is inputted to the pretreatment layer of the neural network model pre-established, with according to preprocessing rule
Handle each location data and obtain the corresponding sample data in the target area；
The hidden layer that each sample data is inputted into the neural network model is to carry out sample training；
When getting prediction instruction, receive current position determination data and input into the neural network model after trained with
The demand for obtaining the target area predicts the outcome.
Preferably, if the target area is more than presumptive area, in addition to：The target area is divided into many height
Region；
Wherein, it regard all subregion as new target area.
Preferably, the preprocessing rule includes singular value decomposition method or PCA.
Preferably, the hidden layer in the neural network model is multiple.
Preferably, the shared equipment is shared bicycle or shared automobile.
In order to solve the above technical problems, the present invention also provides a kind of Demand Forecast system of shared equipment, including：
Collecting unit, for gathering the location data of target area within a predetermined period of time；Wherein, the location data bag
Include the quantity of positional information, temporal information and shared equipment；
Pretreatment unit, for the location data to be inputted to the pretreatment layer of the neural network model pre-established,
The corresponding sample data in the target area is obtained to handle each location data according to preprocessing rule；
Sample training unit, for the hidden layer for inputting each sample data into the neural network model to enter
Row sample training；
Predicting unit, for when getting prediction instruction, receiving current position determination data and inputting to the god after trained
Demand through obtaining the target area in network model predicts the outcome.
Preferably, if the target area is more than presumptive area, in addition to：Division unit, for by the target area
Domain is divided into many sub-regions；
Wherein, it regard all subregion as new target area.
Preferably, the preprocessing rule of the pretreatment unit includes singular value decomposition method or PCA.
Preferably, the hidden layer in the neural network model is multiple.
Preferably, the shared equipment is shared bicycle or shared automobile.
The Demand Forecast method and system of shared equipment provided by the present invention, wherein, this method utilizes neutral net
Algorithm sets up neural network model, and the sample data largely obtained is trained by deep learning method, so that in input
After current position determination data, predicting the outcome for the corresponding demand in target area can be obtained.In addition, getting location data
Afterwards, location data can be pre-processed, therefore, it is possible to improve the accuracy predicted the outcome.As can be seen here, this method can not only
Real-time estimate is enough realized, and the accuracy predicted the outcome is guaranteed.
Brief description of the drawings
In order to illustrate the embodiments of the present invention more clearly, the required accompanying drawing used in embodiment will be done simply below
Introduce, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for ordinary skill people
For member, on the premise of not paying creative work, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 is a kind of flow chart of the Demand Forecast method of shared equipment provided in an embodiment of the present invention；
Fig. 2 is a kind of structure chart of the Demand Forecast system of shared equipment provided in an embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.Based on this
Embodiment in invention, those of ordinary skill in the art are not under the premise of creative work is made, and what is obtained is every other
Embodiment, belongs to the scope of the present invention.
The core of the present invention is to provide a kind of Demand Forecast method and system of shared equipment, for look-ahead one
The demand of the shared equipment in area, so as to improve the utilization rate of shared equipment.
In order that those skilled in the art more fully understand the present invention program, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.
Fig. 1 is a kind of flow chart of the Demand Forecast method of shared equipment provided in an embodiment of the present invention.Such as Fig. 1 institutes
Show, the Demand Forecast method of shared equipment includes：
S11：Gather the location data of target area within a predetermined period of time；Wherein, location data include positional information, when
Between information and shared equipment quantity.
S12：Location data is inputted to the pretreatment layer of the neutral net pre-established, to be handled according to preprocessing rule
Each location data obtains the corresponding sample data in target area.
S13：The hidden layer that each sample data is inputted into neural network model is to carry out sample training.
S14：When getting prediction instruction, receive current position determination data and input to the neural network model after trained
In predicted the outcome with the demand for obtaining target area.
It should be noted that needing to pre-establish neural network model, the foundation foundation of neural network model in the present invention
The principle of neutral net is built, and the field being well known to those skilled in the art, the present invention is repeated no more.Preferably implement
Mode, it is shared bicycle or shared automobile to share equipment., can be with it is to be understood that shared equipment is in addition to above two
It is the equipment of other species, the present embodiment is repeated no more.
In step s 11, can be that 1 hour or half are small depending on the size of predetermined amount of time can be according to actual conditions
When.In specific implementation, equipment is shared, for example, shares bicycle or shared automobile, is fitted with vehicle positioning module, passes through GPS
Alignment system can just obtain the location data of each shared equipment in real time.It is exactly to be obtained by GPS positioning system in the present embodiment
To the location data of each shared equipment.
Due in the present embodiment, selection is location data in a predetermined amount of time, and therefore, these location datas have
There may be repetition.Such as within certain a period of time, the shared number of devices in the s of certain region does not become always, i.e., previous collection
The location data that the location data and rear a cycle that cycle collects are collected has repetition, therefore, within a predetermined period of time, adopts
There will be more redundant data in the location data collected, if these data are fully entered into neural network model
If, then training result is not only influenceed, substantial amounts of invalid computing is also increased.For another example, within a predetermined period of time, certain is shared and set
Although the standby front and rear positional information gathered twice is different, two positional informations all in the target area, then such data
It is also unnecessary.It is understood that the repeated data rejected is more, then the speed of sample training is faster, but if pre- place
The improper of rule setting is managed, then easily causes sample data missing, the precision reduction predicted the outcome.Preferably embodiment party
Formula, preprocessing rule includes singular value decomposition method or PCA.
After sample data is obtained, sample data is inputted into neural network model and carries out sample training to be instructed
Neural network model after white silk, after current position determination data is received, it is possible to obtained currently by the neutral net after training
The demand of the corresponding target area of location data predicts the outcome.Here predict the outcome be exactly neural network model output
As a result, predict the outcome the quantity comprising temporal information and shared equipment.By predicting the outcome, staff can just set shared
Before standby quantity is at the time of temporal information correspondence, it is thrown in target area.
It is emphasized that because the amount of sample data is more, frequency of training is more, then the accuracy predicted the outcome is got over
Height, therefore, it is necessary to by multiple, substantial amounts of sample training in specific implementation.Preferably embodiment, neutral net
Hidden layer in model is multiple.
The Demand Forecast method for the shared equipment that the present embodiment is provided, neutral net mould is set up using neural network algorithm
Type, is trained by deep learning method to the sample data largely obtained, so that after input current position determination data, can
Obtain predicting the outcome for the corresponding demand in target area.In addition, after location data is got, can be carried out to location data pre-
Processing, therefore, it is possible to improve the accuracy predicted the outcome.As can be seen here, this method, can not only realize real-time estimate, and
The accuracy predicted the outcome is guaranteed.
Preferably embodiment, on the basis of above-described embodiment, if target area is more than presumptive area, is also wrapped
Include：Target area is divided into many sub-regions；
Wherein, it regard all subregion as new target area.
In specific implementation, if a target area is larger, the demand for sharing equipment in the region is just
Can be of a relatively high.It is contemplated that, convenience that user uses is, it is necessary to more accurate in placement position, so as to reduce user
The time asked for.In the present embodiment, if target area is larger, first the target area is divided, is divided into multiple sub-districts
Domain, is all then independent, new relative to one target area between every sub-regions, i.e., is performed according to step S11-S14
.It so can be obtained by that many sub-regions are corresponding to predict the outcome.Staff predicts the outcome according to this, in every height
The shared equipment of respective amount is delivered in region.
Above-mentioned part describes the corresponding embodiment of Demand Forecast method of shared equipment, the present invention also provide it is a kind of with
The embodiment of the Demand Forecast system of the corresponding shared equipment of this method.Embodiment and method part due to device part
Embodiment mutually correspond to, therefore device part embodiment refer to method part embodiment description, wouldn't repeat here.
Fig. 2 is a kind of structure chart of the Demand Forecast system of shared equipment provided in an embodiment of the present invention.Such as Fig. 2 institutes
Show, the Demand Forecast system of shared equipment includes：
Collecting unit 11, for gathering the location data of target area within a predetermined period of time；Wherein, location data includes
The quantity of positional information, temporal information and shared equipment.
Pretreatment unit 12, for location data to be inputted to the pretreatment layer of the neutral net pre-established, with according to
Each location data of preprocessing rule processing obtains the corresponding sample data in target area.
Sample training unit 13, for the hidden layer for inputting each sample data into neural network model to carry out sample
Train
Predicting unit 14, for when getting prediction instruction, receiving current position determination data and inputting to after trained
Predicted the outcome in neural network model with the demand for obtaining target area.
Preferably embodiment, if target area is more than presumptive area, in addition to：Division unit, for by mesh
Mark region division is many sub-regions；
Wherein, it regard all subregion as new target area.
Preferably embodiment, the preprocessing rule of pretreatment unit 12 includes singular value decomposition method or principal component point
Analysis method.
Hidden layer in preferably embodiment, neural network model is multiple.
Preferably embodiment, shares equipment to share bicycle or shared automobile.
The Demand Forecast system for the shared equipment that the present embodiment is provided, neutral net mould is set up using neural network algorithm
Type, is trained by deep learning method to the sample data largely obtained, so that after input current position determination data, can
Obtain predicting the outcome for the corresponding demand in target area.In addition, after location data is got, can be carried out to location data pre-
Processing, therefore, it is possible to improve the accuracy predicted the outcome.As can be seen here, this method, can not only realize real-time estimate, and
The accuracy predicted the outcome is guaranteed.
The Demand Forecast method and system to shared equipment provided by the present invention are described in detail above.Explanation
The embodiment of each in book is described by the way of progressive, what each embodiment was stressed be it is different from other embodiment it
Place, between each embodiment identical similar portion mutually referring to.For device disclosed in embodiment, due to itself and reality
Apply that method disclosed in example is corresponding, so description is fairly simple, related part is referring to method part illustration.It should refer to
Go out, for those skilled in the art, under the premise without departing from the principles of the invention, can also be to the present invention
Some improvement and modification are carried out, these are improved and modification is also fallen into the protection domain of the claims in the present invention.
It should also be noted that, in this manual, such as first and second or the like relational terms be used merely to by
One entity or operation make a distinction with another entity or operation, and not necessarily require or imply these entities or operation
Between there is any this actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant meaning
Covering including for nonexcludability, so that process, method, article or equipment including a series of key elements not only include that
A little key elements, but also other key elements including being not expressly set out, or also include be this process, method, article or
The intrinsic key element of equipment.In the absence of more restrictions, the key element limited by sentence "including a ...", is not arranged
Except also there is other identical element in the process including the key element, method, article or equipment.
Claims (10)
1. a kind of Demand Forecast method of shared equipment, it is characterised in that including：
Gather the location data of target area within a predetermined period of time；Wherein, the location data includes positional information, time letter
The quantity of breath and shared equipment；
The location data is inputted to the pretreatment layer of the neural network model pre-established, to be handled according to preprocessing rule
Each location data obtains the corresponding sample data in the target area；
The hidden layer that each sample data is inputted into the neural network model is to carry out sample training；
When getting prediction instruction, receive current position determination data and input into the neural network model after trained to obtain
The demand of the target area predicts the outcome.
2. the Demand Forecast method of shared equipment according to claim 1, it is characterised in that if the target area is big
In presumptive area, then also include：The target area is divided into many sub-regions；
Wherein, it regard all subregion as new target area.
3. the Demand Forecast method of shared equipment according to claim 1, it is characterised in that the preprocessing rule bag
Include singular value decomposition method or PCA.
4. the Demand Forecast method of shared equipment according to claim 1, it is characterised in that the neural network model
In the hidden layer to be multiple.
5. the Demand Forecast method of shared equipment according to claim 1, it is characterised in that the shared equipment is common
Enjoy bicycle or shared automobile.
6. a kind of Demand Forecast system of shared equipment, it is characterised in that including：
Collecting unit, for gathering the location data of target area within a predetermined period of time；Wherein, the location data includes position
The quantity of confidence breath, temporal information and shared equipment；
Pretreatment unit, for the location data to be inputted to the pretreatment layer of the neural network model pre-established, with by
The corresponding sample data in the target area is obtained according to each location data of preprocessing rule processing；
Sample training unit, for the hidden layer for inputting each sample data into the neural network model to carry out sample
This training；
Predicting unit, for when getting prediction instruction, receiving current position determination data and inputting to the nerve net after trained
Predicted the outcome in network model with the demand for obtaining the target area.
7. the Demand Forecast system of shared equipment according to claim 6, it is characterised in that if the target area is big
In presumptive area, then also include：Division unit, for the target area to be divided into many sub-regions；
Wherein, it regard all subregion as new target area.
8. the Demand Forecast system of shared equipment according to claim 6, it is characterised in that the pretreatment unit
Preprocessing rule includes singular value decomposition method or PCA.
9. the Demand Forecast system of shared equipment according to claim 6, it is characterised in that the neural network model
In the hidden layer to be multiple.
10. the Demand Forecast system of shared equipment according to claim 6, it is characterised in that the shared equipment is
Shared bicycle or shared automobile.
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| CN201710253210.5A CN107038503A (en) | 2017-04-18 | 2017-04-18 | A kind of Demand Forecast method and system of shared equipment |
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| US11107135B2 (en) | 2017-11-20 | 2021-08-31 | Advanced New Technologies Co., Ltd. | Assessing shared vehicle popularities |
| CN107944618A (en) * | 2017-11-20 | 2018-04-20 | 阿里巴巴集团控股有限公司 | Planing method of layouting, device and the electronic equipment of shared vehicle |
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| CN109543922B (en) * | 2018-12-20 | 2021-04-20 | 西安电子科技大学 | Time-period borrowing and returning amount prediction method for single-vehicle station group shared by piles |
| CN111507541B (en) * | 2020-04-30 | 2021-01-29 | 南京福佑在线电子商务有限公司 | Goods quantity prediction model construction method, goods quantity measurement device and electronic equipment |
| CN111507541A (en) * | 2020-04-30 | 2020-08-07 | 南京福佑在线电子商务有限公司 | Goods quantity prediction model construction method, goods quantity measurement device and electronic equipment |
| CN112381560A (en) * | 2020-10-23 | 2021-02-19 | 东北石油大学 | Shared equipment product market prediction system and method |
| CN112381560B (en) * | 2020-10-23 | 2022-10-21 | 东北石油大学 | Shared equipment product market prediction system and method |
| CN113452379A (en) * | 2021-07-16 | 2021-09-28 | 燕山大学 | Section contour dimension reduction model training method and system and data compression method and system |
| CN113452379B (en) * | 2021-07-16 | 2022-08-02 | 燕山大学 | Section contour dimension reduction model training method and system and data compression method and system |
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