CN109754168A - Charging station site selecting method and device - Google Patents
Charging station site selecting method and device Download PDFInfo
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- CN109754168A CN109754168A CN201811578050.2A CN201811578050A CN109754168A CN 109754168 A CN109754168 A CN 109754168A CN 201811578050 A CN201811578050 A CN 201811578050A CN 109754168 A CN109754168 A CN 109754168A
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Abstract
The invention discloses a kind of charging station site selecting method and devices.Wherein, this method comprises: obtaining the achievement data of multiple selected cells, wherein each selected cell is corresponding at least one charging station；It is handled based on achievement data of the preset model to multiple selected cells, obtain the charging station utilization rate of each selected cell, wherein, preset model is obtained using multi-group data by machine learning algorithm, and every group of data in multi-group data include: the achievement data of each selected cell and the charging station utilization rate of each selected cell；Target selected cell is determined from multiple selected cells according to charging station utilization rate.The present invention is solved since charging station addressing is unreasonable, the technical problem for causing the service efficiency of charging station low.
Description
Technical field
The present invention relates to electric automobile charging station addressing fields, in particular to a kind of charging station site selecting method and dress
It sets.
Background technique
With domestic economy rapid development and environmental problem it is increasingly prominent, since electric car is mainly with vehicle power supply
It for power, has fewer environmental impacts, utilization rate is higher and higher, and in turn, the demand to electric automobile charging station also increasingly increases.
Currently, mainly realizing the addressing of electric automobile charging station by following methods:
Mode one: charging station and gas station are built jointly, by existing gas station site directly as the site of charging station.It should
Mode does not consider the distribution ability on charging station periphery.Charging station periphery facility in the higher situation of the demand of electric energy,
The implementation of building a station for building charging station here will substantially reduce.
Mode two: it based on the power distribution network space layout based on " gridding ", is built using each distribution grid as one
If the address of charging station.Which does not consider the factors such as road network status, the volume of the flow of passengers, is unfavorable for making full use of for charging station.
It can be seen that above-mentioned several charging station site selecting methods, Consideration is more unilateral, may cause the construction of charging station
Or use is unreasonable, so that the utilization rate of charging station reduces, waste of resource.
For above-mentioned problem, currently no effective solution has been proposed.
Summary of the invention
The embodiment of the invention provides a kind of charging station site selecting method and devices, at least to solve due to charging station addressing not
Rationally, the technical problem for causing the service efficiency of charging station low.
According to an aspect of an embodiment of the present invention, a kind of charging station site selecting method is provided, comprising: obtain multiple addressings
The achievement data of unit, wherein each selected cell is corresponding at least one charging station；Based on preset model to multiple addressing lists
The achievement data of member is handled, and the charging station utilization rate of each selected cell is obtained, wherein preset model is to use multiple groups number
According to what is obtained by machine learning algorithm, every group of data in multi-group data include: each selected cell achievement data and
The charging station utilization rate of each selected cell；Target selected cell is determined from multiple selected cells according to charging station utilization rate.
Further, charging station site selecting method further include: obtain the corresponding area information of multiple selected cells；Based on default
Index system extracts the corresponding achievement data of multiple selected cells from the corresponding area information of multiple selected cells, wherein pre-
If index system includes multiple index grades, each index grade corresponds to multiple indexs.
Further, charging station site selecting method further include: obtain history charge station information, wherein history charge station information
Including at least one following: the utilization rate of each corresponding achievement data of index grade and each charging station；Based on random gloomy
Woods algorithm carries out Supervised machine learning to history charge station information, obtains preset model.
Further, charging station site selecting method further include: charging station utilization rate and default benefit to multiple selected cells
It is compared with rate, obtains comparison result；The corresponding charging station service rating of multiple selected cells is determined according to comparison result；Root
Target selected cell is determined from multiple selected cells according to charging station service rating.
Further, charging station site selecting method further include: determined from multiple selected cells according to charging station service rating
At least one candidate unit；Determine the discrete value of the corresponding correction index of at least one candidate unit；According to correction index from
It dissipates value and determines the corresponding index score of each candidate unit；Index for selection score is greater than the candidate unit conduct of pre-set level score
Target selected cell.
Further, charging station site selecting method further include: correction index includes at least one following: distribution resource metrics,
Assessment of cost index, road network resource metrics.
According to another aspect of an embodiment of the present invention, a kind of charging station addressing device is additionally provided, comprising: module is obtained,
For obtaining the achievement data of multiple selected cells, wherein each selected cell is corresponding at least one charging station；Handle mould
Block obtains the charging station of each selected cell for handling based on achievement data of the preset model to multiple selected cells
Utilization rate, wherein preset model is obtained using multi-group data by machine learning algorithm, every group of data in multi-group data
It include: the achievement data of each selected cell and the charging station utilization rate of each selected cell；Determining module is filled for basis
Power station utilization rate determines target selected cell from multiple selected cells.
Further, it is determined that module includes: comparison module, for the charging station utilization rate to multiple selected cells and in advance
If utilization rate is compared, comparison result is obtained；First determining module, for determining multiple selected cells pair according to comparison result
The charging station service rating answered；Second determining module, for determining mesh from multiple selected cells according to charging station service rating
Mark selected cell.
According to another aspect of an embodiment of the present invention, a kind of storage medium is additionally provided, which includes storage
Program, wherein equipment where control storage medium executes charging station site selecting method in program operation.
According to another aspect of an embodiment of the present invention, a kind of storage medium is additionally provided, which is used to run program,
Wherein, charging station site selecting method is executed when program is run.
In embodiments of the present invention, multiple obtaining in such a way that big data technology plans charging station addressing
After the achievement data of selected cell, handled based on achievement data of the preset model to multiple selected cells, it is every to obtain
The charging station utilization rate of a selected cell finally determines target addressing list according to charging station utilization rate from excessively multiple selected cells
Member as charging station site.
In above process, the achievement data of multiple selected cells is the key factor for influencing charging station addressing, with machine
Based on learning algorithm, the key factor for influencing charging station addressing is handled, the target of charging station can be accurately determined
Position.Due to during the site to charging station carries out addressing, it is contemplated that the key factor for influencing charging station addressing, because
This, obtained target selected cell is user's selected cell more using charging station, to improve the use effect of charging station
Rate avoids the wasting of resources.
It can be seen that scheme provided herein can solve since charging station addressing is unreasonable, lead to charging station
The low technical problem of service efficiency.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is a kind of flow chart of charging station site selecting method according to an embodiment of the present invention；
Fig. 2 is a kind of flow chart of optional charging station site selecting method according to an embodiment of the present invention；And
Fig. 3 is a kind of structural schematic diagram of charging station addressing device according to an embodiment of the present invention.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work
It encloses.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to the embodiment of the present invention described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
Firstly, the part noun or term that occur during the embodiment of the present application is described are suitable for following solution
It releases:
The power distribution network space layout of " gridding " refers to that " gridding " distribution is planned in the way of " from bottom to top ", with
Plot power demand is guiding, is sorted out according to regional regulatory control to the plot of different land use property and Exploitation Depth, in conjunction with allusion quotation
Type load forecasting model carries out systematization load prediction；According to the Planning Standard of differentiation, to promote power supply reliability as mesh
Mark, 10 kilovolts of rack layouts are carried out in subregion, and extend 110 kv grids of planning and pipe trench layout, ultimately form polynary
Change program results, and is effectively docked with regional development planning.
Embodiment 1
According to embodiments of the present invention, a kind of charging station site selecting method embodiment is provided, it should be noted that in attached drawing
The step of process illustrates can execute in a computer system such as a set of computer executable instructions, although also,
Logical order is shown in flow chart, but in some cases, it can be to be different from shown by sequence execution herein or retouch
The step of stating.
Fig. 1 is the flow chart of charging station site selecting method according to an embodiment of the present invention, as shown in Figure 1, this method includes such as
Lower step:
Step S102 obtains the achievement data of multiple selected cells, wherein each selected cell and at least one charging station
It is corresponding.
It should be noted that above-mentioned selected cell can be that predeterminable area is divided into the obtained charging station of multiple portions
Selected cell, wherein charging station can be but be not limited to the charging station of electric car.
Optionally, the achievement data of multiple selected cells is the key factor having an impact to charging station addressing.It can be according to
The addressing region (i.e. above-mentioned predeterminable area) of province, city, area or county, small towns, village or street as charging station, wherein can be by village
The village or street are as addressing base unit (i.e. above-mentioned selected cell).Then, each administrative unit, provinces and cities is obtained by internet
Attribute information, for example, the distribution of the size of population, the density of population, building facility, electrical network capacity, power quality etc..Finally, really
The initial data that the attribute information (i.e. These parameters data) of fixed each selected cell is analyzed as data.
In addition it is also necessary to illustrate, a charging station can be set in each selected cell, multiple fill also can be set
Power station, for example, multiple charging stations are arranged in the higher selected cell of the density of population.
Step S104 is handled based on achievement data of the preset model to multiple selected cells, obtains each addressing list
The charging station utilization rate of member, wherein preset model is obtained using multi-group data by machine learning algorithm, in multi-group data
Every group of data include: the achievement data of each selected cell and the charging station utilization rate of each selected cell.
It should be noted that above-mentioned preset model be according to existing charge station information (i.e. history charge station information), it is comprehensive
Various dimensions variable is closed, by achievement data variable in response, using the charging station utilization rate of each selected cell as target variable
The model being trained, i.e., the achievement data of each selected cell are the input data of above-mentioned preset model, each addressing
The charging station utilization rate of unit is the output data of above-mentioned preset model.
Step S106 determines target selected cell according to charging station utilization rate from multiple selected cells.
It, can be according to charging station after obtaining the charging station utilization rate of each selected cell in a kind of optional scheme
The size of utilization rate is ranked up selected cell, and the selected cell for preferentially selecting charging station utilization rate big is as target addressing list
Member, to install charging station.
It, can be to multiple chargings in the case where in selected cell including multiple charging stations in another optional scheme
The charging station utilization rate averaged stood as each selected cell charging station utilization rate, alternatively, determining each charging
The weighted value stood, then further according to the weighted value of charging station each in selected cell to the charging station utilization rate of each charging station into
Row weighted average, using calculated result as the charging station utilization rate of each selected cell.Obtaining the charging of each selected cell
Utilization rate of standing and then selected cell is ranked up according to the size of charging station utilization rate, it is preferential to select charging station utilization rate
Big selected cell is as target selected cell, to install charging station.
Based on scheme defined by above-mentioned steps S102 to step S106, it can know, using big data technology to charging
The mode that addressing of standing is planned, after the achievement data for obtaining multiple selected cells, based on preset model to multiple addressings
The achievement data of unit is handled, to obtain the charging station utilization rate of each selected cell, finally according to charging station utilization rate
Determine target selected cell as the site of charging station from excessively multiple selected cells.
It is easily noted that, the achievement data of multiple selected cells is the key factor for influencing charging station addressing, with machine
Based on device learning algorithm, the key factor for influencing charging station addressing is handled, the mesh of charging station can be accurately determined
Cursor position.Due to during the site to charging station carries out addressing, it is contemplated that the key factor of charging station addressing is influenced,
Therefore, the target selected cell obtained is user's selected cell more using charging station, to improve the use of charging station
Efficiency avoids the wasting of resources.
It can be seen that scheme provided herein can solve since charging station addressing is unreasonable, lead to charging station
The low technical problem of service efficiency.
In a kind of optional scheme, carry out handling it based on achievement data of the preset model to multiple selected cells
Before, need to obtain the achievement data of multiple selected cells.Specifically, the corresponding area information of multiple selected cells is obtained first,
Then the corresponding finger of multiple selected cells is extracted from the corresponding area information of multiple selected cells based on pre-set level system again
Mark data.
It should be noted that pre-set level system includes multiple index grades, each index grade corresponds to multiple indexs.It can
Choosing, pre-set level system can be determined according to business diagnosis and site selection model, wherein pre-set level system may include one
Grade index and two-level index.First class index can include but is not limited to the quantity of building facility, the longitude and latitude of building facility, population
Density, road network speed, electrical network capacity, power quality, the power balance state of power distribution network, charging station utilization rate etc., wherein building
The longitude and latitude of facility can include but is not limited to school, hospital, market, parking lot, cell, office building, railway station position letter
Breath；Two-level index can include but is not limited to the information of building facility, for example, the daily vehicle flowrate in parking lot, school's classification with
And student's quantity, hospital grade, time series of outpatient amount, hospital bed digit, market rank and the daily volume of the flow of passengers, cell amount and
Number, office building or hotel's rank and area, the average daily volume of the flow of passengers in railway station, the area in railway station, railway station are dispatched a car day
It counts, the vehicle number etc. that arrives at a station day in railway station.
In a kind of optional scheme, handles, obtain based on achievement data of the preset model to multiple selected cells
To before the charging station utilization rate of each selected cell, firstly, obtaining history charge station information, it is then based on random forests algorithm
Supervised machine learning is carried out to history charge station information, obtains preset model.Wherein, history charge station information include such as down toward
It is one of few: the utilization rate of each corresponding achievement data of index grade and each charging station.
It should be noted that above-mentioned history charge information is the charging station number being drawn into from existing charge station information
According to.
Optionally, using existing charge station information, integrated multidimensional degree variable, by building facility (for example, hospital, school,
Airport, parking lot, market, cell, office building, railway station etc.) quantity, build facility location information and two-level index pair
Variable, the utilization rate of each charging station become response using random forests algorithm as target variable the data answered in response
Amount and target variable carry out Supervised machine learning, obtain preset model.Then again by big data technology constantly to default mould
Type is trained iteration, and then preset model is enable to export the utilization rate of accurate charging station.
In a kind of optional scheme, after obtaining the corresponding charging station utilization rate of each selected cell, further root
Target selected cell is determined from multiple selected cells according to charging station utilization rate.Specifically, to the charging station of multiple selected cells
Utilization rate and default utilization rate are compared, and obtain comparison result, then determine multiple selected cells pair according to comparison result
The charging station service rating answered, and target selected cell is determined from multiple selected cells according to charging station service rating.
It should be noted that the quantity of default utilization rate can be multiple, wherein default utilization rate can be passed through by expert
Method is tested to obtain.For example, charging station service rating is level-one in the case where charging station utilization rate is more than or equal to m1；It is charging
Utilization rate of standing is more than or equal to m2, but less than in the case where m1, charging station service rating is second level；It is less than m2 in charging station utilization rate
In the case where, charging station service rating is three-level.Optionally, m1 > m2, m1 0.8, m2 0.6.
Optionally, if the charging station service rating of selected cell is level-one, which, which is very suitable to establish, fills
Power station preferentially selects the selected cell as charging station site；If the charging station service rating of selected cell is second level, should
Selected cell is relatively suitble to establish charging station, secondary to select the selected cell as charging station site；If the charging of selected cell
Service rating of standing is three-level, then the selected cell is not suitable for establishing charging station, does not select the selected cell as charging station site.
It should be noted that being the selected cell of basis property by the selected cell that charging station service rating obtains, obtain
Selected cell may not be optimal selected cell.To obtain optimal selected cell, it is being determined that charging station uses
It grade and then is corrected according to distribution resource, assessment of cost, road network resource information, and is only to charging station service rating
Level-one, second level selected cell be corrected, charging station service rating be three-level selected cell directly give up.
Specifically, determining at least one candidate unit from multiple selected cells according to charging station service rating first, so
It determines the discrete value of the corresponding correction index of at least one candidate unit again afterwards, and is determined each according to the discrete value of correction index
The corresponding index score of candidate unit, last index for selection score are greater than the candidate unit of pre-set level as target addressing list
Member.
It should be noted that correction index includes at least one following: distribution resource metrics, assessment of cost index, road network
Resource metrics.Wherein, distribution resource metrics include but is not limited to power matching network electrical network capacity, power balance, power quality；Cost
Evaluation index includes but is not limited to the system losses expense after cost of investment, operating cost, maintenance cost, access charging station；Road
It includes but is not limited to road mileage, speed that net resource, which relates to index,.
Optionally, the corresponding index score of each candidate unit can be calculated by following formula:
In above formula, siIndicate the corresponding index score of i-th of candidate unit, wherein siNumerical value it is bigger, show the time
Menu member is more suitable as target selected cell；wjFor the weighted value of each correction index；pijIt is j-th in i-th of candidate unit
Index value specific gravity；M indicates the corresponding index number of candidate unit.
It further, can be according to index score and pre- after the corresponding index score of each candidate unit is calculated
If the size of index score determines whether the candidate unit can be used as target selected cell.Optionally, if index score is small
In being equal to the first index score, for example, index score is greater than 0, it is less than or equal to 0.5, then illustrates the distribution money in the candidate unit
The indexs such as source and road network resource do not meet the necessary condition of construction charging station, it is not recommended that establish charging station in this position；If referred to
It marks score and is greater than the first index score, and less than the second index score, for example, index score is greater than 0.5, less than 1, then explanation should
Candidate unit meets the standard of building a station, it is proposed that establishes charging station in this position.
In a kind of optional scheme, Fig. 2 shows a kind of flow charts of optional charging station site selecting method, can by Fig. 2
Know, charging station site selecting method provided herein mainly includes four steps, that is, establishes addressing basis, building and extract addressing
Index, charging station addressing and correction addressing result.Wherein, it is corresponding with step S102 to establish addressing basis, that is, obtains multiple
The achievement data of selected cell；Building and extraction addressing index are building pre-set level system, and are obtained based on pre-set level system
Take the process of achievement data.In the charging station addressing the step of, each selected cell can be determined by way of machine learning
Charging station utilization rate, and target selected cell is determined according to charging station utilization rate.It is selected as obtained in charging station addressing step
Location unit may not be optimal selected cell, to guarantee to obtain optimal charging station selected cell, can rationally be divided by Information Entropy
Weight with various factors optimizes candidate site, to obtain optimal charging station site.
As shown in the above, scheme provided herein utilizes big data technology, in conjunction with engineerings such as random forests
Algorithm is practised, is accurately positioned and filters out candidate charging station site；According to the key factor for influencing charging station addressing, using entropy
Method, the weight of reasonable distribution various factors optimize candidate site.So as to obtain, the method for the present invention can be rapid and accurate
Ground is that regional charging station selects optimum location.
Embodiment 2
According to embodiments of the present invention, a kind of embodiment of charging station addressing device is additionally provided, it should be noted that the dress
Set charging station site selecting method provided by executable embodiment 1, wherein Fig. 3 is charging station addressing according to an embodiment of the present invention
The structural schematic diagram of device, as shown in figure 3, the device includes: to obtain module 301, processing module 303 and determining module 305.
Wherein, obtain module 301, for obtaining the achievement data of multiple selected cells, wherein each selected cell with extremely
A few charging station is corresponding；Processing module 303, for based on preset model to the achievement data of multiple selected cells at
Reason, obtains the charging station utilization rate of each selected cell, wherein preset model is to pass through machine learning algorithm using multi-group data
It obtains, every group of data in multi-group data include: the charging of the achievement data and each selected cell of each selected cell
It stands utilization rate；Determining module 305, for determining target selected cell from multiple selected cells according to charging station utilization rate.
Herein it should be noted that above-mentioned acquisition module 301, processing module 303 and determining module 305 correspond to implementation
Step S102 to step S106 in example 1, three modules are identical as example and application scenarios that corresponding step is realized, but not
It is limited to one disclosure of that of above-described embodiment.
In a kind of optional scheme, obtaining module includes: the first acquisition module and abstraction module.Wherein, it first obtains
Modulus block, for obtaining the corresponding area information of multiple selected cells；Abstraction module, for based on pre-set level system from multiple
The corresponding achievement data of multiple selected cells is extracted in the corresponding area information of selected cell, wherein pre-set level system includes
Multiple index grades, each index grade correspond to multiple indexs.
In a kind of optional scheme, charging station addressing device further include: second obtains module and building module.Its
In, second obtains module, for obtaining history charge station information, wherein history charge station information includes at least one following: every
The utilization rate of a corresponding achievement data of index grade and each charging station；Module is constructed, for being based on random forests algorithm
Supervised machine learning is carried out to history charge station information, obtains preset model.
In a kind of optional scheme, determining module includes: comparison module, the first determining module and the second determining mould
Block.Wherein, comparison module, for multiple selected cells charging station utilization rate and default utilization rate be compared, obtain
Comparison result；First determining module, for determining the corresponding charging station service rating of multiple selected cells according to comparison result；The
Two determining modules, for determining target selected cell from multiple selected cells according to charging station service rating.
In a kind of optional scheme, the second determining module include: third determining module, the 4th determining module, the 5th really
Cover half block and the 6th determining module.Wherein, third determining module, for according to charging station service rating from multiple selected cells
At least one candidate unit of middle determination；4th determining module, for determining the corresponding correction index of at least one candidate unit
Discrete value；5th determining module, for determining the corresponding index score of each candidate unit according to the discrete value of correction index；The
Six determining modules are greater than the candidate unit of pre-set level score as target selected cell for index for selection score.Wherein, school
Direct index includes at least one following: distribution resource metrics, assessment of cost index, road network resource metrics.
Embodiment 3
According to another aspect of an embodiment of the present invention, a kind of storage medium is additionally provided, which includes storage
Program, wherein equipment where control storage medium executes charging station site selecting method provided by embodiment 1 in program operation.
Embodiment 4
According to another aspect of an embodiment of the present invention, a kind of storage medium is additionally provided, which is used to run program,
Wherein, charging station site selecting method provided by embodiment 1 is executed when program is run.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
In the above embodiment of the invention, it all emphasizes particularly on different fields to the description of each embodiment, does not have in some embodiment
The part of detailed description, reference can be made to the related descriptions of other embodiments.
In several embodiments provided herein, it should be understood that disclosed technology contents can pass through others
Mode is realized.Wherein, the apparatus embodiments described above are merely exemplary, such as the division of the unit, Ke Yiwei
A kind of logical function partition, there may be another division manner in actual implementation, for example, multiple units or components can combine or
Person is desirably integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Between coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or communication link of unit or module
It connects, can be electrical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
On unit.It can some or all of the units may be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can for personal computer, server or network equipment etc.) execute each embodiment the method for the present invention whole or
Part steps.And storage medium above-mentioned includes: that USB flash disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), mobile hard disk, magnetic or disk etc. be various to can store program code
Medium.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of charging station site selecting method characterized by comprising
Obtain the achievement data of multiple selected cells, wherein each selected cell is corresponding at least one charging station；
It is handled based on achievement data of the preset model to the multiple selected cell, obtains filling for each selected cell
Power station utilization rate, wherein the preset model is obtained using multi-group data by machine learning algorithm, the multi-group data
In every group of data include: each selected cell achievement data and each selected cell charging station utilize
Rate；
Target selected cell is determined from the multiple selected cell according to the charging station utilization rate.
2. the method according to claim 1, wherein obtaining the achievement data of multiple selected cells, comprising:
Obtain the corresponding area information of the multiple selected cell；
The multiple selected cell pair is extracted from the corresponding area information of the multiple selected cell based on pre-set level system
The achievement data answered, wherein the pre-set level system includes multiple index grades, and each index grade corresponds to multiple indexs.
3. the method according to claim 1, wherein in the finger based on preset model to the multiple selected cell
Mark data are handled, before obtaining the charging station utilization rate of each selected cell, the method also includes:
Obtain history charge station information, wherein the history charge station information includes at least one following: each index grade pair
The utilization rate of the achievement data and each charging station answered；
Supervised machine learning is carried out to the history charge station information based on random forests algorithm, obtains the preset model.
4. the method according to claim 1, wherein according to the charging station utilization rate from the multiple addressing list
Target selected cell is determined in member, comprising:
The charging station utilization rate and default utilization rate of the multiple selected cell are compared, comparison result is obtained；
The corresponding charging station service rating of the multiple selected cell is determined according to the comparison result；
The target selected cell is determined from the multiple selected cell according to the charging station service rating.
5. according to the method described in claim 4, it is characterized in that, according to the charging station service rating from the multiple addressing
The target selected cell is determined in unit, comprising:
At least one candidate unit is determined from the multiple selected cell according to the charging station service rating；
Determine the discrete value of the corresponding correction index of at least one described candidate unit；
The corresponding index score of each candidate unit is determined according to the discrete value of the correction index；
Candidate unit of the index score greater than pre-set level score is chosen as the target selected cell.
6. according to the method described in claim 5, it is characterized in that, the correction index includes at least one following: distribution money
Source index, assessment of cost index, road network resource metrics.
7. a kind of charging station addressing device characterized by comprising
Module is obtained, for obtaining the achievement data of multiple selected cells, wherein each selected cell and at least one charging station
It is corresponding；
Processing module is obtained described every for being handled based on achievement data of the preset model to the multiple selected cell
The charging station utilization rate of a selected cell, wherein the preset model is to be obtained using multi-group data by machine learning algorithm
, every group of data in the multi-group data include: the achievement data and each addressing list of each selected cell
The charging station utilization rate of member；
Determining module, for determining target selected cell from the multiple selected cell according to the charging station utilization rate.
8. device according to claim 7, which is characterized in that the determining module includes:
Comparison module, for the multiple selected cell charging station utilization rate and default utilization rate be compared, obtain
Comparison result；
First determining module, for determining that the corresponding charging station of the multiple selected cell uses according to the comparison result
Grade；
Second determining module, for determining that the target is selected from the multiple selected cell according to the charging station service rating
Location unit.
9. a kind of storage medium, which is characterized in that the storage medium includes the program of storage, wherein run in described program
When control the storage medium where equipment perform claim require any one of 1 to 6 described in charging station site selecting method.
10. a kind of processor, which is characterized in that the processor is for running program, wherein right of execution when described program is run
Benefit require any one of 1 to 6 described in charging station site selecting method.
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