CN109740879A - The method and apparatus for handling charging pile data - Google Patents
The method and apparatus for handling charging pile data Download PDFInfo
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- CN109740879A CN109740879A CN201811565572.9A CN201811565572A CN109740879A CN 109740879 A CN109740879 A CN 109740879A CN 201811565572 A CN201811565572 A CN 201811565572A CN 109740879 A CN109740879 A CN 109740879A
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Abstract
The invention discloses a kind of method and apparatus for handling charging pile data.Wherein, this method comprises: obtaining the attribute data of multiple users and the charging pile utilization rate of at least one charging pile in predeterminable area in predeterminable area；Attribute data and charging pile utilization rate are handled, key index is obtained, wherein key index is the factor for influencing the utilization rate of charging pile；Target protocol is determined according to key index, wherein target protocol is used to improve the charging pile utilization rate of at least one charging pile.The present invention solves the low technical problem of accuracy of the factor for the influence charging pile utilization rate manually inferred.
Description
Technical field
The present invention relates to electric vehicle charging fields, in particular to a kind of method and dress for handling charging pile data
It sets.
Background technique
In recent years, with the rapid development of our country's economy, informatization of power industry had obtained quick development, China's electric power
Enterprise is from initial power system automation to financial computerization, then arrives large scale business enterprise's informatization in recent years, special companion
It is the IT technology of new generation of representative in power industry using Internet of Things and cloud computing with the all-round construction of next-generation intelligent power network
Extensive use, the sharp increase of electric power data resource simultaneously reaches a certain scale.
Electric power data resource has the characteristics that the scale of construction is big, type is more, fireballing.In addition, big data field informatization
There is a marked difference with conventional information project construction, core is business understandability, data resource management ability and big
Data technique ability, three are indispensable.However, particle degree of the electric power data resource in acquisition, the timeliness of data acquisition,
Integrality, consistency etc. are not fully up to expectations, and data source is unique, and the timeliness and accuracy of electric power data resource are poor, portion
Divided data need to be manually entered.In addition, the data magnitude of electric power data resource has reached TB grades, after the substantially propulsion of smart grid,
Traditional data processing mode (i.e. relational database schema), has been unable to satisfy the demand of magnanimity and efficient process, has only adopted
With big data tupe, it is just able to achieve low cost, the processing of magnanimity, high-performance data.
Although many industries are (for example, electric power, finance, interconnection with the continuous mature and popularization and application of big data technology
The industries such as net) using data mining algorithm and big data technology progress business model, handle practical problem.However, big data skill
Art is in electric vehicle charging field using less.
In addition, the construction address of traditional charging pile, generally determines building site using empirical method, without any data conduct
It builds a station foundation, artificial subjective factor interference is larger, so that charging pile utilization rate is lower.Charging station lower for utilization rate leads to
Charging pile utilization rate is improved frequently with preferential and advertising mode is made a price reduction, but is had little effect.
For above-mentioned problem, currently no effective solution has been proposed.
Summary of the invention
The embodiment of the invention provides a kind of method and apparatus for handling charging pile data, at least solve manually to infer
Influence the low technical problem of the accuracy of the factor of charging pile utilization rate.
According to an aspect of an embodiment of the present invention, a kind of method for handling charging pile data is provided, comprising: obtain pre-
If the charging pile utilization rate of the attribute data and at least one charging pile in predeterminable area of multiple users in region；To attribute
Data and charging pile utilization rate are handled, and key index is obtained, wherein key index is the utilization rate for influencing charging pile
Factor；Target protocol is determined according to key index, wherein the charging pile that target protocol is used to improve at least one charging pile utilizes
Rate.
Further, the method for charging pile data is handled further include: obtain based on big data technology multiple in predeterminable area
The user data of user；Screening Treatment is carried out to user data, obtains pending data；Based on multiple interpolation algorithm to be processed
Data carry out difference operation, obtain attribute data.
Further, the method for charging pile data is handled further include: obtain each user in predeterminable area and fill using identical
The charging duration and charging times of electric stake；It is utilized according to the charging pile that charging duration and charging times calculate each charging pile
Rate.
Further, the method for charging pile data is handled further include: according to the size of charging pile utilization rate to predeterminable area
Interior multiple users are ranked up, and obtain ranking results；Attribute data is screened according to ranking results, obtains the first attribute
Data, wherein the first attribute data includes multiple pre-set levels；Construct the coefficient matrix of the first attribute data；According to coefficient square
Battle array obtains the corresponding contribution rate of each pre-set level；Determine that contribution rate refers to greater than the pre-set level of default contribution rate as key
Mark.
Further, the method for charging pile data is handled further include: it is small to filter out charging pile utilization rate according to ranking results
In the charging pile of default utilization rate, preset charged stake is obtained；Using the attribute data of the corresponding multiple users of preset charged stake as
First attribute data.
Further, the method for charging pile data is handled further include: be standardized, obtain to the first attribute data
First matrix；Element in first matrix is handled, multiple coefficients are obtained；Coefficient matrix is constructed based on multiple coefficients.
Further, the method for charging pile data is handled further include: determine the characteristic value and feature vector of coefficient matrix；
The second matrix is constructed according to characteristic value and feature vector；Dimension-reduction treatment is carried out to the second matrix, obtains third matrix；Based on
Three matrixes determine the corresponding contribution rate of each pre-set level.
According to another aspect of an embodiment of the present invention, a kind of device for handling charging pile data is additionally provided, comprising: obtain
Module, for obtaining the charging of at least one charging pile in predeterminable area in the attribute data and predeterminable area of multiple users
Stake utilization rate；Processing module obtains key index for handling attribute data and charging pile utilization rate, wherein closes
Key index is to influence the factor of the utilization rate of charging pile；Determining module, for determining target protocol according to key index, wherein
Target protocol is used to improve the charging pile utilization rate of at least one charging pile.
According to another aspect of an embodiment of the present invention, a kind of storage medium is additionally provided, which includes storage
Program, wherein the method that equipment where control storage medium executes processing charging pile data in program operation.
According to another aspect of an embodiment of the present invention, a kind of processor is additionally provided, which is used to run program,
In, the method for processing charging pile data is executed when program is run.
In embodiments of the present invention, pre- obtaining in such a way that big data technology carries out electric automobile charging pile excavation
If right in region after the attribute data of multiple users and the charging pile utilization rate of at least one charging pile in predeterminable area
Attribute data and charging pile utilization rate are handled, obtain influence charging pile utilization rate key index, then further according to
Key index determines the target protocol for improving the charging pile utilization rate of at least one charging pile.
It is easily noted that, after the charging pile utilization rate of the attribute data and charging pile that obtain multiple users,
Without manually participating in, the key factor for influencing charging pile utilization rate can be accurately determined, avoid the influence charging manually inferred
The low problem of the accuracy of the factor of stake utilization rate, improves the accuracy for determining the key factor for influencing charging pile utilization rate,
Have also achieved labor-saving purpose.In addition, determined influence charging pile utilization rate key factor after, for it is crucial because
Element determines that the target protocol for improving charging pile utilization rate can more effectively improve the utilization rate of charging pile.
It can be seen that scheme provided herein can solve the factor for the influence charging pile utilization rate manually inferred
The low technical problem of accuracy.
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 method flow diagram for handling charging pile data according to an embodiment of the present invention；
Fig. 2 is a kind of schematic diagram of optional charging pile utilization rate according to an embodiment of the present invention；
Fig. 3 is a kind of method flow diagram of optional processing charging pile data according to an embodiment of the present invention；And
Fig. 4 is a kind of apparatus structure schematic diagram for handling charging pile data 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.
Embodiment 1
According to embodiments of the present invention, a kind of embodiment of the method for handling charging pile data is provided, it should be noted that
The step of process of attached drawing illustrates can execute in a computer system such as a set of computer executable instructions, also,
It, in some cases, can be to be different from shown in sequence execution herein although logical order is shown in flow charts
The step of out or describing.
Fig. 1 is the method flow diagram of processing charging pile data according to an embodiment of the present invention, as shown in Figure 1, this method packet
Include following steps:
Step S102 obtains the attribute data of multiple users and at least one charging in predeterminable area in predeterminable area
The charging pile utilization rate of stake.
It should be noted that multiple users in predeterminable area can be obtained based on big data technology in step S102
Attribute data, wherein the source of the attribute data of multiple users can be but be not limited to electric power generate and marketing etc. Intranets system
System data, state's net system data, power industry external system data, the system data of power department and scientific data etc..It is above-mentioned
Charging pile can be the charging pile of electric car charging.
In addition it is also necessary to explanation, the attribute data of multiple users includes but is not limited to charging pile used by a user
Location information, user charges date, the state of charging pile used by a user, the entry time of user's vehicle and when outbound
Between, charging pile installation used by a user, the charge capacity of charging pile used by a user, charging pile used by a user
Producer, the power of charging pile used by a user, the model of charging pile used by a user, charging pile used by a user peak
Rate and spike rate, the flat rate of charging pile used by a user and paddy rate, charging pile used by a user it is shared
Mode etc..
Step S104 handles attribute data and charging pile utilization rate, obtains key index, wherein key refers to
It is designated as influencing the factor of the utilization rate of charging pile.
Optionally, key index is to contribute higher factor to charging pile utilization rate, for example, charging peak date, charging
Stake charge efficiency, charging pile position, charge sharing mode etc., wherein charge sharing mode includes but is not limited to that client is shared
Mode, advertisement sharing mode and website sharing mode.
Step S106 determines target protocol according to key index, wherein target protocol is for improving at least one charging pile
Charging pile utilization rate.
It should be noted that the quantity for improving charging pile utilization rate scheme can be multiple, wherein different key indexes
Corresponding different target protocol.It therefore, can be with using optimal case corresponding with key index after key index has been determined
The charging pile utilization rate of charging pile is more effectively improved, and then improves the low problem of electric automobile charging pile utilization rate.
It based on scheme defined by above-mentioned steps S102 to step S106, can know, electricity is carried out using big data technology
The mode that electrical automobile charging pile excavates, in acquisition predeterminable area in the attribute data and predeterminable area of multiple users at least
After the charging pile utilization rate of one charging pile, attribute data and charging pile utilization rate are handled, obtain influencing charging
Then the key index of the utilization rate of stake determines that the charging pile for improving at least one charging pile utilizes further according to key index
The target protocol of rate.
It is easily noted that, after the charging pile utilization rate of the attribute data and charging pile that obtain multiple users,
Without manually participating in, the key factor for influencing charging pile utilization rate can be accurately determined, avoid the influence charging manually inferred
The low problem of the accuracy of the factor of stake utilization rate, improves the accuracy for determining the key factor for influencing charging pile utilization rate,
Have also achieved labor-saving purpose.In addition, determined influence charging pile utilization rate key factor after, for it is crucial because
Element determines that the target protocol for improving charging pile utilization rate can more effectively improve the utilization rate of charging pile.
It can be seen that scheme provided herein can solve the factor for the influence charging pile utilization rate manually inferred
The low technical problem of accuracy.
In a kind of optional scheme, the step of attribute data of multiple users, may include: in acquisition predeterminable area
Step S10 obtains the user data of multiple users in predeterminable area based on big data technology；
Step S12 carries out Screening Treatment to user data, obtains pending data；
Step S14 carries out difference operation to pending data based on multiple interpolation algorithm, obtains attribute data.
Optionally, data analyst uses big data technology to get user data by Data Analysis Platform, and will
To handle user data in the Python program that obtained user data is linked into Data Analysis Platform.It is specific,
Data Analysis Platform analyzes user data, obtains the attributive character of user data, and the attribute based on user data is special
Sign carries out Screening Treatment to user data, obtains pending data.Optionally, the attributive character of user data shows the user
The action value of data, Data Analysis Platform are used to filter out the user data that action value is greater than default action value from user data
As pending data.Further, after obtaining pending data, Data Analysis Platform is calculated using multiple interpolation model
Method fills up pending data.
In a kind of optional scheme, Screening Treatment is being carried out to user data, after obtaining pending data, data point
It analyses platform and also determines the second data volume of the first data volume of pending data and the user data of each user, and calculate first
The data volume difference of data volume and the second data volume, and in the case where data volume difference is greater than preset threshold, from number to be processed
According to the middle user data for deleting the user.I.e. in the biggish situation of the missing values of pending data, the user of the user is deleted
Data.
In a kind of optional scheme, Data Analysis Platform uses identical charging by obtaining each user in predeterminable area
The charging duration and charging times of stake, and the charging pile for calculating according to charging duration and charging times each charging pile utilizes
Rate.Wherein, charging pile utilization rate can be calculated by following formula:
In above formula, x is charging pile utilization rate, and i is Customs Assigned Number,It indicates in one day using the total of the charging pile
Charging duration.
It should be noted that after having obtained charging pile utilization rate, Data Analysis Platform further to attribute data with
And charging pile utilization rate is handled, and key index is obtained.
Specific, Data Analysis Platform arranges multiple users in predeterminable area according to the size of charging pile utilization rate
Sequence obtains ranking results, is then screened according to ranking results to attribute data, obtains the first attribute data, and constructs the
Then the coefficient matrix of one attribute data determines that contribution rate is greater than the pre-set level of default contribution rate as key index.Wherein,
First attribute data includes multiple pre-set levels.
Optionally, Data Analysis Platform filters out the charging that charging pile utilization rate is less than default utilization rate according to ranking results
Stake, obtains preset charged stake, and using the attribute data of the corresponding multiple users of preset charged stake as the first attribute data.Such as figure
Shown in 2, it is ranked up by the size of charging pile utilization rate and the location information of each charging pile.Wherein, Data Analysis Platform
Preset charged stake of the charging pile utilization rate less than 50% (i.e. default utilization rate) is filtered out, and charging pile utilization rate will be used to be less than
The attribute data of the user of 50% charging pile is as the first attribute data.
It should be noted that since user tends to that exchange, power is selected to be in medium charging pile at night, and white
It, user tends to the slightly larger charging pile of power.In addition, with the promotion of new-energy automobile cruising ability, most of new energy
Automobile is lower without charging, the utilization rate for thereby resulting in part charging pile daily.On the other hand, most of public charging station,
It while electricity payment, also needs to pay parking fee, is reluctant that public charging station charges so as to cause car owner, and private charging pile
Installation cost it is larger, and the number that car owner charges daily is less, low so as to cause private charging pile utilization rate.
For the utilization rate for improving charging pile, need to determine the key index for influencing charging pile utilization rate first.Optionally, number
The first attribute data obtained above can be further processed according to analysis platform, to obtain influencing charging pile utilization rate
Key index.
Specifically, Data Analysis Platform is standardized the first attribute data, the first matrix is obtained, then to
Element in one matrix is handled, and obtains multiple coefficients, and construct coefficient matrix based on multiple coefficients.In building sytem matrix
Later, Data Analysis Platform further determines that the characteristic value and feature vector of coefficient matrix, then according to characteristic value and spy
It levies vector and constructs the second matrix, and dimension-reduction treatment is carried out to the second matrix, obtain third matrix, finally determined based on third matrix
The corresponding contribution rate of each pre-set level.
Optionally, the first attribute data A is standardized, obtains the first matrix X, and based in the first matrix X
Each element obtain coefficient matrix R, wherein coefficient matrix R can be indicated by following formula:
Wherein,Wherein (i, j=1,2,3 ... ..., P), xiFor i-th in matrix X
A element.
Further, the eigen vector of Data Analysis Platform design factor matrix R, and characteristic value is carried out by big
To small sequence, K form the second matrix S by row before taking.By Y=SX, dimension-reduction treatment is carried out to the second matrix, obtains dimensionality reduction
Third matrix Y afterwards.Finally, calculating the contribution rate l of each feature in third matrix Yi,According to the big of contribution rate
It is small that index is ranked up, the biggish index of contribution rate is chosen as key index, for example, choosing contribution rate is greater than default contribution
The index of rate (for example, 50%) is as key index.
It should be noted that by analysis, determine the use date of charging pile, charging pile charge efficiency, charging pile position
The contribution rate of the sharing mode of confidence breath and charging pile is higher, this several indexs are that the key of influence charging pile utilization rate refers to
Mark.
In addition it is also necessary to explanation, as shown in figure 3, as shown in the above, processing charging pile provided herein
The method of data mainly includes data pick-up, data cleansing, determines attribute data, data mining, data analysis and determine pass
Six parts such as key factor.
As shown in the above, scheme provided herein uses big data technology, is based on mass historical data, passes through
Data Integration, data mining realize the effective implementation and application for improving charging pile utilization rate, avoid the wasting of resources, be subsequent digging
Pick ensures.In addition, ensureing accuracy, uniqueness, the value of charging pile data by data processing, allowing to carry out model
It is as a result more acurrate when algorithm is analyzed.Finally, the present invention, on the basis of real business demand, in conjunction with big data technology, expert refers to
Determine threshold value standard, charging pile is divided into two classes, respectively utilization rate is low and the high charging pile of utilization rate, excavates charging pile utilization
The low reason of rate.By algorithm model-principal component analysis of mainstream, the principal element for influencing charging pile utilization rate is determined.With regard to benefit
With the low problem of rate, proposes constructive suggestions, effectively improve charging pile utilization rate.
Embodiment 2
According to embodiments of the present invention, a kind of Installation practice for handling charging pile data is additionally provided, it should be noted that
The method that processing charging pile data provided by embodiment 1 can be performed in the device.Wherein, Fig. 4 is according to an embodiment of the present invention
Handle charging pile data apparatus structure schematic diagram, as shown in figure 4, the device include: obtain module 401, processing module 403 with
And determining module 405.
Wherein, module 401 is obtained, for obtaining in predeterminable area in the attribute data and predeterminable area of multiple users
The charging pile utilization rate of at least one charging pile；Processing module 403, to attribute data and charging pile utilization rate
Reason, obtains key index, wherein key index is the factor for influencing the utilization rate of charging pile；Determining module 405 is used for basis
Key index determines target protocol, wherein target protocol is used to improve the charging pile utilization rate of at least one charging pile.
Herein it should be noted that above-mentioned acquisition module 401, processing module 403 and determining module 405 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, first processing module and second processing
Module.Wherein, first module is obtained, for obtaining the user data of multiple users in predeterminable area based on big data technology；The
One processing module obtains pending data for carrying out Screening Treatment to user data；Second processing module, for based on more
Weight interpolation algorithm carries out difference operation to pending data, obtains attribute data.
Herein it should be noted that above-mentioned first acquisition module, first processing module and Second processing module correspond to
Step S10 to step S14 in embodiment 1, three modules are identical as example and application scenarios that corresponding step is realized, but
It is not limited to the above embodiments a disclosure of that.
In a kind of optional scheme, obtaining module includes: the second acquisition module and third processing module.Wherein,
Two obtain module, and the charging duration and charging times of identical charging pile are used for obtaining each user in predeterminable area；
Third processing module, for calculating the charging pile utilization rate of each charging pile according to charging duration and charging times.
In a kind of optional scheme, processing module include: sorting module, screening module, the first building module, the everywhere
Manage module and the first determining module.Wherein, sorting module, for the size according to charging pile utilization rate in predeterminable area
Multiple users are ranked up, and obtain ranking results；Screening module is obtained for being screened according to ranking results to attribute data
To the first attribute data, wherein the first attribute data includes multiple pre-set levels；First building module, belongs to for constructing first
The coefficient matrix of property data；Fourth processing module, for obtaining the corresponding contribution rate of each pre-set level according to coefficient matrix；The
One determining module, for determining that contribution rate is greater than the pre-set level of default contribution rate as key index.
In a kind of optional scheme, screening module includes: the 5th processing module and the 6th processing module.Wherein,
Five processing modules are preset for filtering out the charging pile that charging pile utilization rate is less than default utilization rate according to ranking results
Charging pile；6th processing module, for using the attribute data of the corresponding multiple users of preset charged stake as the first attribute data.
In a kind of optional scheme, building module includes: the 7th processing module, the 8th processing module and the second building
Module.Wherein, the 7th processing module obtains the first matrix for being standardized to the first attribute data；8th processing
Module obtains multiple coefficients for handling the element in the first matrix；Second building module, for being based on multiple systems
Number building coefficient matrix.
In a kind of optional scheme, fourth processing module includes: the second determining module, third building module, at the 9th
Manage module and third determining module.Wherein, the second determining module, for determine coefficient matrix characteristic value and feature to
Amount；Third constructs module, for constructing the second matrix according to characteristic value and feature vector；9th processing module, for the
Two matrixes carry out dimension-reduction treatment, obtain third matrix；Third determining module, for determining each pre-set level based on third matrix
Corresponding contribution rate.
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 the side of the processing charging pile data in embodiment 1 in program operation
Method.
Embodiment 4
According to another aspect of an embodiment of the present invention, a kind of processor is additionally provided, which is used to run program,
In, the method for the processing charging pile data in 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 method for handling charging pile data characterized by comprising
Obtain the charging of the attribute data and at least one charging pile in the predeterminable area of multiple users in predeterminable area
Stake utilization rate；
The attribute data and the charging pile utilization rate are handled, key index is obtained, wherein the key index
For the factor of the utilization rate of influence charging pile；
Target protocol is determined according to the key index, wherein the target protocol is for improving at least one described charging pile
Charging pile utilization rate.
2. being wrapped the method according to claim 1, wherein obtaining the attribute data of multiple users in predeterminable area
It includes:
The user data of the multiple user in the predeterminable area is obtained based on big data technology；
Screening Treatment is carried out to the user data, obtains pending data；
Difference operation is carried out to the pending data based on multiple interpolation algorithm, obtains the attribute data.
3. the method according to claim 1, wherein obtaining at least one charging pile in the predeterminable area
Charging pile utilization rate, comprising:
Obtain charging duration and charging times that each user in the predeterminable area uses identical charging pile；
The charging pile utilization rate of each charging pile is calculated according to the charging duration and the charging times.
4. the method according to claim 1, wherein to the attribute data and the charging pile utilization rate into
Row processing, obtains key index, comprising:
Multiple users in the predeterminable area are ranked up according to the size of the charging pile utilization rate, obtain sequence knot
Fruit；
The attribute data is screened according to the ranking results, obtains the first attribute data, wherein first attribute
Data include multiple pre-set levels；
Construct the coefficient matrix of first attribute data；
The corresponding contribution rate of each pre-set level is obtained according to the coefficient matrix；
Determine that the contribution rate is greater than the pre-set level of default contribution rate as the key index.
5. according to the method described in claim 4, it is characterized in that, being sieved according to the ranking results to the attribute data
Choosing, obtains the first attribute data, comprising:
The charging pile that the charging pile utilization rate is less than default utilization rate is filtered out according to the ranking results, obtains preset charged
Stake；
Using the attribute data of the corresponding multiple users of the preset charged stake as first attribute data.
6. according to the method described in claim 4, it is characterized in that, the coefficient matrix of building first attribute data, comprising:
First attribute data is standardized, the first matrix is obtained；
Element in first matrix is handled, multiple coefficients are obtained；
The coefficient matrix is constructed based on the multiple coefficient.
7. according to the method described in claim 4, being corresponded to it is characterized in that, obtaining each pre-set level according to the coefficient matrix
Contribution rate, comprising:
Determine the characteristic value and feature vector of the coefficient matrix；
The second matrix is constructed according to the characteristic value and described eigenvector；
Dimension-reduction treatment is carried out to second matrix, obtains third matrix；
The corresponding contribution rate of each pre-set level is determined based on the third matrix.
8. a kind of device for handling charging pile data characterized by comprising
Module is obtained, for obtaining the attribute data of multiple users in predeterminable area and at least one in the predeterminable area
The charging pile utilization rate of charging pile；
Processing module obtains key index for handling the attribute data and the charging pile utilization rate,
In, the key index is the factor for influencing the utilization rate of charging pile；
Determining module, for determining target protocol according to the key index, wherein the target protocol for improve it is described extremely
The charging pile utilization rate of a few charging pile.
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 7 described in processing charging pile data side
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 7 described in handle charging pile data method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811565572.9A CN109740879A (en) | 2018-12-20 | 2018-12-20 | The method and apparatus for handling charging pile data |
Applications Claiming Priority (1)
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