CN118070981A - Charging pile intelligent site selection analysis method and system based on electric power big data analysis - Google Patents
Charging pile intelligent site selection analysis method and system based on electric power big data analysis Download PDFInfo
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Abstract
The invention discloses a method and a system for intelligent site selection analysis of charging piles based on power big data analysis, which relate to the technical field of charging pile layout and comprise the steps of dividing a target area, acquiring characteristic data of each subarea, analyzing the charging pile demand of each subarea, obtaining site selection demand index of each subarea, further analyzing recommended charging pile site selection priority of each subarea, obtaining recommended charging pile site selection of the target area through selection, helping to fully consider the characteristics and the demand of the target area, comprehensively evaluating the data of the existing charging pile distribution site, providing scientific basis for intelligent site selection of the charging piles through analysis and optimization of the data of the existing charging pile distribution site of the target area, realizing reasonable layout and optimal configuration of charging facilities, and improving the efficiency and quality of charging service.
Description
Technical Field
The invention relates to the technical field of charging pile layout, in particular to an intelligent site selection analysis method and system for charging piles based on power big data analysis.
Background
As the popularity of electric vehicles and market demand increases, charging piles become increasingly important as an infrastructure supporting charging of electric vehicles. However, the current layout and distribution of the charging piles often have unbalanced and unreasonable conditions, so that the problems of mismatching of supply and demand of the charging piles, low charging efficiency and the like are caused. Therefore, the intelligent site selection analysis of the charging pile by using the power big data analysis has become one of important ways for solving the problem.
The prior art is as disclosed in the publication No.: the invention patent application of CN113642757A discloses a method and a system for planning construction of an Internet of things charging pile based on artificial intelligence. The system mainly comprises: and the charging pile locating and sizing module and the charging pile utilization rate evaluation module are used for evaluating the charging pile utilization rate. The charging pile locating and sizing module is used for primarily exploring a charging pile locating and sizing scheme and is mainly divided into four sub-modules, wherein the four sub-modules comprise data acquisition, a charging pile locating model for minimizing cost and time cost, a variable step length firefly algorithm solution based on simulated annealing, and a charging pile locating and sizing primary result. The charging pile utilization rate evaluation module is used for further evaluating and analyzing the result obtained in the charging pile locating and sizing module and is mainly divided into four sub-modules, and the charging pile utilization rate evaluation module comprises: and acquiring the data of the Internet of things, preprocessing the data, training an artificial intelligent model, and predicting and evaluating the utilization rate of the charging pile. And finally, feeding back the result obtained by the charging pile utilization rate evaluation module to the charging pile site selection and volume determination module, so as to obtain an optimal scheme of the charging pile construction planning.
The prior art is as disclosed in the publication No.: the invention patent application of CN117556952A discloses an intelligent site selection method for a charging pile of a new energy automobile, which comprises the following steps: step 1, basic data are collected; the basic data comprise basic operation data of the new energy automobile in the area to be selected; step 2, performing data slicing processing on the basic data; step 3, calculating a bicycle index for site selection evaluation of the charging piles by adopting the sliced basic data; acquiring a multi-vehicle analysis index based on single-vehicle index statistics; and 4, comprehensively scoring the settable address points in the area to be addressed according to the vehicle use evaluation dimension, the charging behavior evaluation dimension and the driving behavior evaluation dimension based on the multi-vehicle analysis index. The invention can effectively optimize the layout of the charging pile, improve the adaptation degree of the charging pile and the new energy automobile and improve the utilization rate of the charging pile.
By combining the scheme, in the current intelligent site selection of the charging pile, few important influencing factors, such as population density and the like, aiming at the site selection of the charging pile are considered; in practical application, the method is also lack of further carrying out reprocessing on specific data of the existing charging pile facilities, so that site selection construction conditions are unreasonable, slow progress or shutdown of construction of the charging facilities can be caused, and further the construction of the urban charging infrastructure is affected.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a charging pile intelligent site selection analysis method and a charging pile intelligent site selection analysis system based on power big data analysis, which can effectively solve the problems related to the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the intelligent site selection analysis method and system for the charging pile based on the electric power big data analysis comprise the steps of dividing a target area into equal areas to obtain each subarea, acquiring characteristic data of each subarea, and analyzing to obtain site selection demand indexes of each subarea.
And acquiring the existing charging pile facility data of each subarea, and analyzing the charging pile site selection fit value of each subarea.
And comprehensively analyzing the charging pile site selection priority of each subarea according to the site selection demand index of each subarea and the charging pile site selection fit value of each subarea, and selecting and obtaining the recommended charging pile site selection of the target area.
Further, the characteristic data of each subarea specifically comprises population density, road total coverage area, electric vehicle possession and monthly average road electric vehicle traffic total.
The existing charging pile facility data of each subarea comprises a common charging pile duty ratio, an own duty ratio, the number of charging piles, a month average load rate and a month average Gu Fuzai rate, and the existing charging pile facility data of each subarea further comprises: the operation capacity of each charging pile distribution site, average peak electric quantity of month and average use rate.
Further, the analyzing obtains the index of the site selection requirement of each subarea, and the specific analyzing process comprises the following steps: and extracting the total road coverage area of each subarea, extracting the total traffic amount of the electric vehicles on the month average road of each subarea, and calculating the traffic vehicle density of the month average electric vehicles of each subarea.
And according to population density and electric vehicle possession of each subarea, extracting average electric vehicle passing vehicle density of reference month of each subarea stored in a database, and processing to obtain a first site selection demand representation value of each subarea.
And according to the month average road electric vehicle passing total quantity of each subarea, the charging pile month average load rate and the charging pile month average Gu Fuzai rate, extracting the charging pile reference month average load rate and the charging pile reference month average Gu Fuzai rate stored in the database, and processing to obtain a second site selection demand representation value of each subarea.
And comprehensively calculating the addressing demand index of each subarea according to the first addressing demand representation value of each subarea and the second addressing demand representation value of each subarea.
Further, the analyzing the charging pile address matching value of each sub-area comprises the following specific analysis processes: and extracting the reference charging pile public duty ratio and the reference charging pile self duty ratio stored in the database according to the charging pile public duty ratio and the self duty ratio of each sub-region and the number of the charging piles of each sub-region, and processing to obtain the charging pile number optimization characterization value of each sub-region.
And extracting the area of the subareas, extracting the number of charging pile distribution sites in each subarea, calculating the density of the charging pile distribution sites in each subarea, and extracting the reference density of each charging pile distribution site stored in a database.
And extracting the reference utilization rate of each charging pile distribution site in each subarea stored in the database according to the average utilization rate of each charging pile distribution site in each subarea.
And according to the running capacity of each charging pile distribution site in each subarea and the average peak electric quantity of the month, obtaining the charging pile running optimization characterization value of each subarea through processing.
And (3) comprehensively calculating the charging pile site selection fit value of each subarea according to the charging pile quantity optimization characterization value of each subarea and the charging pile operation optimization characterization value of each subarea.
Further, the index of the site selection requirement of each subarea represents a quantized result which is obtained by analyzing and processing the characteristic data of each subarea and is used for analyzing the site selection requirement degree of the charging piles of each subarea, and the quantized result is used as an analysis basis for selecting and obtaining the recommended charging pile site selection of the target area.
Further, the charging pile demand optimization characterization value of each subarea represents a data quantification result for analyzing the charging pile site selection fit degree of each subarea, which is obtained by analyzing the existing charging pile facility data of each subarea, and is used as an analysis basis of the recommended charging pile site selection of the target area.
Further, the selecting and selecting the recommended charging pile site of the target area comprises the following specific processes: and comprehensively calculating the recommended charging pile site selection priority of each subarea according to the site selection demand index of each subarea and the charging pile site selection fit value of each subarea.
And sequencing the recommended charging pile site selection priorities of all the subareas in sequence from large to small, and extracting the subarea with the first recommended charging pile site selection priority sequence as the recommended charging pile site selection of the target area.
Further, the index of the site selection requirement of each subarea is calculated according to the following formula: ; wherein/> Index of site selection requirement for jth sub-region,/>Representing a value for the first addressing demand for the jth sub-region,/>Representing a value for the second addressing demand for the jth sub-region,/>For the weight factor corresponding to the set first addressing demand representation value,/>For the weight factor corresponding to the set second site selection requirement representation value, e represents a natural constant, j is the number of each subarea,/>N is the number of subregions.
Further, the charging pile address matching value of each sub-area has a calculation formula: ; wherein/> Selecting address fitting value for charging pile of jth sub-area,/>Optimizing characterization values for the number of charging piles in the jth sub-area,/>Optimizing characterization values for charging pile operation of jth sub-region,/>Optimizing weight factors corresponding to characterization values for the set number of charging piles,/>And optimizing the weight factor corresponding to the characterization value for the set charging pile operation.
The invention provides an intelligent site selection analysis system for a charging pile based on power big data analysis, which comprises a region division module, wherein the region division module is used for dividing a target region into equal areas to obtain each sub-region, acquiring characteristic data of each sub-region, and analyzing to obtain site selection demand indexes of each sub-region.
And the charging pile site selection analysis module is used for acquiring the existing charging pile facility data of each subarea and analyzing the charging pile site selection fit value of each subarea.
And the recommended charging pile address selecting and selecting module is used for comprehensively analyzing the charging pile address selecting priority of each subarea according to the charging pile address matching value of each subarea and selecting and obtaining the recommended charging pile address of the target area.
The database is used for storing the average passing vehicle density of the electric vehicles in the reference month of each subarea, the average loading rate of the reference month and the average Gu Fuzai rate of the reference month of the charging piles, the common duty ratio of the reference charging piles, the self duty ratio of the reference charging piles, the reference density of the charging pile distribution sites and the reference utilization rate of the charging pile distribution sites in each subarea of the charging piles.
The invention has the following beneficial effects:
(1) According to the intelligent site selection analysis method for the charging piles based on the power big data analysis, the target area is divided, the characteristic data of each subarea is obtained, the characteristic and the demand of the target area are fully considered by analyzing the site selection fit value of the charging piles of each subarea, the characteristic data of the charging piles are comprehensively evaluated, the subarea with the most construction demand characteristic is found out, the facility data of the existing charging piles of the target area are analyzed, scientific basis can be provided for intelligent site selection of the charging piles, reasonable layout and optimal configuration of charging facilities are realized, and the efficiency and quality of charging service are improved.
(2) The invention is helpful for comprehensively understanding the charging demand of each subarea and the layout demand of the charging piles by evaluating the site selection demand index of each subarea, can more accurately determine the area with high charging demand by combining factors such as vehicle density, population density, monthly average road electric vehicle traffic total amount and the like, provides scientific basis for intelligent site selection of the charging piles, and simultaneously considers factors such as the loading rate of the charging piles and the monthly average road electric vehicle traffic total amount, can further optimize the layout of the charging piles, improve the utilization rate and service quality of charging facilities, comprehensively analyze each factor, and can help a decision maker to more effectively formulate the layout and planning strategy of the charging piles so as to realize the optimization and the precision of the intelligent site selection analysis of the charging piles.
(3) According to the invention, the number distribution, the space distribution and the use condition of the charging piles can be comprehensively considered by analyzing the site selection fit value of the charging piles in each subarea, meanwhile, the supply condition of the charging facilities is effectively measured by combining the density of the charging pile distribution sites, the operation condition and the optimization potential of the charging facilities can be more accurately estimated by combining the factors such as the average use rate, the operation capacity and the average monthly peak electric quantity of each charging pile distribution site, and the invention is beneficial to further formulating a more scientific and more accurate site selection strategy of the charging piles, improving the utilization efficiency and the service quality of the charging facilities and promoting the popularization of electric vehicles.
(4) According to the invention, the recommended charging pile site selection priority of each subarea is combined with each factor to comprehensively evaluate each subarea, so that the most potential charging pile site selection is found, the site selection priority of each area can be intuitively known through sequencing site selection characterization values, a decision maker is facilitated to make a more intelligent site selection decision, the scientificity and accuracy of the charging pile site selection can be improved, a powerful support is provided for the layout and planning of charging facilities, and the intelligent development of the charging piles is promoted.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "open," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like indicate orientation or positional relationships, merely for convenience in describing the present invention and to simplify the description, and do not indicate or imply that the components or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
Referring to fig. 1, a first aspect of the present invention provides an intelligent site selection analysis method for a charging pile based on power big data analysis, which includes performing equal area division on a target area to obtain each sub-area, obtaining feature data of each sub-area, and analyzing to obtain site selection requirement indexes of each sub-area.
And acquiring the existing charging pile facility data of each subarea, and analyzing the charging pile site selection fit value of each subarea.
Comprehensively analyzing the charging pile site selection priority of each sub-region according to the charging pile site selection fit value of each sub-region, and selecting and obtaining the recommended charging pile site selection of the target region.
Specifically, the characteristic data of each subarea specifically includes population density, road total coverage area, electric vehicle possession, and monthly average road electric vehicle traffic total.
The existing charging pile facility data of each subarea comprises a common charging pile duty ratio, an own charging pile duty ratio, the number of charging piles, the average utilization rate of each charging pile distribution site, a month average load rate and a month average Gu Fuzai rate.
Specifically, the pre-construction requirement value of the charging pile of each subarea is obtained through analysis, and the specific analysis process comprises the following steps: and extracting the total road coverage area of each subarea, extracting the total traffic amount of the electric vehicles on the month average road of each subarea, and calculating the traffic vehicle density of the month average electric vehicles of each subarea.
And according to population density and electric vehicle possession of each subarea, extracting average electric vehicle passing vehicle density of reference month of each subarea stored in a database, and processing to obtain a first site selection demand representation value of each subarea.
And according to the month average road electric vehicle passing total quantity of each subarea and the month average load rate and monthly Gu Fuzai rate of the charging piles, extracting the reference month average load rate and the reference month average Gu Fuzai rate of the charging piles stored in the database, and processing to obtain a second site selection demand representation value of each subarea.
And comprehensively calculating the addressing demand index of each subarea according to the first addressing demand representation value of each subarea and the second addressing demand representation value of each subarea.
It should be noted that, the average monthly electric vehicle passing vehicle density of each sub-area may not only be analyzed by using mobile phone signal data, vehicle-mounted GPS data or social media geographical tag data, and these data sources may provide rich information about the vehicle movement mode, and by analyzing these data, the vehicle flow and the vehicle density in a specific area may be estimated, but also may be calculated by extracting the average monthly electric vehicle passing number of each sub-area stored in the database according to the total road coverage area, and the average monthly electric vehicle passing vehicle density of each sub-area may be calculated by the following formula: in the above, the ratio of/> Represents the average electric vehicle passing density of the month of the jth sub-area,/>Represents the total amount of electric vehicle traffic on the month average road of the jth sub-area,/>Representing the total coverage area of the road in the jth sub-area, j being the number of each sub-area,/>N is the number of subregions.
In this embodiment, by calculating the vehicle density, it is possible to determine areas with higher traffic flow, which may be the locations where the charging piles are installed with priority, and high traffic flow means more potential charging demands, so that the charging piles are arranged in these areas to better satisfy the user demands, and by knowing the vehicle density distribution conditions of the areas, the distribution of the charging piles can be planned better, ensuring wider and more uniform coverage of the charging piles, installing more charging piles in areas with dense traffic, and reducing the number of charging piles in areas with less traffic, so as to optimize the utilization rate of the charging facilities.
The first site selection demand representation value of each subarea represents a quantitative result of site selection demand data, which is obtained by analyzing and processing population density and electric vehicle possession of each subarea and is used as an analysis basis of site selection demand indexes of each subarea.
It should be noted that, the first site selection requirement characterization value of each sub-region can be obtained through a more accurate calculation method besides being obtained through analysis of the historical preparation operation data evaluation platform, and the specific calculation method is as follows: in the above, the ratio of/> Representing a value for the first addressing demand for the jth sub-region,/>Representing population density of the jth sub-region,/>Representing the electric vehicle possession of the jth subregion,/>Represents the average electric vehicle passing density of the month of the jth sub-area,/>Representing site selection demand factors corresponding to set unit population densities,/>Representing site selection demand factors corresponding to set unit electric automobile possessionAverage electric vehicle passing vehicle density of reference month indicating jth sub-region,/>Indicating the correction factor corresponding to the set vehicle density.
It should be noted that, the average load rate of the charging piles in the month of each sub-area is represented in one month, the average load level of the charging piles in the normal period of the charging piles, usually in the daytime or in the peak period, reflects the use condition of the charging piles in the daytime or in the peak period, and is generally represented by percentage, and the higher the average load rate of the charging piles in the month is, the heavier the load of the charging piles in the flat period is, and the higher the utilization rate is; the month average Gu Fuzai rate represents the average load level of the valley period, usually the night or the low peak period, of the charging pile in one month, reflects the service condition of the charging pile in the night or the low peak period, and is also represented by percentage, and the higher the month average Gu Fuzai rate is, the heavier the load of the charging pile in the valley period is, and the higher the utilization rate is.
The second site selection requirement representation value of each subarea is obtained by analyzing and processing the road traffic total quantity of each subarea, the charging pile month average load rate and monthly Gu Fuzai rate, and the obtained quantized result for analyzing the site selection requirement degree of the charging pile of each subarea can be obtained through a more accurate calculation method besides being obtained through analysis by a historical preparation operation data evaluation platform, and the specific calculation method is as follows: in the above, the ratio of/> Representing a value for the second addressing demand for the jth sub-region,/>Represents the total amount of electric vehicle traffic on the month average road of the jth sub-area,/>Charging pile month average load rate representing jth sub-area,/>, andMonthly Gu Fuzai rate of charging pile distribution site representing jth sub-area,/>Charging pile representing jth sub-area refers to month average load rate,/>Reference month uniform Gu Fuzai rate of charging pile distribution site representing jth sub-area,/>, andRepresenting a correction factor corresponding to the set monthly average road electric vehicle traffic total amount,/>Representing a correction factor corresponding to the set charging pile month average load rate,/>And the correction factors corresponding to the set charging pile month uniform Gu Fuzai rate are shown.
Specifically, the charging pile site selection fit value of each subarea is analyzed, and the specific analysis process is as follows: and extracting the reference charging pile public duty ratio and the reference charging pile self duty ratio stored in the database according to the charging pile public duty ratio and the self duty ratio of each sub-region and the number of the charging piles of each sub-region, and processing to obtain the charging pile number optimization characterization value of each sub-region.
And extracting the area of the subareas, extracting the number of charging pile distribution sites in each subarea, calculating the density of the charging pile distribution sites in each subarea, and extracting the reference density of the charging pile distribution sites stored in the database.
And extracting the reference utilization rate of each charging pile distribution site in each subarea of the charging piles stored in the database according to the average utilization rate of each charging pile distribution site in each subarea.
And according to the running capacity of each charging pile distribution site in each subarea and the average peak electric quantity of the month, obtaining the charging pile running optimization characterization value of each subarea through processing.
And (3) comprehensively calculating the charging pile site selection fit value of each subarea according to the charging pile quantity optimization characterization value of each subarea and the charging pile operation optimization characterization value of each subarea.
It should be noted that, the address selection fit value of the number of the charging piles in each sub-area indicates a quantization result obtained by analyzing the common duty ratio, the own duty ratio and the number of the charging piles in each sub-area, wherein the quantization result is used for analyzing the optimization demand degree of the number of the charging piles in each sub-area, and the historical data can be modeled and predicted by using a machine learning algorithm, such as regression analysis, a support vector machine, a neural network and the like, so as to obtain the address selection fit value of the number of the charging piles in each sub-area through analysis, and in the embodiment, the method is obtained by the following calculation method, and the specific calculation method is as follows:
in the above, the ratio of/> Selecting address fitting values for the number of charging piles in the jth sub-area,/>Fill pile sharing ratio for jth sub-area,/>A private duty cycle for the charging pile for the jth sub-zone,Number of charging piles for jth sub-area,/>Fill pile common duty ratio for reference of jth sub-region,/>Private duty ratio of reference charging pile for jth sub-region,/>Representing the correction factor corresponding to the public duty ratio of the set charging pile,/>Representing the correction factor corresponding to the set private duty ratio of the charging pile,/>Indicating the compensation factors corresponding to the set single charging piles.
It should be noted that, the charging pile operation site selection fit value of each sub-area indicates that the quantized result for analyzing the charging pile operation optimization requirement of each sub-area is obtained by analyzing and processing the average usage rate, the operation capacity and the average peak electric quantity of the month of the charging pile of each sub-area, and not only can the future operation condition of the charging pile of each sub-area be predicted by using mathematical modeling and optimization algorithm and combining historical data and real-time monitoring data to establish an operation model of the charging pile, but also the future operation condition of the charging pile of each sub-area is compared with the site selection requirement, and the charging pile operation site selection fit value of each sub-area is obtained by analyzing and obtaining the following calculation method in the embodiment:
in the above, the ratio of/> Selecting address fitting values for charging pile operation of the d charging pile distribution site in the j-th sub-area,/>For density of charging pile distribution sites in jth sub-region,/>Average utilization rate of charging piles for d charging pile distribution sites in jth sub-area,/>For the operation capacity of the d charging pile distribution site in the j-th sub-area,/>Monthly average peak power for the d charging pile distribution site in the j-th sub-area,/>For reference density of charging pile distribution sites stored in database,/>Referring to average utilization rate of charging piles for the d charging pile distribution site in the j-th sub-area,/>For the reference operating capacity of the d charging pile distribution site in the j-th sub-area stored in the database,/>Average peak electric quantity for reference month of d charging pile distribution sites in j-th sub-area stored in database,/>Correction factor corresponding to density of charging pile distribution siteCorrection factor corresponding to average utilization rate of charging pileRepresenting a compensation factor corresponding to the operating capacity of the charging pile set,/>The correction factor corresponding to the set monthly average peak power of the charging piles is represented, d represents the number of each charging pile distribution site, and/(m)U is the number of charging pile distribution sites.
It should be noted that, the density of the distribution sites of the charging piles may be calculated by using a spatial interpolation method, such as kriging interpolation or inverse distance weighted interpolation, according to the position data of the known charging piles, the distribution condition of the density of the charging piles is calculated in space, the continuous density of the charging piles is obtained from the discrete point data, and the density of the distribution sites of the charging piles in each sub-region may be calculated by the area of the sub-region and the number of the distribution sites of the charging piles in each sub-region, where the calculation formula is as follows: in the above, the ratio of/> Representing the density of charging pile distribution sites in the jth sub-region,/>Representing the number of charging pile distribution sites in the jth sub-region,/>Representing the area of the sub-region.
Specifically, the site selection demand index of each sub-area represents a quantized result obtained by analyzing the characteristic data of each sub-area and used for analyzing the site selection demand degree of the charging piles of each sub-area, and is used as an analysis basis for selecting a target area to obtain the recommended charging pile site selection of the target area.
Specifically, the charging pile demand optimization characterization value of each subarea represents the analysis result which is obtained by analyzing the data of the address matching degree of each subarea and is used for analyzing the charging pile data quantization result of each subarea, and is used as the analysis basis of the recommended charging pile address of the target area.
Specifically, selecting and obtaining the recommended charging pile site selection of the target area, wherein the specific process is as follows: and comprehensively calculating the recommended charging pile site selection priority of each subarea according to the site selection demand index of each subarea and the charging pile site selection fit value of each subarea.
It should be noted that, the recommended charging pile address priority of each sub-area indicates a quantized result obtained by analyzing the address requirement index of each sub-area and the address matching value of the charging pile of each sub-area, where the recommended charging pile address priority of each sub-area is obtained by considering the matching situation of public facilities of each sub-area, such as a business center, a residential area, an office area, etc., the address requirement of the charging pile may be affected by surrounding public facilities, so that the factor setting weights may be included in the priority analysis of the recommended charging pile address to obtain the recommended charging pile address priority of each sub-area, and in this embodiment, the recommended charging pile address priority of each sub-area is obtained by the following calculation method:
in the above, the ratio of/> The recommended charging pile addressing priority for the jth sub-region,Index of site selection requirement for jth sub-region,/>Selecting address fitting value for charging pile of jth sub-area,/>Weight factor corresponding to the set address demand index,/>And the weight factor corresponding to the set charging pile address matching value is represented.
And sequencing the recommended charging pile site selection priorities of all the subareas in sequence from large to small, and extracting the subarea with the first recommended charging pile site selection priority sequence as the recommended charging pile site selection of the target area.
In this embodiment, according to the index of the site selection requirement and the site selection fit value of the charging piles in each sub-region, the site selection priority of the recommended charging piles in each sub-region is comprehensively calculated, then the site selection priority of the recommended charging piles in each sub-region is sequenced, the sub-region with the first sequence is selected as the site selection of the recommended charging piles in the target region, which is conducive to the decision process of intelligent site selection of the charging piles, by comprehensively considering the site selection requirement and the site selection fit condition of the charging piles in each sub-region, the region with the most urgent requirement can be preferentially considered, thereby the utilization efficiency and coverage area of the charging facility are improved to the greatest extent, by sequencing the priority of the site selection of the recommended charging piles, the decision maker can know the priority of each region more clearly, which is conducive to reasonably distributing resources and formulating a priority charging pile construction plan, thereby realizing more effective charging pile layout and coverage, and improving the quality and user satisfaction of charging services.
Specifically, the index of the site selection requirement of each subarea can be obtained by performing pattern recognition and analysis by using historical data, and can also be obtained by the following calculation method, wherein the specific calculation method is as follows:
; wherein/> The index of demand for addressing for the jth sub-zone,Representing a value for the first addressing demand for the jth sub-region,/>A second addressing demand characterization value for the jth sub-region,For the weight factor corresponding to the set first addressing demand representation value,/>For the weight factor corresponding to the set second site selection requirement representation value, e represents a natural constant, j is the number of each subarea,/>N is the number of subregions.
Specifically, the charging pile address matching value of each sub-area can be obtained by performing pattern recognition and analysis by using historical data, and can also be obtained by the following calculation method, wherein the specific calculation method is as follows:
; wherein/> Selecting address fitting value for charging pile of jth sub-area,/>Selecting address fitting values for the number of charging piles in the jth sub-area,/>Selecting address fitting values for charging pile operation of jth sub-area,/>Selecting weight factors corresponding to address fitting values for the set number of charging piles,/>And (5) operating the weighting factors corresponding to the address matching values for the set charging piles.
As shown in fig. 2, the second aspect of the present invention provides an intelligent site selection analysis system for a charging pile based on power big data analysis, which includes a region division module, configured to divide a target region by equal areas to obtain each sub-region, obtain feature data of each sub-region, and analyze to obtain a site selection demand index of each sub-region.
And the charging pile site selection analysis module is used for acquiring the existing charging pile facility data of each subarea and analyzing the charging pile site selection fit value of each subarea.
And the recommended charging pile address selecting and selecting module is used for comprehensively analyzing the charging pile address selecting priority of each subarea according to the charging pile address matching value of each subarea and selecting and obtaining the recommended charging pile address of the target area.
The database is used for storing the average passing vehicle density of the electric vehicles in the reference month of each subarea, the average loading rate of the reference month of the charging piles and the average Gu Fuzai rate of the reference month, the common duty ratio of the reference charging piles, the self duty ratio of the reference charging piles, the reference density of the distribution sites of the charging piles and the reference utilization rate of the distribution sites of the charging piles in each subarea of the charging piles.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The intelligent site selection analysis method for the charging pile based on the power big data analysis is characterized by comprising the following steps of:
dividing the target area by equal area to obtain each subarea, acquiring characteristic data of each subarea, and analyzing to obtain the site selection requirement index of each subarea;
Acquiring existing charging pile facility data of each subarea, and analyzing charging pile site selection fit values of each subarea;
And comprehensively analyzing the charging pile site selection priority of each subarea according to the site selection demand index of each subarea and the charging pile site selection fit value of each subarea, and selecting and obtaining the recommended charging pile site selection of the target area.
2. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 1, wherein the method is characterized by comprising the following steps of: the characteristic data of each subarea specifically comprises road total coverage area, population density, electric vehicle possession and month average road electric vehicle passing total amount;
The existing charging pile facility data of each subarea comprises a common charging pile duty ratio, an own duty ratio, the number of charging piles, a month average load rate and a month average Gu Fuzai rate, and the existing charging pile facility data of each subarea further comprises: the operation capacity of each charging pile distribution site, average peak electric quantity of month and average use rate.
3. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 1, wherein the method is characterized by comprising the following steps of: the analysis obtains the site selection demand index of each subarea, and the specific analysis process comprises the following steps:
extracting the total road coverage area of each subarea, extracting the total traffic amount of the electric vehicles on the month average road of each subarea, and calculating the traffic vehicle density of the month average electric vehicles of each subarea;
According to population density and electric vehicle possession of each subarea, extracting average electric vehicle passing vehicle density of reference month of each subarea stored in a database, and processing to obtain a first site selection demand representation value of each subarea;
According to the month average road electric vehicle passing total quantity of each subarea, the charging pile month average load rate and the charging pile month average Gu Fuzai rate, extracting the charging pile reference month average load rate and the charging pile reference month average Gu Fuzai rate stored in a database, and processing to obtain a second site selection demand representation value of each subarea;
And comprehensively calculating the addressing demand index of each subarea according to the first addressing demand representation value of each subarea and the second addressing demand representation value of each subarea.
4. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 2, which is characterized in that: the charging pile site selection fit values of all the subareas are analyzed, and the specific analysis process is as follows:
Extracting the reference charging pile public duty ratio and the reference charging pile self duty ratio stored in the database according to the charging pile public duty ratio and the self duty ratio of each sub-region and the number of the charging piles of each sub-region, and processing to obtain the charging pile number optimization characterization value of each sub-region;
Extracting the area of the subareas, extracting the number of charging pile distribution sites in each subarea, calculating the density of the charging pile distribution sites in each subarea, and extracting the reference density of each charging pile distribution site stored in a database;
extracting the reference utilization rate of each charging pile distribution site in each subarea stored in a database according to the average utilization rate of each charging pile distribution site in each subarea;
According to the running capacity of each charging pile distribution site in each subarea and the average peak electric quantity of the month, obtaining the charging pile running optimization characterization value of each subarea through processing;
And (3) comprehensively calculating the charging pile site selection fit value of each subarea according to the charging pile quantity optimization characterization value of each subarea and the charging pile operation optimization characterization value of each subarea.
5. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 1, wherein the method is characterized by comprising the following steps of: and the site selection demand index of each subarea represents a quantized result which is obtained by analyzing and processing the characteristic data of each subarea and is used for analyzing the site selection demand degree of the charging piles of each subarea, and the quantized result is used as an analysis basis for selecting and obtaining the recommended charging pile site selection of the target area.
6. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 1, wherein the method is characterized by comprising the following steps of: and the charging pile demand optimization characterization value of each subarea represents a data quantification result which is obtained by analyzing and processing the existing charging pile facility data of each subarea and is used for analyzing the matching degree of the charging pile site selection of each subarea, and is used as an analysis basis of the recommended charging pile site selection of the target area.
7. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 1, wherein the method is characterized by comprising the following steps of: the recommended charging pile site selection of the target area is obtained through selection, and the specific process is as follows:
Comprehensively calculating recommended charging pile site selection priority of each subarea according to site selection demand indexes of each subarea and charging pile site selection fit values of each subarea;
And sequencing the recommended charging pile site selection priorities of all the subareas in sequence from large to small, and extracting the subarea with the first recommended charging pile site selection priority sequence as the recommended charging pile site selection of the target area.
8. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 2, which is characterized in that: the address selecting requirement index of each subarea is calculated according to the following formula:
；
wherein, Index of site selection requirement for jth sub-region,/>Representing a value for the first addressing demand for the jth sub-region,/>Representing a value for the second addressing demand for the jth sub-region,/>For the weight factor corresponding to the set first addressing demand representation value,/>For the weight factor corresponding to the set second site selection requirement representation value, e represents a natural constant, j is the number of each subarea,/>N is the number of subregions.
9. The intelligent site selection analysis method for the charging pile based on the power big data analysis of claim 2, which is characterized in that: the charging pile address matching value of each subarea has a calculation formula as follows:
；
wherein, Selecting address fitting value for charging pile of jth sub-area,/>Optimizing characterization values for the number of charging piles in the jth sub-area,/>Optimizing characterization values for charging pile operation of jth sub-region,/>Optimizing weight factors corresponding to characterization values for the set number of charging piles,/>And optimizing the weight factor corresponding to the characterization value for the set charging pile operation.
10. Charging stake intelligence site selection analytic system based on electric power big data analysis, its characterized in that includes:
The regional division module is used for carrying out equal area division on the target region to obtain each subarea, acquiring characteristic data of each subarea, and analyzing to obtain the site selection requirement index of each subarea;
The charging pile site selection analysis module is used for acquiring the existing charging pile facility data of each subarea and analyzing the charging pile site selection fit value of each subarea;
the recommended charging pile address selecting and selecting module is used for comprehensively analyzing the charging pile address selecting priority of each subarea according to the charging pile address matching value of each subarea and selecting and obtaining the recommended charging pile address of the target area;
The database is used for storing the average passing vehicle density of the electric vehicles in the reference month of each subarea, the average loading rate of the reference month of the charging piles and the average Gu Fuzai rate of the reference month, the common duty ratio of the reference charging piles, the self duty ratio of the reference charging piles, the reference density of the charging pile distribution sites and the reference utilization rate of the charging pile distribution sites.
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