CN114048920A - Site selection layout method, device, equipment and storage medium for charging facility construction - Google Patents
Site selection layout method, device, equipment and storage medium for charging facility construction Download PDFInfo
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Abstract
The invention discloses a site selection layout method, a site selection layout device, site selection equipment and a storage medium for charging facility construction. The method comprises the following steps: acquiring layer data in an electronic map, and carrying out gridding processing on the layer data to obtain at least one grid area; determining charging performance information of each grid area according to charging associated information of existing charging facilities in each grid area and area attribute information of the corresponding grid area; and determining the site selection layout result of each grid region relative to the charging facility by combining a preset site selection analysis model and a layout optimization model according to the charging performance information of each grid region. By the technical scheme, scientific rationalization of site selection layout of the charging facilities is realized, the charging requirements of users are met, the satisfaction degree of the users is improved, further, the full utilization of resources is promoted, and the utilization efficiency of the resources is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data, in particular to a site selection layout method, a site selection layout device, site selection equipment and a storage medium for charging facility construction.
Background
Along with the improvement of society and economic level, people pay more and more attention to the requirements on energy conservation and emission reduction, the emission of waste gas of the traditional automobile is a main factor of urban air pollution, the development of electric automobiles and new energy automobiles has important significance on improving urban environment, and the establishment of perfect scientific charging infrastructure site selection layout is the basic premise and important guarantee of wide application of the electric automobiles.
However, in the existing site selection planning analysis of the electric vehicle charging facility, power factors such as power data requirements and station area capacity are not considered, and influence factors of the operation state of the charging station are not considered, so that the site selection of the charging facility is not clear, and after the site selection is determined, the problems of not deepening research and inaccurate quantification exist, and the scientific and reasonable site selection layout of the charging facility construction is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a site selection layout method, a site selection layout device, site selection layout equipment and a storage medium for charging facility construction, so that scientific rationalization of site selection layout of charging facility construction is realized, charging requirements are met, and user satisfaction is improved.
In a first aspect, an embodiment of the present invention provides a site selection layout method for charging facility construction, including:
acquiring layer data in an electronic map, and carrying out gridding processing on the layer data to obtain at least one grid area;
determining charging performance information of each grid area according to charging associated information of existing charging facilities in each grid area and area attribute information of the corresponding grid area;
and determining the site selection layout result of each grid region relative to the charging facility by combining a preset site selection analysis model and a layout optimization model according to the charging performance information of each grid region.
In a second aspect, an embodiment of the present invention further provides an address selecting layout apparatus for charging facility construction, including:
the area acquisition module is used for acquiring layer data in an electronic map and carrying out gridding processing on the layer data to obtain at least one grid area;
the data determining module is used for determining charging performance information of each grid area according to charging related information of existing charging facilities in each grid area and area attribute information of the corresponding grid area;
and the site selection determining module is used for determining site selection layout results of the grid areas relative to the charging facilities according to the charging performance information of the grid areas by combining a preset site selection analysis model and a layout optimization model.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, the one or more programs cause the one or more processors to implement the site selection layout method for construction of the charging facility according to any one of the embodiments of the present invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the address layout method for charging facility construction according to any one of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, at least one grid area is obtained by obtaining the layer data in the electronic map and carrying out gridding processing on the layer data; determining charging performance information of each grid region according to charging associated information of existing charging facilities in each grid region and region attribute information of the corresponding grid region; and determining the site selection layout result of each grid area relative to the charging facility by combining a preset site selection analysis model and a layout optimization model according to the charging performance information of each grid area. According to the technical scheme, the problems that site selection of site selection layout of the charging facility is not clear, research is not deepened and the like in the prior art are solved according to the charging performance information of each grid area and a method combining the site selection analysis model and the layout optimization model, scientific rationalization of the site selection layout of the charging facility is realized, the charging requirement of a user is met, the satisfaction degree of the user is improved, the full utilization of resources is promoted, and the utilization efficiency of the resources is improved.
Drawings
Fig. 1 is a design flowchart of a site selection layout method for charging facility construction according to an embodiment of the present invention;
fig. 2 is a construction flowchart of an address selection layout method for charging facility construction according to a second embodiment of the present invention;
fig. 3 is a diagram illustrating an implementation effect of an address analysis model in the address selection layout method for charging facility construction according to the second embodiment of the present invention;
fig. 4 is a diagram illustrating an implementation effect of a layout optimization model in the site selection layout method for charging facility construction according to the second embodiment of the present invention;
fig. 5 is a diagram illustrating an implementation effect of calculating scores of operating states of charging facilities in the site selection layout method for charging facility construction according to the second embodiment of the present invention;
fig. 6 is a schematic structural diagram of an address selection layout device for charging facility construction according to a third embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a design flowchart of a site selection layout method for charging facility construction according to an embodiment of the present invention, where the embodiment is applicable to site selection layout for charging facility construction, for example, a site selection layout of an electric vehicle charging station/charging pile, and the method may be executed by a site selection layout device for charging facility construction, and the device may be implemented in a hardware and/or software manner, referring to fig. 1, where the method provided by the embodiment of the present invention specifically includes the following steps:
s110, obtaining layer data in the electronic map, and carrying out meshing processing on the layer data to obtain at least one mesh area.
The electronic map may be a digital map that is stored and referred to digitally, and may be used to find various places, various positions, driving routes, and the like. The electronic map may also be considered to be formed by superimposing one or more layer data, and each layer data may represent a part of geographical or traffic information. The layer data can comprise standard maps, satellite maps, traffic maps and other types of data. The gridding process may be a method of dividing the layer data into one or more grid regions according to a gridding standard, where the gridding standard may be to divide the layer data in units of preset kilometers, in units of administrative regions, or in units of latitudes and longitudes. The grid area may be a grid area obtained by dividing the layer data according to a grid standard. Each grid region may have different information, such as region location information, traffic information, and charging facilities.
According to the embodiment of the invention, the layer data of the electronic map can be obtained by obtaining an Application Programming Interface (API) of the electronic map, and the obtained layer data can be subjected to gridding processing, so that one or more grid areas can be obtained, and the information can be conveniently analyzed according to the grid areas in the follow-up process.
And S120, determining the charging performance information of each grid region according to the charging related information of the existing charging facilities in each grid region and the region attribute information of the corresponding grid region.
The charging-related information may be information related to existing charging facilities in each grid area, and may be information such as a charging station file, a 96-point charging pile power curve, a daily charging quantity of a charging pile, and the like; information irrelevant to the existing charging facility in each grid area is area attribute information, for example, the area attribute information may be information such as a parking lot position, a Point of Interest (POI) Point of Interest, a road network structure, and an electric vehicle holding capacity in each grid area; certainly, in practical application, if information such as the daily charging amount of the charging pile and the 96-point power curve of the charging pile is missing and abnormal, the abnormal value of the information such as the daily charging amount of the charging pile and the 96-point power curve of the charging pile can be identified and deleted through an isolated Forest (Isolation Forest) algorithm, and then the original missing value and the missing value generated after the abnormal value is removed can be completed through a Lagrange Interpolation method (Lagrange Interpolation).
The charging performance information may be considered as key charging information in each grid area, and may be information such as a charging demand, a power supply capacity, and a charging facility operation capacity in each grid area.
According to the embodiment of the invention, the existing charging facility information in each grid area can be obtained according to each grid area, so that the charging associated information in each grid area can be obtained, and the area attribute information of each grid area can also be correspondingly obtained, so that the charging performance information of each grid area can be obtained according to the charging associated information of the existing charging facility in each grid area and the corresponding area attribute information, and the subsequent processing can be conveniently carried out according to the charging performance information.
And S130, determining the site selection layout result of each grid region relative to the charging facility by combining a preset site selection analysis model and a layout optimization model according to the charging performance information of each grid region.
The site selection analysis model can be a classification model constructed by a machine learning classification algorithm and can be used for predicting whether a new charging facility is needed in each grid area, and the layout optimization model can be a regression model constructed by a machine learning regression algorithm and can be used for predicting the number of immigration and emigration of specific charging facilities. The site selection layout result can be result information of new construction, migration and migration of the charging facilities in each grid area.
According to the embodiment of the invention, after the charging performance information of each grid area is obtained, the site selection layout result of the corresponding charging facility of each grid area can be obtained according to the charging performance information in each grid area by combining the preset site selection analysis model and the layout optimization model, so that the specific results of new construction, immigration and emigration of the charging facility of each grid area can be obtained.
According to the technical scheme of the embodiment of the invention, at least one grid area is obtained by obtaining the layer data in the electronic map and carrying out gridding processing on the layer data; determining charging performance information of each grid region according to charging associated information of existing charging facilities in each grid region and region attribute information of the corresponding grid region; and determining the site selection layout result of each grid area relative to the charging facility by combining a preset site selection analysis model and a layout optimization model according to the charging performance information of each grid area. According to the technical scheme, the problems that site selection of site selection layout of the charging facility is not clear, research is not deepened and the like in the prior art are solved according to the charging performance information of each grid area and a method combining the site selection analysis model and the layout optimization model, scientific rationalization of the site selection layout of the charging facility is realized, the charging requirement of a user is met, the satisfaction degree of the user is improved, the full utilization of resources is promoted, and the utilization efficiency of the resources is improved.
Example two
Fig. 2 is a construction flowchart of an address selection layout method for charging facility construction according to a second embodiment of the present invention, which is embodied on the basis of the second embodiment of the present invention, and referring to fig. 2, the method provided by the embodiment of the present invention specifically includes the following steps:
s201, obtaining layer data in the electronic map, and dividing the layer data into at least one unit grid area by taking 1 kilometer as a unit.
According to the embodiment of the invention, the API interface of the electronic map can be called, then the layer data in the electronic map can be obtained, and the layer data can be divided into one or more unit grid areas according to the unit of 1 kilometer.
And S202, clustering each unit grid area according to the vehicle travel data and the vehicle charging data related to each unit grid area.
The trip data of the vehicle can be regarded as trip heat of a grid area, and the trip heat of the grid area can be obtained by obtaining taxi order origin-destination data and driving navigation origin-destination data and analyzing the data. Of course, in practical application, if the grid region trip heat degree is missing, the missing value may be filled by using the average value of the trip heat degrees of the grid region on the current day and the previous day of the grid region trip heat degree. The vehicle charging electricity consumption data can be obtained by acquiring a 96-point power curve of the charging pile and a daily charging amount of the charging pile and analyzing the curve.
After the electronic map is divided into a plurality of unit grid areas, the number of the unit grid areas is large, and the unit grid areas are small, which is not beneficial to the subsequent analysis of the grid areas, so that the unit grid areas need to be clustered.
In the embodiment of the present invention, each unit grid region may be clustered according to vehicle travel data and vehicle charging data associated with each unit grid region, specifically, a clustering algorithm may be used for clustering, for example, a K-means clustering algorithm (KMeans) may be used for clustering each unit grid region.
The specific clustering process may be: firstly, K unit grid areas are selected from a unit grid area set consisting of all unit grid areas as an initial clustering center, wherein the K value can be determined by adopting a CH index (CalinsKi-Harabasz, CH index). Then calculating the distance between all unit grid areas and K cluster centers, allocating each unit grid area to the cluster closest to the unit grid area, wherein the cluster center and the unit grid area allocated to the cluster center represent a cluster, each unit grid area is allocated, the cluster center of the cluster is recalculated according to the existing unit grid area in the cluster, and the process is repeated until a certain termination condition is met, wherein the termination condition can be that no unit grid area is reallocated to different clusters, no cluster center is changed again or the square sum of errors is locally minimum. And outputting a clustering result after the termination condition is met.
S203, acquiring the multi-class grid region formed after the clustering processing.
According to the embodiment of the invention, after clustering processing is carried out by using a clustering algorithm, a plurality of unit grid areas with similar distances are clustered into a new grid area, so that one or more new grid areas after clustering processing is carried out on all the unit grid areas can be obtained, and subsequent analysis on the grid areas is facilitated.
It should be noted that after the multiple types of grid regions formed after the clustering process are obtained, the charging performance information of each grid region may be determined according to the charging related information of the existing charging facility in each grid region and the region attribute information of the corresponding grid region.
Optionally, on the basis of the foregoing embodiment, the charging performance information includes: charging demand and charging capacity matching information of the grid area, and charging facility operation state information.
The charging requirement and charging capacity matching information of the grid area can be information whether the charging requirement and the charging capacity of the grid area are matched or not; the charging facility operation state information may be information on whether the charging facility operation state is good or not.
S204, determining the existing charging facilities and the corresponding charging related information in each grid area according to the position information of the installed charging facilities and the area longitude and latitude of the grid area.
It should be noted that, the grid area and the corresponding charging facility and the charging facility related information may be associated by using the latitude and longitude information. The method can be used as a basis for judging whether charging facilities and corresponding charging associated information exist in the grid area.
According to the embodiment of the invention, the position information of the installed charging facility and the area longitude and latitude of the grid area can be obtained firstly, then whether the position information of the installed charging facility is in the area longitude and latitude is judged, if the position information of the installed charging facility is in the area longitude and latitude, the charging facility can be considered to be in the grid area, the charging facility related information of the grid area can be correspondingly obtained, and further the existing charging facility and the corresponding charging related information included in each grid area can be obtained.
S205, determining the matching information of the charging requirement and the charging capacity of each grid area through a pre-constructed charging requirement prediction basic model based on the area attribute information and the charging related information of each existing charging facility aiming at each grid area.
The charging demand prediction base model can be a regression model constructed by adopting a machine learning regression algorithm.
According to the embodiment of the invention, for each grid area, the area attribute information and the charging associated information of the existing charging facility can be used as the input data of the corresponding grid area, the input data of each grid area is input into the charging demand prediction basic model, and the charging demand prediction basic model can output the matching information of the charging demand and the charging capacity of the corresponding grid area.
And S206, determining the charging facility operation state information of the grid area through a pre-constructed operation state analysis model based on the charging related information of each existing charging facility.
The operation state analysis model may be a model for calculating a composite score of the operation state of the charging facility.
According to the embodiment of the invention, the comprehensive score of the operation state of the charging facility can be calculated through the pre-constructed operation state analysis model based on the charging related information of each existing charging facility, so that the operation state information of the charging facility in each grid area can be determined.
And S207, determining the operation level of each existing charging facility in the grid area based on the charging facility operation state information in the corresponding charging performance information for each grid area.
In the embodiment of the invention, the operation level of each existing charging facility in the corresponding grid area can be obtained by judging whether the charging facility operation state information in the corresponding charging performance information meets the preset threshold value or not in each grid area. For example, it is determined whether the integrated score of the operating status of the charging facility satisfies a preset threshold condition, the operating level of the charging facility may be one level if the integrated score of the operating status of the charging facility exceeds the threshold condition of 80 points, the operating level of the charging facility may be two levels if the integrated score of the operating status of the charging facility exceeds the threshold condition of 60 points and does not exceed 80 points, and the operating level of the charging facility may be three levels if the integrated score of the operating status of the charging facility is below the threshold condition of 60 points.
And S208, if the operation state information of the charging facilities does not meet the operation threshold condition, obtaining the migration result of the existing charging facilities in the grid area by combining the layout optimization model with each operation level.
According to the embodiment of the invention, whether the operation state information of the charging facility meets the operation threshold condition or not can be judged firstly, if the operation state information of the charging facility does not meet the operation threshold condition, the charging facility can be migrated in the corresponding grid area, and the result of the migrated quantity of the existing charging facility in the grid area can be obtained by combining the layout optimization model with each operation level. For example, if the score of the operating state of the charging facility is 50 minutes and the operating threshold condition is 60 minutes, the score of the operating state of the charging facility is lower than the operating threshold condition, and if it is determined that the operating state information of the charging facility does not satisfy the operating threshold condition, it indicates that the operating state of the charging facility is not qualified, the charging facility may be migrated in the corresponding grid area.
And S209, otherwise, determining whether the grid area needs to be newly built with a charging facility according to the traffic flow heat data, the peripheral state data and the charging requirement and charging capacity matching information in the corresponding charging performance information of the grid area.
The traffic flow heat data can be regarded as grid area travel heat. The peripheral state data may be the state of the charging facility, such as the traffic state-related business, the company distribution state, the shopping state, and the residence state, in the grid area.
According to the embodiment of the invention, if the operation state information of the charging facility meets the operation threshold condition, the operation state of the charging facility can be considered to be good, and whether the charging facility needs to be newly built in a grid area can be judged. Specifically, whether a new charging facility is needed in the grid area can be judged through the traffic flow heat data, the peripheral state data, and the charging requirement and charging capability matching information in the corresponding charging performance information in the grid area.
S210, if a new charging facility is required, obtaining new building/immigration results of the existing charging facilities in the grid area through the site selection analysis model in combination with each operation level.
In the embodiment of the invention, if the grid area needs to be newly built with the charging facilities, the newly built/migrated result of the existing charging facilities in the grid area can be obtained by combining the site selection analysis model with each operation level. For example, if the operation level is divided into B, C levels, for a charging station with operation level B, the charging requirement is large, and there is a situation that a user queues up to charge, it is necessary to add a charging facility to provide better service for the user of the electric vehicle, so that the result of needing to newly build the charging facility can be predicted through the site selection analysis model, and compared with the actual charging facility result with operation level B, the result of newly building/migrating the existing charging facility is determined.
For example, the traffic flow heat data, the peripheral state data, and the charging requirement and charging capability matching information in the corresponding charging performance information in the grid region may be used as input data of the site selection analysis model, the site selection analysis model may be constructed by using an XGBoost algorithm (XGeoXBoost) classification algorithm, and whether a charging facility is newly established in the grid region is used as a prediction variable; the model input data consists of a positive example and a negative example, the balance sample data is obtained, and charging electric quantity, traffic flow heat and peripheral state data of 50 grid areas with the grid area charging electric quantity being more than 3000 kilowatt hours are randomly selected as the positive example; randomly selecting charging electric quantity, traffic flow heat and peripheral state data of 50 grid areas with the charging electric quantity of the grid areas being less than 500 kilowatt-hours as a counter example; and (3) using 80% of input data as training data and the rest as test data, fitting a training set to the maximum extent by continuously adjusting model parameters, and testing the test data by using the trained model. As shown in fig. 3.
Fig. 3 is a diagram illustrating an implementation effect of an address analysis model in the address selection layout method for charging facility construction according to the second embodiment of the present invention.
Firstly, taking traffic flow heat data, peripheral state data and charging requirement and charging capability matching information in corresponding charging performance information of a grid area as input data, then taking 80% of the input data as a training set and 20% of the input data as a test set, using the training set to train an addressing analysis model, then using the test set to test the addressing analysis model, dividing the training set into a plurality of sampling sets such as a sampling set 1, a sampling set 2, … and a sampling set N, voting by an XGboost algorithm, and finally outputting a result whether a new charging facility is needed in the grid area.
And S211, otherwise, obtaining the migration result of the existing charging facilities in the grid area by combining the layout optimization model with each operation level.
According to the embodiment of the invention, if the grid area does not need to be newly built with the charging facilities, the migration result of the existing charging facilities in the grid area can be obtained by combining the layout optimization model with each operation level. For example, if the operation level is divided into B, C levels, for a charging station with the operation level C, the charging pile utilization rate is low, and there is an idle situation of the charging pile, and it is necessary to reduce the charging piles, so that the migration result of the existing charging facilities can be output by combining the layout optimization model with each operation level. For example, a Gradient Boosting Decision Tree (GBDT) regression algorithm may be used to predict the reasonable number of charging facilities, and the number of charging facilities migrated is determined by comparing the predicted number with the actual number of charging facilities of the operation level C, as shown in fig. 4.
Fig. 4 is a diagram illustrating an implementation effect of a layout optimization model in the site selection layout method for charging facility construction according to the second embodiment of the present invention.
Firstly, taking traffic flow heat data, peripheral state data and charging requirement and charging capability matching information in corresponding charging performance information of a grid area as input data, then taking 80% of the input data as a training set and 20% of the input data as a test set, training a layout optimization model by using the training set, then testing the layout optimization model by using the test set, fitting the training set by using a gradient lifting decision tree algorithm, namely a regression tree, and finally outputting the result of the number of charging facilities required to be newly built in the grid area.
And S212, taking the emigration result and/or the newly built/emigration result as an addressing layout result of the grid area relative to the charging facilities.
In the embodiment of the invention, after the emigration result and/or the newly-built/emigration result are/is obtained, the emigration result and/or the newly-built/emigration result are/is used as the site selection layout result of each grid area relative to the charging facilities.
Further, on the basis of the above embodiment, determining the matching information between the charging demand and the charging capacity of the grid area by using a pre-constructed charging demand prediction base model based on the area attribute information and the charging related information of each existing charging facility includes:
a1, extracting vehicle travel data in the area attribute information and historical charging quantity in each charging related information according to at least one attribute dimension of season, holidays, weather and temperature.
According to the embodiment of the invention, according to one or more attribute dimensions of seasons, holidays, weather and temperature, vehicle travel data in the area attribute information of the grid area and historical charging quantity in each charging related information can be respectively extracted under each attribute dimension.
b1, inputting corresponding vehicle travel data and various historical charging quantities as input data into a charging demand prediction basic model according to each attribute dimension, and obtaining charging demand and charging capacity information of the grid area in the attribute dimension.
The charging demand prediction basic model can be a regression model constructed by utilizing a gradient lifting regression and a random forest regression algorithm; and the grid search and time series cross validation method can be utilized to optimize the hyperparameter of the charging demand prediction basic model, the optimal hyperparameter value is found, and the accuracy of the charging demand prediction basic model can be improved through an integrated learning Stacking framework.
According to the embodiment of the invention, the vehicle travel data and the historical charging quantities of the grid area under the corresponding attribute dimension can be input into the charging demand prediction basic model as input data under each attribute dimension, and the charging demand prediction basic model can output the charging demand and the charging capacity information of the corresponding attribute dimension, so that the charging demand and the charging capacity information of the grid area under each attribute dimension can be obtained.
And c1, summarizing the charging requirement and the charging capacity information under each attribute dimension as the charging requirement and the charging capacity information of the grid area.
According to the embodiment of the invention, the charging requirement and the charging capacity information of the grid area under each attribute dimension can be summarized to obtain the charging requirement and the charging capacity information of the grid area. The specific summarizing method may be a method of accumulating the charging demand and the charging capability information under each attribute dimension, or may be a method of setting a corresponding weight to an attribute dimension, that is, a method of weighted sum of the charging demand and the charging capability information of each attribute dimension, where the cumulative sum of the weights of each attribute dimension is 1.
Further, on the basis of the above embodiment, determining the charging facility operation state information of the grid area through a pre-constructed operation state analysis model based on the charging related information of each existing charging facility includes:
a2, acquiring a preset multi-level index evaluation table.
The multi-level index evaluation table comprises a first-level index column, a second-level index column and a third-level index column, wherein each first-level index item in the first-level index column corresponds to at least one second-level index item in the second-level index column, and each second-level index item in the second-level index column corresponds to at least one third-level index item in the third-level index column.
The multi-level index evaluation table may be an evaluation table for evaluating an operation state of the charging facility, and may include a first-level index column, a second-level index column and a third-level index column, where each of the first-level index column may include a corresponding first-level index item, and there may be a plurality of first-level index items, and each first-level index item in the first-level index column corresponds to one or more second-level index items in the second-level index column, and each second-level index item in the second-level index column corresponds to one or more third-level index items in the third-level index column.
According to the embodiment of the invention, the preset multi-level index evaluation table is obtained to evaluate and analyze the operation state of the charging station, so that a basis is provided for the optimized layout of the charging facility.
Illustratively, the multi-level index evaluation table is shown in table 1, a first-level index column corresponds to a first-level index in table 1, a second-level index column corresponds to a second-level index in table 1, a third-level index column corresponds to a third-level index in table 1, and a first-level index item may include an operation level, a service level, and a management level; the secondary index items can comprise operation capacity, economic benefit, work order processing, satisfaction degree, equipment operation and maintenance operation; the three-level index items can comprise the number of direct-current charging piles, the number of alternating-current charging piles, the monthly average load of a charging station, the monthly average charging times, the monthly average charging time, the monthly average charging electric quantity, the monthly average charging pile utilization rate, the monthly average charging cost, the hourly charging unit price, the monthly average parking fee, the investment benefit rate, the customer service work order processing timeliness rate, the customer service work order processing completion rate, the customer service satisfaction rate, the equipment utilization rate, the equipment fault rate, the repair work order reporting timeliness rate, the patrol work order timeliness rate, the patrol plan making rate and the patrol work order completion rate; the operation level of the first-level index item corresponds to the operation capacity and economic benefit of the second-level index item; the operation capacity of the second-level index item corresponds to the number of the direct-current charging piles, the number of the alternating-current charging piles, the monthly average load of the charging station, the monthly average charging times, the monthly average charging time, the monthly average charging electric quantity and the monthly average charging pile utilization rate of the third-level index item. The specific multi-level index evaluation table is shown in table 1.
TABLE 1 Multi-level index evaluation Table
b2, determining the three-level index weight and the three-level index score relative to each three-level index item according to each charging related information.
It should be noted that the specific information of the corresponding three-level index items in the multi-level index evaluation table can be obtained by obtaining the charging correlation information, so that the score of each three-level index and the weight of each three-level index can be conveniently calculated in the following. For example, by acquiring the charging related information, the specific number of the dc charging piles can be obtained.
In the embodiment of the present invention, the three-level index weight and the three-level index score corresponding to each three-level index item may be determined according to the charging correlation information of each grid region, and specifically, the three-level index weight and the three-level index score of the three-level index item may be respectively calculated by using an entropy weight method and a quality and inferiority distance method (TOPSIS method).
It should be noted that, the third-level index score of the third-level index item is calculated, and specifically, the third-level index score may be calculated by combining charging related information of each grid region through a good-bad solution distance method. The process of calculating the third-level index weight of the third-level index item may be: firstly, smoothing the value of a three-level index item through a sigmoid function, and then calculating the weight of the three-level index through an entropy weight method; specifically, the specific gravity P of the index value of the jth index in the ith grid region can be calculated by the formula (1)ij：
Wherein r isijThe specific value of the jth index item in the three-level index items under the ith grid area, and m is the number of the grid areas.
Then, the entropy value e of the jth index item in the three-level index items can be calculated by the formula (2)j：
Wherein the content of the first and second substances,
finally, the tertiary index weight of the jth index item in the tertiary index items can be calculated by formula (3)
Wherein, gj＝1-ej(j＝1，2，…，n)。
And c2, determining the secondary index score of the corresponding secondary index item based on the tertiary index weight and the tertiary index score of each tertiary index item, and determining the secondary index weight of the corresponding secondary index item by a multi-scheme decision-making analytic hierarchy process.
In the embodiment of the present invention, each secondary index item in the secondary index column corresponds to one or more tertiary index items in the tertiary index column, so that the secondary index score of the corresponding secondary index item can be obtained by performing weighted combination on the tertiary index weight and the tertiary index score of each tertiary index item, thereby obtaining the secondary index score of each secondary index item, and then the secondary index weight of each secondary index item can be obtained by performing computation through an Analytic Hierarchy Process (AHP). Among them, the multi-scheme decision-making analytic hierarchy process can be described as:
step 1: and establishing a hierarchical structure model. The method is generally divided into three layers, wherein the first layer is a target layer, the third layer is a scheme layer, and the second layer is a criterion layer or an index layer.
In practical applications, the specific content of each layer may be specifically set according to specific situations.
Step 2: a contrast matrix is constructed. Starting from the second layer, the pair-wise comparison matrix and the scale 1-9 are used. If every factor of the upper layer dominates or is affected by all factors of the next layer, it is called complete hierarchy, otherwise it is called incomplete hierarchy. The specific values of the elements in the contrast matrix can be calculated by the contrast matrix scaling method of table 2, which is shown in table 2.
TABLE 2 comparative matrix scaling method
Let a layer have z factors, X ═ X1，x2，......，xzComparing their influence on a criterion (or target) of the previous layer,and determining the proportion of the layer relative to a certain rule, namely sequencing the influence degree of z factors on a certain target of the upper layer.
The comparison is carried out between every two factors, and 1-9 scales are taken during comparison. The result of the comparison of the xth factor with respect to the yth factor is denoted by a, and a is called a pairwise comparison matrix.
And step 3: and (4) judging the matrix consistency check through a formula (4).
Wherein the content of the first and second substances,
λmaxfor judging the maximum eigenvalue of the matrix, CI is a consistency index, RI is a random consistency index, and CR is a random consistency ratio. If CR is<0.1, the judgment matrix is considered to pass the consistency check, the step 4 is successfully executed, otherwise, the consistency is not satisfactory, and a comparison matrix needs to be reconstructed.
And 4, step 4: and calculating the secondary index weight of the secondary index item.
Multiplying each row of elements in A and opening the k power to obtain a vector
Wherein the content of the first and second substances,
to W*Performing normalization processing to obtain a second-level index weight vector W(A)＝(w1，w2，…，wk)TWherein, in the step (A),
d2, determining the primary index score of the corresponding primary index item based on the secondary index weight and the secondary index score of each secondary index item, and determining the primary index weight of the corresponding primary index item by a multi-scheme decision-making analytic hierarchy process.
In the embodiment of the invention, each first-level index item in the first-level index column corresponds to one or more second-level index items in the second-level index column, so that the first-level index score of the corresponding first-level index item can be obtained by weighted combination of the second-level index weight and the second-level index score of each corresponding second-level index item, the first-level index score of each first-level index item can be obtained, and then the first-level index weight of each first-level index item can be obtained by calculation of a multi-scheme decision-based analytic hierarchy process.
e2, weighting the primary index weight and the primary index score of each primary index item to obtain the charging facility operation state information of the grid area.
According to the embodiment of the invention, the primary index weight and the primary index score of each primary index item are weighted and processed, so that the score of the operation state of the charging facility in each grid area can be obtained, and the operation state information of the charging facility in the grid area can be further obtained.
For example, fig. 5 is a diagram illustrating an implementation effect of calculating a score of an operating state of a charging facility in a site selection layout method for building the charging facility according to a second embodiment of the present invention.
Firstly, a multi-level index evaluation table is obtained S301, then the value of a three-level index item is smoothed by a sigmoid function S302, and then a three-level index weight is calculated by an entropy weight method S303; obtaining a third-level index score S304 by using a good-bad solution distance method; the second-level index score S305 of the corresponding second-level index item can be obtained through the weighted combination of the third-level index weight and the third-level index score of each third-level index item, and the second-level index weight S306 of each second-level index item can be obtained through the calculation of a multi-scheme decision-making analytic hierarchy process; the primary index score of the corresponding primary index item can be obtained through the weighted combination of the secondary index weight of each secondary index item and the secondary index score S307, the primary index weight of each primary index item can be obtained through the calculation of a multi-scheme decision-making analytic hierarchy process S308, and finally the weighted combination of the primary index weight of each primary index item and the primary index score is carried out to obtain the comprehensive score of the charging facility operation state of the grid area S309.
Further, on the basis of the above embodiment, obtaining migration results of existing charging facilities in the grid area by combining the layout optimization model with each operation level includes:
a3, determining the charging facility migratory list needing to migrate out in the grid area based on each operation level.
According to the embodiment of the invention, the charging facility migratory list needing to be migrated in each grid area can be determined according to the operation level of each charging facility. For example, if the operation level is divided into B, C levels, for a charging station with operation level C, the charging pile utilization rate is low, and there is a situation that the charging pile is idle, so the charging facility for operation level C can be used as an emigration list of charging facilities that need to be emigration.
b3, determining the migration number of the charging facilities to be migrated in the grid area through the layout optimization model and the charging facility migration list, and outputting the migration number as a migration result.
According to the embodiment of the invention, the number of the charging facilities which are migrated out can be predicted through the layout optimization model, then the number of the charging facilities which are to be migrated out in each grid area is determined by combining the charging facility migration list, and the number can be output as a migration out result.
Correspondingly, the newly built/migrated result of the existing charging facilities in the grid area is obtained by combining the site selection analysis model with each operation level, and the method comprises the following steps:
a4, determining a new building/immigration list of charging facilities needing to be immigrated in the grid area based on each operation level.
According to the embodiment of the invention, the new establishment/immigration list of the charging facilities needing to be immigrated in the grid area can be determined according to the operation level of each charging facility. For example, if the operation level is divided into B, C two levels, the charging demand is large for the charging station with operation level B, and there is a case where the user queues up to charge, and it is necessary to add a charging facility, so the charging facility with operation level B can be used as a new building/immigration list of charging facilities that need to be immigrated.
b4, determining the number of new charging facilities to be newly built/migrated in the grid area through the layout optimization model and the new building/migration list of the charging facilities, and outputting the new charging facilities as new building/migration results.
According to the embodiment of the invention, the number of the charging facilities which are migrated in can be predicted through the layout optimization model, then the new construction/migration list of the charging facilities is combined, and finally the new construction/migration number of the charging facilities to be newly constructed/migrated in each grid area is determined and can be output as the new construction/migration result.
EXAMPLE III
Fig. 6 is a schematic structural diagram of a site selection layout device for charging facility construction according to a third embodiment of the present invention, which is capable of executing a site selection method for charging facility construction according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. The device can be implemented by software and/or hardware, and specifically comprises: an area acquisition module 401, a data determination module 402 and an addressing determination module 403.
The area acquisition module 401 is configured to acquire layer data in an electronic map, and perform meshing processing on the layer data to obtain at least one mesh area;
a data determining module 402, configured to determine charging performance information of each grid region according to charging-related information of existing charging facilities in each grid region and region attribute information of a corresponding grid region;
an address selection determining module 403, configured to determine, according to the charging performance information of each grid area, an address selection layout result of each grid area relative to the charging facility by combining a preset address selection analysis model and a layout optimization model.
According to the technical scheme of the embodiment of the invention, the layer data in the electronic map is obtained through the area obtaining module, and gridding processing is carried out on the layer data to obtain at least one grid area; determining charging performance information of each grid region according to charging associated information of existing charging facilities in each grid region and region attribute information of the corresponding grid region through a data determination module; and determining the site selection layout result of each grid area relative to the charging facility by the site selection determination module according to the charging performance information of each grid area in combination with a preset site selection analysis model and a layout optimization model. According to the technical scheme, the problems that site selection of site selection layout of the charging facility is not clear, research is not deepened and the like in the prior art are solved according to the charging performance information of each grid area and a method combining the site selection analysis model and the layout optimization model, scientific rationalization of the site selection layout of the charging facility is realized, the charging requirement of a user is met, the satisfaction degree of the user is improved, the full utilization of resources is promoted, and the utilization efficiency of the resources is improved.
Further, on the basis of the above embodiment of the present invention, the area obtaining module 401 includes:
the dividing unit is used for acquiring layer data in the electronic map and dividing the layer data into at least one unit grid area by taking 1 kilometer as a unit;
the clustering unit is used for clustering each unit grid area according to vehicle travel data and vehicle charging electricity data associated with each unit grid area;
and the acquisition unit is used for acquiring at least one grid area formed after the clustering processing.
Further, on the basis of the above embodiment of the present invention, the charging performance information includes: matching information of charging requirements and charging capacity of the grid area and operation state information of the charging facility;
accordingly, on the basis of the above embodiment of the present invention, the data determining module 402 includes:
the first determination unit is used for determining the existing charging facilities and corresponding charging related information in each grid area according to the position information of the installed charging facilities and the area longitude and latitude of the grid area;
a second determining unit, configured to determine, for each grid area, matching information between a charging demand and a charging capability of the grid area through a pre-constructed charging demand prediction base model based on the area attribute information and charging related information of each existing charging facility;
a third determining unit, configured to determine, based on the charging-related information of each existing charging facility, charging facility operation state information of the grid area through a pre-constructed operation state analysis model.
Further, on the basis of the above embodiment of the present invention, the second determining unit is specifically configured to:
according to at least one attribute dimension of season, holidays, weather and temperature, vehicle travel data in the area attribute information and historical charging quantity in each piece of charging related information are respectively extracted;
for each attribute dimension, inputting corresponding vehicle travel data and each historical charging amount as input data into the charging demand prediction basic model to obtain charging demand and charging capacity information of the grid area under the attribute dimension;
and summarizing the charging requirements and the charging capacity information to serve as the charging requirements and the charging capacity information of the grid area.
Further, on the basis of the foregoing embodiment of the present invention, the third determining unit is specifically configured to:
acquiring a preset multi-level index evaluation table, wherein the multi-level index evaluation table comprises a first-level index column, a second-level index column and a third-level index column, each first-level index item in the first-level index column corresponds to at least one second-level index item in the second-level index column, and each second-level index item in the second-level index column corresponds to at least one third-level index item in the third-level index column;
determining the three-level index weight and the three-level index score relative to each three-level index item according to each charging associated information;
determining the secondary index score of the corresponding secondary index item based on the tertiary index weight and the tertiary index score of each tertiary index item, and determining the secondary index weight of the corresponding secondary index item by a multi-scheme decision-making analytic hierarchy process;
determining the primary index score of the corresponding primary index item based on the secondary index weight and the secondary index score of each secondary index item, and determining the primary index weight of the corresponding primary index item by a multi-scheme decision-making analytic hierarchy process;
and performing weighting processing on the primary index weight and the primary index score of each primary index item to obtain the charging facility operation state information of the grid area.
Further, on the basis of the above embodiment of the present invention, the address selection determining module 403 includes:
a level unit configured to determine, for each grid area, an operation level of each existing charging facility in the grid area based on charging facility operation state information in corresponding charging performance information;
a fourth determining unit, configured to, if the charging facility operation state information does not meet an operation threshold condition, obtain, by using the layout optimization model in combination with each operation level, a migration result of an existing charging facility in the grid area; otherwise, determining whether a new charging facility is needed in the grid area according to the traffic flow heat data, the peripheral state data and the charging requirement and charging capacity matching information in the corresponding charging performance information of the grid area;
an obtaining unit, configured to, if a new charging facility needs to be created, obtain a new creation/migration result of an existing charging facility in the grid area through the site selection analysis model in combination with each operation level; otherwise, obtaining the migration result of the existing charging facilities in the grid area by combining the layout optimization model with the operation levels;
and the result determining unit is used for taking the emigration result and/or the newly-built/emigration result as the site selection layout result of the grid area relative to the charging facility.
Further, on the basis of the above embodiment of the present invention, the obtaining unit is specifically configured to:
determining a charging facility migration list needing to be migrated in the grid area based on each operation level;
determining the number of charging facilities to be migrated in the grid area through the layout optimization model and the charging facility migration list, and outputting the number as a migration result;
correspondingly, the obtaining of the newly built/migrated result of the existing charging facility in the grid area by combining the site selection analysis model with each operation level includes:
determining a new building/migrating list of charging facilities needing to migrate into the grid area based on each operation level;
and determining the number of new buildings/immigrations of the charging facilities to be newly built/immigrated in the grid area through the layout optimization model and the new building/immigration list of the charging facilities, and outputting the new buildings/immigrations as new building/immigration results.
Example four
Fig. 7 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention, and fig. 7 shows a block diagram of a computer device 712 suitable for implementing an embodiment of the present invention. The computer device 712 shown in fig. 7 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention. Device 712 is typically a computing device implementing the siting layout method of charging facility construction.
As shown in fig. 7, computer device 712 is embodied in the form of a general purpose computing device. Components of computer device 712 may include, but are not limited to: one or more processors 716, a storage device 728, and a bus 718 that couples the various system components (including the storage device 728 and the processors 716).
The processor 716 executes various functional applications and data processing by running programs stored in the storage device 728, for example, implementing the site selection layout method for charging facility construction provided by the above-described embodiment of the present invention.
EXAMPLE five
Embodiments of the present invention provide a computer-readable storage medium having stored thereon a computer program that, when executed by a processing device, implements an addressing layout method for charging facility construction as in embodiments of the present invention. The computer readable medium of the present invention described above may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the computer device; or may exist separately and not be incorporated into the computer device.
The computer readable medium carries one or more programs which, when executed by the computing device, cause the computing device to: acquiring layer data in an electronic map, and carrying out gridding processing on the layer data to obtain at least one grid area;
determining charging performance information of each grid area according to charging associated information of existing charging facilities in each grid area and area attribute information of the corresponding grid area;
and determining the site selection layout result of each grid region relative to the charging facility by combining a preset site selection analysis model and a layout optimization model according to the charging performance information of each grid region.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A site selection layout method for charging facility construction is characterized by comprising the following steps:
acquiring layer data in an electronic map, and carrying out gridding processing on the layer data to obtain at least one grid area;
determining charging performance information of each grid area according to charging associated information of existing charging facilities in each grid area and area attribute information of the corresponding grid area;
and determining the site selection layout result of each grid region relative to the charging facility by combining a preset site selection analysis model and a layout optimization model according to the charging performance information of each grid region.
2. The method according to claim 1, wherein the obtaining layer data in an electronic map and performing gridding processing on the layer data to obtain at least one grid area comprises:
acquiring layer data in an electronic map, and dividing the layer data into at least one unit grid area by taking 1 kilometer as a unit;
clustering each unit grid area according to vehicle travel data and vehicle charging electricity data related to each unit grid area;
and acquiring the multi-type grid area formed after the clustering treatment.
3. The method of claim 1, wherein the charging performance information comprises: matching information of charging requirements and charging capacity of the grid area and operation state information of the charging facility;
correspondingly, the determining the charging performance information of each grid area according to the charging related information of the existing charging facility in each grid area and the area attribute information of the corresponding grid area includes:
determining the existing charging facilities and corresponding charging associated information in each grid area according to the position information of the installed charging facilities and the area longitude and latitude of the grid area;
for each grid area, determining the matching information of the charging requirement and the charging capacity of the grid area through a pre-constructed charging requirement prediction basic model based on the area attribute information and the charging associated information of the existing charging facilities;
and determining the charging facility operation state information of the grid area through a pre-constructed operation state analysis model based on the charging related information of each existing charging facility.
4. The method of claim 3, wherein determining the charging demand and charging capacity matching information for the grid area through a pre-constructed charging demand prediction base model based on the area attribute information and charging correlation information of each of the existing charging facilities comprises:
according to at least one attribute dimension of season, holidays, weather and temperature, vehicle travel data in the area attribute information and historical charging quantity in each piece of charging related information are respectively extracted;
for each attribute dimension, inputting corresponding vehicle travel data and each historical charging amount as input data into the charging demand prediction basic model to obtain charging demand and charging capacity information of the grid area under the attribute dimension;
and summarizing the charging requirement and charging capacity information under each attribute dimension to serve as the charging requirement and charging capacity information of the grid area.
5. The method of claim 3, wherein determining the charging facility operating state information of the grid area through a pre-constructed operating state analysis model based on the charging related information of each existing charging facility comprises:
acquiring a preset multi-level index evaluation table, wherein the multi-level index evaluation table comprises a first-level index column, a second-level index column and a third-level index column, each first-level index item in the first-level index column corresponds to at least one second-level index item in the second-level index column, and each second-level index item in the second-level index column corresponds to at least one third-level index item in the third-level index column;
determining the three-level index weight and the three-level index score relative to each three-level index item according to each charging associated information;
determining the secondary index score of the corresponding secondary index item based on the tertiary index weight and the tertiary index score of each tertiary index item, and determining the secondary index weight of the corresponding secondary index item by a multi-scheme decision-making analytic hierarchy process;
determining the primary index score of the corresponding primary index item based on the secondary index weight and the secondary index score of each secondary index item, and determining the primary index weight of the corresponding primary index item by a multi-scheme decision-making analytic hierarchy process;
and performing weighting processing on the primary index weight and the primary index score of each primary index item to obtain the charging facility operation state information of the grid area.
6. The method according to any one of claims 1 to 5, wherein the determining, according to the charging performance information of each grid region, an addressing layout result of each grid region relative to a charging facility in combination with a preset addressing analysis model and a layout optimization model comprises:
for each grid area, determining the operation level of each existing charging facility in the grid area based on the charging facility operation state information in the corresponding charging performance information;
if the charging facility operation state information does not meet the operation threshold condition, obtaining the migration result of the existing charging facility in the grid area by combining the layout optimization model with each operation level; otherwise, determining whether a new charging facility is needed in the grid area according to the traffic flow heat data, the peripheral state data and the charging requirement and charging capacity matching information in the corresponding charging performance information of the grid area;
if a charging facility needs to be newly built, obtaining a newly built/migrated result of the existing charging facility in the grid area through the site selection analysis model in combination with each operation level; otherwise, obtaining the migration result of the existing charging facilities in the grid area by combining the layout optimization model with the operation levels;
and taking the emigration result and/or the newly built/emigration result as the site selection layout result of the grid area relative to the charging facility.
7. The method of claim 6, wherein obtaining the migration result of the existing charging facilities in the grid area through the layout optimization model in combination with each operation level comprises:
determining a charging facility migration list needing to be migrated in the grid area based on each operation level;
determining the number of charging facilities to be migrated in the grid area through the layout optimization model and the charging facility migration list, and outputting the number as a migration result;
correspondingly, the obtaining of the newly built/migrated result of the existing charging facility in the grid area by combining the site selection analysis model with each operation level includes:
determining a new building/migrating list of charging facilities needing to migrate into the grid area based on each operation level;
and determining the number of new buildings/immigrations of the charging facilities to be newly built/immigrated in the grid area through the layout optimization model and the new building/immigration list of the charging facilities, and outputting the new buildings/immigrations as new building/immigration results.
8. A site selection layout device for construction of charging facilities is characterized by comprising:
the area acquisition module is used for acquiring layer data in an electronic map and carrying out gridding processing on the layer data to obtain at least one grid area;
the data determining module is used for determining charging performance information of each grid area according to charging related information of existing charging facilities in each grid area and area attribute information of the corresponding grid area;
and the site selection determining module is used for determining site selection layout results of the grid areas relative to the charging facilities according to the charging performance information of the grid areas by combining a preset site selection analysis model and a layout optimization model.
9. A computer device, characterized in that the computer device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the siting layout method for charging facility construction according to any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements an addressing layout method for the construction of a charging facility according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112381313A (en) * | 2020-11-23 | 2021-02-19 | 国网北京市电力公司 | Charging pile address determination method and device |
| CN115374236A (en) * | 2022-10-26 | 2022-11-22 | 北京宾理信息科技有限公司 | Method, device, equipment and medium for generating intelligent public charging service network |
| CN115375209A (en) * | 2022-10-26 | 2022-11-22 | 北京宾理信息科技有限公司 | Supply and demand analysis method and device for public charging station, storage medium and equipment |
| CN117057626A (en) * | 2023-08-15 | 2023-11-14 | 深圳橙电新能源科技有限公司 | Optical storage and charge energy gain conversion analysis method based on big data |
| CN117669993A (en) * | 2024-01-30 | 2024-03-08 | 南方科技大学 | Progressive charging facility planning method, progressive charging facility planning device, terminal and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112381313A (en) * | 2020-11-23 | 2021-02-19 | 国网北京市电力公司 | Charging pile address determination method and device |
| CN112381313B (en) * | 2020-11-23 | 2024-07-05 | 国网北京市电力公司 | Method and device for determining charging pile address |
| CN115374236A (en) * | 2022-10-26 | 2022-11-22 | 北京宾理信息科技有限公司 | Method, device, equipment and medium for generating intelligent public charging service network |
| CN115375209A (en) * | 2022-10-26 | 2022-11-22 | 北京宾理信息科技有限公司 | Supply and demand analysis method and device for public charging station, storage medium and equipment |
| CN115374236B (en) * | 2022-10-26 | 2023-01-13 | 北京宾理信息科技有限公司 | Method, device, equipment and medium for generating intelligent public charging service network |
| CN117057626A (en) * | 2023-08-15 | 2023-11-14 | 深圳橙电新能源科技有限公司 | Optical storage and charge energy gain conversion analysis method based on big data |
| CN117669993A (en) * | 2024-01-30 | 2024-03-08 | 南方科技大学 | Progressive charging facility planning method, progressive charging facility planning device, terminal and storage medium |
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