Comprehensive evaluation method for competitiveness of charging station

Abstract

The invention discloses a comprehensive evaluation method for competitiveness of a charging station. The method comprises the following steps: determining a final index set for evaluating the competitiveness of the station; based on the index weight model, objective weighting is carried out on each index by using an entropy weight method; constructing a station competitiveness comprehensive evaluation model I based on a grey relational analysis method; constructing a station competitiveness comprehensive evaluation model II based on a TOPSIS analysis method; the station competitiveness indexes solved by the first model and the second model are sorted and compared, the effectiveness of the result is obtained through cross validation, and a final station competitiveness sequence is obtained through an average algorithm; and visualizing the competitiveness data of the target station. The comprehensive evaluation method for the competitiveness of the charging station has the advantages and characteristics that index data can be reached, double evaluation models can be subjected to cross validation, and the competitive environment around the charging station is fully considered, and the evaluation method has relatively high practical applicability by adding a follow-up matched evaluation result visualization scheme.

Description

Technical Field

[0001] The invention relates to a comprehensive evaluation method for competitiveness of a charging station. Background Art

[0002] Faced with the increasingly fierce competition in the charging market, self-operated charging stations need accurate service positioning and efficient pricing strategies to enhance their competitiveness. Charging station operators need a reasonable competitiveness analysis model to analyze the basic data of the target station and its surrounding stations to evaluate the competitiveness of the target station and provide guidance for subsequent self-operated station management and operation optimization. The evaluation model mainly includes two parts: evaluation indicators and evaluation methods.

[0003] There are many studies in China that evaluate the development of service-related industries by building a multi-index competitiveness evaluation model. For example, Zhang Zhong and Jin Qing (2015) selected 16 indicators from the four perspectives of strategy, process, performance, and customer value to build an evaluation index system for service-oriented manufacturing, and used a three-layer BP neural network with forward feedback as the evaluation model. Bian Guoli (2017) selected 6 secondary indicators and 12 tertiary indicators from the three perspectives of basic factors, industrial factors, and market factors, and evaluated the service outsourcing industry based on the grey AHP method.

[0004] There are also some related studies on the specific object of electric vehicle charging stations, including: Xie Menghua (2021) set 12 indicators from four dimensions, including planning and operation integration indicators, distribution network operation impact indicators, transportation network operation impact indicators, and user experience indicators, including the number of charging piles, maintenance costs, traffic operation index, average charging time, average queuing time, parking fee level, etc., and then used the Analytic Hierarchy Process to calculate and evaluate. Dong Hua (2023) selected 19 indicators from four aspects, namely planning services, operator services, user services, and social services, to construct a comprehensive evaluation index system for electric vehicle charging station services, and then used the combined weighting method, using the subjective weighting method (AHP method, ordinal relationship method) and the objective weighting method (entropy weight method, coefficient of variation method) to calculate the weights, and then determined the comprehensive weights based on the moment estimation theory. Liu Si (2016) proposed a method for setting electric vehicle charging prices based on the comprehensive use of cost-benefit analysis, starting from the factors affecting the operating benefits of charging stations and consumer benefits. Zhang Wei (2017) selected 14 indicators from three perspectives: operating status, customer service, and impact on transportation and distribution networks, and constructed a combined evaluation model based on the "average method", "Borda method" and "Copeland method".

[0005] However, considering the actual scenarios of charging station actual competitiveness evaluation and service pricing adjustment, the above models have three core problems: it is difficult to obtain indicator data, the indicator selection lacks regional competition considerations, and there is a lack of appropriate data visualization presentation methods, which makes the models difficult to apply in actual scenarios. First, the existing models have too high requirements for the required calculation data, and the data acquisition methods are not universal. It is difficult to obtain data on public platforms, which makes the data requirements of the models high and the application and promotion difficult to carry out. Second, the basic operating conditions and regional competition conditions of different charging stations are different. The above existing charging station operation service evaluation models and indicator systems lack analysis of regional competition conditions, that is, lack of comparative analysis of the pricing and operating conditions of other competitive stations in the region. Therefore, the evaluation model cannot give an evaluation of the relative competitiveness of the target station and its competitive environment, and has no guiding significance for its subsequent pricing adjustment and service improvement. Third, the data presentation results calculated by most models are highly complex, the analysis is difficult, and there is a lack of effective visualization methods to present the results for application in actual management work. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the prior art and provide a comprehensive evaluation method for the competitiveness of charging stations. The comprehensive evaluation of the competitiveness of charging stations is performed based on the entropy weight method, grey correlation analysis and TOPSIS method. The method has the advantages and characteristics of accessible indicator data, cross-validation of dual evaluation models, and full consideration of the competitive environment around the charging station. The subsequent matching evaluation result visualization scheme makes the evaluation method have strong practical applicability.

[0007] The technical solution to achieve the above purpose is: a comprehensive evaluation method for the competitiveness of charging stations, comprising the following steps:

[0008] S1, determine the final set of indicators for evaluating the competitiveness of the site;

[0009] S2, based on the indicator weight model, objectively weights each indicator using the entropy weight method;

[0010] S3, construct a comprehensive evaluation model of station competitiveness based on grey correlation analysis method;

[0011] S4, based on the TOPSIS analysis method (Technique for Order Preference by Similarity to an Ideal Solution, also known as the superior and inferior solution distance method), the second comprehensive evaluation model of the competitiveness of the station is constructed to calculate the competitiveness index and cross-validate the first comprehensive evaluation model of the competitiveness of the station;

[0012] S5, ranking and comparing the station competitiveness indexes solved by the station competitiveness comprehensive evaluation model 1 and the station competitiveness comprehensive evaluation model 2, cross-validating the validity of the results, and obtaining the final ranking of station competitiveness through an average algorithm;

[0013] S6, visualize the competitiveness data of the target station; calculate the correlation coefficient based on the grey correlation coefficient, obtain the correlation coefficient between each indicator data of the object and the optimal value, convert it into a percentage score, and draw a station competitiveness radar chart as a reference for station competitiveness improvement analysis.

[0014] In the above-mentioned charging station competitiveness comprehensive evaluation method, in step S1, after screening and integrating the existing evaluation index set, regional competition related indicators are added to obtain a preliminary index set containing 18 indicators. The preliminary index set is shown in Table 1:

[0015] Table 1, the preliminary indicator set integrating existing charging station evaluation indicators:

[0016]

[0017] Table 2, the final set of indicators after screening based on data accessibility:

[0018]

[0019] Based on the goal of improving station operation and regional competitiveness, redundant indicators with overlapping impacts on each other were eliminated from the preliminary indicator set, and the final indicator set for evaluating station competitiveness was determined based on the data fields disclosed by the platform to ensure data accessibility. The final indicator set contains 9 indicators, see Table 2.

[0020] In the above-mentioned charging station competitiveness comprehensive evaluation method, in step S2, the calculation step of objectively weighting each indicator using the entropy weight method based on the indicator weight model includes:

[0021] S21, construct the evaluation index matrix;

[0022] S22, indicator data standardization;

[0023] S23, normalization of indicator data;

[0024] S24, calculate the information entropy of each indicator;

[0025] S25, determining the weight of each indicator.

[0026] In the above-mentioned comprehensive evaluation method for competitiveness of charging stations, each calculation step is specifically as follows:

[0027] S21, construct the evaluation index matrix: Assuming that there are m evaluation objects and n evaluation indicators, construct the evaluation index matrix X according to the actual data of each indicator:

[0028]

[0029] S22, indicator data standardization: There are three ways to evaluate the indicator value and judge the quality based on the indicator characteristics, which are:

[0030] When the indicator value is larger, the better, then the indicator is a positive indicator, and its standardized formula is:

[0031]

[0032] When the indicator value is as small as possible, the indicator is an inverse indicator, and its standardized formula is:

[0033]

[0034] When the index value is closer to a certain value, the evaluation result is better, and the value is recorded as That is, this indicator is a moderation indicator, and its standardized formula is:

[0035]

[0036] In formula (2-2), formula (2-3) and formula (2-4), x ij Represents the i-th index value of the j-th evaluation object:

[0037] According to the standardization formulas, the matrix X is processed to obtain the normalized matrix X * :

[0038]

[0039] S23, indicator data normalization, the normalization formula is:

[0040]

[0041] According to the normalization formula (2-6), for the matrix X * Perform normalization to obtain the standardized matrix F:

[0042]

[0043] S24, find the information entropy of each indicator:

[0044]

[0045] In formula (2-8), K is H i is the information entropy of the jth index, when f ij = 0, f ij ln ij=0; Determine the entropy weight of each indicator:

[0046]

[0047] S25, determine the weight of each indicator: After the above calculation steps, the entropy weight vector W of n evaluation indicators can be obtained. n ) T ,in

[0048] In the above-mentioned charging station competitiveness comprehensive evaluation method, in step S3, the calculation steps of constructing a first comprehensive evaluation model of charging station competitiveness based on the grey correlation analysis method include:

[0049] S31, establish an evaluation index matrix;

[0050] S32, construct a comprehensive evaluation model based on grey relational analysis;

[0051] S33, determining the optimal indicator set;

[0052] S34, standardization of indicator matrix;

[0053] S35, calculate the grey correlation coefficient;

[0054] S36, calculating and sorting the grey relational degree.

[0055] The above-mentioned comprehensive evaluation method for competitiveness of charging stations includes the following calculation steps:

[0056] S31, establish an evaluation index matrix: First, assuming that there are m evaluation objects (electric vehicle charging stations) and n evaluation indicators, first establish an evaluation index matrix X, x ij Represents the i-th index value of the j-th evaluation object:

[0057]

[0058] The comprehensive evaluation model based on grey relational analysis is:

[0059] P=E×W (3-2)

[0060] In formula (3-2), P = (p1, p2, ..., p m ) T is the comprehensive evaluation result vector of m evaluation objects, p j represents the grey relational degree of the jth evaluation object;

[0061] W=(w1,w2,...,w n ) T is the weight distribution vector of n evaluation indicators, and E is the grey correlation coefficient matrix:

[0062]

[0063] In formula (3-3), ε ij is the correlation coefficient between the ith indicator of the jth evaluated object and the ith optimal indicator, which represents the degree of correlation between the data series of each evaluation indicator and the optimal data series; ε ij The larger it is, the greater the correlation between the two series on the i-th index;

[0064] Finally, according to p j The values ​​of are used to sort the evaluated objects;

[0065] S32, determine the optimal indicator set: Assume is the optimal sequence, and each element of the optimal sequence is composed of the optimal value selected from each indicator data series; the selection principles of evaluation indicators of different natures are also different; the selection principle is: if a certain evaluation indicator is a positive indicator, that is, the larger the indicator value, the better, then the maximum value of all evaluated objects is selected; on the contrary, if a certain evaluation indicator is an inverse indicator, that is, the smaller the indicator, the better, then the minimum value of all evaluated objects is selected; if a certain evaluation indicator is a moderation indicator, that is, when it is close to a certain suitable value, it is good, then this moderate value is selected as the element of the reference sequence;

[0066] After determining the optimal indicator set, construct the matrix D:

[0067]

[0068] In formula (3-4), x ij represents the i-th indicator of the j-th evaluation object, represents the optimal value of the i-th indicator;

[0069] S33, standardization of the indicator matrix D: In the evaluation process, the different dimensions of each evaluation indicator will affect the evaluation results. It is necessary to perform dimensionless quantification on the evaluation indicators and convert the values ​​of each evaluation indicator into a relatively unified measurement. This process is called evaluation indicator data standardization; Assume X = (x ij ) m×n After data standardization, we get R = (r ij ) m×n , r ij ∈[0, 1]; refer to formula (2-2), formula (2-3), and formula (2-4) for three cases of standardization:

[0070] When the indicator value is larger, the better, then the indicator is a positive indicator, and its standardized formula is:

[0071]

[0072] When the indicator value is as small as possible, the indicator is an inverse indicator, and its standardized formula is:

[0073]

[0074] When the index value is closer to a certain value, the evaluation result is better, and the value is recorded as That is, this indicator is a moderation indicator, and its standardized formula is:

[0075]

[0076] The matrix X is processed according to the standardization formula to obtain the normalized matrix R:

[0077]

[0078] S34, calculate the grey correlation coefficient: correlation coefficient ε ij The calculation formula is:

[0079]

[0080] In formula (3-9), ρ is the resolution coefficient, which is between 0 and 1. The introduction of the resolution coefficient is to reduce the influence of the extreme value of the evaluation index on the calculation, thereby improving the discrimination of the correlation coefficient; the smaller the resolution coefficient, the greater the resolution; in general, the resolution coefficient is usually taken as 0.5;

[0081] S35, calculate the grey correlation degree: when comparing the correlation coefficient between the series of each scheme and the optimal series on a certain indicator, it cannot fully reflect the pros and cons of the scheme; in order to grasp the correlation degree between the series of schemes as a whole, it is necessary to weight the correlation coefficients and accumulate them into a single number, that is, the correlation degree p, then the correlation degree of the comparison series to the optimal series is:

[0082]

[0083] In formula (3-10), w i is the weight of the i-th indicator in the evaluation index system;

[0084] S36, sorting: grey relational degree p j The larger the value is, the closer the object sequence is to the optimal sequence, indicating that the jth evaluated object is better, so the gray correlation degree p j The size of the objects will be sorted.

[0085] In the above-mentioned comprehensive evaluation method for the competitiveness of charging stations, in step S4, the principle of TOPSIS analysis is to determine the standardized decision matrix through the standardized evaluation index data and the weights of each index, and then find the optimal solution and the worst solution in the standardized decision matrix, that is, the positive and negative ideal solutions; calculate the distance between each evaluation solution and the positive and negative ideal solutions respectively, and finally use the relative proximity of each solution to the optimal solution to evaluate the pros and cons of the solution.

[0086] The above-mentioned charging station competitiveness comprehensive evaluation method, based on the TOPSIS analysis method, constructs the calculation steps of the second charging station competitiveness comprehensive evaluation model, including:

[0087] S41, establish an evaluation index matrix;

[0088] S42, standardization of evaluation indicators;

[0089] S43, determining the indication entropy weight;

[0090] S44, construct a decision matrix;

[0091] S45, determining the positive and negative ideal solutions by the optimal values ​​of the matrix V index;

[0092] S46, calculating the Euclidean distance between each evaluation object and the positive and negative ideal solutions;

[0093] S47, calculating the relative proximity to the optimal object.

[0094] The above-mentioned comprehensive evaluation method for competitiveness of charging stations includes the following calculation steps:

[0095] S41, establish an evaluation index matrix: First, assuming that there are m evaluation objects (electric vehicle charging stations) and n evaluation indicators, establish an evaluation index matrix X, x ij Represents the i-th indicator value of the j-th evaluation object:

[0096]

[0097] S42, standardization of evaluation indicators: refer to formula (2-2), formula (2-3), and formula (2-4) to standardize the evaluation indicator matrix.

[0098] When the indicator value is larger, the better, then the indicator is a positive indicator, and its standardized formula is:

[0099]

[0100] When the indicator value is as small as possible, the indicator is an inverse indicator, and its standardized formula is:

[0101]

[0102] When the index value is closer to a certain value, the evaluation result is better, and the value is recorded as That is, this indicator is a moderation indicator, and its standardized formula is:

[0103]

[0104] The matrix X is processed according to the standardization formula to obtain the normalized matrix R:

[0105]

[0106] S43, determine the index entropy weight: refer to the steps of the entropy weight method, and calculate the entropy weight matrix W = (w1, w2, ..., w n ) T ,in

[0107] S44, construct decision matrix V: construct decision matrix v based on evaluation index and entropy weight ij =r ij × i :

[0108]

[0109] S45, determine the positive and negative ideal solutions Z by the optimal values ​​of the evaluation indicators of the decision matrix + and Z - :

[0110]

[0111] S46, calculating the Euclidean distance D between each evaluation object and the positive and negative ideal solutions j + and D j -

[0112]

[0113]

[0114] S47, calculate the relative proximity C between each evaluation object and the optimal object j :

[0115]

[0116] In formula (4-11), C j The larger it is, the closer the j-th evaluation object is to the optimal object level.

[0117] In the above-mentioned comprehensive evaluation method for charging station competitiveness, in step S5, the TOPSIS analysis method calculates the weighted Euclidean distance and relative closeness, and sorts according to the relative closeness; while the principle of the grey correlation analysis method is to calculate the correlation and sort the evaluation objects according to the correlation. The two models obtain different results, and the final ranking of station competitiveness is obtained by the average algorithm.

[0118] Compared with the prior art, the charging station competitiveness comprehensive evaluation method of the present invention has the following beneficial effects:

[0119] (1) The evaluation index system in the present invention is integrated, screened, and de-redundant through the existing index system, and 9 indicators in 3 directions are selected for evaluation. All index data can be obtained from the public data of the online charging platform, and the data is accessible;

[0120] (2) The present invention uses the grey correlation analysis method and the TOPSIS superior and inferior solution distance method to evaluate the index data, which can cross-validate the ranking results, and the final ranking result takes the average of the two, thereby reducing the one-sidedness of a single evaluation method;

[0121] (3) The selection of indicators in the present invention fully considers the competitive environment around the target station, including the number of surrounding competitive stations and the pricing of service fees. This makes the evaluation model targeted at the target station, and the evaluation results are more valuable for reference in actual station adjustments;

[0122] (4) By converting the scores into grey correlation coefficients and visualizing the radar chart data, the evaluation results can be presented more clearly, effectively presenting the advantages and disadvantages of the target site in terms of multiple competitiveness evaluation indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] Figure 1 A flowchart of the comprehensive evaluation method for charging station competitiveness of the present invention;

[0124] Figure 2 This is an example of calculating the competitiveness of a station based on the dual model of grey correlation method and TOPSIS method;

[0125] Figure 3 Schematic diagram of radar comparing competitiveness indicators of target station A and comparison station B / F. DETAILED DESCRIPTION

[0126] In order to enable those skilled in the art to better understand the technical solution of the present invention, the specific implementation methods thereof are described in detail below in conjunction with the accompanying drawings:

[0127] See also Figure 1 , Figure 2 and Figure 3In the embodiment of the present invention, data is first requested from the database of Shanghai's mainstream electric vehicle charging platforms (Lianlian Charging Pro, eXiangdian, etc.), and after deduplication and error elimination, a total of 10,085 basic information of Shanghai charging stations is obtained, including a total of about 173,000 DC charging piles and AC charging piles. The important information of charging stations that can be obtained on the charging platform includes: electricity fee, service fee, total charging fee, number of fast charging piles, number of slow charging piles, parking fee, location tag, parking discount tag, site level tag, etc.

[0128] A charging station in the southern area of ​​Shinan District and operated by State Grid Electric Vehicles (B&Q Electric Vehicle Charging Station in Minhang District, Shanghai) was selected as the target station A* and location benchmark for competitiveness evaluation. Ten surrounding charging stations were selected as the objects of competitiveness evaluation. The peak electricity price on September 18 (8:00-15:00) was selected as the cost and time benchmark. After data collation and quantification of qualitative indicators, the basic data is shown in Table 3 below.

[0129] Table 3, data of a certain state-owned electric vehicle station and its surrounding competitive stations:

[0130]

[0131]

[0132] After obtaining the example data, substitute it into the entropy weight method to calculate the weight of each indicator, and the indicator weight matrix can be obtained, as shown in Table 4.

[0133] Table 4, calculation results of entropy weight method example:

[0134] index Information Entropy Variation index d Entropy weight w Fast recharge 0.7375 0.2625 0.1616 Slow charging 0.9399 0.0601 0.0370 7 days charging times 0.7935 0.2065 0.1271 Total cost (peak) 0.9363 0.0637 0.0392 Parking Fee 0.2598 0.7402 0.4555 Site Rating 0.8824 0.1176 0.0724 Service Offers 0.9543 0.0457 0.0282 Number of stations within 2km 0.9352 0.0648 0.0399 The lowest price difference around 0.9363 0.0637 0.0392

[0135] Then, the grey correlation model (model 1) and TOPSIS model (model 2) are substituted into the near-optimality calculation to obtain the near-optimality calculation values ​​of all stations under the two calculation models, as well as the competitiveness ranking among the 10 competing stations. Finally, the final ranking of station competitiveness is obtained through the two sets of ranking data and the average algorithm, see Figure 2 and Table 5. It can be seen from the algorithm results that the results obtained by the two algorithms are close, and the sorting results are more accurate.

[0136] Table 5, the ranking results of station competitiveness calculated by grey correlation method and TOPSIS method:

[0137] Station Grey relational degree p Sort 1 Station Relative proximity c Sort 2 Final sorting A 0.4363 7 A 0.0932 7 7 B 0.4012 10 B 0.0745 10 10 C 0.4670 5 C 0.1079 6 5 D 0.4198 9 D 0.1650 2 5 E 0.4210 8 E 0.0812 9 9 F 0.7028 1 F 0.3407 1 1 G 0.4963 4 G 0.1143 5 4 H 0.4445 6 H 0.0825 8 7 I 0.5498 2 I 0.1647 3 2 J 0.5297 3 J 0.1491 4 3

[0138] Finally, the data analysis of the grey correlation coefficients of the nine indicators was carried out for the target stations, and the scores were converted into specific percentages to intuitively display the competitiveness of the stations in various dimensions. At the same time, a radar chart was drawn. Figure 3 and Table 6.

[0139] Table 6, Grey correlation coefficients and score conversion of each index of target station A and comparison station B / F:

[0140] Target station A score B score F score Fast recharge 0.4706 47 0.4211 42 0.3333 33 Slow charging 0.6000 60 0.3333 33 1.0000 100 7 days charging times 0.3353 34 0.4225 42 0.3333 33 Total cost (peak) 0.5024 50 0.5261 53 0.3333 33 Parking Fee 0.3333 33 0.3333 33 1.0000 100 Site Rating 0.5000 50 0.3333 33 0.5000 50 Service Offers 1.0000 100 1.0000 100 1.0000 100 Number of stations within 2km 1.0000 100 0.5472 55 0.5918 59 The lowest price difference around 0.5024 50 0.5261 53 0.3333 33

[0141] According to the overall ranking of the target station A (B&Q Electric Vehicle Charging Station in Minhang District, Shanghai) in the surrounding stations, and the score comparison of the 9 major indicator dimensions, it can be concluded that the comprehensive ranking of station A in the surrounding station competition is low (7th), and the overall competitiveness is weak. The indicators with strong competitiveness are the number of surrounding competitive stations, additional services and preferential settings. To further enhance the competitiveness of the station, it is necessary to consider competitiveness enhancement measures from the perspectives of parking fees, number of charging times in 7 days, number of fast charging piles, total charging costs, price difference with the lowest surrounding station pricing, station rating, etc.

[0142] In summary, the comprehensive evaluation method for charging station competitiveness of the present invention is based on the entropy weight method, grey correlation analysis and TOPSIS analysis method to conduct a comprehensive evaluation of the competitiveness of charging stations. It has the advantages and characteristics of accessible indicator data, cross-validation of dual evaluation models, and full consideration of the competitive environment around the charging station. The subsequent matching evaluation result visualization scheme makes the evaluation method have strong practical applicability.

[0143] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A comprehensive evaluation method for competitiveness of charging stations, characterized in that: The following steps are involved: S1, determine the final set of indicators for evaluating the competitiveness of the site; S2, based on the indicator weight model, objectively weights each indicator using the entropy weight method; S3, construct a comprehensive evaluation model of station competitiveness based on grey correlation analysis method; S4, based on the TOPSIS analysis method, the second comprehensive evaluation model of the competitiveness of the station was constructed to calculate the competitiveness index and to cross-validate the first comprehensive evaluation model of the competitiveness of the station; S5, ranking and comparing the station competitiveness indexes solved by the station competitiveness comprehensive evaluation model 1 and the station competitiveness comprehensive evaluation model 2, cross-validating the validity of the results, and obtaining the final ranking of station competitiveness through an average algorithm; S6, visualize the competitiveness data of the target station; calculate the correlation coefficient based on the grey correlation coefficient, obtain the correlation coefficient between each indicator data of the object and the optimal value, convert it into a percentage score, and draw a station competitiveness radar chart as a reference for station competitiveness improvement analysis.

2. A charging station competitiveness comprehensive evaluation method according to claim 1, characterized in that: In step S1, after screening and integrating the existing evaluation indicator set, and adding the regional competition related indicators, a preliminary indicator set containing 18 indicators is obtained. The preliminary indicator set is shown in Table 1: Table 1, the preliminary indicator set integrating existing charging station evaluation indicators: Table 2, the final set of indicators after screening based on data accessibility: Based on the goal of improving station operation and regional competitiveness, redundant indicators with overlapping impacts on each other were eliminated from the preliminary indicator set, and the final indicator set for evaluating station competitiveness was determined based on the data fields disclosed by the platform to ensure data accessibility. The final indicator set contains 9 indicators, see Table 2.

3. A charging station competitiveness comprehensive evaluation method according to claim 1, characterized in that: In step S2, based on the indicator weight model, the calculation steps of objectively weighting each indicator using the entropy weight method include: S21, construct the evaluation index matrix; S22, indicator data standardization; S23, normalization of indicator data; S24, calculate the information entropy of each indicator; S25, determining the weight of each indicator.

4. A charging station competitiveness comprehensive evaluation method according to claim 3, characterized in that: Each calculation step is as follows: S21, construct the evaluation index matrix: Assuming that there are m evaluation objects and n evaluation indicators, construct the evaluation index matrix X according to the actual data of each indicator: S22, indicator data standardization: There are three ways to evaluate the indicator value and judge the quality based on the indicator characteristics, which are: When the indicator value is larger, the better, then the indicator is a positive indicator, and its standardized formula is: When the indicator value is as small as possible, the indicator is an inverse indicator, and its standardized formula is: When the index value is closer to a certain value, the evaluation result is better, and the value is recorded as That is, this indicator is a moderation indicator, and its standardized formula is: In formula (2-2), formula (2-3) and formula (2-4), x ij Represents the i-th indicator value of the j-th evaluation object: According to the standardization formulas, the matrix X is processed to obtain the normalized matrix X * : S23, indicator data normalization, the normalization formula is: According to the normalization formula (2-6), for the matrix X * Perform normalization to obtain the standardized matrix F: S24, find the information entropy of each indicator: In formula (2-8), K is H i is the information entropy of the jth index, when f ij = 0, f ij lnf ij =0; Determine the entropy weight of each indicator: S25, determine the weight of each indicator: After the above calculation steps, the entropy weight vector W of n evaluation indicators can be obtained. n ) T ,in 5. A charging station competitiveness comprehensive evaluation method according to claim 1, characterized in that: In step S3, the calculation steps of constructing the first comprehensive evaluation model of station competitiveness based on the grey correlation analysis method include: S31, establish an evaluation index matrix; S32, construct a comprehensive evaluation model based on grey relational analysis; S33, determining the optimal indicator set; S34, standardization of indicator matrix; S35, calculate the grey correlation coefficient; S36, calculating and sorting the grey relational degree.

6. A charging station competitiveness comprehensive evaluation method according to claim 5, characterized in that: The specific calculation steps are as follows: S31, establish an evaluation index matrix: First, assuming there are m evaluation objects and n evaluation indicators, first establish an evaluation index matrix X, x ij Represents the i-th index value of the j-th evaluation object: The comprehensive evaluation model based on grey relational analysis is: P=E×W (3-2) In formula (3-2), P = (p1, p2, ..., p m ) T is the comprehensive evaluation result vector of m evaluation objects, p j represents the grey relational degree of the jth evaluation object; W=(w1,w2,...,w n ) T is the weight distribution vector of n evaluation indicators, and E is the grey correlation coefficient matrix: In formula (3-3), ε ij is the correlation coefficient between the ith indicator of the jth evaluated object and the ith optimal indicator, which represents the degree of correlation between the data series of each evaluation indicator and the optimal data series; ε ij The larger it is, the greater the correlation between the two series on the i-th index; Finally, according to p j The values ​​of are used to sort the evaluated objects; S32, determine the optimal indicator set: Assume is the optimal sequence, and each element of the optimal sequence is composed of the optimal value selected from each indicator data series; the selection principles of evaluation indicators of different natures are also different; the selection principle is: if a certain evaluation indicator is a positive indicator, that is, the larger the indicator value, the better, then the maximum value of all evaluated objects is selected; on the contrary, if a certain evaluation indicator is an inverse indicator, that is, the smaller the indicator, the better, then the minimum value of all evaluated objects is selected; if a certain evaluation indicator is a moderation indicator, that is, when it is close to a certain suitable value, it is good, then this moderate value is selected as the element of the reference sequence; After determining the optimal indicator set, construct the matrix D: In formula (3-4), x ij represents the i-th indicator of the j-th evaluation object, represents the optimal value of the i-th indicator; S33, standardization of the indicator matrix D: In the evaluation process, the different dimensions of each evaluation indicator will affect the evaluation results. It is necessary to perform dimensionless quantification on the evaluation indicators and convert the values ​​of each evaluation indicator into a relatively unified measurement. This process is called evaluation indicator data standardization; Assume X = (x ij ) m×n After data standardization, we get R = (r ij ) m×n , r ij ∈[0, 1]; refer to formula (2-2), formula (2-3), and formula (2-4) for three cases of standardization: When the indicator value is larger, the better, then the indicator is a positive indicator, and its standardized formula is: When the indicator value is as small as possible, the indicator is an inverse indicator, and its standardized formula is: When the index value is closer to a certain value, the evaluation result is better, and the value is recorded as That is, this indicator is a moderation indicator, and its standardized formula is: The matrix X is processed according to the standardization formula to obtain the normalized matrix R: S34, calculate the grey correlation coefficient: correlation coefficient ε ij The calculation formula is: In formula (3-9), ρ is the resolution coefficient, which is between 0 and 1. The introduction of the resolution coefficient is to reduce the influence of the extreme value of the evaluation index on the calculation, thereby improving the discrimination of the correlation coefficient; the smaller the resolution coefficient, the greater the resolution; In general, the resolution factor is usually taken as 0.5; S35, calculate the grey correlation degree: when comparing the correlation coefficient between the series of each scheme and the optimal series on a certain indicator, it cannot fully reflect the pros and cons of the scheme; in order to grasp the correlation degree between the series of schemes as a whole, it is necessary to weight the correlation coefficients and accumulate them into a single number, that is, the correlation degree p, then the correlation degree of the comparison series to the optimal series is: In formula (3-10), w i is the weight of the i-th indicator in the evaluation index system; S36, sorting: grey relational degree p j The larger the value is, the closer the object sequence is to the optimal sequence, indicating that the jth evaluated object is better, so the gray correlation degree p j The size of the objects will be sorted.

7. A charging station competitiveness comprehensive evaluation method according to claim 1, characterized in that: In step S4, the principle of TOPSIS analysis is to determine the standardized decision matrix through the standardized evaluation index data and the weight of each index, and then find the best solution and the worst solution in the standardized decision matrix, that is, the positive and negative ideal solutions; The distance between each evaluation scheme and the positive and negative ideal solutions is calculated respectively, and finally the relative proximity of each scheme to the optimal scheme is used to evaluate the pros and cons of the scheme.

8. A charging station competitiveness comprehensive evaluation method according to claim 7, characterized in that: The calculation steps of the second comprehensive evaluation model of station competitiveness based on TOPSIS analysis method include: S41, establish an evaluation index matrix; S42, standardization of evaluation indicators; S43, determining the indication entropy weight; S44, construct a decision matrix; S45, determining the positive and negative ideal solutions by the optimal values ​​of the matrix V index; S46, calculating the Euclidean distance between each evaluation object and the positive and negative ideal solutions; S47, calculating the relative proximity to the optimal object.

9. A charging station competitiveness comprehensive evaluation method according to claim 7, characterized in that: The specific calculation steps are as follows: S41, establish an evaluation index matrix: First, assuming there are m evaluation objects and n evaluation indicators, establish an evaluation index matrix X, x ij Represents the i-th indicator value of the j-th evaluation object: S42, standardization of evaluation indicators: refer to formula (2-2), formula (2-3), and formula (2-4) to standardize the evaluation indicator matrix. When the indicator value is larger, the better, then the indicator is a positive indicator, and its standardized formula is: When the indicator value is as small as possible, the indicator is an inverse indicator, and its standardized formula is: When the index value is closer to a certain value, the evaluation result is better, and the value is recorded as That is, this indicator is a moderation indicator, and its standardized formula is: The matrix X is processed according to the standardization formula to obtain the normalized matrix R: S43, determine the index entropy weight: refer to the steps of the entropy weight method, and calculate the entropy weight matrix W = (w1, w2..., w n ) T ,in S44, construct decision matrix V: construct decision matrix v based on evaluation index and entropy weight ij =r ij × i : S45, determine the positive and negative ideal solutions Z by the optimal values ​​of the evaluation indicators of the decision matrix + and Z-: S46, calculating the Euclidean distance D between each evaluation object and the positive and negative ideal solutions j + and D j - S47, calculate the relative proximity C between each evaluation object and the optimal object j : In formula (4-11), C j The larger it is, the closer the j-th evaluation object is to the optimal object level.

10. A charging station competitiveness comprehensive evaluation method according to claim 1, characterized in that: In step S5, the TOPSIS analysis method calculates the weighted Euclidean distance and relative closeness, and sorts according to the relative closeness; while the principle of the grey correlation analysis method is to calculate the correlation and sort the evaluation objects according to the correlation. The two models obtain different results, and the final ranking of station competitiveness is obtained through the average algorithm.

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