Charging station evaluation method and device, electronic equipment and storage medium

Abstract

The invention discloses a charging station evaluation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a first index scale matrix corresponding to a charging station; determining a fuzzification evaluation matrix corresponding to the charging station according to a preset first index fuzzy number and the first index scale matrix; determining an index weight corresponding to the charging station, and determining a comprehensive evaluation matrix corresponding to the charging station according to the fuzzification evaluation matrix and the index weight; and evaluating the charging station according to the comprehensive evaluation matrix. According to the method, the charging station can be evaluated based on the comprehensive evaluation matrix, and the evaluation condition corresponding to each scoring index in the charging station can be determined by using the index weight, the first index scale matrix and the first index fuzzy number, so that the problem that no feasible method for evaluating the charging station exists at present is solved; and comprehensive and accurate evaluation of the charging station is realized.

Description

Technical Field

[0001] The present invention relates to the field of planning and design, and in particular to an evaluation method, device, electronic equipment and storage medium for a charging station. Background Art

[0002] Charging stations are designed to provide electric vehicle users with safe, convenient and efficient charging services to support daily commuting and long-distance travel needs of electric vehicles. They are a key factor in promoting the popularization of electric vehicles.

[0003] As the sales of electric vehicles increase, more and more charging stations are being added to meet the needs of electric vehicle users. However, since the specific operating environment and user needs of charging stations have a great impact on charging stations, the design or optimization requirements for charging stations with different operating environments or different user needs are also different. Therefore, how to reasonably evaluate charging stations has become an important issue. Summary of the invention

[0004] The present invention provides a charging station evaluation method, device, electronic device and storage medium, which solves the problem that there is currently no feasible method to evaluate charging stations and realizes accurate evaluation of charging stations.

[0005] According to one aspect of the present invention, a method for evaluating a charging station is provided, the method comprising:

[0006] Obtain a first indicator scale matrix corresponding to the charging station, wherein the first indicator scale matrix includes multiple scale elements, one scale element corresponds to a scoring indicator, and the scale element is used to represent the relative score of the charging station for the scoring indicator among all charging stations in the same application scenario; determine the fuzzy evaluation matrix corresponding to the charging station according to a preset first indicator fuzzy number and the first indicator scale matrix; determine the indicator weight corresponding to the charging station, and determine the comprehensive evaluation matrix corresponding to the charging station according to the fuzzy evaluation matrix and the indicator weight; and evaluate the charging station according to the comprehensive evaluation matrix.

[0007] Optionally, according to the preset first indicator fuzzy number and the first indicator scale matrix, the fuzzy evaluation matrix corresponding to the charging station is determined, including: according to the first indicator fuzzy number, determining the membership corresponding to each scale element; replacing each scale element with the membership corresponding to the scale element to obtain the fuzzy evaluation matrix. Optionally, determining the indicator weight corresponding to the charging station includes: obtaining the second indicator scale matrix corresponding to the charging station, and determining the clear evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix; calculating the geometric mean corresponding to each row of matrix elements in the clear evaluation matrix, and using the geometric mean as the indicator weight. Optionally, after calculating the geometric mean corresponding to each row of matrix elements in the clear evaluation matrix, and before using the geometric mean as the indicator weight, the method also includes: setting the indicator correlation of the charging station according to the application scenario in which the charging station is located; adjusting the geometric mean according to the indicator correlation and the preset adjustment factor. Optionally, after taking the geometric mean as the indicator weight, the method also includes: determining the maximum eigenvalue of the clarification evaluation matrix according to the indicator weight and the clarification evaluation matrix; determining the consistency check index of the clarification evaluation matrix according to the order of the clarification evaluation matrix and the maximum eigenvalue of the clarification evaluation matrix; determining the single-sort consistency check result of the clarification evaluation matrix according to the consistency check index of the clarification evaluation matrix and the random consistency check correction term; when the single-sort consistency check result of the clarification evaluation matrix is ​​failure, re-obtaining the second indicator scale matrix, and returning to execute the step of determining the clarification evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix, until the single-sort consistency check result of the clarification evaluation matrix is ​​success. Optionally, the method further includes: obtaining the weight value corresponding to the third indicator scale matrix, wherein the charging station indicator corresponding to the matrix element in the third indicator scale matrix is ​​the superior indicator of the charging station indicator corresponding to the matrix element in the second indicator scale matrix; determining the total ranking weight value according to the weight value corresponding to the third indicator scale matrix and the indicator weight corresponding to the second indicator scale matrix, and determining the total ranking consistency test result of the clarity evaluation matrix according to the total ranking weight value and the third indicator scale matrix; when the total ranking consistency test result of the clarity evaluation matrix fails, re-obtaining the second indicator scale matrix, and returning to execute the step of determining the clarity evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix. Optionally, evaluating the charging station according to the comprehensive evaluation matrix includes: calculating the first parameter value according to each matrix element in the comprehensive evaluation matrix; calculating the second parameter value according to each matrix element in the comprehensive evaluation matrix and the indicator score corresponding to each matrix element; determining the score of the charging station according to the first parameter value and the second parameter value, and evaluating the charging station according to the score of the charging station.

[0008] According to another aspect of the present invention, there is provided an evaluation device for a charging station, the device comprising:

[0009] An acquisition module, used to acquire a first indicator scale matrix corresponding to the charging station, wherein the first indicator scale matrix includes multiple scale elements, one scale element corresponds to one scoring indicator, and the scale element is used to indicate the relative score of the charging station for the scoring indicator among all charging stations in the same application scenario;

[0010] A first determination module, configured to determine a fuzzy evaluation matrix corresponding to the charging station according to a preset first indicator fuzzy number and a first indicator scale matrix;

[0011] The second determination module is used to determine the indicator weights corresponding to the charging station, and determine the comprehensive evaluation matrix corresponding to the charging station according to the fuzzy evaluation matrix and the indicator weights;

[0012] The evaluation module is used to evaluate the charging stations based on the comprehensive evaluation matrix.

[0013] Optionally, the first determination module 302 is specifically used to: determine the membership corresponding to each scale element according to the first indicator fuzzy number; replace each scale element with the membership corresponding to the scale element to obtain a fuzzy evaluation matrix. Optionally, the indicator weight corresponding to the charging station is determined, and the second determination module 303 is specifically used to: obtain the second indicator scale matrix corresponding to the charging station, and determine the clear evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix; calculate the geometric mean corresponding to each row of matrix elements in the clear evaluation matrix, and use the geometric mean as the indicator weight. Optionally, after calculating the geometric mean corresponding to each row of matrix elements in the clear evaluation matrix, and before using the geometric mean as the indicator weight, the second determination module 303 is also used to: set the indicator correlation of the charging station according to the application scenario in which the charging station is located; adjust the geometric mean according to the indicator correlation and the preset adjustment factor. Optionally, after taking the geometric mean as the indicator weight, the second determination module 303 is also used to: determine the maximum eigenvalue of the clarity evaluation matrix according to the indicator weight and the clarity evaluation matrix; determine the consistency check index of the clarity evaluation matrix according to the order of the clarity evaluation matrix and the maximum eigenvalue of the clarity evaluation matrix; determine the single-sort consistency check result of the clarity evaluation matrix according to the consistency check index of the clarity evaluation matrix and the random consistency check correction term; when the single-sort consistency check result of the clarity evaluation matrix is ​​failure, re-acquire the second indicator scale matrix, and return to execute the step of determining the clarity evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix, until the single-sort consistency check result of the clarity evaluation matrix is ​​success. Optionally, the second determination module 303 is also used to: obtain the weight value corresponding to the third indicator scale matrix, wherein the charging station indicator corresponding to the matrix element in the third indicator scale matrix is ​​the upper-level indicator of the charging station indicator corresponding to the matrix element in the second indicator scale matrix; determine the total sorting weight value according to the weight value corresponding to the third indicator scale matrix and the indicator weight corresponding to the second indicator scale matrix, and determine the total sorting consistency test result of the clarity evaluation matrix according to the total sorting weight value and the third indicator scale matrix; when the total sorting consistency test result of the clarity evaluation matrix fails, re-acquire the second indicator scale matrix, and return to execute the step of determining the clarity evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix. Optionally, the evaluation module 304 is specifically used to: calculate the first parameter value according to each matrix element in the comprehensive evaluation matrix; calculate the second parameter value according to each matrix element in the comprehensive evaluation matrix and the indicator score corresponding to each matrix element; determine the score of the charging station according to the first parameter value and the second parameter value, and evaluate the charging station according to the score of the charging station.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the charging station evaluation method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the charging station evaluation method described in any embodiment of the present invention when executed.

[0017] The evaluation method of the charging station provided by the embodiment of the present invention obtains the first indicator scale matrix corresponding to the charging station; determines the fuzzy evaluation matrix corresponding to the charging station according to the preset first indicator fuzzy number and the first indicator scale matrix; determines the indicator weight corresponding to the charging station, and determines the comprehensive evaluation matrix corresponding to the charging station according to the fuzzy evaluation matrix and the indicator weight; and evaluates the charging station according to the comprehensive evaluation matrix. In the above technical scheme, on the one hand, the first indicator scale matrix is ​​processed using the first indicator fuzzy number to obtain the fuzzy evaluation matrix corresponding to the charging station, that is, the method of fuzzifying the score can reduce the complexity and random subjectivity of the first indicator scale matrix formed based on various complexities and random subjectivities, thereby ensuring the accuracy and reliability of the subsequent evaluation of the charging station. On the other hand, by using the indicator weight corresponding to each scoring indicator, each matrix element in the fuzzy evaluation matrix is ​​processed, which can reflect the importance of different scoring indicators of a charging station in the application scenario, and based on the fuzzy score corresponding to each scoring indicator, the weight indicator is increased, thereby achieving the accuracy and pertinence of the evaluation matrix used to evaluate the charging station. Finally, the charging station can be evaluated based on the comprehensive evaluation matrix, or the evaluation status of each scoring indicator in the charging station can be determined by using the indicator weight, the first indicator scaling matrix and the first indicator fuzzy number, which solves the problem that there is no feasible method to evaluate the charging station at present, and realizes a comprehensive and accurate evaluation of the charging station. At the same time, it also realizes the comparative evaluation of multiple charging stations in the same application scenario.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a charging station evaluation method provided by an embodiment of the present invention;

[0021] Figure 2 A flowchart of another charging station evaluation method provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the structure of an evaluation device for a charging station provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Figure 1This is a flowchart of a charging station evaluation method provided by an embodiment of the present invention. This embodiment can be applied to evaluate the score of any charging station among all charging stations in the same application scenario relative to other charging stations. The method can be executed by an evaluation device of the charging station. The evaluation device of the charging station can be implemented in the form of hardware and / or software. The evaluation device of the charging station can be configured in an electronic device. In this embodiment, the electronic device can be a device such as a computer or a server. Figure 1 As shown, the method includes:

[0027] S101. Obtain a first indicator scale matrix corresponding to a charging station.

[0028] Among them, the first indicator scaling matrix includes multiple scaling elements, one scaling element corresponds to one scoring indicator, and the scaling element is used to represent the relative score of the charging station for the scoring indicator among all charging stations in the same application scenario.

[0029] First, the same application scenario is the application scenario where the charging station is located. For example, the charging station in the city center is the same application scenario, the charging station on the rural street is the same application scenario, or the charging station on the highway is the same application scenario, etc. Secondly, all charging stations can use the same scoring indicators to evaluate the charging station, that is, no matter how many charging stations exist in the same application scenario, the corresponding scoring indicators are the same. Among them, the scoring indicators can be shown in the following table:

[0030] Table 1 Scoring index table for electric vehicle charging stations

[0031]

[0032] It is worth noting that in this embodiment, the scoring indicator corresponding to each scale element in the first indicator scale matrix is ​​a secondary indicator. Finally, each scale element in the first indicator scale matrix refers to the relative score of the station on this scoring indicator compared with other charging stations in the same application scenario. For example, there are currently 3 charging stations in the same application scenario, and there is 1 scoring indicator. At this time, when the user learns about the scoring indicator, he can score the scoring indicator of the three charging stations according to his own feelings, and obtain the scores of the three charging stations on this scoring indicator. That is, the user compares the three charging stations based on his own feelings and obtains the score. Among them, in addition to using user scoring, charging stations can also be scored using charging station simulation equipment. Or use multiple experts to score based on experience, which is not limited here.

[0033] Specifically, after scoring each scoring indicator of the charging station, all scores corresponding to the charging station can be arranged to obtain the first indicator scale matrix corresponding to the charging station. Optionally, since each scale element in the first indicator scale matrix is ​​a score obtained after comparison with other charging stations, when obtaining the first indicator scale matrix, the first indicator scale matrix corresponding to each charging station in the same application scenario can be obtained. The first indicator scale matrix corresponding to each charging station can be expressed as a 1*n matrix. Where n is the number of scoring indicators.

[0034] In this embodiment, the first indicator scale matrix of a charging station is determined by the relative scores of other charging stations in the same application scenario under one scoring indicator, which provides a basis for the subsequent evaluation of the charging station. In addition, it is also possible to determine the first indicator scale matrix corresponding to each charging station in the same application scenario, which provides a comparative basis for the subsequent comparative evaluation of all charging stations in the same application scenario.

[0035] S102: Determine a fuzzy evaluation matrix corresponding to the charging station according to a preset first indicator fuzzy number and a first indicator scaling matrix.

[0036] The first indicator fuzzy number includes a membership degree. The membership degree indicates the importance of a scoring indicator. In this embodiment, the first indicator fuzzy number can predefine fuzzy rules, and the user can determine the fuzzy number of each indicator according to the fuzzy rules; the user can also directly determine the fuzzy number corresponding to the charging station by comparing it with other charging stations. Optionally, the first indicator fuzzy number can be a Pythagorean fuzzy number.

[0037] Specifically, since each scale element in the first indicator scale matrix corresponds to a scoring indicator, and the first indicator fuzzy number is also pre-set or determined according to each scoring indicator, the fuzzy evaluation matrix can be determined based on the scoring indicator and the first indicator fuzzy number corresponding to each scoring indicator and the scale elements in the first indicator scale matrix.

[0038] In this embodiment, the first indicator fuzzy number is used to process the first indicator scale matrix to obtain the fuzzy evaluation matrix corresponding to the charging station. That is, the method of fuzzifying the score can reduce the complexity and random subjectivity of the first indicator scale matrix formed based on various uncertainties and random subjectivities, thereby ensuring the accuracy and reliability of subsequent evaluations of the charging stations.

[0039] S103: Determine the indicator weights corresponding to the charging station, and determine the comprehensive evaluation matrix corresponding to the charging station based on the fuzzy evaluation matrix and the indicator weights.

[0040] Among them, the indicator weight is the weight corresponding to the scoring indicator. Each scoring indicator corresponds to an indicator weight.

[0041] Specifically, in one implementation, the indicator weight corresponding to each scoring indicator can be determined based on the score ratio corresponding to each scoring indicator of different charging stations in the same application scenario. In another implementation, the score of each scoring indicator of all charging stations can be added up and compared after the first implementation to determine the indicator weight corresponding to each scoring indicator. Specifically, since each matrix element in the fuzzy evaluation matrix corresponds to a scoring indicator, the indicator weight corresponding to the scoring indicator can be multiplied by the matrix element in the fuzzy evaluation matrix corresponding to the scoring indicator. Finally, the comprehensive evaluation matrix corresponding to the charging station can be obtained.

[0042] In this embodiment, the indicator weight corresponding to each scoring indicator is used to process each matrix element in the fuzzy evaluation matrix, which can reflect the importance of different scoring indicators of a charging station in the application scenario. The weight indicator is increased based on the fuzzy score corresponding to each scoring indicator, thereby achieving the accuracy and pertinence of the evaluation matrix used to evaluate the charging station.

[0043] S104. Evaluate the charging station based on the comprehensive evaluation matrix.

[0044] Specifically, in the above steps, in order to ensure the accuracy and reliability of the first indicator scale matrix, the fuzzy number is used to fuzzify the matrix. Therefore, after obtaining the comprehensive evaluation matrix, the comprehensive evaluation matrix can be defuzzified, and the final result can be used to evaluate the charging station. Exemplarily, when evaluating the charging station, the relative position of the charging station relative to other charging stations competing in the same application scenario can be judged based on the comprehensive evaluation matrix after defuzzification. And, according to the degree of membership corresponding to each indicator in the first indicator fuzzy number and the scale element corresponding to each indicator in the first indicator scale matrix, its contribution to the final charging station can be determined, and the advantages and weaknesses can be found. And, if the indicator weight of a certain scoring indicator is high, the size of the scale element corresponding to the scoring indicator in the first indicator scale matrix can be determined, so as to determine the advantages and weaknesses of the charging station.

[0045] In this embodiment, the charging station can be evaluated based on the comprehensive evaluation matrix, and the evaluation status corresponding to each scoring indicator in the charging station can also be determined by using the indicator weight, the first indicator scaling matrix and the first indicator fuzzy number, which solves the problem that there is no feasible method to evaluate the charging station at present, and realizes a comprehensive and accurate evaluation of the charging station. At the same time, it also realizes the comparative evaluation of multiple charging stations in the same application scenario.

[0046] The evaluation method of the charging station provided by the embodiment of the present invention obtains the first indicator scale matrix corresponding to the charging station; determines the fuzzy evaluation matrix corresponding to the charging station according to the preset first indicator fuzzy number and the first indicator scale matrix; determines the indicator weight corresponding to the charging station, and determines the comprehensive evaluation matrix corresponding to the charging station according to the fuzzy evaluation matrix and the indicator weight; and evaluates the charging station according to the comprehensive evaluation matrix. In the above technical scheme, on the one hand, the first indicator scale matrix is ​​processed using the first indicator fuzzy number to obtain the fuzzy evaluation matrix corresponding to the charging station, that is, the method of fuzzifying the score can reduce the complexity and random subjectivity of the first indicator scale matrix formed based on various complexities and random subjectivities, thereby ensuring the accuracy and reliability of the subsequent evaluation of the charging station. On the other hand, by using the indicator weight corresponding to each scoring indicator, each matrix element in the fuzzy evaluation matrix is ​​processed, which can reflect the importance of different scoring indicators of a charging station in the application scenario, and based on the fuzzy score corresponding to each scoring indicator, the weight indicator is increased, thereby achieving the accuracy and pertinence of the evaluation matrix used to evaluate the charging station. Finally, the charging station can be evaluated based on the comprehensive evaluation matrix, or the evaluation status of each scoring indicator in the charging station can be determined by using the indicator weight, the first indicator scaling matrix and the first indicator fuzzy number, which solves the problem that there is no feasible method to evaluate the charging station at present, and realizes a comprehensive and accurate evaluation of the charging station. At the same time, it also realizes the comparative evaluation of multiple charging stations in the same application scenario.

[0047] When evaluating charging stations, the importance of scoring indicators will vary depending on the specific operating environment and user needs. For example, in the two scenarios of commercial stations in the central city (Case A) and highway service area stations (Case B), different operating environments have obvious differences in the priority of indicators. For another example, for commercial stations in the central city, charging stations usually serve short-term shopping or office users, so geographical location and coverage are key indicators, and the station location must be close to high-traffic areas such as office buildings or shopping centers. In addition, fast charging capability is the key to improving user experience, and DC fast charging services that can meet users' short-term charging needs are essential. In commercial areas with fierce price competition, preferential pricing strategies (such as free first hour or member discounts) are also very important to attract users. In contrast, although sustainability and green energy are plus points, they have a relatively low priority because of their low direct impact on user decisions. In highway service area stations, the stations serve vehicles traveling long distances, and their main needs are concentrated on the reasonable distribution of geographical locations to cover the main routes of long-distance travel. Compatibility and standardization are also core indicators of high-speed stations to ensure support for multiple vehicle models and charging standards. Furthermore, perfect customer service (such as rest areas, dining facilities) and immediate fault handling capabilities can significantly improve user satisfaction. In this case, the importance of price and charging mode is relatively low, because highway users pay more attention to efficiency rather than price. Based on this, the present invention aims to evaluate different charging stations under the same application scenario, so that the different indicators of the charging station itself can be compared, and different charging stations under the same application scenario can be compared with each other. Furthermore, when designing a charging station, it can refer to the advantages and disadvantages of each charging station under the same application scenario as the currently designed station, and guide the currently designed station to learn from each other's strengths and weaknesses.

[0048] Figure 2 This is a flow chart of another charging station evaluation method provided by an embodiment of the present invention. Based on the above embodiment, this embodiment describes some steps in detail. Figure 2 As shown, the method includes:

[0049] S201. Obtain a first indicator scale matrix corresponding to a charging station.

[0050] Specifically, in a feasible implementation, the first indicator scale matrix can be obtained as follows:

[0051] Based on a scoring indicator, determine the score of a charging station in the same application scenario relative to other charging stations. For example, there are three charging stations A, B and C in the same application scenario. Taking the scoring indicator of charging cost as an example, if the charging cost is 1.2 yuan / 1 kWh at charging station A, the charging cost is 1.7 yuan / 1 kWh at charging station B, and the charging cost is 2.2 yuan / 1 kWh at charging station C. If 10 points are the full score, the above description can be used for scoring. The scale element corresponding to the charging cost in the first indicator scale matrix corresponding to charging station A is 8 points, the scale element corresponding to the charging cost in the first indicator scale matrix corresponding to charging station B is 4 points, and the scale element corresponding to the charging cost in the first indicator scale matrix corresponding to charging station C is 2 points. Furthermore, according to the above-mentioned scoring of charging costs, other scoring indicators can also be scored in the same way, and finally the first indicator scale matrix corresponding to charging station A, the first indicator scale matrix corresponding to charging station B, and the first indicator scale matrix corresponding to charging station C can be obtained.

[0052] S202. Determine the degree of membership corresponding to each scale element according to the first indicator fuzzy number.

[0053] Specifically, the membership degree corresponding to each scoring indicator can be preset in combination with the demand for charging stations in the current application scenario. For example, in the current application scenario, the membership degree corresponding to the charging cost scoring indicator can be preset as follows: 0-1 points is 0.0, 2-6 points is 0.4, and 7-10 points is 0.6. For another example, the membership degree corresponding to the user experience scoring indicator can be preset as follows: experience satisfaction 70% and above is 0.8, experience satisfaction 25% to 69% is 0.2, and experience satisfaction 24% and below is 0.0.

[0054] And, when the first indicator fuzzy number is obtained, the membership degree corresponding to each scale element in the first indicator fuzzy number can be determined. For example, following the above example, the scale element corresponding to the charging cost in the first indicator scale matrix corresponding to charging station A is 8 points, then the scale element 8 corresponding to the charging cost in the first indicator fuzzy number of charging station A can be determined, and its corresponding membership degree is 0.6. Similarly, the scale element 4 corresponding to the charging cost in the first indicator fuzzy number of charging station B has a corresponding membership degree of 0.4. The scale element 2 corresponding to the charging cost in the first indicator fuzzy number of charging station C has a corresponding membership degree of 0.4.

[0055] S203, replacing each scale element with the membership degree corresponding to the scale element to obtain a fuzzy evaluation matrix.

[0056] Specifically, after determining the membership degree corresponding to each scale element in the first index fuzzy number, each scale element can be directly replaced by the membership degree corresponding to the scale element. Finally, the replaced matrix is ​​the fuzzy evaluation matrix.

[0057] S204. Obtain a second indicator scale matrix corresponding to the charging station.

[0058] The second indicator scale matrix includes multiple indicator elements, one indicator element corresponds to one scoring indicator, and the indicator element is the relative score of a certain scoring indicator among all the scoring indicators of the charging station. That is, a comparative score is performed between every two scoring indicators in the charging station. Specifically, the second indicator scale matrix can be determined according to a preset importance table:

[0059] For example, the importance level table may be shown in Table 2 below.

[0060] Table 2 Importance table

[0061] Scale meaning 1 Indicates that indicator i is equally important as indicator j 3 Indicates that indicator i is slightly more important than indicator j 5 Indicates that indicator i is significantly more important than indicator j 7 Indicates that indicator i is much more important than indicator j 9 Indicates that indicator i is absolutely more important than indicator j 2，4，6，8 Indicates the middle value of two adjacent scales

[0062] The scoring method uses a 1-9 scale, where "1" means that the two indicators are equally important, and "9" means that one indicator is absolutely more important than the other. In addition, the middle value of two adjacent scales, such as 2, 4, 6, and 8, is included to indicate the slight difference in relative importance. The comparison of the importance of each two indicators constitutes the second indicator scale matrix. In addition, the matrix has a ij =1 / a ji , a ij Indicates the relative importance of indicator i relative to indicator j. ji Indicates the relative importance of indicator j relative to indicator i.

[0063] For example, take the three secondary indicators under the “first-level indicator technology and innovation” in Table 1 as an example. The comparison relationship among experts is as follows: “Compatibility and standardization” is considered slightly more important than “sustainability and green energy”, with a score of 12 =3; "Compatibility and standardization" is considered to be significantly more important than "technological innovation and upgrading capabilities", with a score of a 13 =5; "Sustainability and green energy" is considered slightly more important than "technological innovation and upgrading capabilities", with a score of a 23 =3. The second indicator scale matrix determined after the scoring is:

[0064]

[0065] Among them, P b The "b" in the above table represents the second scoring indicator "Technology and Innovation" in the first-level scoring indicators. 12The “1” in the index represents the first secondary indicator “Compatibility and Standardization” under the “Technology and Innovation” indicator, the “2” represents the second secondary indicator “Sustainability and Green Energy” under the “Technology and Innovation” indicator, and a 13 In and a 23 The "3" in the table represents the third secondary indicator "Technology Innovation and Upgrading Capability" under the "Technology and Innovation" indicator. The second indicator scale matrix of the secondary scoring indicators corresponding to other primary scoring indicators can all be determined according to the above method.

[0066] S205. Determine a clarity evaluation matrix corresponding to the charging station according to a preset second indicator fuzzy number and a second indicator scaling matrix.

[0067] Among them, the second indicator fuzzy number and the first indicator fuzzy number can be set to different parameters when pre-set. When the second indicator fuzzy number is pre-set, in addition to the membership, it can also include non-membership and uncertainty. Non-membership indicates the degree of unimportance of a certain indicator, and uncertainty is used to quantify the uncertainty when evaluating a certain indicator. Exemplarily, following the above example, the corresponding second indicator fuzzy number is pre-set for each scale in the importance table. For example, the second indicator fuzzy number corresponding to scale 1 is (0.5, 0.4, 0.3); the second indicator fuzzy number corresponding to scale 3 is (0.7, 0.3, 0.64); the second indicator fuzzy number corresponding to scale 5 is (0.8, 0.2, 0.6). Among them, the first value in the brackets is the membership, the second value is the non-membership, and the third value is the uncertainty. The last uncertainty can be expressed using √[1-(membership) 2 -(non-membership) 2 ] is calculated. And, after obtaining the second indicator scale matrix, the second indicator scale matrix can be fuzzy processed based on the second indicator fuzzy number, and then clarified to obtain the clarified evaluation matrix corresponding to the charging station. For example, following the above example, after obtaining the second indicator scale matrix P b After that, the second indicator scale matrix can be fuzzified according to the second indicator fuzzy number. After processing, the second indicator scale fuzzy matrix is ​​as follows:

[0068]

[0069] Among them, the inverse scale (such as 1 / 3) takes the symmetric value of the corresponding second indicator fuzzy number [such as (0.3, 0.7, 0.64)]. After obtaining the second indicator scale fuzzy matrix, the second indicator scale fuzzy matrix is ​​clarified. For example, continuing the above example, the clarified evaluation matrix is ​​as follows:

[0070]

[0071] That is, the membership degree is subtracted from the non-membership degree to obtain a clarified value. b The element (0.5, 0.4, 0.3) in the first row and first column of the fuzzy matrix is ​​obtained by using 0.5-0.4=0.1. b Clear the first row and first column elements in the matrix.

[0072] S206, calculating the geometric mean corresponding to each row of matrix elements in the clarity evaluation matrix.

[0073] Specifically, the geometric mean corresponding to each row of matrix elements in the clarity evaluation matrix can be calculated using the following formula:

[0074]

[0075] Where i represents the i-th row of the clarity evaluation matrix, n represents the order of the clarity evaluation matrix (i.e., the number of scoring indicators), and j represents the j-th column of the clarity evaluation matrix. ij Represents the matrix element of the i-th row and j-th column of the clarity evaluation matrix. Optionally, for the geometric mean corresponding to each row of matrix elements in the clarity evaluation matrix, a normalization formula can be used to normalize them to obtain a normalized geometric mean. The normalization formula is as follows:

[0076]

[0077] Among them, due to G i is the geometric mean value corresponding to the matrix elements in the ith row. Therefore, after calculating each row of the entire clarity evaluation matrix according to the above calculation, we can get W = (w 1 , w 2 , ..., w n ) is the normalized geometric mean.

[0078] S207: Setting the indicator correlation of the charging station according to the application scenario of the charging station.

[0079] Among them, the indicator correlation is used to quantify the correlation between the current demand of the charging station in the application scenario and each scoring indicator. For example, if the application scenario of the charging station is located in the city center, the correlation of each secondary scoring indicator can be set according to the application scenario of the charging station in the city center, for example: geographical location and coverage is 0.9, charging speed is 0.95, number and type of charging piles is 0.7, intelligence and convenience is 0.6, customer service and support is 0.5; compatibility and standardization is 0.8, sustainability and green energy is 0.6, technological innovation and upgrading capability is 0.7; price and charging model is 0.7, partnership is 0.5.

[0080] S208. Adjust the geometric mean according to the indicator correlation and the preset adjustment factor.

[0081] The adjustment factor is used to optimize the final geometric mean. Specifically, according to the above steps, after obtaining the set mean value w corresponding to each row i After that, we can use (1+index correlation*adjustment factor)*w i Calculate the new geometric mean after adjusting the geometric mean.

[0082] For example, it is assumed that the geometric mean corresponding to each row of matrix elements in the clarification evaluation matrix calculated above is W = (0.35, 0.30, 0.35). Among them, the first 0.35 represents the geometric mean of the first row, 0.30 represents the geometric mean of the second row, and the second 0.35 represents the geometric mean of the third row. At this time, if the adjustment factor is 0.2, the geometric mean of the adjusted first row can be calculated as: 0.35*(1+0.2*0.8)=0.4060, the geometric mean of the second row is: 0.30*(1+0.2*0.6)=0.3360, and the geometric mean of the third row is: 0.35*(1+0.2*0.7)=0.3990. Among them, the first row corresponds to the indicator correlation degree of 0.8 between compatibility and standardization, the second row corresponds to the indicator correlation degree of 0.6 between sustainability and green energy, and the third row corresponds to the indicator correlation degree of 0.7 between technological innovation and upgrading capability.

[0083] S209. Use the geometric mean as the indicator weight.

[0084] Specifically, after the above steps, the adjusted geometric mean can be used as the indicator weight. For example, following the above example, the final indicator weight W=(0.4060, 0.3360, 0.3990).

[0085] S210: Determine the maximum eigenvalue of the clarification evaluation matrix according to the indicator weights and the clarification evaluation matrix.

[0086] Among them, the maximum eigenvalue is used to evaluate whether there is inconsistency in the clarity evaluation matrix. The closer the eigenvalue is to the matrix dimension, the better the consistency. Specifically, the maximum eigenvalue can be calculated according to the following formula:

[0087]

[0088] Among them, λ max is the maximum eigenvalue, P is the clarification evaluation matrix, and W is the indicator weight.

[0089] S211. Determine a consistency test index of the clarity evaluation matrix according to the order of the clarity evaluation matrix and the maximum eigenvalue of the clarity evaluation matrix.

[0090] Specifically, the consistency test index of the clarification evaluation matrix can be determined according to the following formula:

[0091]

[0092] Where n is the order of the clarity evaluation matrix, λ max is the maximum eigenvalue.

[0093] For example, when λ max =n, CI = 0, and the clarity evaluation matrix has complete consistency; the larger the CI, the greater the deviation from consistency. As the order n of the matrix increases, the calculation error tends to increase and the consistency tends to decrease.

[0094] S212: Determine a single-rank consistency test result of the clarification evaluation matrix according to the consistency test index of the clarification evaluation matrix and the random consistency test correction item.

[0095] Among them, the random consistency test correction term is used to evaluate the consistency level of the matrix. Specifically, the single-rank consistency test result of the clarity evaluation matrix can be determined according to the following formula:

[0096]

[0097] Wherein, RI is the random consistency check correction term. The calculated CR is the single-rank consistency check result of the clarity evaluation matrix. Exemplarily, the random consistency check correction term can be determined according to the following table:

[0098] Table 3 Random consistency test correction items

[0099] Order n 3 4 5 6 RI 0.58 0.90 1.12 1.24

[0100] S213, determining whether the single-sort consistency test result of the clarity evaluation matrix is ​​successful; if so, continue to execute S215; if not, execute S214.

[0101] Specifically, when CR≥0.1, it can be determined that the single-sort consistency test result of the clarity evaluation matrix fails, and S214 needs to be executed. When CR<0.1, it can be determined that the single-sort consistency test result of the clarity evaluation matrix succeeds, and S215 can be executed.

[0102] S214. Re-acquire the second indicator scale matrix, and return to execute S205.

[0103] Specifically, if the result of the single-rank consistency check of the clarity evaluation matrix fails, it means that the initial second indicator scale matrix corresponding to the clarity evaluation matrix obtained this time may have inconsistent scoring logic. Therefore, it is necessary to re-acquire the second indicator scale matrix and return to the step of determining the clarity evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix.

[0104] S215. Obtain weight values ​​corresponding to the third indicator scaling matrix.

[0105] Among them, the charging station indicator corresponding to the matrix element in the third indicator scale matrix is ​​the superior indicator of the charging station indicator corresponding to the matrix element in the second indicator scale matrix. That is, in this embodiment, the source of the third indicator scale matrix is ​​consistent with the second indicator scale matrix. The only difference is that each matrix element in the second indicator scale matrix corresponds to a secondary scoring indicator, while the third indicator scale matrix summarizes each matrix element corresponding to a primary scoring indicator. Specifically, the weight value corresponding to the third indicator scale matrix can be obtained according to the method of obtaining the weight value of the second indicator scale matrix as described above.

[0106] S216. Determine the total ranking weight value according to the weight value corresponding to the third indicator scale matrix and the indicator weight corresponding to the second indicator scale matrix, and determine the total ranking consistency test result of the clarified evaluation matrix according to the total ranking weight value and the third indicator scale matrix.

[0107] Specifically, the total ranking consistency test result of the clarity evaluation matrix can be determined according to the following table and formula:

[0108] Table 4 Total ranking weight values

[0109]

[0110] Among them, layer A represents each indicator in the first scoring indicator, and layer B represents each indicator in the second scoring indicator. m is the total number of scoring indicators in the first scoring indicator, and n is the total number of scoring indicators in the second scoring indicator. 1 to a m are the weight values ​​corresponding to the first scoring indicator. Layer B corresponds to the indicator weight of each row, b 1j , b 2j , b 3j , ..., b nj , j = 1, 2, ..., m. b ij It can be expressed as a vector (b i1 , b i2 , ..., b im ), i = 1, 2, 3, ..., n. Finally, the total ranking weight can be And, the CI and RI calculated above can be used to determine the total sorting consistency test result. For example:

[0111]

[0112] S217, determining whether the total ranking consistency test result of the clarity evaluation matrix is ​​successful; if so, continue to execute S218; if not, execute S214.

[0113] Specifically, if the calculated CI is less than 0.1, it can be determined that the total ranking consistency test result of the clarity evaluation matrix is ​​successful, and at this time, S218 can be continued. If the calculated CI is greater than or equal to 0.1, S214 needs to be executed.

[0114] In this embodiment, the rationality and consistency of the evaluation according to different scoring indicators can be ensured through consistency testing. If both the single sorting consistency result and the total sorting consistency result pass the test, it can be determined that the calculated indicator weight is valid and can be used for subsequent analysis. Among them, the single sorting consistency test is to verify whether the indicator weight calculation logic in the entire hierarchy is consistent, and to ensure that the comprehensive effect of the weight of each level meets the overall requirements. The single sorting consistency test result will indirectly affect the total sorting consistency test result, that is, it will affect the accuracy of the overall indicator weight.

[0115] S218. Determine the comprehensive evaluation matrix corresponding to the charging station based on the fuzzy evaluation matrix and indicator weights.

[0116] Specifically, after the indicator weights pass the consistency test, the comprehensive evaluation matrix corresponding to the charging station can be determined according to the fuzzy evaluation matrix and the indicator weights. For example, the matrix elements of each row (a row is a matrix element) in the fuzzy evaluation matrix are multiplied by the corresponding indicator weights to finally obtain the comprehensive evaluation matrix corresponding to the charging station.

[0117] S219. Calculate a first parameter value according to each matrix element in the comprehensive evaluation matrix.

[0118] Specifically, each matrix element in the comprehensive evaluation matrix may be added to obtain the first parameter value.

[0119] S220. Calculate a second parameter value according to each matrix element in the comprehensive evaluation matrix and the index score corresponding to each matrix element.

[0120] Specifically, each matrix element in the comprehensive evaluation matrix may be multiplied by the indicator score corresponding to each matrix element to obtain the indicator score, and then all the indicator scores may be added together to obtain the second parameter value.

[0121] S221. Determine a score of the charging station according to the first parameter value and the second parameter value, and evaluate the charging station according to the score of the charging station.

[0122] Specifically, the score of the charging station can be determined by dividing the second parameter value by the first parameter value. At this point, the charging station can be evaluated according to the score of the charging station. Exemplarily, a simple example is used to illustrate how to evaluate the charging station based on the score of the charging station. Assume that the score of a charging station is 72 points, and the total score is 100. Among them, the scoring indicators involved in the above-mentioned first indicator scaling matrix are "user experience", "charging cost" and "technological innovation". Among them, user experience (weight 0.5): score 80; charging cost (weight 0.3): score 60; technological innovation (weight 0.2): score 70.

[0123] At this point, you can analyze and make transformation suggestions based on different scores:

[0124] For example, user experience (score 80, good performance); Advantages: The high score of user experience indicates that the geographical location, service attitude, charging convenience, etc. of the station are recognized by users. Improvement: Continue to strengthen intelligent services, such as launching a mobile phone reservation function and optimizing the parking guidance system.

[0125] Charging cost (score 60, poor performance); Problem: The high charging cost leads to a low score for this indicator, which may be due to the high charging standards or the inflexible pricing model. Improvement suggestions: (1) Adjust the pricing strategy, such as offering preferential prices during off-peak hours to attract more users; (2) Cooperate with the government or enterprises to obtain subsidies for charging station operations and reduce operating costs.

[0126] Technological innovation (score 70, medium level); Problem: The score for technological innovation is average, which may be due to the fact that the compatibility of charging equipment or charging efficiency has not reached advanced levels. Suggestions for improvement: (1) Upgrade charging equipment to improve compatibility with new energy vehicles (such as fast-charging vehicles); (2) Introduce high-power fast-charging technology to reduce user waiting time.

[0127] Comprehensive transformation plan: Short-term optimization: (1) Implement preferential price activities to improve cost-effectiveness; introduce parking guidance system and mobile reservation function to further enhance user experience. Medium- and long-term optimization:) Upgrade charging pile equipment to improve charging efficiency and equipment compatibility; optimize station layout through data analysis and improve service capabilities.

[0128] It is worth noting that the above example is only a simple application example and should not be used as a limitation of the method proposed in this case. In practice, the scores of multiple charging stations can also be compared with each other to optimize the construction of charging stations or provide a basis for the design of charging stations.

[0129] The evaluation method of the charging station provided by the embodiment of the present invention, in order to reduce the ambiguity and uncertainty caused by the evaluation of the scoring index by relying on the initial manual or empirical value, uses the second index scale matrix and the second index fuzzy number to calculate the clear evaluation matrix, and calculates the index weight accordingly. When using the membership and non-membership in the second index fuzzy number, since the uncertainty is calculated based on the sum of the squares of the membership and non-membership, it is limited to a certain range. Based on this property, when the second index scale matrix is ​​finally determined, a scale matrix with better accuracy and consistency can be obtained, which is used to guide the subsequent calculation of the score of the charging station. It has the advantages of being more flexible and particularly suitable for complex multi-indicator and multi-dimensional evaluation scenarios. In addition, the use of this method to obtain the final score of the charging station can provide objective quantitative support for the construction and operation optimization of the charging station, help to rationally plan resources, and improve charging efficiency and user satisfaction. At the same time, the method has a wide range of applicability, not only limited to the field of charging stations, but also can be extended to other complex system evaluation scenarios that require multi-dimensional and fuzzy decision-making, thereby playing an important guiding role in multiple industries.

[0130] Figure 3 A schematic diagram of the structure of an evaluation device for a charging station provided in an embodiment of the present invention.

[0131] like Figure 3 As shown, the device comprises:

[0132] An acquisition module 301 is used to acquire a first indicator scale matrix corresponding to the charging station, wherein the first indicator scale matrix includes a plurality of scale elements, one scale element corresponds to one scoring indicator, and the scale element is used to indicate a relative score of the charging station for the scoring indicator among all charging stations in the same application scenario;

[0133] A first determination module 302, configured to determine a fuzzy evaluation matrix corresponding to the charging station according to a preset first indicator fuzzy number and a first indicator scale matrix;

[0134] The second determination module 303 is used to determine the indicator weights corresponding to the charging station, and determine the comprehensive evaluation matrix corresponding to the charging station according to the fuzzy evaluation matrix and the indicator weights;

[0135] The evaluation module 304 is used to evaluate the charging station according to the comprehensive evaluation matrix.

[0136] Optionally, the first determination module 302 is specifically used to: determine the membership degree corresponding to each scale element according to the first indicator fuzzy number; replace each scale element with the membership degree corresponding to the scale element to obtain a fuzzy evaluation matrix.

[0137] Optionally, to determine the indicator weight corresponding to the charging station, the second determination module 303 is specifically configured to:

[0138] The second indicator scale matrix corresponding to the charging station is obtained, and the clarity evaluation matrix corresponding to the charging station is determined according to the preset second indicator fuzzy number and the second indicator scale matrix; the geometric mean corresponding to each row of matrix elements in the clarity evaluation matrix is ​​calculated, and the geometric mean is used as the indicator weight.

[0139] Optionally, after calculating the geometric mean corresponding to each row of matrix elements in the clarity evaluation matrix and before using the geometric mean as the indicator weight, the second determination module 303 is further used to:

[0140] According to the application scenarios of the charging stations, the indicator correlation of the charging stations is set; according to the indicator correlation and the pre-set adjustment factors, the geometric mean is adjusted.

[0141] Optionally, after taking the geometric mean as the indicator weight, the second determination module 303 is also used to: determine the maximum eigenvalue of the clarity evaluation matrix according to the indicator weight and the clarity evaluation matrix; determine the consistency check index of the clarity evaluation matrix according to the order of the clarity evaluation matrix and the maximum eigenvalue of the clarity evaluation matrix; determine the single-sort consistency check result of the clarity evaluation matrix according to the consistency check index of the clarity evaluation matrix and the random consistency check correction term; when the single-sort consistency check result of the clarity evaluation matrix is ​​failure, re-acquire the second indicator scale matrix, and return to execute the step of determining the clarity evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix, until the single-sort consistency check result of the clarity evaluation matrix is ​​success.

[0142] Optionally, the second determining module 303 is further configured to:

[0143] Obtain weight values ​​corresponding to the third indicator scale matrix, wherein the charging station indicators corresponding to the matrix elements in the third indicator scale matrix are superior indicators of the charging station indicators corresponding to the matrix elements in the second indicator scale matrix; determine the total ranking weight values ​​according to the weight values ​​corresponding to the third indicator scale matrix and the indicator weights corresponding to the second indicator scale matrix, and determine the total ranking consistency test result of the clarification evaluation matrix according to the total ranking weight values ​​and the third indicator scale matrix; when the total ranking consistency test result of the clarification evaluation matrix is ​​failure, re-obtain the second indicator scale matrix, and return to execute the step of determining the clarification evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix.

[0144] Optionally, the evaluation module 304 is specifically used for:

[0145] A first parameter value is calculated according to each matrix element in the comprehensive evaluation matrix; a second parameter value is calculated according to each matrix element in the comprehensive evaluation matrix and the indicator score corresponding to each matrix element; a score of the charging station is determined according to the first parameter value and the second parameter value, and the charging station is evaluated according to the score of the charging station.

[0146] The charging station evaluation device provided in the embodiment of the present invention can execute the charging station evaluation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0147] Figure 4 A schematic diagram of the structure of an electronic device 10 provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0148] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0149] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0150] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the evaluation method of the charging station.

[0151] In some embodiments, the evaluation method of the charging station may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the evaluation method of the charging station described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the evaluation method of the charging station in any other appropriate manner (e.g., by means of firmware).

[0152] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0154] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0157] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0158] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0159] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging station evaluation method, characterized in that: include: Obtaining a first indicator scale matrix corresponding to the charging station, wherein the first indicator scale matrix includes a plurality of scale elements, one of the scale elements corresponds to a scoring indicator, and the scale element is used to indicate a relative score of the charging station for the scoring indicator among all charging stations in the same application scenario; Determining a fuzzy evaluation matrix corresponding to the charging station according to a preset first indicator fuzzy number and the first indicator scaling matrix; Determining the indicator weights corresponding to the charging station, and determining the comprehensive evaluation matrix corresponding to the charging station according to the fuzzy evaluation matrix and the indicator weights; The charging station is evaluated according to the comprehensive evaluation matrix.

2. The charging station evaluation method according to claim 1, characterized in that: The step of determining the fuzzy evaluation matrix corresponding to the charging station according to the preset first indicator fuzzy number and the first indicator scale matrix includes: Determining the degree of membership corresponding to each of the scale elements according to the first indicator fuzzy number; Each of the scale elements is replaced by the membership degree corresponding to the scale element to obtain the fuzzy evaluation matrix.

3. The charging station evaluation method according to claim 1, characterized in that: The determining the indicator weight corresponding to the charging station includes: Obtaining a second indicator scale matrix corresponding to the charging station, and determining a clarity evaluation matrix corresponding to the charging station according to a preset second indicator fuzzy number and the second indicator scale matrix; The geometric mean corresponding to each row of matrix elements in the clarity evaluation matrix is ​​calculated, and the geometric mean is used as the indicator weight.

4. The charging station evaluation method according to claim 3, characterized in that: After calculating the geometric mean corresponding to each row of matrix elements in the clarity evaluation matrix and before using the geometric mean as the indicator weight, the method further includes: According to the application scenario of the charging station, the indicator correlation degree of the charging station is set; The geometric mean is adjusted according to the indicator correlation and a preset adjustment factor.

5. The charging station evaluation method according to claim 3 or 4, characterized in that: After taking the geometric mean as the indicator weight, the method further comprises: Determining the maximum eigenvalue of the clarity evaluation matrix according to the indicator weights and the clarity evaluation matrix; Determining a consistency test index of the clarity evaluation matrix according to the order of the clarity evaluation matrix and the maximum eigenvalue of the clarity evaluation matrix; Determining a single-rank consistency test result of the clarity evaluation matrix according to the consistency test index of the clarity evaluation matrix and the random consistency test correction item; When the single-sort consistency check result of the clarity evaluation matrix fails, the second indicator scale matrix is ​​re-acquired, and the step of determining the clarity evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix is ​​returned to execute until the single-sort consistency check result of the clarity evaluation matrix is ​​successful.

6. The charging station evaluation method according to claim 5, characterized in that: The method further includes: Obtaining weight values ​​corresponding to a third indicator scale matrix, wherein the charging station indicators corresponding to the matrix elements in the third indicator scale matrix are superior indicators of the charging station indicators corresponding to the matrix elements in the second indicator scale matrix; Determine a total ranking weight value according to the weight value corresponding to the third indicator scale matrix and the indicator weight corresponding to the second indicator scale matrix, and determine a total ranking consistency test result of the clarification evaluation matrix according to the total ranking weight value and the third indicator scale matrix; When the total ranking consistency check result of the clarification evaluation matrix fails, the second indicator scale matrix is ​​re-acquired, and the step of returning to execute the step of determining the clarification evaluation matrix corresponding to the charging station according to the preset second indicator fuzzy number and the second indicator scale matrix.

7. The charging station evaluation method according to claim 1, characterized in that: The step of evaluating the charging station according to the comprehensive evaluation matrix includes: Calculating a first parameter value according to each matrix element in the comprehensive evaluation matrix; Calculating a second parameter value according to each matrix element in the comprehensive evaluation matrix and the index score corresponding to each matrix element; A score of the charging station is determined according to the first parameter value and the second parameter value, and the charging station is evaluated according to the score of the charging station.

8. An evaluation device for a charging station, characterized in that: include: An acquisition module, configured to acquire a first indicator scale matrix corresponding to the charging station, wherein the first indicator scale matrix includes a plurality of scale elements, one of the scale elements corresponds to a scoring indicator, and the scale element is used to indicate a relative score of the charging station for the scoring indicator among all charging stations in the same application scenario; A first determination module, configured to determine a fuzzy evaluation matrix corresponding to the charging station according to a preset first indicator fuzzy number and the first indicator scale matrix; A second determination module is used to determine the indicator weight corresponding to the charging station, and determine the comprehensive evaluation matrix corresponding to the charging station according to the fuzzy evaluation matrix and the indicator weight; An evaluation module is used to evaluate the charging station according to the comprehensive evaluation matrix.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the charging station evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the charging station evaluation method according to any one of claims 1 to 7 when executed.

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