Charging station key index determination method, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a charging station key index determination method, electronic equipment and a storage medium, and the method comprises the steps: obtaining a data value of each charging amount correlation index of a plurality of sample charging stations, obtaining a plurality of sample index data, and converting each sample index data into a fuzzy variable; determining a condition variable and a result variable based on the fuzzy variable; and determining a charging quantity key index influencing the charging quantity of the charging station from each charging quantity correlation index based on the condition variable and the result variable. According to the embodiment of the invention, the charging quantity key index of the charging station can be accurately determined.

Description

Technical Field

[0001] The present invention relates to the technical field of charging stations, and in particular to a method for determining key indicators of a charging station, an electronic device and a storage medium. Background Art

[0002] Electric vehicles are gradually attracting attention from the reform of the automobile industry due to their low energy consumption and intelligent features. Their large-scale application can effectively reduce automobile carbon emissions and dependence on oil resources. Therefore, the current market demand for electric vehicles is growing sharply. The growing trend of demand for electric vehicles urgently requires the construction of more and more efficient electric vehicle charging stations and other infrastructure to support the popularization and use of electric vehicles. Correctly configuring the key indicators related to the charging capacity of the charging station can help improve the service efficiency and user satisfaction of the charging station, and maximize the charging capacity of users at the charging station. Therefore, a solution that can accurately determine the key indicators of the charging station is urgently needed. Summary of the invention

[0003] The embodiments of the present invention provide a method for determining a key indicator of a charging station, an electronic device, and a storage medium, which can accurately determine the key indicator of the charging amount of the charging station.

[0004] In a first aspect, an embodiment of the present invention provides a method for determining key indicators of a charging station, including:

[0005] Acquire data values ​​of various charging quantity-related indicators of multiple sample charging stations to obtain multiple sample indicator data, and convert each sample indicator data into a fuzzy variable;

[0006] Determining conditional variables and outcome variables based on the fuzzy variables; and

[0007] Based on the condition variable and the result variable, a key charging quantity indicator that affects the charging quantity of the charging station is determined from the various charging quantity associated indicators.

[0008] In a second aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for determining key indicators of a charging station as described in any one of the embodiments of the present invention is implemented.

[0009] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for determining key indicators of a charging station as described in any one of the embodiments of the present invention.

[0010] The embodiments of the present invention provide a method for determining key indicators of a charging station, an electronic device, and a storage medium. The method obtains sample indicator data values ​​corresponding to each charging amount-related indicator, converts each sample indicator data value into a fuzzy variable, and then determines a conditional variable and a result variable based on the fuzzy variable. The charging amount-related indicators that affect the charging amount of the charging station are further determined from each charging amount-related indicator based on the conditional variable and the result variable. The method can comprehensively consider and analyze the complex interactive effects of each charging amount-related indicator on the charging amount of the charging station based on the idea of ​​fuzzy set quality comparison analysis, accurately evaluate the degree of influence of each charging amount-related indicator on the charging amount of the charging station, thereby accurately determining the key charging amount indicators of the charging station, thereby improving the service efficiency and user satisfaction of the charging station, and maximizing the user charging amount of the charging station.

[0011] In addition, the embodiment of the present invention is based on the fuzzy set mass comparison analysis method, which can identify and explain the complex mechanism behind the charging capacity of the charging station, especially how to achieve the optimal charging efficiency through the combination of factors; it can also perform effective analysis and provide reliable analysis results when the sample size is small, such as when there are data from dozens to hundreds of sample charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 It is a flow chart of a method for determining key indicators of a charging station provided by an embodiment of the present invention;

[0014] Figure 2 is another flow chart of the method for determining key indicators of a charging station provided by an embodiment of the present invention;

[0015] Figure 3 is another flow chart of the method for determining key indicators of a charging station provided by an embodiment of the present invention;

[0016] Figure 4 It is a structural schematic diagram of a device for determining key indicators of a charging station provided by an embodiment of the present invention;

[0017] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] 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.

[0020] Figure 1 A flowchart of a method for determining key indicators of a charging station provided in an embodiment of the present invention is provided. This embodiment can be applied to scenarios where configuration indicators of a target charging station are optimized or a new charging station is built. The method can be performed by a device for determining key indicators of a charging station provided in an embodiment of the present invention, and the device can be implemented in software and / or hardware. In a specific embodiment, the device can be integrated into an electronic device, such as a computer, a server, etc. The following embodiments will be described using the device integrated into an electronic device as an example. Reference Figure 1 , the method may specifically include the following steps:

[0021] Step 101, obtain the data values ​​of each charging amount related index of multiple sample charging stations to obtain multiple sample index data, and convert each sample index data into a fuzzy variable. This step can convert quantitative data into a fuzzy membership reflecting the degree of satisfaction, reflect the fuzziness and uncertainty of the index, and facilitate the determination of conditional variables and result variables based on fuzzy variables.

[0022] Specifically, the above-mentioned charging capacity-related indicators can be understood as indicators related to the charging capacity of the charging station.

[0023] Optionally, before step 101, a correlation index system including various charge capacity correlation indicators is established.

[0024] It is understandable that the above-mentioned related indicator system needs to take into account existing data and resources, and reserve space for future technological upgrades and scalability of charging stations, so as to ensure a comprehensive assessment of various factors affecting the charging capacity of charging stations, thereby facilitating the optimization of key factors and improving the operational efficiency and user satisfaction of charging stations.

[0025] Optionally, the above-mentioned charging amount related indicators are: distance D from the city center, traffic convenience T, number of charging times per day N, average charging time L, power of charging pile P, equipment failure rate F, average charging cost C, discount for each charging Discount, user rating S or user return rate Retention Rate.

[0026] Optionally, each of the above-mentioned charging capacity associated indicators is a charging capacity associated indicator of one indicator type among multiple indicator types, and the above-mentioned multiple types may include, for example: geographic location type, user behavior type, equipment performance type, cost data type and user evaluation type.

[0027] Specifically, the above-mentioned various charging capacity related indicators can be specifically shown in Table 1:

[0028]

[0029] Table 1

[0030] Specifically, each charge capacity related indicator may also be of the same type.

[0031] Optionally, the above-mentioned charging capacity related indicators can be determined by consulting experts, reading literature and / or analyzing cases.

[0032] Optionally, the process of obtaining multiple sample indicator data by acquiring the data values ​​of each charging quantity-related indicator of multiple sample charging stations includes: acquiring the original data values ​​of each charging quantity-related indicator from a storage location such as a database storing the original data values ​​of each charging quantity-related indicator; and preprocessing the original data values ​​of each charging quantity-related indicator to obtain sample indicator data corresponding to each charging quantity-related indicator.

[0033] Optionally, the process of preprocessing the original data values ​​of the above-mentioned various charging capacity related indicators includes: data cleaning, data classification and data correction of the original data values ​​of each charging capacity related indicator, so as to ensure that the data entering the next step of processing is reliable and valid.

[0034] Optionally, the above process of converting each sample index data into a fuzzy variable includes: normalizing each sample index data to obtain normalized sample index data corresponding to each sample index data; and based on a predetermined fuzziness membership function, fuzzifying each normalized sample index data to obtain a fuzzy variable corresponding to each sample index data.

[0035] Optionally, the above process of normalizing each sample indicator data includes: normalizing each sample indicator data using a minimum-maximum normalization method or a Z-score normalization method.

[0036] For example, the maximum value of the charging power of each sample charging station is 100kw, and the minimum value is 10kw. When the charging power of the current sample charging station is 50kw, the minimum-maximum normalization method is used to plan the charging power of the current sample charging station: (50-10) / (100-10)=0.44, that is, the normalized sample indicator data 0.44 corresponding to the charging power of the previous sample charging station is obtained.

[0037] It is understandable that the units or dimensions of various sample data may be inconsistent. Normalizing the data of each sample indicator can eliminate the differences in dimensions and value ranges between different indicators, so that each indicator can be compared and analyzed on the same scale.

[0038] Specifically, the fuzziness membership function may be determined based on experience, and may be a trigonometric function, a trapezoidal function, a linear function or a Gaussian function.

[0039] Optionally, the predetermined fuzziness membership function is the following trapezoidal membership function:

[0040]

[0041] Among them, μ(x′) represents the fuzzy variable; x′ represents the normalized sample index data; a, b, c and d represent the parameters of the membership function.

[0042] Step 102, determining conditional variables and result variables based on fuzzy variables. This step can help determine the key charging capacity indicators that affect the charging capacity of the charging station from various charging capacity-related indicators based on the conditional variables and result variables.

[0043] Specifically, the above-mentioned outcome variables can be understood as user satisfaction scores.

[0044] Optionally, the above process of determining conditional variables and result variables based on fuzzy variables includes: directly determining each fuzzy variable as a conditional variable, and performing weighted summation on each conditional variable based on the result variable weight corresponding to each indicator type to obtain the result variable of the corresponding sample charging station.

[0045] Optionally, the above process of determining conditional variables based on fuzzy variables includes: based on the conditional variable weights corresponding to each fuzzy variable, weighted summing up each fuzzy variable of the same type of charging quantity associated indicators of the same sample charging station to obtain the conditional variables corresponding to the corresponding type of charging quantity associated indicators.

[0046] Specifically, the above-mentioned conditional variable weights and outcome variable weights can be obtained through expert scoring, hierarchical analysis method or entropy method.

[0047] Step 103, based on the conditional variables and the result variables, determine the key charging quantity indicators that affect the charging quantity of the charging station from the various charging quantity-related indicators. This step is based on step 101 and step 102, by obtaining the sample indicator data values ​​corresponding to the various charging quantity-related indicators, and converting the various sample indicator data values ​​into fuzzy variables, and then determining the conditional variables and the result variables based on the fuzzy variables, and further determining the charging quantity-related indicators that affect the charging quantity of the charging station from the various charging quantity-related indicators based on the conditional variables and the result variables. Based on the idea of ​​fuzzy set quality comparison analysis, it can comprehensively consider and analyze the complex interactive effects of various charging quantity-related indicators on the charging quantity of the charging station, accurately evaluate the degree of influence of various charging quantity-related indicators on the charging quantity of the charging station, and thus accurately determine the key charging quantity indicators of the charging station, thereby improving the service efficiency and user satisfaction of the charging station, and maximizing the charging quantity of users of the charging station.

[0048] In an optional specific example of the present invention, the plurality of sample charging stations include a plurality of first sample charging stations and a plurality of second sample charging stations.

[0049] Optionally, the process of determining the key charging capacity indicator that affects the charging capacity of the charging station from various charging capacity-related indicators based on the conditional variables and the result variables includes:

[0050] Based on the condition variables and result variables corresponding to multiple first sample charging stations, key indicators of charging capacity to be verified are determined from various charging capacity-related indicators; based on the condition variables and result variables corresponding to multiple second sample charging stations, verification reference indicators are determined from various charging capacity-related indicators; based on the verification reference indicators, the key indicators of charging capacity to be verified are verified, and after the verification is passed, the key indicators of the charging station to be verified are determined as the key indicators of charging capacity.

[0051] Optionally, the process of verifying the key indicator of the charging amount to be verified based on the verification reference indicator includes: verifying whether the key indicator of the charging amount to be verified is consistent with the verification reference indicator.

[0052] It can be understood that by verifying the consistency of the method for determining key indicators of charging stations provided by the embodiment of the present invention on different data sets, the accuracy of the method of the present invention can be ensured.

[0053] The following further introduces the method for determining key indicators of a charging station provided by an embodiment of the present invention.

[0054] Optionally, when each charge capacity associated indicator is a charge capacity associated indicator of one type among multiple types, such as Figure 2 As shown, Figure 1 Step 102 in may include the following steps:

[0055] Step 1021 , based on the conditional variable weights corresponding to the fuzzy variables, weighted sum is performed on the fuzzy variables of the same type of charging amount associated indicators of the same sample charging station to obtain the intermediate conditional variables corresponding to each indicator type of each charging station.

[0056] In a specific example, the weights of the conditional variables corresponding to the distance from the city center and the degree of transportation convenience in the geographical location indicators are 0.4 and 0.6 respectively, and the fuzzy variables corresponding to the distance from the city center and the degree of transportation convenience of the current sample power station are 0.5 and 0.8 respectively; then the intermediate conditional variable of the geographical location indicator of the current sample power station is: 0.5×0.4+0.6×0.8=0.68.

[0057] Step 1022: Based on the weight of the result variable corresponding to each indicator type, weighted sum is performed on each intermediate condition variable corresponding to each sample charging station to obtain the intermediate result variable corresponding to the corresponding sample charging station.

[0058] In a specific example, the weights of the result variables of the geographic location class, user behavior class, equipment performance class, cost data class and user evaluation class are 0.3, 0.1, 0.1, 0.2 and 0.3 respectively. The intermediate conditional variables of the geographic location class, user behavior class, equipment performance class, cost data class and user evaluation class of the current sample power station are 0.88, 0.55, 0.68, 0.46 and 0.76 respectively. Then the intermediate result variable of the current sample power station is 0.88×0.3+0.55×0.1+0.68×0.1+0.46×0.2+0.76×0.3=0.707.

[0059] Step 1023: determining a condition variable and a result variable based on the intermediate condition variable and the intermediate result variable.

[0060] Optionally, the above process of determining conditional variables and result variables based on intermediate conditional variables and intermediate result variables includes: performing outlier detection and consistency check on the intermediate conditional variables and intermediate result variables; adjusting and optimizing the fuzzy membership function, conditional variable weights and result variable weights based on the outlier detection results and the consistency check results; and determining the conditional variables and result variables based on the adjusted and optimized fuzzy membership function, conditional variable weights and result variable weights.

[0061] Optionally, the process of adjusting and optimizing the fuzzy membership function, the conditional variable weight, and the result variable weight based on the outlier detection result and the consistency check result includes:

[0062] When the outlier detection result is that an outlier exists or the consistency check result is that the consistency is lower than the first consistency threshold, the fuzzy membership function, the conditional variable weight and the result variable weight are adjusted and optimized.

[0063] Optionally, an embodiment of the present invention also includes, when the above-mentioned outlier detection result is that there is no outlier or the consistency check result is that the consistency is not lower than the first consistency threshold, determining the corresponding intermediate conditional variables as the conditional variables corresponding to each indicator type of each charging station, and determining the corresponding intermediate result variables as the intermediate result variables corresponding to each sample charging station.

[0064] Specifically, the first consistency threshold may be set based on expert experience data.

[0065] Specifically, the corresponding intermediate condition variables may also be directly determined as condition variables corresponding to each indicator type of each charging station.

[0066] Specifically, the corresponding intermediate result variable may also be determined as the intermediate result variable corresponding to each sample charging station.

[0067] The implementation of the present invention can classify and fuse fuzzy variables, that is, fuse fuzzy variables of the same indicator type of the same charging station into conditional variables of the corresponding type of the corresponding charging station, which can be beneficial to consider and analyze more charging amount related indicators. In addition, by performing outlier detection and consistency check on intermediate conditional variables and intermediate result variables, and adjusting and optimizing the fuzzy membership function, conditional variable weights and result variable weights based on the outlier detection results and consistency check results, it can be beneficial to improve the accuracy of obtaining key indicators.

[0068] The following further introduces the method for determining key indicators of a charging station provided by an embodiment of the present invention.

[0069] Optionally, the process of determining the key charging capacity indicator that affects the charging capacity of the charging station from various charging capacity-related indicators based on the conditional variables and the result variables includes:

[0070] Step 1031, construct a truth table based on the condition variables and the result variables.

[0071] Optionally, the above process of constructing a truth table based on conditional variables and result variables includes: determining a conditional logic state value based on the conditional variable and a preset first variable threshold; determining a result logic state value based on the result variable and a preset second variable threshold; and constructing a truth table based on the conditional logic state value and the result logic state value.

[0072] Optionally, based on setting the first variable threshold λ j and the second variable threshold λ Y , the conditional variables and result variables are converted into binary logic states through the following formula, and the conditional logic state value and result logic state value are obtained accordingly:

[0073]

[0074] Among them, C ij Indicates the conditional logic state value; Y i Indicates the result logic state value; indicates μ ij represents the jth conditional variable of the i-th charging station; μ Yi Represents the outcome variable corresponding to the i-th charging station.

[0075] In a specific example, the truth table is shown in Table 2:

[0076]

[0077] Table 2

[0078] Step 1032, determining multiple condition combinations based on the truth table.

[0079] Specifically, when the number of the above condition variables is n and the number of condition combinations is m, it can be simplified based on Boolean algebra and logic simplification method as follows:

[0080] Y1=f1(C1,C2...C n )

[0081] Y2=f2(C1,C2...C n )

[0082] …

[0083] Y m =f m (C1,C2...C n )

[0084] Step 1033, determining the key indicator of the charging capacity based on a combination of multiple conditions.

[0085] Optionally, the above process of determining the key indicator of charging capacity based on multiple condition combinations includes: calculating the consistency and coverage corresponding to each condition combination, and determining a target condition combination from multiple condition combinations based on the consistency and coverage of each condition combination.

[0086] Specifically, a condition combination in which the corresponding consistency is greater than the second consistency threshold and the coverage is greater than the coverage threshold can be determined as the target condition combination.

[0087] Specifically, the second consistency threshold and coverage threshold can be set based on expert experience.

[0088] Optionally, the process of determining the key indicator of charging capacity based on the target condition combination includes:

[0089] Perform confirmatory adjustments on the fuzzy membership function, conditional variable weights, and outcome variable weights, and obtain the range of consistency and coverage corresponding to the target condition combination during the confirmatory adjustment process; and

[0090] When the variation range of consistency and coverage meets the preset requirements, the key indicator of charging capacity is determined based on the logical expression corresponding to the target condition combination, otherwise the target condition combination is re-determined until the variation range of consistency and coverage meets the preset requirements.

[0091] It can be understood that by verifying the sensitivity of consistency and coverage to fuzzy membership functions, conditional variable weights and result variable weights, the robustness of the method for determining key indicators of charging stations provided in an embodiment of the present invention can be verified, thereby ensuring the accuracy of the key indicators of charging capacity ultimately determined using the method of the present invention.

[0092] In a specific example, when the logical expression corresponding to the target condition combination is:

[0093] Y=D′·T·P+N·L′·S

[0094] Among them, Y represents high user satisfaction; D′ indicates that the charging station is close to the city center; T represents high traffic convenience; P represents high charging pile power; N represents many charging times per day; L′ represents short average charging time; and S represents high user evaluation.

[0095] The key indicators of charging capacity are identified as:

[0096] Geographical location indicators: proximity to the city center and convenient transportation T improve the convenience of users reaching the charging station; equipment performance indicators: high-power equipment P can shorten the charging time and improve service efficiency; user behavior indicators: high charging frequency N and short charging time L′ indicate that the charging station has a high operating efficiency; user evaluation indicators: high user evaluation S directly reflects the user's recognition and loyalty.

[0097] In practical applications, the configuration indicators of charging stations can be optimized or set based on the above-mentioned key indicators of charging volume. For example, geographical location optimization (D′, T) can be performed: charging stations can be arranged in areas close to the city center and with convenient transportation to improve accessibility; equipment performance can be improved (P): high-power charging piles can be installed, equipment maintenance processes can be optimized, equipment failure rates can be reduced, and service stability can be ensured; user behavior indicators (N, L′) can be optimized: the number of charging piles can be increased to meet peak demand, the average charging time can be shortened, and service efficiency can be improved; user satisfaction can be improved (S): high-quality services can be provided, user relationship management can be strengthened, membership points or discount plans can be launched, and user evaluation and return rate can be improved.

[0098] Figure 4 is a structural diagram of a device for determining key indicators of a charging station provided in an embodiment of the present invention, and the device is suitable for executing a method for determining key indicators of a charging station provided in an embodiment of the present invention. Figure 4 As shown, the device may specifically include:

[0099] The fuzzy variable acquisition module 401 is used to obtain the data values ​​of each charging amount related indicator of multiple sample charging stations to obtain multiple sample indicator data, and convert each sample indicator data into a fuzzy variable. It can convert quantitative data into a fuzzy membership degree reflecting the degree of satisfaction, reflect the fuzziness and uncertainty of the indicator, and facilitate the determination of conditional variables and result variables based on fuzzy variables.

[0100] Optionally, the indicator type of all charging amount related indicators includes multiple indicator types.

[0101] Optionally, the plurality of sample charging stations include a plurality of first sample charging stations and a plurality of second sample charging stations.

[0102] Optionally, the above-mentioned fuzzy variable acquisition module 401 can be specifically used to normalize each sample index data to obtain normalized sample index data corresponding to each sample index data; and based on a predetermined fuzziness membership function, fuzzy process each normalized sample index data to obtain a fuzzy variable corresponding to each sample index data.

[0103] The condition and result variable acquisition module 402 is used to determine the condition variables and result variables based on the fuzzy variables, and can help determine the key charging quantity indicators that affect the charging quantity of the charging station from various charging quantity related indicators based on the condition variables and result variables.

[0104] Optionally, the above-mentioned condition and result variable acquisition module 402 can be specifically used to perform weighted summation of various fuzzy variables of the same type of charging amount related indicators of the same sample charging station based on the condition variable weights corresponding to each fuzzy variable, so as to obtain intermediate condition variables corresponding to each indicator type of each charging station; perform weighted summation of various intermediate condition variables corresponding to each sample charging station based on the result variable weights corresponding to each indicator type, so as to obtain intermediate result variables corresponding to the corresponding sample charging station; and determine condition variables and result variables based on the intermediate condition variables and intermediate result variables.

[0105] Optionally, the above-mentioned condition and result variable acquisition module 402 can be specifically used to perform outlier detection and consistency check on intermediate condition variables and intermediate result variables; adjust and optimize the fuzzy membership function, condition variable weights and result variable weights based on the outlier detection results and consistency check results; and determine the condition variables and result variables based on the adjusted and optimized fuzzy membership function, condition variable weights and result variable weights.

[0106] The key indicator acquisition module 403 is used to determine the key charging amount indicator that affects the charging amount of the charging station from various charging amount related indicators based on the condition variable and the result variable.

[0107] Optionally, the key indicator acquisition module 403 can be specifically used to construct a truth table based on conditional variables and result variables; determine multiple conditional combinations based on the truth table; and determine the key indicators of charging capacity based on multiple conditional combinations. In combination with modules 401 and 402, by obtaining the sample indicator data values ​​corresponding to each charging capacity-related indicator, and converting each sample indicator data value into a fuzzy variable, then determining the conditional variables and result variables based on the fuzzy variables, and further determining the charging capacity-related indicators that affect the charging capacity of the charging station from each charging capacity-related indicator based on the conditional variables and result variables, it can be based on the idea of ​​fuzzy set quality comparison analysis, comprehensively consider and analyze the complex interactive effects of each charging capacity-related indicator on the charging capacity of the charging station, accurately evaluate the degree of influence of each charging capacity-related indicator on the charging capacity of the charging station, and thus accurately determine the key indicators of the charging capacity of the charging station, thereby improving the service efficiency and user satisfaction of the charging station, and maximizing the charging capacity of users of the charging station.

[0108] Optionally, the key indicator acquisition module 403 can be specifically used to calculate the consistency and coverage corresponding to each condition combination, and determine a target condition combination from multiple condition combinations based on the consistency and coverage of each condition combination.

[0109] Optionally, the key indicator acquisition module 403 can be specifically used to determine the key indicator of charging capacity to be verified from various charging capacity related indicators based on the condition variables and result variables corresponding to multiple first sample charging stations; determine the verification reference indicator from various charging capacity related indicators based on the condition variables and result variables corresponding to multiple second sample charging stations; and verify the key indicator of charging capacity to be verified based on the verification reference indicator, and determine the key indicator of the charging station to be verified as the key indicator of charging capacity after the verification is passed.

[0110] Optionally, the key indicator acquisition module 403 can be specifically used to perform verification adjustments on the fuzzy membership function, conditional variable weights and result variable weights, and obtain the consistency and coverage variation range corresponding to the target condition combination during the verification adjustment process; and when the consistency and coverage variation range meets the preset requirements, determine the key indicator of charging capacity based on the logical expression corresponding to the target condition combination, otherwise redetermine the target condition combination until the consistency and coverage variation range meets the preset requirements.

[0111] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0112] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining key indicators of a charging station provided in any of the above embodiments is implemented.

[0113] An embodiment of the present invention further provides a computer-readable medium having a computer program stored thereon, and when the program is executed by a processor, the method for determining key indicators of a charging station provided in any of the above embodiments is implemented.

[0114] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements a method for determining a key indicator of a charging station as described in any one of the embodiments of the present invention.

[0115] Reference below Figure 5, which shows a schematic diagram of the structure of a computer system 500 of an electronic device suitable for implementing an embodiment of the present invention. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0116] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0117] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0118] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0119] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0120] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] The modules and / or units involved in the embodiments of the present invention may be implemented in software or hardware. The modules and / or units described may also be set in a processor, for example, it may be described as: a processor including: a fuzzy variable acquisition module, a condition and result variable acquisition module and a key indicator acquisition module. The names of these modules do not constitute limitations on the modules themselves in some cases.

[0122] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: obtaining data values ​​of each charging quantity-related indicator of multiple sample charging stations to obtain multiple sample indicator data, and converting each sample indicator data into a fuzzy variable; determining conditional variables and result variables based on the fuzzy variables; and determining the key charging quantity indicators that affect the charging quantity of the charging station from each charging quantity-related indicator based on the conditional variables and the result variables.

[0123] 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 may occur depending on design requirements and other indicators. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining key indicators of a charging station, characterized in that: include: Acquire data values ​​of various charging quantity-related indicators of multiple sample charging stations to obtain multiple sample indicator data, and convert each sample indicator data into a fuzzy variable; Determine conditional variables and result variables based on the fuzzy variables; as well as Based on the condition variable and the result variable, a key charging quantity indicator that affects the charging quantity of the charging station is determined from the various charging quantity associated indicators.

2. The method for determining key indicators of a charging station according to claim 1, characterized in that: The step of converting each sample indicator data into a fuzzy variable includes: Normalizing each of the sample index data to obtain normalized sample index data corresponding to each of the sample index data; and Based on the predetermined fuzzy membership function, each normalized sample index data is fuzzified to obtain the fuzzy variables corresponding to each sample index data.

3. The method for determining key indicators of a charging station according to claim 2, characterized in that: The indicator types of all the charging amount related indicators include multiple indicator types; the determining of conditional variables and result variables based on the fuzzy variables includes: Based on the weights of the conditional variables corresponding to each fuzzy variable, the weighted sum of the fuzzy variables of the same type of charging capacity-related indicators of the same sample charging station is performed to obtain the intermediate conditional variables corresponding to each indicator type of each charging station; Based on the result variable weights corresponding to each indicator type, weighted summation is performed on each of the intermediate condition variables corresponding to the same sample charging station to obtain an intermediate result variable corresponding to each sample charging station; and The condition variable and the result variable are determined based on the intermediate condition variable and the intermediate result variable.

4. The method for determining key indicators of a charging station according to claim 3, characterized in that: The determining the condition variable and the result variable based on the intermediate condition variable and the intermediate result variable comprises: Performing outlier detection and consistency check on the intermediate condition variables and the intermediate result variables; Based on the outlier detection result and the consistency check result, the fuzziness membership function, the condition variable weight and the result variable weight are adjusted and optimized; and The condition variable and the result variable are determined based on the adjusted and optimized fuzzy membership function, the condition variable weight and the result variable weight.

5. The method for determining key indicators of a charging station according to any one of claims 1 to 4, characterized in that: The determining of the key charging amount index that affects the charging amount of the charging station from the various charging amount associated indexes based on the condition variable and the result variable includes: constructing a truth table based on the conditional variables and the result variables; determining a plurality of condition combinations based on the truth table; and The key indicator of the charge capacity is determined based on the combination of the multiple conditions.

6. The method for determining key indicators of a charging station according to claim 5, characterized in that: The determining the key indicator of the charge capacity based on the combination of the multiple conditions includes: Calculating the consistency and coverage corresponding to each condition combination, and determining a target condition combination from the multiple condition combinations based on the consistency and coverage of each condition combination; and The key indicator of the charging capacity is determined based on the target condition combination.

7. The method for determining key indicators of a charging station according to claim 6, characterized in that: The determining the key indicator of the charging amount based on the target condition combination includes: Performing confirmatory adjustments on the fuzzy membership function, the condition variable weights, and the result variable weights, and obtaining a variation range of consistency and coverage corresponding to the target condition combination during the confirmatory adjustment; and When the variation range of the consistency and coverage rate meets the preset requirements, the key indicator of the charging amount is determined based on the logical expression corresponding to the target condition combination, otherwise the target condition combination is re-determined until the variation range of the consistency and coverage rate meets the preset requirements.

8. The method for determining key indicators of a charging station according to claim 1, characterized in that: The plurality of sample charging stations include a plurality of first sample charging stations and a plurality of second sample charging stations; The determining of the key charging amount index that affects the charging amount of the charging station from the various charging amount associated indexes based on the condition variable and the result variable includes: Based on the condition variables and result variables corresponding to the plurality of first sample charging stations, determining the key charging capacity indicator to be verified from the respective charging capacity associated indicators; Determining a verification reference indicator from the respective charging amount-related indicators based on the condition variables and result variables corresponding to the plurality of second sample charging stations; and The key indicator of the charging amount to be verified is verified based on the verification reference indicator, and after the verification passes, the key indicator of the charging station to be verified is determined as the key indicator of the charging amount.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for determining key indicators of a charging station as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for determining key indicators of a charging station as described in any one of claims 1 to 8 is implemented.

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