Charging station load characteristic prediction method, device, equipment and storage medium

Abstract

The present disclosure relates to a charging station load feature prediction method, device, equipment and storage medium. The present disclosure can fully consider the charging behavior characteristics of users of various vehicle types by combining the total satisfaction of the users of various vehicle types to the charging station after charging at the charging station and the attributes of the charging station itself, taking the charging load curve of the charging station when the total satisfaction of the users to the charging station is maximum under the preset charging price of the charging station as the final charging load curve of the charging station, improving the accuracy of the charging station load feature prediction, thereby providing scientific guidance for the operation of the charging station.

Description

Technical Field

[0001] This disclosure relates to the field of power technology, and in particular to a method, apparatus, equipment and storage medium for predicting the load characteristics of charging stations. Background Technology

[0002] New energy vehicles, as a green and clean means of transportation and a superior alternative to gasoline vehicles, are gradually becoming a new development direction for the future automotive industry. The charging load of new energy vehicles is flexible, and their charging behavior is highly unpredictable. Fully utilizing the adjustment capabilities of large-scale new energy vehicle charging loads will provide charging station operators with more diversified profit-making and control methods.

[0003] Currently, the charging load characteristics of charging stations are mainly estimated through their own attributes (such as charging equipment capacity and number of charging devices) to guide charging station operators in the operation and control of charging stations. However, since only the attributes of the charging station itself are considered, the charging load characteristics obtained are relatively limited, and the charging load curve reflecting the charging load characteristics of the charging station has a large gap with the actual situation, which is not accurate enough. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a method, apparatus, equipment, and storage medium for predicting the load characteristics of charging stations.

[0005] The first aspect of this disclosure provides a method for predicting the load characteristics of charging stations, including:

[0006] Based on the distribution pattern of historical vehicle charging data of charging stations, the target charging data of a preset number of target vehicles in a preset future time period is determined, and the target charging data of the target vehicles satisfies the distribution pattern.

[0007] Based on the target charging data, construct the initial charging load curve of the charging station corresponding to a preset number of target vehicles in a preset future time period;

[0008] Based on the preset charging price of each target vehicle at the charging station, the arrival time of each target vehicle at the charging station, the charging time and charging cost at the charging station, describe the overall user satisfaction of a preset number of target vehicles with the charging station under the preset charging price.

[0009] With the goal of maximizing overall satisfaction under the preset charging price, the load parameters in the initial charging load curve are optimized to obtain the corrected charging load curve of the charging station. The corrected charging load curve maximizes overall satisfaction under the preset charging price.

[0010] A second aspect of this disclosure provides a charging station load characteristic prediction device, comprising:

[0011] The first determining module is used to determine the target charging data of a preset number of target vehicles at the charging station for a preset future time period based on the distribution pattern of the historical vehicle charging data of the charging station. The target charging data of the target vehicles satisfies the distribution pattern.

[0012] The module is used to construct the initial charging load curve of the charging station corresponding to a preset number of target vehicles in a preset future time period based on the target charging data.

[0013] The description module is used to describe the total user satisfaction of a preset number of target vehicles with the charging station under the preset charging price, based on the arrival time of each target vehicle at the charging station, the charging time and charging cost before and after each target vehicle responds to the preset charging price of the charging station.

[0014] The solution module is used to optimize the load parameters in the initial charging load curve with the goal of maximizing the overall satisfaction under the preset charging price, and obtain the corrected charging load curve of the charging station. The corrected charging load curve maximizes the overall satisfaction under the preset charging price.

[0015] A third aspect of this disclosure provides a computer device including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, can implement the charging station load characteristic prediction method of the first aspect described above.

[0016] The fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the charging station load characteristic prediction method of the first aspect described above.

[0017] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0018] This disclosure determines the target charging data for a predetermined number of target vehicles at a charging station within a preset future time period by analyzing the distribution patterns of historical vehicle charging data at the charging station. The target charging data for these vehicles conforms to a distribution pattern. Based on this target charging data, an initial charging load curve for the charging station corresponding to the predetermined number of target vehicles within the preset future time period is constructed. Based on the arrival time, charging duration, and charging cost of each target vehicle before and after responding to the preset charging price at the charging station, the overall user satisfaction of the predetermined number of target vehicles at the preset charging price is described. With the objective of maximizing the overall satisfaction at the preset charging price, the load parameters in the initial charging load curve are optimized to obtain a corrected charging load curve for the charging station. The corrected charging load curve maximizes the overall satisfaction at the preset charging price. This disclosure can combine the overall user satisfaction of different vehicle types after charging at the charging station with the attributes of the charging station itself. The final charging load curve of the charging station is the one where the overall user satisfaction is maximized at the preset charging price. This fully considers the charging behavior characteristics of different vehicle types, improves the accuracy of charging station load characteristic prediction, and thus provides scientific guidance for charging station operation. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0020] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for predicting the load characteristics of a charging station provided in an embodiment of this disclosure;

[0022] Figure 2 This is a flowchart of a method for determining target charging data provided in an embodiment of this disclosure;

[0023] Figure 3 This is a flowchart of a comprehensive satisfaction determination method provided in an embodiment of this disclosure;

[0024] Figure 4 This is a flowchart of a method for determining a modified charging load curve provided in an embodiment of this disclosure;

[0025] Figure 5 This is a schematic diagram of the structure of a charging station load characteristic prediction device provided in an embodiment of this disclosure;

[0026] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0027] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0028] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0029] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0031] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0032] The charging station load characteristic prediction method and other methods provided in this disclosure can be executed by a computer device. This device can be understood as any device with processing and computing capabilities. This device may include, but is not limited to, mobile terminals such as smartphones, laptops, and tablet computers, as well as fixed electronic devices such as digital TVs and desktop computers.

[0033] To better understand the inventive concept of the embodiments of this disclosure, the technical solutions of the embodiments of this disclosure will be described below in conjunction with exemplary embodiments.

[0034] Figure 1 This is a flowchart of a method for predicting the load characteristics of a charging station according to an embodiment of this disclosure. This method can be executed by a computer device, such as... Figure 1 As shown, the charging station load characteristic prediction method provided in this embodiment includes the following steps:

[0035] Step 110: Based on the distribution pattern of historical vehicle charging data of the charging station, determine the target charging data of a preset number of target vehicles at the charging station in a preset future time period, wherein the target charging data satisfies the distribution pattern.

[0036] In this embodiment of the disclosure, a computer device can determine the target charging data of a preset number of target vehicles at a charging station for a preset future time period based on the distribution pattern of historical vehicle charging data at the charging station, wherein the target charging data satisfies the distribution pattern.

[0037] Historical vehicle charging data can be understood as vehicle charging data at charging stations during a certain historical period (such as the past year).

[0038] The preset future time period can be understood as a preset future time period, such as the next day or the next week. It can be set as needed, and there is no limitation here.

[0039] The preset quantity can be set as needed, such as 100, but there is no limit here.

[0040] Step 120: Based on the target charging data, construct the initial charging load curve of the charging station corresponding to a preset number of target vehicles in a preset future time period.

[0041] In this embodiment of the disclosure, the initial charging load curve of the charging station can be understood as the total average charging power of the charging station in each time period.

[0042] Step 130: Based on the arrival time of each target vehicle at the charging station, the charging duration and charging cost at the charging station before and after the preset charging price for each target vehicle, describe the overall user satisfaction of the preset number of target vehicles with the charging station under the preset charging price.

[0043] In this embodiment of the disclosure, the computer device can describe the overall satisfaction of users of a preset number of target vehicles with the charging station under the preset charging price, based on the arrival time of each target vehicle at the charging station, the charging time and charging cost at the charging station before and after each target vehicle responds to the preset charging price of the charging station.

[0044] The preset charging price is the current charging price set by the charging station operator. The preset charging price can be set by the charging station operator as needed, and there is no restriction here.

[0045] Step 140: With the goal of maximizing the overall satisfaction under the preset charging price, optimize the load parameters in the initial charging load curve to obtain the corrected charging load curve of the charging station. The corrected charging load curve maximizes the overall satisfaction under the preset charging price.

[0046] In this embodiment of the disclosure, the load parameter in the initial charging load curve can be understood as the total average charging power of vehicles charging in the charging station for each time period.

[0047] Therefore, by combining the overall satisfaction of users of different vehicle types with the charging station after charging and the attributes of the charging station itself, the charging load curve of the charging station when the overall user satisfaction is maximized under the preset charging price can be used as the final charging load curve of the charging station. This can fully take into account the charging behavior characteristics of users of different vehicle types, improve the accuracy of charging station load characteristic prediction, and thus provide scientific guidance for the operation of charging stations.

[0048] In some embodiments of this disclosure, the above-mentioned determination of the target charging data of a preset number of target vehicles at the charging station based on the distribution pattern of historical vehicle charging data of the charging station in a preset future time period can be executed by computer equipment. Figure 2 A flowchart of a method for determining target charging data is provided, such as... Figure 2 As shown, the target charging data determination method provided in this embodiment includes the following steps:

[0049] Step 210: Classify the historical vehicle charging data of the charging station by vehicle type to obtain the vehicle charging data corresponding to each vehicle type.

[0050] Vehicle types can include private cars, commercial vehicles, etc.

[0051] Step 220: From the vehicle charging data corresponding to each vehicle type, obtain the arrival time of each vehicle at the charging station, the battery state of charge of each vehicle at the charging station, the charging amount of each vehicle at the charging station, and the average charging power.

[0052] For example, historical vehicle charging data can be represented as:

[0053]

[0054] in, Let i be the arrival time of the i-th vehicle at the charging station;

[0055] The state of charge of the battery when the i-th vehicle arrives at the charging station;

[0056] E(i) represents the amount of charge received by the i-th vehicle at the charging station.

[0057] P(i) represents the average charging power of the i-th vehicle at the charging station;

[0058] Let i be the vehicle type of the i-th vehicle, which can be divided into two categories: commercial vehicles and private vehicles.

[0059] N represents the number of vehicles.

[0060] Step 230: Based on the arrival times of vehicles of each vehicle type, construct the distribution characteristics of arrival times of vehicles of each vehicle type.

[0061] The distribution characteristics of arrival times can be understood as the distribution patterns of arrival times.

[0062] For example, when the vehicle type is a commercial vehicle (CV), the corresponding vehicle arrival time dataset can be:

[0063] ;

[0064] When the vehicle type is private car (PV), the corresponding arrival time dataset for the vehicle can be:

[0065] Based on the data grouped by vehicle type, the statistics show that the number of vehicles arriving at time t0 is the minimum. Therefore, the arrival times of the vehicles are transformed, and...

[0066] ;

[0067] Obtain the arrival time after time conversion;

[0068] Let the vehicle arrival change times be defined in the data for each vehicle type. It follows a Gaussian distribution with specific parameters, i.e. , The probability density function of its arrival time distribution characteristics is as follows:

[0069] ;

[0070] Based on historical vehicle charging data from charging stations, the parameters of the aforementioned Gaussian distribution were obtained using the maximum likelihood estimation method:

[0071] For vehicles classified as commercial vehicles, the characteristic parameters of their arrival time distribution are as follows: , It can be obtained from the following formula:

[0072] ;

[0073] Where, n CV Indicates the number of vehicles in operation;

[0074] For private cars, the characteristic parameters of their arrival time distribution are... , It can be obtained from the following formula:

[0075] ;

[0076] Where, n PV This indicates the number of private cars.

[0077] Step 240: Based on the battery state of charge of vehicles of each vehicle type at each arrival time, construct the battery state of charge distribution characteristics of vehicles of each vehicle type at each preset arrival time.

[0078] The preset arrival time can be set as needed, such as 0:00-6:00, 6:00-18:00 and 18:00-24:00, without any limitation.

[0079] The State of Charge (SOC) is the ratio between the current remaining capacity of a battery and its capacity when fully charged. This ratio is usually expressed as a percentage, ranging from 0 to 100%, and intuitively reflects the state of the battery from being fully discharged (SOC is 0) to being fully charged (SOC is 100%).

[0080] For example, the historical vehicle charging data of charging stations can be further divided into multiple groups for commercial vehicles and private cars. Here, we take three groups for each group based on arrival time periods as an example. The historical vehicle charging data will be further divided into Group 6:

[0081]

[0082] The subset of data regarding vehicle arrival SOC in the above 6 sets of data is defined as follows: ;

[0083] ;

[0084] ;

[0085] Assume that the vehicle arrival SOC in each of the above data sets follows a Gaussian distribution with specific parameters, i.e.

[0086]

[0087]

[0088] The SOC probability density functions for vehicles arriving at charging stations between 0:00 and 6:00, 6:00 and 18:00, and 18:00 and 24:00 are as follows:

[0089] ;

[0090] The SOC probability density functions for private cars arriving at charging stations between 0:00 and 6:00, 6:00 and 18:00, and 18:00 and 24:00 are as follows:

[0091] ;

[0092] Based on historical vehicle charging data from charging stations, the parameters of the aforementioned Gaussian distribution were obtained using the maximum likelihood estimation method:

[0093] For operating vehicles arriving between 0:00 and 6:00, 6:00 and 18:00, and 18:00 and 24:00, the characteristic parameters of their SOC distribution are... , It can be obtained through the following formulas:

[0094] ;

[0095] ;

[0096] ;

[0097] For private cars arriving between 0:00 and 6:00, 6:00 and 18:00, and 18:00 and 24:00, the characteristic parameters of their SOC distribution are... and It can be obtained through the following formulas:

[0098] ;

[0099] ;

[0100] ;

[0101] .

[0102] Step 250: Based on the charging amount of vehicles of each vehicle type under each battery state of charge, construct the charging amount distribution characteristics of vehicles of each vehicle type under each battery state of charge.

[0103] For example, firstly, based on the SOC distribution characteristics of vehicles arriving at each preset arrival time, the data for commercial vehicles and private cars are further divided into multiple groups. Here, we take a three-group SOC as an example. Historical vehicle charging data can be further divided into 18 groups as shown in the table below:

[0104] ;

[0105] by Taking two groups as an example, the definitions of vehicle charging data upon arrival at the station in the above groups are as follows, and the relevant definitions for other groups can be deduced by analogy.

[0106] ;

[0107] Assume that the charging amount of vehicles coming to this station in each of the above data sets follows a Gaussian distribution with specific parameters, i.e.

[0108] ;

[0109] Based on historical vehicle charging data from charging stations, the maximum likelihood estimation method is used to estimate the characteristic parameters of the charging quantity distribution of operating vehicles under different battery states of charge. and Characteristic parameters of the charging amount distribution of private cars under different battery states of charge. and ,Right now:

[0110] .

[0111] Step 260: Based on the average charging power of vehicles of each vehicle type under each battery state of charge, construct the distribution characteristics of the average charging power of vehicles of each vehicle type under each battery state of charge.

[0112] For example, historical vehicle charging data can be divided into 18 groups based on vehicle type, arrival time, and arrival SOC, as shown in the table below:

[0113]

[0114] ;

[0115] by Taking two groups as examples, the average charging power of the vehicles arriving at the station for the above groups is... Average charging power of private cars at the station The definitions are as follows, and the relevant definitions for other groups can be deduced similarly:

[0116] ;

[0117] Assume that the average charging power of vehicles arriving at the station in each of the above data sets follows a Gaussian distribution with specific parameters, namely:

[0118]

[0119] The probability density function of the average charging power of vehicles arriving at the station is as follows:

[0120] ;

[0121] Based on historical vehicle charging data from charging stations, the parameters of the Gaussian distribution are estimated using the maximum likelihood estimation method. These parameters represent the characteristic parameters of the average charging power distribution of vehicles (operating vehicles) under various battery states of charge. and Characteristic parameters of the average charging power distribution of vehicles of the private car type under various battery states of charge. and They are respectively:

[0122] .

[0123] Step 270: Based on the arrival time distribution characteristics, battery state of charge distribution characteristics, charging amount distribution characteristics, and average charging power distribution characteristics, determine the target arrival time, target charging amount, and target average charging power of each target vehicle in a preset number of target vehicles in a preset future time period.

[0124] In some embodiments, step 270 above may include steps 2701-2705:

[0125] Step 2701: Sampling of arrival time data based on the probability density function of the arrival time distribution characteristics of vehicles of each vehicle type to obtain the target arrival time of each target vehicle in the preset future time period.

[0126] Step 2702: Based on the probability density function of the battery state of charge distribution characteristics of vehicles of each vehicle type in each preset arrival time period, sample the battery state of charge data to obtain the target battery state of charge of each target vehicle in the preset future time period.

[0127] Step 2703: Based on the probability density function of the charging amount distribution characteristics of vehicles of each vehicle type under each battery state of charge, sample the charging amount data to obtain the target charging amount for each target vehicle in a preset future time period.

[0128] Step 2704: Based on the probability density function of the average charging power distribution characteristics of vehicles of each vehicle type under each battery charge state, perform data sampling to obtain the target average charging power of each target vehicle in the preset future time period.

[0129] Step 280: According to the target arrival time of each target vehicle, the target average charging power of each time period to which the target arrival time belongs is superimposed to obtain the total average charging power corresponding to each time period.

[0130] For example, if the target arrival times of three target vehicles a, b, and c are 12:00, 13:00, and 13:30 respectively, and the time period of 12:00, 13:00, and 13:30 is 12:00-14:00, then the target average charging power of the three target vehicles a, b, and c can be added together to obtain the total average charging power of the charging station corresponding to the time period of 12:00-14:00.

[0131] Step 290: Based on the total average charging power corresponding to each time period, construct the initial charging load curve of the charging station.

[0132] Therefore, by analyzing the distribution patterns of historical vehicle charging data at charging stations, the target charging data for a predetermined number of vehicles in a future time period can be obtained relatively accurately, and the initial charging load curve of the charging station corresponding to the predetermined number of target vehicles in the future time period can be constructed relatively accurately.

[0133] In some embodiments of this disclosure, the arrival time of each target vehicle at the charging station, the charging duration at the charging station, and the charging cost before and after the preset charging price for each target vehicle at the charging station are described to represent the overall user satisfaction of a preset number of target vehicles at the charging station under the preset charging price. The computer device can execute... Figure 3 A flowchart of a method for determining overall satisfaction is provided, such as... Figure 3 As shown, the method for determining overall satisfaction provided in this embodiment includes the following steps:

[0134] Step 310: Calculate the sensitivity coefficient of users of each vehicle type to the charging cost of the charging station, the sensitivity coefficient of users of each vehicle type to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of users of each vehicle type to the charging time of the charging station.

[0135] The charging cost sensitivity coefficient can be understood as an evaluation of how sensitive users of each vehicle type are to charging costs. The greater the sensitivity of users of a certain vehicle type to charging costs, the more concerned users are about charging costs, and the higher the charging cost sensitivity coefficient.

[0136] In some embodiments, the calculation of the user's sensitivity coefficient to charging station costs for each vehicle type may include S11-S15:

[0137] S11. For each vehicle type of user, obtain the satisfaction level of a target number of users with the historical charging fees of the charging station, and obtain the target number of first satisfaction levels for that vehicle type of user.

[0138] The target number can be set as needed, such as 100; there is no limit here.

[0139] S12. Standardize each first satisfaction level to obtain the first standard satisfaction level corresponding to each first satisfaction level.

[0140] S13. Calculate the first ratio of each first standard satisfaction level to the sum of the target number of first standard satisfaction levels.

[0141] Specifically, one can calculate the sum of the target number of first standard satisfaction levels, and then calculate the first ratio of each first standard satisfaction level to the sum of the target number of first standard satisfaction levels.

[0142] S14. Based on the first ratio, calculate the entropy value of the target number of first standard satisfactions to obtain the first entropy value.

[0143] S15. Based on the first entropy value, calculate the user sensitivity coefficient of this vehicle type to the charging cost of the charging station.

[0144] The arrival time sensitivity coefficient can be understood as the degree to which users of each vehicle type are sensitive to the arrival time of their vehicle at the charging station. The greater the sensitivity of users of a certain vehicle type to the arrival time, the more they care about the arrival time, and the higher the arrival time sensitivity coefficient.

[0145] In some embodiments, the above calculation of the user sensitivity coefficient for each vehicle type to the time period of vehicle arrival at the charging station may include S21-S25:

[0146] S21. For each vehicle type of user, obtain the satisfaction level of a target number of users with the historical arrival time at the charging station, and obtain the target number of second satisfaction levels for each vehicle type of user.

[0147] S22. Standardize each second satisfaction level to obtain the second standard satisfaction level corresponding to each second satisfaction level.

[0148] S23. Calculate the second ratio of each second standard satisfaction level to the sum of the second standard satisfaction levels.

[0149] S24. Based on the second ratio, calculate the entropy value of the second standard satisfaction of the target number, and obtain the second entropy value.

[0150] S25. Based on the second entropy value, calculate the sensitivity coefficient of vehicle type users to the arrival time of charging stations.

[0151] The charging time sensitivity coefficient can be understood as the degree to which users of each vehicle type are sensitive to the charging time at charging stations. The greater the sensitivity of users of a certain vehicle type to charging time, the more they care about the charging time, and the higher the charging time sensitivity coefficient.

[0152] In some embodiments, the calculation of the user's sensitivity coefficient to charging time at charging stations for each vehicle type may include S31-S35:

[0153] S31. For each type of user, obtain a target number of users' satisfaction with the historical charging time of the charging station, and obtain a target number of third satisfaction levels for the users of the vehicle type.

[0154] S32. Standardize each third satisfaction level to obtain the third standard satisfaction level corresponding to each third satisfaction level.

[0155] S33. Calculate the third ratio of each third standard satisfaction level to the sum of the third standard satisfaction levels.

[0156] S34. Based on the third ratio, calculate the entropy value of the third standard satisfaction of the target number to obtain the third entropy value.

[0157] S35. Based on the third entropy value, calculate the user sensitivity coefficient of vehicle type to charging time at charging stations.

[0158] Specifically, the satisfaction level of a target number of users with historical charging costs at charging stations can be collected by sampling surveys (e.g., questionnaires) for each vehicle type, thus obtaining the target number of first satisfaction levels for each vehicle type; the satisfaction level of a target number of users with historical arrival times at charging stations can be collected, thus obtaining the target number of second satisfaction levels for each vehicle type; and the satisfaction level of a target number of users with historical charging durations at charging stations can be collected, thus obtaining the target number of third satisfaction levels for each vehicle type.

[0159] Specifically, to eliminate the influence of dimensions, each of the first, second, and third satisfaction levels can be standardized separately. For example, the Min-Max standardization method can be used, and the standardization expression can be:

[0160] ;

[0161] Where i is the user's ID; j is the satisfaction level ID, where j=1 indicates the first satisfaction level, j=2 indicates the second satisfaction level, and j=3 indicates the third satisfaction level; Let j be the minimum satisfaction level among the target number of users. Z represents the maximum satisfaction score of the j-th user among the target number of users. ij Let be the standard satisfaction level corresponding to the j-th satisfaction level of the i-th user;

[0162] For example, when i is 3 and j is 1, x ij The first satisfaction level of the third user out of the target number of users; The minimum satisfaction level among the target number of users. Z represents the maximum first satisfaction level among the target number of users. ij This represents the first standard satisfaction level corresponding to the first satisfaction level of the third user.

[0163] Specifically, the first ratio of each first standard satisfaction level to the sum of the target number of first standard satisfaction levels, the second ratio of each second standard satisfaction level to the sum of the second standard satisfaction levels, and the third ratio of each third standard satisfaction level to the sum of the third standard satisfaction levels can be calculated using the following formula:

[0164] ;

[0165] Where n represents the target quantity; P ij This represents the ratio corresponding to the j-th satisfaction level of the i-th user; when j is 1, P ij P is the first ratio corresponding to the first satisfaction level of the i-th user; when j is 2, P ij P is the second ratio corresponding to the second satisfaction level of the i-th user; when j is 3, P ij Let be the third ratio corresponding to the third satisfaction level of the i-th user.

[0166] Specifically, based on the first ratio, the entropy value of the target number of first standard satisfaction levels is calculated to obtain the first entropy value; based on the second ratio, the entropy value of the target number of second standard satisfaction levels is calculated to obtain the second entropy value; and based on the third ratio, the entropy value of the target number of third standard satisfaction levels is calculated to obtain the third entropy value, which can be calculated using the following formula:

[0167] ;

[0168] Among them, e j This represents the entropy value corresponding to the j-th satisfaction level. When j is 1, e j This represents the first entropy value corresponding to the first satisfaction level; when j is 2, e j This represents the second entropy value corresponding to the second satisfaction level; when j is 3, e j This represents the third entropy value corresponding to the third satisfaction level.

[0169] Specifically, based on the first entropy value, the sensitivity coefficient of vehicle type users to charging station fees is calculated; based on the second entropy value, the sensitivity coefficient of vehicle type users to charging station arrival time is calculated; and based on the third entropy value, the sensitivity coefficient of vehicle type users to charging station charging time is calculated. This can be done using the following formula:

[0170] ;

[0171] in, This represents the sensitivity coefficient corresponding to the j-th satisfaction level. When j is 1, This represents the charging cost sensitivity coefficient; when j is 2, This represents the sensitivity coefficient for arrival time; when j is 3, This represents the sensitivity coefficient to charging time.

[0172] Step 320: Describe the user satisfaction with the charging fee, the planned arrival time of the target vehicle at the charging station, and the charging time of the charging station for each target vehicle under the preset charging price of the charging station.

[0173] In this embodiment of the disclosure, the computer device can describe the satisfaction of each target vehicle user with the charging fee of the charging station, the satisfaction with the planned arrival time of the target vehicle at the charging station, and the satisfaction with the charging time of the charging station under the influence of the preset charging price of the charging station.

[0174] The planned arrival time can be understood as the arrival time recommended by the charging station operator to the user under the preset charging price of the charging station.

[0175] In some embodiments, under the preset charging price of the charging station described above, the satisfaction of each target vehicle user with the charging fee, the planned arrival time of the target vehicle at the charging station, and the charging time of the charging station may include steps 3201-3207:

[0176] Step 3201: For each target vehicle, obtain the historical charging cost incurred after the target vehicle arrives at the charging station at the target arrival time and is fully charged to the preset charging amount, before the target vehicle responds to the preset charging price.

[0177] The preset charging amount can be set as needed; there is no limitation here.

[0178] Step 3202: After obtaining the target vehicle's response preset charging price, determine the actual charging cost to fully charge the preset amount of electricity after arriving at the charging station at the planned arrival time, as well as the additional revenue brought to the charging station by fully charging the preset amount of electricity.

[0179] Step 3203: Based on historical charging costs, actual charging costs, and additional revenue, predict the user satisfaction of the target vehicle with the charging station's charging costs.

[0180] For example, the user satisfaction A with the charging costs at the charging station for the target vehicle can be calculated using the following formula:

[0181] ;

[0182] ;

[0183] ;

[0184] Wherein, CIC is the revenue attractiveness constant, the value of which reflects the amount of revenue that is attractive to users;

[0185] cost0 represents the historical charging cost incurred by the target vehicle after it arrives at the charging station at the target arrival time and is fully charged to the preset amount of charge, before the target vehicle responds to the preset charging price.

[0186] cost1 represents the actual charging cost incurred by the target vehicle after responding to the preset charging price and arriving at the charging station at the planned arrival time to fully charge the preset amount of charge.

[0187] E0 indicates the preset charging amount;

[0188] This represents the daily time-of-use electricity price curve in the electricity market before the target vehicle responds to the preset charging price;

[0189] This represents the daily time-of-use electricity price curve in the electricity market after the target vehicle responds to the preset charging price;

[0190] Profit represents the additional revenue generated by a charging station after a target vehicle responds to a preset charging price and arrives at the charging station at the planned arrival time, fully charged to the preset amount.

[0191] Step 3204: For each target vehicle, calculate the time difference between the planned arrival time after the target vehicle responds to the preset charging price and the target arrival time before the target vehicle responds to the preset charging price.

[0192] Step 3205: Based on the time difference, predict the user satisfaction of the target vehicle with the planned arrival time of the target vehicle at the charging station.

[0193] For example, the user satisfaction B of the target vehicle with the planned arrival time at the charging station can be calculated using the following formula:

[0194] ;

[0195] ;

[0196] Among them, t aI This indicates the target arrival time of the target vehicle before responding to the preset charging price;

[0197] t aR This indicates the planned arrival time of the target vehicle after responding to the preset charging price;

[0198] Indicates time difference.

[0199] Step 3206: For each target vehicle, obtain the first average charging power after the target vehicle responds to the preset charging price and the first total number of vehicles visiting the charging station during the time period of the planned arrival time, as well as the target average charging power before the target vehicle responds to the preset charging price and the second total number of vehicles visiting the charging station during the time period of the target arrival time.

[0200] Step 3207: Based on the first average charging power, the first total number of vehicles, the target average charging power, and the second total number of vehicles, calculate the user satisfaction with the charging time of the charging station for the target vehicle.

[0201] For example, the user satisfaction C of the target vehicle with the charging time at the charging station can be calculated using the following formula:

[0202] ;

[0203] Among them, P R This represents the first average charging power of the target vehicle after responding to the preset charging price.

[0204] P I This indicates the target average charging power of the target vehicle before it responds to the preset charging price;

[0205] This represents the total number of second-generation vehicles that visited the charging station during the time period in which the target vehicle arrived before responding to the preset charging price;

[0206] This indicates the total number of the first vehicles to visit the charging station during the time period when the target vehicle plans to arrive after responding to the preset charging price.

[0207] Step 330: For each target vehicle, sum the products of the target vehicle's charging cost satisfaction and the target vehicle's charging cost sensitivity coefficient, the target vehicle's planned arrival time satisfaction and the target vehicle's arrival time sensitivity coefficient, and the target vehicle's charging duration satisfaction and the target vehicle's charging duration sensitivity coefficient to obtain the target vehicle's overall user satisfaction with the charging station under the preset charging price.

[0208] For example, the overall satisfaction of users of the target vehicle with the charging station under the preset charging price. It can be calculated using the following formula:

[0209] ;

[0210] in, This represents the charging cost sensitivity coefficient for the target vehicle.

[0211] This represents the sensitivity coefficient for the arrival time period corresponding to the target vehicle;

[0212] This represents the charging time sensitivity coefficient for the target vehicle.

[0213] A represents the satisfaction level with the charging costs associated with the target vehicle;

[0214] B represents the satisfaction level with the planned arrival time of the target vehicle;

[0215] C represents the satisfaction level with the charging time corresponding to the target vehicle.

[0216] Step 340: Sum the overall satisfaction of users of the target number of vehicles with the charging station to obtain the total satisfaction of users of the target number of vehicles with the charging station under the preset charging price.

[0217] Therefore, based on the arrival time of each target vehicle at the charging station, the charging duration and charging cost before and after each target vehicle responds to the preset charging price, the overall satisfaction of users of a preset number of target vehicles with the charging station under the preset charging price can be accurately predicted.

[0218] In some embodiments of this disclosure, the load parameters in the initial charging load curve are adjusted to maximize overall satisfaction under a preset charging price, resulting in a corrected charging load curve for the charging station. The computer equipment can then execute this adjustment. Figure 4 A flowchart of a method for determining a modified charging load curve is provided, such as... Figure 4 As shown, the method for determining the modified charging load curve provided in this embodiment includes the following steps:

[0219] Step 410: With the goal of maximizing the total satisfaction under the preset charging price, calculate the planned arrival time and first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized.

[0220] Step 420: Determine the planned arrival time of each target vehicle after responding to the preset charging price when the overall satisfaction is maximized as the ideal arrival time of each target vehicle, and determine the first average charging power of each target vehicle after responding to the preset charging price when the overall satisfaction is maximized as the ideal average charging power of each target vehicle.

[0221] Step 430: Based on the ideal arrival time of each target vehicle, sum up the ideal average charging power for each time period corresponding to the ideal arrival time to obtain the ideal average charging power for each time period.

[0222] For example, if the ideal arrival times for three target vehicles a, b, and c are 12:05, 13:05, and 13:40 respectively, and the time period between 12:00 and 14:00 is 12:00, then the ideal average charging power of the three target vehicles a, b, and c can be added together to obtain the ideal average charging power of the charging station during the 12:00-14:00 time period.

[0223] Step 440: Adjust the total average charging power corresponding to each time period in the initial charging load curve to the ideal average charging power to obtain the corrected charging load curve of the charging station. The corrected charging load curve maximizes the overall satisfaction under the preset charging price.

[0224] Therefore, by combining the overall satisfaction of users of different vehicle types with the charging station after charging and the attributes of the charging station itself, the charging load curve of the charging station when the overall user satisfaction is maximized under the preset charging price can be used as the final charging load curve of the charging station. This can fully take into account the charging behavior characteristics of users of different vehicle types, improve the accuracy of charging station load characteristic prediction, and thus provide scientific guidance for the operation of charging stations.

[0225] Figure 5 This is a schematic diagram of the structure of a charging station load characteristic prediction device provided in an embodiment of this disclosure. This device can be understood as the aforementioned computer equipment or a functional module within the aforementioned computer equipment. Figure 5 As shown, the charging station load characteristic prediction device 500 includes:

[0226] The first determining module 510 is used to determine the target charging data of a preset number of target vehicles at the charging station for a preset future time period based on the distribution pattern of the historical vehicle charging data of the charging station, and the target charging data of the target vehicles satisfies the distribution pattern.

[0227] The construction module 520 is used to construct the initial charging load curve of the charging station corresponding to a preset number of target vehicles in a preset future time period based on the target charging data.

[0228] The description module 530 is used to describe the total user satisfaction of a preset number of target vehicles with the charging station under the preset charging price, based on the arrival time of each target vehicle at the charging station, the charging time and charging cost at the charging station before and after each target vehicle responds to the preset charging price of the charging station.

[0229] The solution module 540 is used to optimize the load parameters in the initial charging load curve with the goal of maximizing the overall satisfaction under the preset charging price, and obtain the corrected charging load curve of the charging station. The corrected charging load curve maximizes the overall satisfaction under the preset charging price.

[0230] Optionally, the first determining module mentioned above includes:

[0231] The classification submodule is used to classify the historical vehicle charging data of the charging station by vehicle type, and obtain the vehicle charging data corresponding to each vehicle type.

[0232] The acquisition submodule is used to obtain the arrival time of each vehicle at the charging station, the battery state of charge of each vehicle at the charging station, the amount of charging of each vehicle at the charging station, and the average charging power from the vehicle charging data corresponding to each vehicle type.

[0233] The first construction submodule is used to construct the arrival time distribution characteristics of vehicles of each vehicle type based on the arrival time of each vehicle type.

[0234] The second construction submodule is used to construct the battery charge distribution characteristics of vehicles of each vehicle type in each preset arrival time period based on the battery charge status of vehicles of each vehicle type at each arrival time.

[0235] The third construction submodule is used to construct the charging amount distribution characteristics of vehicles of each vehicle type under each battery state of charge based on the charging amount of vehicles of each vehicle type under each battery state of charge.

[0236] The fourth submodule is used to construct the average charging power distribution characteristics of vehicles of each vehicle type under each battery state of charge, based on the average charging power of vehicles of each vehicle type under each battery state of charge.

[0237] The first determining submodule is used to determine the target arrival time, target charging amount and target average charging power of each target vehicle in a preset number of target vehicles in a preset future time period based on the arrival time distribution characteristics, battery state of charge distribution characteristics, charging amount distribution characteristics and average charging power distribution characteristics.

[0238] The first superposition submodule is used to superimpose the target average charging power of each time period to which the target arrival time belongs according to the target arrival time of each target vehicle, so as to obtain the total average charging power corresponding to each time period.

[0239] The fifth submodule is used to construct the initial charging load curve of the charging station based on the total average charging power corresponding to each time period.

[0240] Optionally, the first determining submodule mentioned above includes:

[0241] The first sampling unit is used to sample arrival time data based on the probability density function of the arrival time distribution characteristics of vehicles of each vehicle type, so as to obtain the target arrival time of each target vehicle in a preset future time period.

[0242] The second sampling unit is used to sample the battery state of charge data based on the probability density function of the battery state of charge distribution characteristics of vehicles of each vehicle type in each preset arrival time period, so as to obtain the target battery state of charge of each target vehicle in the preset future time period.

[0243] The third sampling unit is used to sample the charging amount data based on the probability density function of the charging amount distribution characteristics of vehicles of each vehicle type under each battery charge state, so as to obtain the target charging amount of each target vehicle in a preset future time period.

[0244] The fourth sampling unit is used to sample data based on the probability density function of the average charging power distribution characteristics of vehicles of each vehicle type under each battery charge state, so as to obtain the target average charging power of each target vehicle in a preset future time period.

[0245] Optionally, the above-described module includes:

[0246] The sensitivity coefficient calculation submodule is used to calculate the sensitivity coefficient of users of each vehicle type to the charging cost of the charging station, the sensitivity coefficient of users of each vehicle type to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of users of each vehicle type to the charging time of the charging station.

[0247] The description submodule is used to describe the user satisfaction of each target vehicle with the charging fee, the planned arrival time of the target vehicle at the charging station, and the charging time of the charging station under the preset charging price of the charging station.

[0248] The first summation submodule is used to sum the products of the target vehicle's charging cost satisfaction and charging cost sensitivity coefficient, the target vehicle's planned arrival time satisfaction and arrival time sensitivity coefficient, and the target vehicle's charging duration satisfaction and charging duration sensitivity coefficient for each target vehicle, so as to obtain the target vehicle's overall user satisfaction with the charging station under the preset charging price.

[0249] The second summation submodule is used to sum the overall satisfaction of users of a preset number of target vehicles with the charging station, and obtain the total satisfaction of users of a preset number of target vehicles with the charging station under the preset charging price.

[0250] Optionally, the above sensitivity coefficient calculation submodule includes:

[0251] The first acquisition unit is used to acquire, for each vehicle type user, the satisfaction level of a target number of users with the historical charging cost of the charging station, to obtain a target number of first satisfaction levels for the vehicle type user; acquire the satisfaction level of a target number of users with the historical arrival time of the charging station, to obtain a target number of second satisfaction levels for the vehicle type user; and acquire the satisfaction level of a target number of users with the historical charging duration of the charging station, to obtain a target number of third satisfaction levels for the vehicle type user.

[0252] The standardization unit is used to standardize each first satisfaction level, second satisfaction level, and third satisfaction level to obtain the first standard satisfaction level corresponding to each first satisfaction level, the second standard satisfaction level corresponding to each second satisfaction level, and the third standard satisfaction level corresponding to each third satisfaction level.

[0253] The first calculation unit is used to calculate the first ratio of each first standard satisfaction level to the sum of the target number of first standard satisfaction levels, the second ratio of each second standard satisfaction level to the sum of the second standard satisfaction levels, and the third ratio of each third standard satisfaction level to the sum of the third standard satisfaction levels.

[0254] The second calculation unit is used to calculate the entropy value of the target number of first standard satisfaction levels based on the first ratio, to obtain the first entropy value; to calculate the entropy value of the target number of second standard satisfaction levels based on the second ratio, to obtain the second entropy value; and to calculate the entropy value of the target number of third standard satisfaction levels based on the third ratio, to obtain the third entropy value.

[0255] The third calculation unit is used to calculate the sensitivity coefficient of vehicle type users to charging station charging costs based on the first entropy value; to calculate the sensitivity coefficient of vehicle type users to charging station arrival time based on the second entropy value; and to calculate the sensitivity coefficient of vehicle type users to charging station charging duration based on the third entropy value.

[0256] Optionally, the above-described submodule includes:

[0257] The second acquisition unit is used to acquire, for each target vehicle, the historical charging cost used to fully charge the preset amount of electricity after the target vehicle arrives at the charging station at the target arrival time, before the target vehicle responds to the preset charging price.

[0258] The third acquisition unit is used to acquire the actual charging cost of the target vehicle after it responds to the preset charging price and arrives at the charging station at the planned arrival time to fully charge the preset amount of charging, as well as the additional revenue brought to the charging station by fully charging the preset amount of charging.

[0259] The first prediction unit is used to predict the user satisfaction of the target vehicle with the charging station's charging costs based on historical charging costs, actual charging costs, and additional benefits.

[0260] The fourth calculation unit is used to calculate, for each target vehicle, the time difference between the planned arrival time after the target vehicle responds to the preset charging price and the target arrival time before the target vehicle responds to the preset charging price.

[0261] The second prediction unit is used to predict the user satisfaction of the target vehicle with the planned arrival time of the target vehicle at the charging station based on the time difference.

[0262] The fourth acquisition unit is used to acquire, for each target vehicle, the first average charging power of the target vehicle after responding to the preset charging price and the first total number of vehicles visiting the charging station during the time period of the planned arrival time, as well as the target average charging power of the target vehicle before responding to the preset charging price and the second total number of vehicles visiting the charging station during the time period of the target arrival time.

[0263] The fifth calculation unit is used to calculate the user satisfaction with the charging time of the target vehicle at the charging station based on the first average charging power, the first total number of vehicles, the target average charging power, and the second total number of vehicles.

[0264] Optionally, the above solution module includes:

[0265] The calculation submodule is used to calculate the planned arrival time and first average charging power of each target vehicle after responding to the preset charging price, with the goal of maximizing the total satisfaction under the preset charging price.

[0266] The second determining submodule is used to determine the planned arrival time of each target vehicle after responding to the preset charging price when the total satisfaction is maximized as the ideal arrival time of each target vehicle, and to determine the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized as the ideal average charging power of each target vehicle.

[0267] The second superposition submodule is used to superimpose the ideal average charging power of each time period corresponding to the ideal arrival time of each target vehicle, so as to obtain the ideal average charging power of each time period.

[0268] The adjustment submodule is used to adjust the total average charging power corresponding to each time period in the initial charging load curve to the ideal average charging power, so as to obtain the corrected charging load curve of the charging station.

[0269] The charging station load characteristic prediction device provided in this embodiment can implement the method of any of the above embodiments, and its execution mode and beneficial effects are similar, so they will not be described again here.

[0270] This disclosure also provides a computer device, which includes a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, it can implement the methods of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.

[0271] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure, such as... Figure 6 As shown, the computer device 600 may include a processor 610 and a memory 620. The memory 620 stores a computer program 621. When the computer program 621 is executed by the processor 610, it can implement the method provided in any of the above embodiments. The execution mode and beneficial effects are similar and will not be described again here.

[0272] Of course, for the sake of simplicity, Figure 6 Only some of the components of the computer device 600 relevant to the present invention are shown in this illustration; components such as buses, input / output interfaces, input devices, and output devices are omitted. In addition, the computer device 600 may include any other suitable components depending on the specific application.

[0273] This disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the methods of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.

[0274] The aforementioned computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0275] The computer program described above can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer device, partially on the user's device, as a standalone software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.

[0276] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0277] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0278] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the load characteristics of a charging station, characterized in that, include: Based on the distribution pattern of historical vehicle charging data at the charging station, the target charging data of a preset number of target vehicles at the charging station in a preset future time period is determined, and the target charging data of the target vehicles satisfies the distribution pattern. Based on the target charging data, an initial charging load curve for the charging station corresponding to the preset number of target vehicles in the preset future time period is constructed. Based on the arrival time of each target vehicle at the charging station before and after responding to the preset charging price of the charging station, the charging time and charging cost of each target vehicle at the charging station, the overall satisfaction of users of the preset number of target vehicles with the charging station under the preset charging price is described. With the goal of maximizing the overall satisfaction under the preset charging price, the load parameters in the initial charging load curve are optimized and solved to obtain the corrected charging load curve of the charging station. The corrected charging load curve maximizes the overall satisfaction under the preset charging price. The description of the overall user satisfaction with the charging station for a preset number of target vehicles under the preset charging price, based on the arrival time of each target vehicle at the charging station, the charging duration at the charging station, and the charging cost before and after each target vehicle responds to the preset charging price, includes: Calculate the sensitivity coefficient of users of each vehicle type to the charging cost of the charging station, the sensitivity coefficient of users of each vehicle type to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of users of each vehicle type to the charging time of the charging station. The description includes the satisfaction levels of users of each target vehicle with the charging fee, the planned arrival time of the target vehicle at the charging station, and the charging time at the charging station, all under the preset charging price of the charging station. For each target vehicle, the product of the charging cost satisfaction and the charging cost sensitivity coefficient, the product of the planned arrival time satisfaction and the arrival time sensitivity coefficient, and the product of the charging duration satisfaction and the charging duration sensitivity coefficient are summed to obtain the overall user satisfaction of the target vehicle with the charging station under the preset charging price. The total satisfaction of users of the target number of vehicles with the charging station is summed to obtain the total satisfaction of users of the target number of vehicles with the charging station at the preset charging price. The calculation of the user sensitivity coefficients for each vehicle type to the charging cost of the charging station, the user sensitivity coefficients for each vehicle type to the time of arrival at the charging station, and the user sensitivity coefficients for each vehicle type to the charging duration of the charging station includes: For each vehicle type of user, obtain a target number of users' satisfaction with the historical charging fees of the charging station to obtain a target number of first satisfaction levels for the vehicle type of user; obtain a target number of users' satisfaction with the historical arrival time of the charging station to obtain a target number of second satisfaction levels for the vehicle type of user; obtain a target number of users' satisfaction with the historical charging duration of the charging station to obtain a target number of third satisfaction levels for the vehicle type of user. The first satisfaction level, the second satisfaction level, and the third satisfaction level are standardized to obtain the first standard satisfaction level corresponding to each first satisfaction level, the second standard satisfaction level corresponding to each second satisfaction level, and the third standard satisfaction level corresponding to each third satisfaction level. Calculate a first ratio of each first standard satisfaction level to the sum of the target number of first standard satisfaction levels, a second ratio of each second standard satisfaction level to the sum of the second standard satisfaction levels, and a third ratio of each third standard satisfaction level to the sum of the third standard satisfaction levels; Based on the first ratio, the entropy value of the target number of first standard satisfactions is calculated to obtain the first entropy value; based on the second ratio, the entropy value of the target number of second standard satisfactions is calculated to obtain the second entropy value; based on the third ratio, the entropy value of the target number of third standard satisfactions is calculated to obtain the third entropy value. Based on the first entropy value, calculate the user's sensitivity coefficient to the charging cost of the charging station for the vehicle type; based on the second entropy value, calculate the user's sensitivity coefficient to the arrival time of the charging station for the vehicle type; based on the third entropy value, calculate the user's sensitivity coefficient to the charging duration of the charging station for the vehicle type. The description of the charging station's preset charging price includes the following satisfaction levels for each target vehicle user: satisfaction with the charging cost, satisfaction with the planned arrival time, and satisfaction with the charging duration. For each target vehicle, obtain the historical charging cost incurred by the target vehicle after it arrives at the charging station at the target arrival time and is fully charged to the preset amount, before the target vehicle responds to the preset charging price. After the target vehicle responds to the preset charging price, the actual charging cost used to fully charge the preset amount of electricity after arriving at the charging station at the planned arrival time, as well as the additional revenue brought to the charging station by fully charging the preset amount of electricity; Based on the historical charging costs, the actual charging costs, and the additional revenue, predict the user satisfaction of the target vehicle with the charging costs of the charging station. For each target vehicle, calculate the time difference between the planned arrival time of the target vehicle after responding to the preset charging price and the target arrival time of the target vehicle before responding to the preset charging price; Based on the time difference, predict the user satisfaction of the target vehicle with the planned arrival time of the target vehicle at the charging station; For each target vehicle, obtain the first average charging power of the target vehicle after responding to the preset charging price and the first total number of vehicles visiting the charging station during the time period of the planned arrival time, as well as the target average charging power of the target vehicle before responding to the preset charging price and the second total number of vehicles visiting the charging station during the time period of the target arrival time. Based on the first average charging power, the first total number of vehicles, the target average charging power, and the second total number of vehicles, the user satisfaction with the charging time of the target vehicle at the charging station is calculated.

2. The method according to claim 1, characterized in that, The method of determining the target charging data of a preset number of target vehicles at a charging station based on the distribution pattern of historical vehicle charging data at the charging station for a preset future time period includes: The historical vehicle charging data of the charging station is classified by vehicle type to obtain the vehicle charging data corresponding to each vehicle type. From the vehicle charging data corresponding to each vehicle type, obtain the arrival time of each vehicle at the charging station, the battery state of charge of each vehicle at the charging station, the charging amount of each vehicle at the charging station, and the average charging power. Based on the arrival times of vehicles of each vehicle type, the distribution characteristics of arrival times of vehicles of each vehicle type are constructed. Based on the state of charge of the batteries of each vehicle type at each arrival time, the distribution characteristics of the state of charge of the batteries of each vehicle type at each preset arrival time are constructed. Based on the charging amount of vehicles of each vehicle type under each battery state of charge, the charging amount distribution characteristics of vehicles of each vehicle type under each battery state of charge are constructed. Based on the average charging power of vehicles of each vehicle type under each battery state of charge, the distribution characteristics of the average charging power of vehicles of each vehicle type under each battery state of charge are constructed. Based on the arrival time distribution characteristics, battery state of charge distribution characteristics, charging amount distribution characteristics and average charging power distribution characteristics, the target arrival time, target charging amount and target average charging power of each target vehicle in a preset number of target vehicles in a preset future time period are determined. Based on the target arrival time of each target vehicle, the target average charging power within each time period corresponding to the target arrival time is summed to obtain the total average charging power corresponding to each time period. Based on the total average charging power corresponding to each time period, the initial charging load curve of the charging station is constructed.

3. The method according to claim 2, characterized in that, The step of determining the target arrival time, target charging amount, and target average charging power of each target vehicle in a preset number of target vehicles within a preset future time period based on the arrival time distribution characteristics, battery state of charge distribution characteristics, charging amount distribution characteristics, and average charging power distribution characteristics includes: Based on the probability density function of the arrival time distribution characteristics of vehicles of each vehicle type, the arrival time data is sampled to obtain the target arrival time of each target vehicle in a preset future time period. Based on the probability density function of the battery state of charge distribution characteristics of vehicles of each vehicle type in each preset arrival time period, the battery state of charge data is sampled to obtain the target battery state of charge of each target vehicle in the preset future time period. Based on the probability density function of the charging amount distribution characteristics of vehicles of different vehicle types under different battery states of charge, the charging amount data is sampled to obtain the target charging amount of each target vehicle in a preset future time period. Data sampling is performed based on the probability density function of the average charging power distribution characteristics of vehicles of different vehicle types under different battery states of charge to obtain the target average charging power of each target vehicle in a preset future time period.

4. The method according to claim 1, characterized in that, The step of optimizing the load parameters in the initial charging load curve to obtain the corrected charging load curve of the charging station, with the objective of maximizing the overall satisfaction under the preset charging price, includes: With the goal of maximizing the total satisfaction under the preset charging price, calculate the planned arrival time and first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized; The planned arrival time of each target vehicle after responding to the preset charging price when the total satisfaction is maximized is determined as the ideal arrival time of each target vehicle, and the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized is determined as the ideal average charging power of each target vehicle. Based on the ideal arrival time of each target vehicle, the ideal average charging power within each time period corresponding to the ideal arrival time is superimposed to obtain the ideal average charging power corresponding to each time period; The total average charging power corresponding to each time period in the initial charging load curve is adjusted to the ideal average charging power to obtain the corrected charging load curve of the charging station.

5. A charging station load characteristic prediction device, characterized in that, include: The first determining module is used to determine the target charging data of a preset number of target vehicles at the charging station in a preset future time period based on the distribution pattern of the historical vehicle charging data of the charging station, wherein the target charging data of the target vehicles satisfies the distribution pattern. A construction module is used to construct, based on the target charging data, the initial charging load curve of the charging station corresponding to the preset number of target vehicles in the preset future time period; The description module is used to describe the overall satisfaction of users of a preset number of target vehicles with the charging station under the preset charging price, based on the arrival time of each target vehicle at the charging station, the charging time and the charging cost at the charging station before and after each target vehicle responds to the preset charging price of the charging station. The solution module is used to optimize the load parameters in the initial charging load curve with the goal of maximizing the total satisfaction under the preset charging price, so as to obtain the corrected charging load curve of the charging station, which maximizes the total satisfaction under the preset charging price. The description module includes: The sensitivity coefficient calculation submodule is used to calculate the sensitivity coefficient of users of each vehicle type to the charging cost of the charging station, the sensitivity coefficient of users of each vehicle type to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of users of each vehicle type to the charging time of the charging station. The description submodule is used to describe the user satisfaction of each target vehicle with the charging fee, the planned arrival time of the target vehicle at the charging station, and the charging time of the charging station under the preset charging price of the charging station. The first summation submodule is used to sum the products of the target vehicle's charging cost satisfaction and charging cost sensitivity coefficient, the target vehicle's planned arrival time satisfaction and arrival time sensitivity coefficient, and the target vehicle's charging duration satisfaction and charging duration sensitivity coefficient for each target vehicle, so as to obtain the target vehicle's overall user satisfaction with the charging station under the preset charging price. The second summation submodule is used to sum the overall satisfaction of users of a preset number of target vehicles with the charging station, and obtain the total satisfaction of users of a preset number of target vehicles with the charging station under the preset charging price. The sensitivity coefficient calculation submodule includes: The first acquisition unit is used to acquire, for each vehicle type user, the satisfaction level of a target number of users with the historical charging cost of the charging station, to obtain a target number of first satisfaction levels for the vehicle type user; acquire the satisfaction level of a target number of users with the historical arrival time of the charging station, to obtain a target number of second satisfaction levels for the vehicle type user; and acquire the satisfaction level of a target number of users with the historical charging duration of the charging station, to obtain a target number of third satisfaction levels for the vehicle type user. The standardization unit is used to standardize each first satisfaction level, second satisfaction level, and third satisfaction level to obtain the first standard satisfaction level corresponding to each first satisfaction level, the second standard satisfaction level corresponding to each second satisfaction level, and the third standard satisfaction level corresponding to each third satisfaction level. The first calculation unit is used to calculate the first ratio of each first standard satisfaction level to the sum of the target number of first standard satisfaction levels, the second ratio of each second standard satisfaction level to the sum of the second standard satisfaction levels, and the third ratio of each third standard satisfaction level to the sum of the third standard satisfaction levels. The second calculation unit is used to calculate the entropy value of the target number of first standard satisfaction levels based on the first ratio, to obtain the first entropy value; to calculate the entropy value of the target number of second standard satisfaction levels based on the second ratio, to obtain the second entropy value; and to calculate the entropy value of the target number of third standard satisfaction levels based on the third ratio, to obtain the third entropy value. The third calculation unit is used to calculate the sensitivity coefficient of vehicle type users to charging station charging costs based on the first entropy value; to calculate the sensitivity coefficient of vehicle type users to charging station arrival time based on the second entropy value; and to calculate the sensitivity coefficient of vehicle type users to charging station charging duration based on the third entropy value. The description submodule includes: The second acquisition unit is used to acquire, for each target vehicle, the historical charging cost used to fully charge the preset amount of electricity after the target vehicle arrives at the charging station at the target arrival time, before the target vehicle responds to the preset charging price. The third acquisition unit is used to acquire the actual charging cost of the target vehicle after it responds to the preset charging price and arrives at the charging station at the planned arrival time to fully charge the preset amount of charging, as well as the additional revenue brought to the charging station by fully charging the preset amount of charging. The first prediction unit is used to predict the user satisfaction of the target vehicle with the charging station's charging costs based on historical charging costs, actual charging costs, and additional benefits. The fourth calculation unit is used to calculate, for each target vehicle, the time difference between the planned arrival time after the target vehicle responds to the preset charging price and the target arrival time before the target vehicle responds to the preset charging price. The second prediction unit is used to predict the user satisfaction of the target vehicle with the planned arrival time of the target vehicle at the charging station based on the time difference. The fourth acquisition unit is used to acquire, for each target vehicle, the first average charging power of the target vehicle after responding to the preset charging price and the first total number of vehicles visiting the charging station during the time period of the planned arrival time, as well as the target average charging power of the target vehicle before responding to the preset charging price and the second total number of vehicles visiting the charging station during the time period of the target arrival time. The fifth calculation unit is used to calculate the user satisfaction with the charging time of the target vehicle at the charging station based on the first average charging power, the first total number of vehicles, the target average charging power, and the second total number of vehicles.

6. A computer device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the charging station load characteristic prediction method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the charging station load characteristic prediction method as described in any one of claims 1-4.

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