Quick charging station space flexibility analysis method and device based on user preference

Abstract

The invention provides a quick charging station space flexibility analysis method and device based on user preference, and belongs to the field of electric vehicle load flexibility evaluation and the field of electric vehicle load regulation and control. The method comprises the following steps: collecting multi-source data related to selection of a quick charging station; based on the multi-source data, obtaining an alternative charging station set corresponding to each quick charging order and an attribute of each quick charging station in the alternative charging station set, wherein the attribute affects user selection; based on the attributes, an optimization model of user differentiation preference identification is established and solved, and attribute preference weights of the users are obtained; and based on the attribute preference weight, analyzing the spatial flexibility of the fast charging station. Based on quick charging station selection behavior analysis, a city-level quick charging station price incentive-load flexibility relationship is established, and a basis is provided for a charging station operator to use a price incentive means to utilize electric vehicle space flexibility.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicle load flexibility assessment and electric vehicle load regulation, and specifically relates to a method and device for analyzing the spatial flexibility of a fast charging station based on user preferences. Background Art

[0002] In densely populated urban areas, fast charging stations (FCS) have emerged as key infrastructure to address the growing demand for charging and alleviate users' range anxiety. With the rapid expansion of fast charging infrastructure, the spatial flexibility of electric vehicles (EVs) has become a research focus. Spatial flexibility refers to the redistribution of EV charging load among different charging stations by influencing users' choice of charging stations. This flexibility provides important opportunities for the power system to enable EV users to participate in grid ancillary services such as mitigating congestion and absorbing renewable energy.

[0003] As private investment in charging infrastructure increases, charging station operators (CSOs) as decision makers of price incentives have become key players in exploiting the spatial flexibility of electric vehicles. Initially, price incentives were mainly set by power system operators. For example, studies have obtained the spatial demand elasticity of charging loads through traffic assignment problems, and then redistributed charging loads to reduce safety risks in urban power-traffic coupled networks. Today, charging prices include not only the electricity costs set by the power system, but also the service prices set by charging station operators. These operators play an important role in influencing users' choice of charging stations to exploit the spatial flexibility of charging loads.

[0004] In order to fully utilize the spatial flexibility of electric vehicles, it is crucial to identify differentiated user preferences, as these preferences directly affect the spatial distribution of charging station loads. However, many existing studies simplify user preferences and assume that users are completely rational or have uniform preferences. For example, some studies guide users to the station with the shortest charging time based on the total charging time. However, user preferences in reality are uncertain and are affected by multiple conflicting attributes. Although some studies have considered differentiated preferences and bounded rationality, they often rely on theoretical assumptions rather than real data. For example, some studies have modeled user decision-making behavior through mixed Gaussian distribution parameters, and other studies have explored the impact of user preferences on route and charging decisions. Despite the progress of these studies, identifying user preferences through real-world data is still in its early stages.

[0005] In recent years, data-driven models have been increasingly used to identify user preferences, usually using vehicle trajectory data. For commercial electric vehicle fleets, trajectory data is more readily available and charging behavior can be inferred. For example, studies have explored the attributes that affect the choice of charging stations for large fleets of electric taxis based on trajectory data, and other studies have analyzed user preferences using multi-source real-world data, and there is also research based on cumulative prospect theory to model the random charging behavior of electric taxis. Despite these advances, it remains a challenge to identify different user preferences based on real data from charging station operators.

[0006] From the perspective of charging station operators, the load flexibility of fast charging stations means using price incentives to change users' charging station choices, realize the spatial transfer of charging loads, tap into the spatial flexibility of electric vehicles, and provide support for the grid to achieve goals such as congestion elimination and renewable energy consumption. Therefore, one of the key issues for charging station operators to use price incentives to utilize the spatial flexibility of electric vehicles is to use existing data (fast charging station data, fast charging user data, fast charging order data) and data-driven models to analyze fast charging station selection behavior and obtain differentiated user preferences. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the existing technology and propose a method and device for analyzing the spatial flexibility of fast charging stations based on user preferences. Based on the analysis of fast charging station selection behavior, the present invention establishes a price incentive-load flexibility relationship for city-level fast charging stations, providing a basis for charging station operators to use price incentives to utilize the spatial flexibility of electric vehicles, and has high application value.

[0008] The first embodiment of the present invention proposes a method for analyzing the spatial flexibility of a fast charging station based on user preferences, comprising:

[0009] Collect multi-source data related to fast charging station selection, including: fast charging station data, fast charging user data and fast charging order data;

[0010] Based on the multi-source data, a set of candidate charging stations corresponding to each fast charging order and an attribute of each fast charging station in the set of candidate charging stations that affects user selection are obtained;

[0011] Based on the attributes, an optimization model for identifying user differentiated preferences is established and solved to obtain the user's attribute preference weights;

[0012] Based on the attribute preference weights, a fast charging station spatial flexibility analysis is performed.

[0013] In a specific embodiment of the present invention, obtaining the candidate charging station set corresponding to each fast charging order and the attributes of each fast charging station in the candidate charging station set that affect the user's selection includes:

[0014] 1) Based on the multi-source data, each fast charging order is marked to obtain a set of candidate charging stations corresponding to each fast charging order;

[0015] If the sth fast charging order of user n is displayed in scenario s, the user selects fast charging station i, then the fast charging station j within the preset distance δ around fast charging station i is used as the user's alternative charging station in this scenario to form an alternative charging station set. The candidate charging station set includes a fast charging station i;

[0016] Remember a sj Indicates whether the fast charging station j is marked as an alternative charging station in scenario s. If the fast charging station j belongs to Then a sj is 1, otherwise it is 0; record y sj Indicates whether the fast charging station j is a historically selected charging station in scenario s, where if j = i, then y sj is 1, otherwise it is 0;

[0017] 2) Based on the result of step 1), the attributes that affect the user's choice of each fast charging station in the set of candidate charging stations corresponding to each fast charging order are counted;

[0018] Note x nsj is the attribute of fast charging station j in scenario s for user n, x nsjk is the kth attribute of fast charging station j in user n scenario s, and the value range of k is 1 to 10, where x nsj1 to x nsj10 The average charging fee of fast charging station j in scenario s for user n is The number of available fast charging piles at fast charging station j when user n starts charging in scenario s Parking fee type F for fast charging station j j 、Fast charging pile power of fast charging station j The number of supporting facilities Q of fast charging station j j , the number of office-related points of interest (POIs) within the set range around the fast charging station The number of entertainment and shopping POIs within the set range around the fast charging station Number of medical and educational POIs within the set range around the fast charging station The number of residential POIs within the set range around the fast charging station The number of traffic-related POIs within the set range around the fast charging station j

[0019] In a specific embodiment of the present invention, the optimization model expression of the user differentiated preference identification is as follows:

[0020]

[0021] Wherein, formula (1) is the objective function of maximizing the optimization model of user differentiated preference identification, formula (2)-formula (4) are the constraints of the optimization model of user differentiated preference identification; L n is the maximum likelihood function of user n, i.e., the objective function; λ≥0 is a hyperparameter that controls the weight penalty; P nsj is the probability of user n choosing fast charging station j in scenario s; β n is the attribute preference weight of user n; V nsj is the utility of fast charging station j in scenario s for user n; V nsjk is the utility of the kth attribute of fast charging station j in scenario s for user n, β nk is the preference weight of the kth attribute of user n.

[0022] In a specific embodiment of the present invention, before establishing the optimization model for identifying user differentiated preferences, the method further includes:

[0023] The attribute is subjected to square root transformation or piecewise linear transformation so that the transformed attribute is close to normal distribution; wherein,

[0024] The square root conversion expression is as follows:

[0025]

[0026] Then use the square root conversion of formula (5) to get V nsjk Replace formula (3) to obtain the updated optimization model for identifying user differentiated preferences;

[0027] The piecewise linear conversion includes:

[0028] The k-th attribute set {x nsjk |n,s,j} is segmented using k-Means clustering. The number of segments is determined by the elbow method, and the k-th attribute is obtained. The starting point of the l-th segment is b kl , the starting point of the l+1th segment of the kth attribute is b k(l+1) ; The lth segment of the kth attribute of fast charging station j under user n scenario s is x nsjkl , β nkl is the preference weight of the lth segment of the kth attribute of user n;

[0029]

[0030] Using the piecewise linear conversion of formula (6) nsjk Replace equation (3) to obtain the updated optimization model for identifying user differentiated preferences.

[0031] The second embodiment of the present invention provides a fast charging station space flexibility analysis device based on user preferences, comprising:

[0032] A data collection module is used to collect multi-source data related to the selection of fast charging stations, including: fast charging station data, fast charging user data and fast charging order data;

[0033] an attribute statistics module, configured to obtain, based on the multi-source data, a set of candidate charging stations corresponding to each fast charging order and an attribute of each fast charging station in the set of candidate charging stations that affects the user's selection;

[0034] A preference identification module is used to establish and solve an optimization model for user differentiated preference identification based on the attributes to obtain the user's attribute preference weights;

[0035] The spatial flexibility analysis module is used to perform spatial flexibility analysis of fast charging stations based on the attribute preference weights.

[0036] In a specific embodiment of the present invention, obtaining the candidate charging station set corresponding to each fast charging order and the attributes of each fast charging station in the candidate charging station set that affect the user's selection includes:

[0037] 1) Based on the multi-source data, each fast charging order is marked to obtain a set of candidate charging stations corresponding to each fast charging order;

[0038] If the sth fast charging order of user n is displayed in scenario s, the user selects fast charging station i, then the fast charging station j within the preset distance δ around fast charging station i is used as the user's alternative charging station in this scenario to form an alternative charging station set. The candidate charging station set includes a fast charging station i;

[0039] Remember a sj Indicates whether the fast charging station j is marked as an alternative charging station in scenario s. If the fast charging station j belongs to Then a sj is 1, otherwise it is 0; record y sj Indicates whether the fast charging station j is a historically selected charging station in scenario s, where if j = i, then y sj is 1, otherwise it is 0;

[0040] 2) Based on the result of step 1), the attributes that affect the user's choice of each fast charging station in the set of candidate charging stations corresponding to each fast charging order are counted;

[0041] Note x nsj is the attribute of fast charging station j in scenario s for user n, x nsjk is the kth attribute of fast charging station j in user n scenario s, and the value range of k is 1 to 10, where x nsj1 to x nsj10 The average charging fee of fast charging station j in scenario s for user n is The number of available fast charging piles at fast charging station j when user n starts charging in scenario s Parking fee type F for fast charging station j j 、Fast charging pile power of fast charging station j The number of supporting facilities Q of fast charging station j j , the number of office-related points of interest (POIs) within the set range around the fast charging station The number of entertainment and shopping POIs within the set range around the fast charging station Number of medical and educational POIs within the set range around the fast charging station The number of residential POIs within the set range around the fast charging station The number of traffic-related POIs within the set range around the fast charging station j

[0042] In a specific embodiment of the present invention, the optimization model expression of the user differentiated preference identification is as follows:

[0043]

[0044] Wherein, formula (1) is the objective function of maximizing the optimization model of user differentiated preference identification, formula (2)-formula (4) are the constraints of the optimization model of user differentiated preference identification; L n is the maximum likelihood function of user n, i.e., the objective function; λ≥0 is a hyperparameter that controls the weight penalty; P nsj is the probability of user n choosing fast charging station j in scenario s; β n is the attribute preference weight of user n; V nsj is the utility of fast charging station j in scenario s for user n; V nsjk is the utility of the kth attribute of fast charging station j in scenario s for user n, β nk is the preference weight of the kth attribute of user n.

[0045] In a specific embodiment of the present invention, before establishing the optimization model for identifying user differentiated preferences, the method further includes:

[0046] The attribute is subjected to square root transformation or piecewise linear transformation so that the transformed attribute is close to normal distribution; wherein,

[0047] The square root conversion expression is as follows:

[0048]

[0049] Then use the square root conversion of formula (5) to get V nsjk Replace formula (3) to obtain the updated optimization model for identifying user differentiated preferences;

[0050] The piecewise linear conversion includes:

[0051] The k-th attribute set {x nsjk |n,s,j} is segmented using k-Means clustering. The number of segments is determined by the elbow method, and the k-th attribute is obtained. The starting point of the l-th segment is b kl , the starting point of the l+1th segment of the kth attribute is b k(l+1) ; The lth segment of the kth attribute of fast charging station j under user n scenario s is x nsjkl , β nkl is the preference weight of the lth segment of the kth attribute of user n;

[0052]

[0053] Using the piecewise linear conversion of formula (6) nsjk Replace equation (3) to obtain the updated optimization model for identifying user differentiated preferences.

[0054] A third aspect of the present invention provides an electronic device, including:

[0055] at least one processor; and a memory communicatively coupled to the at least one processor;

[0056] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above-mentioned method for analyzing the spatial flexibility of fast charging stations based on user preferences.

[0057] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-mentioned method for analyzing the spatial flexibility of fast charging stations based on user preferences.

[0058] The characteristics and beneficial effects of the present invention are:

[0059] The present invention first uses existing data (fast charging station data, fast charging user data, fast charging order data) and a data-driven discrete choice model to analyze the fast charging station selection behavior, focusing on differentiated user preference analysis, including users' differentiated preferences for multiple influencing attributes (price, number of available piles, parking fee type, charging power, supporting facilities, surrounding POI), and the difference in selection preferences between commercial vehicles and private car users, and then uses the differentiated preferences to analyze the spatial flexibility of fast charging stations. The present invention achieves an accuracy of more than 80% when using the obtained preferences to predict charging station selection behavior. The present invention can be used to explore the load flexibility of fast charging stations through price incentives in the scenario of eliminating congestion in urban-level power grids, and promote the optimal allocation and management of power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is an overall flow chart of a method for analyzing spatial flexibility of fast charging stations based on user preferences according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The present invention proposes a method and device for analyzing the spatial flexibility of a fast charging station based on user preferences, which is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0062] The first embodiment of the present invention proposes a method for analyzing the spatial flexibility of a fast charging station based on user preferences, comprising:

[0063] Collect multi-source data related to fast charging station selection, including: fast charging station data, fast charging user data and fast charging order data;

[0064] Based on the multi-source data, a set of candidate charging stations corresponding to each fast charging order and an attribute of each fast charging station in the set of candidate charging stations that affects user selection are obtained;

[0065] Based on the attributes, an optimization model for identifying user differentiated preferences is established and solved to obtain the user's attribute preference weights;

[0066] Based on the attribute preference weights, a fast charging station spatial flexibility analysis is performed.

[0067] In a specific embodiment of the present invention, the method for analyzing the spatial flexibility of fast charging stations based on user preferences has an overall process as follows: Figure 1 As shown, including:

[0068] 1) Collect multi-source data related to fast charging station selection.

[0069] In this embodiment, the multi-source data includes: fast charging station data, fast charging user data and fast charging order data.

[0070] The fast charging station data needs to include static information of each fast charging station (charging station type, parking fee type, charging power, surrounding points of interest (POI), supporting facilities) and dynamic charging prices during the selected period. In this embodiment, the charging station type is used to determine whether it is a public fast charging station rather than a dedicated charging station. The fast charging stations in this embodiment are all public fast charging stations, and the selected period lasts at least three months.

[0071] The fast charging user data is user data displayed in the fast charging station data that uses the fast charging pile of the fast charging station to charge. In this embodiment, the fast charging user data includes the type of user, which is divided into private car users and commercial vehicle users in this embodiment.

[0072] The fast charging order data is the fast charging order data corresponding to the fast charging station data.

[0073] 2) Based on the data collected in step 1), the charging scene is reproduced to obtain the set of candidate charging stations corresponding to each fast charging order and the attributes of each fast charging station in the set of candidate charging stations that affect the user's choice; the specific steps are as follows:

[0074] 2-1) Based on the data collected in step 1), each fast charging order is marked to obtain a set of candidate charging stations corresponding to each fast charging order.

[0075] Specifically, if the sth fast charging order of user n (i.e., scenario s) shows that the user selects fast charging station i, then all fast charging stations j (including fast charging station i) within a preset distance δ around fast charging station i are taken as the user's alternative charging stations in this scenario to form an alternative charging station set. It is recommended that the value of the preset distance δ be less than or equal to 3. In a specific implementation of the present invention, the value of δ is 3. sj Indicates whether the fast charging station j is marked as an alternative charging station in scenario s. If the fast charging station j belongs to Then a sj is 1, otherwise it is 0. sj Indicates whether the marked fast charging station j is a historically selected fast charging station in scenario s, where if j = i, then y sj is 1, otherwise it is 0. This embodiment assumes that user n selects the sth charging order by comparing All the fast charging stations in the area finally chose the fast charging station i.

[0076] 2-2) Based on the result of step 2-1), the attributes that affect the user's choice of each fast charging station in the set of candidate charging stations corresponding to each fast charging order are counted.

[0077] In this embodiment, x nsj is the attribute of fast charging station j in scenario s for user n, x nsjk is the kth attribute of the fast charging station j in the user n scenario s. In this embodiment, the value range of k is 1 to 10, where x nsj1 to x nsj10 The average charging fee of fast charging station j during the charging period of user n in scenario s is The number of fast charging piles available at fast charging station j when user n starts charging in scenario s Parking fee type F for fast charging station j j 、Fast charging pile power of fast charging station j The number of supporting facilities Q of fast charging station j j , the number of office-related POIs (points of interest) within the set range around the fast charging station j The number of entertainment and shopping POIs within the set range around the fast charging station Number of medical and educational POIs within the set range around the fast charging station The number of residential POIs within the set range around the fast charging station The number of traffic-related POIs within the set range around the fast charging station j In a specific embodiment of the present invention, the set range is 1 km.

[0078] 3) Based on the result of step 2), an optimization model for identifying user differentiated preferences is established, and the user's attribute preference weights are obtained by solving the optimization model.

[0079] In this embodiment, for any user n, the optimization model for identifying user differentiated preferences is expressed as follows:

[0080]

[0081] Among them, formula (1) is the objective function of maximizing the optimization model of user differentiated preference identification, and formula (2)-formula (4) are the constraints of the optimization model of user differentiated preference identification. n is the maximum likelihood function of user n, i.e., the objective function. λ≥0 is a hyperparameter that controls the weight penalty. nsj is the probability of user n choosing fast charging station j in scenario s. n is the attribute preference weight of user n. nsj is the utility of fast charging station j in scenario s for user n. V nsjk is the utility of the kth attribute of fast charging station j in scenario s for user n, β nkis the preference weight of the kth attribute of user n. In the scenario where the attributes are highly collinear, the objective function is The penalty avoids the preference weight β nk The risk of overfitting. Formula (4) is the preference β nk range constraints.

[0082] Specifically, when estimating the preference weights, this embodiment needs to constrain the preference weights according to the preference range of formula (4) based on prior knowledge, where the three terms represent non-negative, non-positive or unconstrained. For example, if other attributes remain unchanged and the average price rises, the selection utility decreases, that is, the preference weight of the average price should be a non-positive constraint. According to prior knowledge, the preference ranges of charging prices and parking fee types are non-positive values, the preference ranges of the number of available fast charging piles, charging power and supporting facilities are non-negative values, and the preference ranges related to POIs are not restricted.

[0083] 4) Based on the results of step 3), perform a fast charging station (FCS) spatial flexibility analysis.

[0084] The spatial flexibility of FCS is the sum of the spatial flexibility at the user level, which is affected by user preferences. Therefore, the spatial flexibility of FCS loads can be reliably analyzed by differentiating user preferences.

[0085] In this example, we first analyze the spatial flexibility at the user level. The user preferences have been obtained, and the set of alternative charging stations is determined based on the user's historical charging behavior. The average charging fee of fast charging station j during the charging period of user n in scenario s under price incentives The user compares the probability P of choosing a charging station from the set of alternative charging stations under price incentives. nsj Determine the fast charging station to go to for charging. The presence or absence of price incentives changes the user's choice of fast charging stations. The user's load is spatially transferred due to price incentives, forming user-level spatial flexibility.

[0086] Secondly, the spatial flexibility of FCS load. The spatial flexibility at the user level is reflected in the FCS axis and time axis. Summarizing the spatial flexibility of all users along the FCS axis and time axis naturally forms the overall spatial flexibility of FCS load.

[0087] Furthermore, before establishing the optimization model for identifying user differentiated preferences, the method of this embodiment may also include:

[0088] Attribute conversion. Specifically:

[0089] The conversion methods of each attribute in this embodiment include square root conversion and piecewise linear conversion. To avoid over-reliance on prior knowledge in determining the attribute conversion method, this embodiment can determine the attribute conversion method in combination with the attribute distribution, and the conversion goal is to make the converted attribute closer to normal distribution.

[0090] Among them, for the square root conversion, taking the number of available piles as an example, its utility V nsjk There is a saturation trend, rather than the linear change trend in formula (3). The saturation trend means that as the number of available piles increases, the user utility increases very slowly or even stops increasing. For attributes with this trend, it is necessary to use formula (5) for square root conversion:

[0091]

[0092] Then use the square root conversion of formula (5) to get V nsjk The replacement formula (3) can better reflect the utility trend.

[0093] For piecewise linear conversion, taking charging power as an example, its utility V nsjk There may be changes in preference weights in different segments, rather than the linear change trend in formula (3). From a physical point of view, the utility of charging power may increase rapidly in the middle and low segments of fast charging, but increase slowly in the super fast charging segment, or have the opposite increase trend for some users. For attributes with this trend, use formula (6) to convert V nsjk The replacement formula (3) can better reflect the utility trend. In this embodiment, the kth attribute set {x nsjk |n,s,j} is segmented using k-Means clustering. The number of segments is determined by the elbow method. The k-th attribute can be obtained. The starting point of the l-th segment is b kl , the starting point of the l+1th segment of the kth attribute is b k(l+1) The lth segment of the kth attribute of fast charging station j under user n scenario s is x nsjkl , β nkl is the preference weight of the lth segment of the kth attribute of user n.

[0094]

[0095] Furthermore, the method described in this embodiment is further described in detail below in conjunction with a specific embodiment.

[0096] In a specific implementation of the present invention, the method for analyzing the spatial flexibility of fast charging stations based on user preferences comprises the following steps:

[0097] 1) Collect multi-source data related to fast charging station selection.

[0098] In this embodiment, the data comes from the actual operation data of the charging station operator Star Charge, including fast charging station data, fast charging user data and fast charging order data. The data collection scope is Taiyuan City, China. To identify user preferences, the specific data used include the following:

[0099] The fast charging station data includes more than 200 fast charging stations with fast charging piles in Taiyuan City, Shanxi Province. According to the Star Charging Operator Operating Rules, charging piles with a charging power greater than 20kW are considered fast charging piles. The data content includes static information of the station, such as charging station type, parking fee type, charging power, surrounding points of interest (POI), supporting facilities, etc., as well as dynamic charging prices from July 25, 2023 to October 31, 2023.

[0100] The fast charging user data includes information on 2,746 users in Taiyuan City, Shanxi Province who used fast charging piles to charge their cars. The user types are divided into private car users and commercial vehicle users (such as taxis and online car-hailing services).

[0101] The fast charging order data refers to 114,781 fast charging order data generated by the 2,746 fast charging users mentioned above at fast charging stations in Taiyuan City, Shanxi Province between July 25, 2023 and October 31, 2023, including 25,805 orders for private cars and 88,976 orders for commercial vehicles.

[0102] 2) Based on the data collected in step 1), the charging scene is reproduced to obtain the set of candidate charging stations corresponding to each fast charging order and the attributes of each fast charging station in the set of candidate charging stations that affect the user's choice; the specific steps are as follows:

[0103] 2-1) Based on the data collected in step 1), each fast charging order is marked to obtain a set of alternative charging stations corresponding to each fast charging order, so as to obtain a set of alternative charging stations corresponding to each fast charging order and attributes of each fast charging station in the set of alternative charging stations that affect user selection.

[0104] In this embodiment, it can be known from the order data that the sth fast charging order of user n (i.e., in scenario s) shows that the user selects fast charging station i, then all fast charging stations j (including fast charging station i) within 3 km around fast charging station i are regarded as the user's alternative charging stations in this scenario to form an alternative charging station set.

[0105] Remember a sj Indicates whether the fast charging station j is marked as an alternative charging station in scenario s. If the fast charging station j belongs to Then a sjis 1, otherwise it is 0. sj Indicates whether the marked fast charging station j is a historically selected fast charging station in scenario s, where if j = i, then y sj is 1, otherwise it is 0. This means that user n compares All the fast charging stations in the final selection of fast charging station i. In this embodiment, the data collected from step 1) can obtain 223 fast charging stations The number of fast charging stations ranges from a minimum of 1 to a maximum of 31, with an average of 14.

[0106] 2-2) Based on the result of step 2-1), the attributes that affect the user's choice of each fast charging station in the set of candidate charging stations corresponding to each fast charging order are counted.

[0107] In this embodiment, x nsj is the attribute of fast charging station j in scenario s for user n, x nsjk is the kth attribute of fast charging station j in scenario s for user n, where k ranges from 1 to 10, where x nsj1 to x nsj10 The average charging fee of fast charging station j during the charging period of user n in scenario s is The number of fast charging piles available at fast charging station j when user n scenario s starts charging Parking fee type F for fast charging station j j 、Fast charging pile power of fast charging station j The number of supporting facilities Q of fast charging station j j , the number of office-related POIs (points of interest) within 1 km of the fast charging station j Number of entertainment and shopping POIs within 1 km of fast charging station j Number of medical and educational POIs within 1km of fast charging station j Number of residential POIs within 1km of fast charging station j Number of traffic-related POIs within 1 km of fast charging station j

[0108] 3) Based on the result of step 2), an optimization model for identifying user differentiated preferences is established, and the user's attribute preference weights are obtained by solving the optimization model.

[0109] In this embodiment, for any user n, the optimization model for identifying user differentiated preferences is expressed as follows:

[0110]

[0111] Among them, formula (1) is the objective function of maximizing the optimization model of user differentiated preference identification, and formula (2)-formula (4) (4) are the constraint functions of the optimization model of user differentiated preference identification. n is the maximum likelihood function of user n, i.e., the objective function. λ≥0 is a hyperparameter that controls the weight penalty. nsj is the probability of user n choosing fast charging station j in scenario s. n is the attribute preference weight of user n. nsj is the utility of fast charging station j in scenario s for user n. V nsjk is the utility of the kth attribute of fast charging station j in scenario s for user n, β nk is the preference weight of the kth attribute of user n. In the scenario where the attributes are highly collinear, the objective function is The penalty avoids the preference weight β nk The risk of overfitting. Formula (4) is the preference β nk range constraints.

[0112] Specifically, when estimating preference weights, it is necessary to constrain the preference weights according to the prior knowledge using the preference range of formula (4), where the three terms represent non-negative, non-positive, or unconstrained. For example, if other attributes remain unchanged and the average price rises, the choice utility decreases, that is, the preference weight of the average price should be a non-positive constraint. According to prior knowledge, the preference ranges of charging prices and parking fee types are non-positive values, the preference ranges of the number of available fast charging piles, charging power, and supporting facilities are non-negative values, and the preference ranges related to POIs are not restricted.

[0113] Furthermore, this embodiment can further improve the accuracy of preference identification through attribute conversion; the details are as follows:

[0114] After analysis, this embodiment found that the attribute conversion method can be determined in combination with the attribute distribution, and the conversion goal is to make the converted attribute closer to the normal distribution. Combined with prior knowledge and distribution, the conversion method of each input attribute is as follows: the average charging fee and parking fee type during charging are not converted, the number of fast charging piles is square root converted, and the fast charging pile power, the number of supporting facilities, and POI related attributes are piecewise linearly converted.

[0115] In this embodiment, the number of private car users is 1411, the number of commercial vehicle users is 1335, the number of private car orders is 25805, and the number of commercial vehicle orders is 88976. By comparing the following three cases, we can see the importance of different user preferences and attribute conversions in improving prediction accuracy. Of each user's sample, 70% is used as a training set and 30% is used as a test set. In Scheme 1, the user selects the most frequently selected FCS in the corresponding charging period. In Scheme 2, a basic model containing preference range restrictions is used to identify user preferences. In Scheme 3, user preferences are determined by an improved model containing attribute conversion (i.e., the model described in this embodiment is used). The prediction accuracy of private car users choosing FCS in the three cases is 70.3%, 85.0%, and 88.3%, respectively, and the prediction accuracy of commercial vehicle users choosing FCS in the three cases is 65.9%, 79.7%, and 83.8%, respectively.

[0116] Among the average charging fee preference weights during charging, the proportion of users with high and medium preference weights for commercial vehicles is 40%, which is higher than the 27% for private cars. The preference weights of this attribute vary greatly between the two types of users. From the perspective of tapping the load flexibility of fast charging stations, commercial vehicles have greater flexibility potential.

[0117] 4) Based on the results of step 3), perform a fast charging station (FCS) spatial flexibility analysis.

[0118] In this example, more than 200 fast charging stations are studied in Taiyuan City, Shanxi Province. By analyzing the load of the studied FCSs from 11:30 to 11:45, the loads of the fast charging stations No. 126 and No. 127 are higher, 4068 kW and 4457 kW, respectively. Assuming that the power system needs to perform congestion management, the CSO's coupon-based price incentives take advantage of the spatial flexibility of FCS No. 126 and No. 127. By issuing coupons of 0.3 yuan / kWh to users, users can charge at neighboring FCSs from 11:00 to 12:00; the spatial flexibility of FCS126 and 127 is as follows. The load of FCS 126 and 127 during 11:30 to 11:45 was reduced by 188 kW and 659 kW, respectively, and the spatial flexibility ratios were -4.6% and -14.8%, respectively. Therefore, due to the differences between the preferences of users at the two stations, the same price incentives have different effects on the spatial flexibility of different FCSs.

[0119] To implement the above embodiment, a second aspect of the present invention provides a fast charging station space flexibility analysis device based on user preferences, comprising:

[0120] A data collection module is used to collect multi-source data related to the selection of fast charging stations, including: fast charging station data, fast charging user data and fast charging order data;

[0121] an attribute statistics module, configured to obtain, based on the multi-source data, a set of candidate charging stations corresponding to each fast charging order and an attribute of each fast charging station in the set of candidate charging stations that affects the user's selection;

[0122] A preference identification module is used to establish and solve an optimization model for user differentiated preference identification based on the attributes to obtain the user's attribute preference weights;

[0123] The spatial flexibility analysis module is used to perform spatial flexibility analysis of fast charging stations based on the attribute preference weights.

[0124] In a specific implementation of the present invention, obtaining the candidate charging station set corresponding to each fast charging order and the attributes of each fast charging station in the candidate charging station set that affect the user's selection includes:

[0125] 1) Based on the multi-source data, each fast charging order is marked to obtain a set of candidate charging stations corresponding to each fast charging order;

[0126] If the sth fast charging order of user n is displayed in scenario s, the user selects fast charging station i, then the fast charging station j within the preset distance δ around fast charging station i is used as the user's alternative charging station in this scenario to form an alternative charging station set. The candidate charging station set includes a fast charging station i;

[0127] Remember a sj Indicates whether the fast charging station j is marked as an alternative charging station in scenario s. If the fast charging station j belongs to Then a sj is 1, otherwise it is 0; record y sj Indicates whether the marked fast charging station j is a historically selected fast charging station in scenario s, where if j = i, then y sj is 1, otherwise it is 0;

[0128] 2) Based on the result of step 1), the attributes that affect the user's choice of each fast charging station in the set of candidate charging stations corresponding to each fast charging order are counted;

[0129] Note x nsj is the attribute of fast charging station j in scenario s for user n, x nsjk is the kth attribute of fast charging station j in user n scenario s, and the value range of k is 1 to 10, where x nsj1 to x nsj10 The average charging fee of fast charging station j in scenario s for user n is The number of available fast charging piles at fast charging station j when user n starts charging in scenario s Parking fee type F for fast charging station j j 、Fast charging pile power of fast charging station j The number of supporting facilities Q of fast charging station j j 、Number of office-related POIs within the set range around the fast charging station The number of entertainment and shopping POIs within the set range around the fast charging station Number of medical and educational POIs within the set range around the fast charging station The number of residential POIs within the set range around the fast charging station The number of traffic-related POIs within the set range around the fast charging station j

[0130] In a specific implementation of the present invention, the optimization model expression of the user differentiated preference identification is as follows:

[0131]

[0132] Wherein, formula (1) is the objective function of maximizing the optimization model of user differentiated preference identification, formula (2)-formula (4) are the constraints of the optimization model of user differentiated preference identification; L n is the maximum likelihood function of user n, i.e., the objective function; λ≥0 is a hyperparameter that controls the weight penalty; P nsj is the probability of user n choosing fast charging station j in scenario s; β n is the attribute preference weight of user n; V nsj is the utility of fast charging station j in scenario s for user n; V nsjk is the utility of the kth attribute of fast charging station j in scenario s for user n, β nk is the preference weight of the kth attribute of user n.

[0133] In a specific implementation of the present invention, before establishing the optimization model for identifying user differentiated preferences, the method further includes:

[0134] The attribute is subjected to square root transformation or piecewise linear transformation so that the transformed attribute is close to normal distribution; wherein,

[0135] The square root conversion expression is as follows:

[0136]

[0137] Then use the square root conversion of formula (5) to get V nsjk Replace formula (3) to obtain the updated optimization model for identifying user differentiated preferences;

[0138] The piecewise linear conversion includes:

[0139] The k-th attribute set {x nsjk |n,s,j} is segmented using k-Means clustering. The number of segments is determined by the elbow method, and the k-th attribute is obtained. The starting point of the l-th segment is b kl , the starting point of the l+1th segment of the kth attribute is b k(l+1) ; The lth segment of the kth attribute of fast charging station j under user n scenario s is x nsjkl , β nkl is the preference weight of the lth segment of the kth attribute of user n;

[0140]

[0141] Using the piecewise linear conversion of formula (6) nsjk Replace equation (3) to obtain the updated optimization model for identifying user differentiated preferences.

[0142] This can be achieved based on the analysis of fast charging station selection behavior, and the price incentive-load flexibility relationship of city-level fast charging stations can be established, which provides a basis for charging station operators to use price incentives to utilize the spatial flexibility of electric vehicles and has high application value.

[0143] To implement the above embodiment, a third aspect of the present invention provides an electronic device, including:

[0144] at least one processor; and a memory communicatively coupled to the at least one processor;

[0145] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above-mentioned method for analyzing the spatial flexibility of fast charging stations based on user preferences.

[0146] To implement the above-mentioned embodiment, a fourth aspect of the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned method for analyzing the spatial flexibility of fast charging stations based on user preferences.

[0147] It should be noted that the computer-readable medium disclosed above 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 disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, 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 suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0148] The computer readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method for analyzing the spatial flexibility of fast charging stations based on user preferences in the above embodiment.

[0149] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0150] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0151] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0152] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0154] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0155] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0156] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0157] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for analyzing the spatial flexibility of fast charging stations based on user preferences, characterized in that: include: Collect multi-source data related to fast charging station selection, including: fast charging station data, fast charging user data and fast charging order data; Based on the multi-source data, a set of candidate charging stations corresponding to each fast charging order and an attribute of each fast charging station in the set of candidate charging stations that affects user selection are obtained; Based on the attributes, an optimization model for identifying user differentiated preferences is established and solved to obtain the user's attribute preference weights; Based on the attribute preference weights, a fast charging station spatial flexibility analysis is performed.

2. The method according to claim 1, characterized in that The step of obtaining a set of candidate charging stations corresponding to each fast charging order and the attributes of each fast charging station in the set of candidate charging stations that affect the user's selection includes: 1) Based on the multi-source data, each fast charging order is marked to obtain a set of candidate charging stations corresponding to each fast charging order; If the sth fast charging order of user n is displayed in scenario s, the user selects fast charging station i, then the fast charging station j within the preset distance δ around fast charging station i is used as the user's alternative charging station in this scenario to form an alternative charging station set. The candidate charging station set includes a fast charging station i; Remember a sj Indicates whether the fast charging station j is marked as an alternative charging station in scenario s. If the fast charging station j belongs to Then a sj is 1, otherwise it is 0; record y sj Indicates whether the fast charging station j is a historically selected charging station in scenario s, where if j = i, then y sj is 1, otherwise it is 0; 2) Based on the result of step 1), the attributes that affect the user's choice of each fast charging station in the set of candidate charging stations corresponding to each fast charging order are counted; Note x nsj is the attribute of fast charging station j in scenario s for user n, x nsjk is the kth attribute of fast charging station j in user n scenario s, and the value range of k is 1 to 10, where x nsj1 to x nsj10 The average charging fee of fast charging station j in scenario s for user n is The number of available fast charging piles at fast charging station j when user n starts charging in scenario s Parking fee type F for fast charging station j j 、Fast charging pile power of fast charging station j The number of supporting facilities Q of fast charging station j j , the number of office-related points of interest (POIs) within the set range around the fast charging station The number of entertainment and shopping POIs within the set range around the fast charging station Number of medical and educational POIs within the set range around the fast charging station The number of residential POIs within the set range around the fast charging station The number of traffic-related POIs within the set range around the fast charging station j 3. The method according to claim 2, characterized in that The optimization model expression of the user differentiated preference identification is as follows: Wherein, formula (1) is the objective function of maximizing the optimization model of user differentiated preference identification, formula (2)-formula (4) are the constraints of the optimization model of user differentiated preference identification; L n is the maximum likelihood function of user n, i.e., the objective function; λ≥0 is a hyperparameter that controls the weight penalty; P nsj is the probability of user n choosing fast charging station j in scenario s; β n is the attribute preference weight of user n; V nsj is the utility of fast charging station j in scenario s for user n; V nsjk is the utility of the kth attribute of fast charging station j in scenario s for user n, β nk is the preference weight of the kth attribute of user n.

4. The method according to claim 3, characterized in that Before establishing the optimization model for identifying user differentiated preferences, the method further includes: The attribute is subjected to square root transformation or piecewise linear transformation so that the transformed attribute is close to normal distribution; wherein, The square root conversion expression is as follows: Then use the square root conversion of formula (5) to get V nsjk Replace formula (3) to obtain the updated optimization model for identifying user differentiated preferences; The piecewise linear conversion includes: The k-th attribute set {x nsjk |n,s,j} is segmented using k-Means clustering. The number of segments is determined by the elbow method, and the k-th attribute is obtained. The starting point of the l-th segment is b kl , the starting point of the l+1th segment of the kth attribute is b k(l+1) ; The lth segment of the kth attribute of fast charging station j in user n scenario s is x nsjkl , β nkl is the preference weight of the lth segment of the kth attribute of user n; Using the piecewise linear conversion of formula (6) nsjk Replace equation (3) to obtain the updated optimization model for identifying user differentiated preferences.

5. A fast charging station space flexibility analysis device based on user preferences, characterized in that: include: A data collection module is used to collect multi-source data related to the selection of fast charging stations, including: fast charging station data, fast charging user data and fast charging order data; an attribute statistics module, configured to obtain, based on the multi-source data, a set of candidate charging stations corresponding to each fast charging order and an attribute of each fast charging station in the set of candidate charging stations that affects the user's selection; A preference identification module is used to establish and solve an optimization model for user differentiated preference identification based on the attributes to obtain the user's attribute preference weights; The spatial flexibility analysis module is used to perform spatial flexibility analysis of fast charging stations based on the attribute preference weights.

6. The device according to claim 5, characterized in that The step of obtaining the candidate charging station set corresponding to each fast charging order and the attributes of each fast charging station in the candidate charging station set that affect the user's selection includes: 1) Based on the multi-source data, each fast charging order is marked to obtain a set of candidate charging stations corresponding to each fast charging order; If the sth fast charging order of user n is displayed in scenario s, the user selects fast charging station i, then the fast charging station j within the preset distance δ around fast charging station i is used as the user's alternative charging station in this scenario to form an alternative charging station set. The candidate charging station set includes a fast charging station i; Remember a sj Indicates whether the fast charging station j is marked as an alternative charging station in scenario s. If the fast charging station j belongs to Then a sj is 1, otherwise it is 0; record y sj Indicates whether the fast charging station j is a historically selected charging station in scenario s, where if j = i, then y sj is 1, otherwise it is 0; 2) Based on the result of step 1), the attributes that affect the user's choice of each fast charging station in the set of candidate charging stations corresponding to each fast charging order are counted; Note x nsj is the attribute of fast charging station j in scenario s for user n, x nsjk is the kth attribute of fast charging station j in user n scenario s, and the value range of k is 1 to 10, where x nsj1 to x nsj10 The average charging fee of fast charging station j in scenario s for user n is The number of available fast charging piles at fast charging station j when user n starts charging in scenario s Parking fee type F for fast charging station j j 、Fast charging pile power of fast charging station j The number of supporting facilities Q of fast charging station j j , the number of office-related points of interest (POIs) within the set range around the fast charging station The number of entertainment and shopping POIs within the set range around the fast charging station Number of medical and educational POIs within the set range around the fast charging station The number of residential POIs within the set range around the fast charging station The number of traffic-related POIs within the set range around the fast charging station j 7. The device according to claim 6, characterized in that The optimization model expression of the user differentiated preference identification is as follows: Wherein, formula (1) is the objective function of maximizing the optimization model of user differentiated preference identification, formula (2)-formula (4) are the constraints of the optimization model of user differentiated preference identification; L n is the maximum likelihood function of user n, i.e., the objective function; λ≥0 is a hyperparameter that controls the weight penalty; P nsj is the probability of user n choosing fast charging station j in scenario s; β n is the attribute preference weight of user n; V nsj is the utility of fast charging station j in scenario s for user n; V nsjk is the utility of the kth attribute of fast charging station j in scenario s for user n, β nk is the preference weight of the kth attribute of user n.

8. The device according to claim 7, characterized in that Before establishing the optimization model for identifying user differentiated preferences, the method further includes: The attribute is subjected to square root transformation or piecewise linear transformation so that the transformed attribute is close to normal distribution; wherein, The square root conversion expression is as follows: Then use the square root conversion of formula (5) to get V nsjk Replace formula (3) to obtain the updated optimization model for identifying user differentiated preferences; The piecewise linear conversion includes: The k-th attribute set {x nsjk |n,s,j} is segmented using k-Means clustering. The number of segments is determined by the elbow method, and the k-th attribute is obtained. The starting point of the l-th segment is b kl , the starting point of the l+1th segment of the kth attribute is b k(l+1) ; The lth segment of the kth attribute of fast charging station j in user n scenario s is x nsjkl , β nkl is the preference weight of the lth segment of the kth attribute of user n; Using the piecewise linear conversion of formula (6) nsjk Replace equation (3) to obtain the updated optimization model for identifying user differentiated preferences.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method according to any one of claims 1 to 4.

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