Electric vehicle orderly charging guidance method and system based on blockchain smart contract

Abstract

The present invention relates to the field of blockchain technology and electric vehicle charging scheduling technology, and specifically to an orderly charging guidance method and system for electric vehicles based on blockchain smart contracts. Electric vehicle users submit charging demand parameters to the charging platform, and the blockchain system obtains the distance between the user and each charging station through a commercial map interface to form a set of charging stations accessible to electric vehicle users and a distance matrix. The charging station nodes take turns to call the orderly guidance model constructed in the smart contract to solve, obtain a recommended set of charging stations, and store the calculated data in the blockchain system through a consensus mechanism. The present invention combines blockchain technology, introduces a fairness index, limits the volatility of single-pile revenue, provides users with a set of charging stations through a user selection model and an orderly charging guidance model, ensures the fairness of the revenue distribution of charging station operators, and improves user charging utility.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology and electric vehicle charging scheduling, and in particular to a method and system for guiding the orderly charging of electric vehicles based on blockchain smart contracts. Background Art

[0002] In recent years, as people pay more attention to environmental protection, electric vehicles have gained space and opportunities for development as an environmentally friendly means of transportation. Although many people have adopted electric vehicles as a means of travel, there are still many obstacles for electric vehicle users and charging station operators in the process of promoting electric vehicles. On the one hand, electric vehicle users are highly concerned about the information of electric vehicle charging facilities (such as the location of charging stations, charging prices, etc.), and the lack of transparency of information between different charging platforms will further lead to users' mileage anxiety. On the other hand, charging station operators develop charging platforms separately to serve registered users of the platforms, and registered users cannot be shared between different platforms, resulting in operators in the charging market acting independently, causing a decrease in the utilization rate of charging piles and a decrease in the income of charging station operators. In summary, breaking the information barriers between charging station operators has a direct impact on user charging experience and charging station profitability.

[0003] At present, building a unified charging platform is the main way to solve the above problems. However, existing third-party centralized charging platforms have trust and security issues. First, charging station operators lack trust in centralized third-party platforms because the demand allocation mechanism dominated by third parties may be unfair. Second, the business information recorded in the centralized system may be leaked or tampered with by malicious third-party platforms, posing a potential threat to both electric vehicle users and charging station operators. Blockchain technology can establish a naturally trusted charging platform between charging station operators and solve the trust problem of demand allocation through smart contracts, while ensuring the security of business information.

[0004] Both blockchain and smart contracts are widely used in various business scenarios with their powerful digital auditing functions, such as resource trading, healthcare, supply chain management, data management and other fields. In previous studies, some scholars proposed a charging platform framework based on blockchain, but the charging guidance strategy deployed in its smart contract did not consider the bilateral interests of users and operators. The utility of electric vehicle users is mainly affected by charging information (such as charging station location and charging price). Improving the overall utility of users is a key factor for charging station operators to increase their revenue. How to describe the relationship between charging station operators and electric vehicle users' choice behavior is the key to the feasibility of the charging platform. The fairness of income distribution guided by demand distribution is the basis for the operator's union. For charging facilities and charging prices with different spatial distributions, the charging demand is uneven, which directly affects the fairness of demand distribution. Therefore, a reasonable and orderly charging guidance method and system are urgently needed to jointly establish a unified charging platform in the charging market and promote the realization of the vision of "ten thousand piles interconnected". Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention aims to provide an orderly charging guidance method and system for electric vehicles based on blockchain smart contracts, so as to solve the problems of unfair distribution of charging demand and low platform trust caused by the uneven distribution of charging facilities and charging prices on different platforms and the uneven charging demand.

[0006] In order to solve the above problems, the present invention adopts the following technical solutions:

[0007] A method for guiding orderly charging of electric vehicles based on blockchain smart contracts, comprising:

[0008] (1) Electric vehicle users submit charging requests to the charging platform and submit charging demand parameters through their personal mobile terminals;

[0009] (2) The blockchain system obtains the distance between the user and each charging station through the commercial map interface, forming a set of charging stations accessible to electric vehicle users and a distance matrix. The charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve the problem, obtain the recommended set of charging stations, and store the calculated data in the blockchain system through the consensus mechanism;

[0010] (3) Returning the recommended charging station set to the electric vehicle user's personal mobile terminal;

[0011] In step (2), the charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve, and the charging station recommendation set obtained includes:

[0012] A. Establish a charging station in-station data set, cluster the charging stations according to the charging prices to form similar price areas, set a fairness index, and limit the fluctuation of the single-pile revenue within the similar price area to not exceed the fairness index;

[0013] B. Based on the multinomial Logit user selection model, the probability of an electric vehicle user charging at the charging station is calculated according to the driving distance from the electric vehicle user's demand point to the charging station and the charging rate; the upper limit of the number of recommendations for a single charging station is the number of idle charging piles in the station, and the upper limit of the number of recommendations for electric vehicle users is determined by negotiation between the charging station operators;

[0014] C. Construct an ordered guidance model based on the fairness index, the user selection model, the upper limit of the number of electric vehicle user recommendations and the upper limit of the recommendation of a single charging station, and construct an equivalent second-order cone programming model based on the ordered guidance model to calculate the recommended charging station set.

[0015] Furthermore, the fairness index f=1-ρ, where is the volatility of the revenue of a single charging station. is the revenue of each charging station. is the average revenue per charging pile of charging stations in similar price areas, L is the total number of similar price areas, K l is the set of charging stations in area l, π i is the expected revenue of charging station i, C i is the number of charging piles at charging station i, K i is the set of charging stations in area i with similar price.

[0016] Furthermore, the multinomial Logit user choice model meets the following conditions:

[0017]

[0018] Among them, r ni is the probability that electric vehicle user n chooses to go to charging station i, α is the initial utility of electric vehicle users for charging, β is the preference weight coefficient of electric vehicle users for driving distance, γ is the preference weight coefficient of electric vehicle users for charging price, d ni is the distance between electric vehicle user n and charging station i, d n_max P is the maximum distance that electric vehicle user n is willing to travel to receive charging services, i is the charging price, p i_max is the maximum charging price, I n ={i∈I:d ni ≤d n_max} is the set of charging stations accessible to user n, x ni is a binary variable, x ni=0 means that user n selects charging station i, otherwise it does not.

[0019] Furthermore, the equivalent second-order cone programming model is:

[0020]

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[0022]

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[0032] Among them, r ni is the probability that user n chooses to go to charging station i, I n ={i∈I:d ni ≤d n_max} is the set of charging stations accessible to user n, α is the initial utility of electric vehicle users for charging, β is the preference weight coefficient of electric vehicle users for driving distance, γ is the preference weight coefficient of electric vehicle users for charging price, d ni is the distance between electric vehicle user n and charging station i, d n_max P is the maximum distance that electric vehicle user n is willing to travel to receive charging services, i is the charging price, p i_max is the maximum charging price, π i is the expected revenue of charging station i, K i is the set of charging stations in area i with similar prices, C i is the number of available charging piles at charging station i, L is the total number of similar price areas, K l is the set of charging stations in area l, x ni is a binary variable, x ni=1 means that charging station i is recommended to user n, otherwise it is not recommended. maxF is the maximum selection probability of all users, E n is the charging demand of user n, r n0 is the probability that the user does not choose any charging station, B i is the number of available idle piles in charging station i, A n is the upper limit of the number of charging stations recommended to user n, C i is the number of charging stations at charging station i.

[0033] Furthermore, before step (1), when the electric vehicle user and the charging station operator join the charging platform, they apply for a digital certificate from the government agency to confirm the legitimacy of their identity. After obtaining the digital certificate, the charging station operator and the electric vehicle user obtain a certified public-private key pair, and the blockchain system automatically creates account information for accessing the platform in the state tree according to the content of the agreement. The account information includes the account information of the electric vehicle user and the account information of the charging station operator.

[0034] The account information of electric vehicle users includes balance and number of transactions;

[0035] The charging station operator account information includes the number of idle charging piles in the station and the charging and connection prices;

[0036] The account information of electric vehicle users and charging station operators is stored in the blockchain system through a consensus mechanism;

[0037] The step (3) also includes that after charging is completed, the electric vehicle user initiates a payment transaction through a personal mobile terminal, and the transaction information is recorded in the blockchain system through a consensus mechanism.

[0038] The present invention also provides an electric vehicle orderly charging guidance system based on blockchain smart contracts, comprising:

[0039] The charging request submission module is used by electric vehicle users to submit charging requests to the charging platform and submit charging demand parameters through personal mobile terminals;

[0040] The charging station recommendation set calculation module is used in the blockchain system to obtain the distance between the user and each charging station through the commercial map interface, forming a charging station set and distance matrix accessible to electric vehicle users. The charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve, obtain the charging station recommendation set, and store the calculated data in the blockchain system through the consensus mechanism;

[0041] A charging station recommendation set push module, used to return the charging station recommendation set to the personal mobile terminal of the electric vehicle user;

[0042] The charging station recommendation set calculation module includes:

[0043] An in-station data set establishing unit, used for establishing an in-station data set of a charging station;

[0044] A price clustering unit, used for clustering the charging stations according to the charging prices to form similar price areas;

[0045] An income fluctuation control unit, used to set a fairness index, and limit the fluctuation of the income of a single pile within the similar price range to not exceed the fairness index;

[0046] A user selection unit, based on a multinomial Logit user selection model, calculates the probability of a user charging at the charging station according to the driving distance from the user's demand point to the charging station and the charging rate;

[0047] A capacity setting unit, used to set the number of recommendations for a single charging station and the number of user recommendations. The upper limit of the number of recommendations for a single charging station is the number of idle charging piles in the station, and the upper limit of the number of user recommendations is determined by negotiation between the charging station operators;

[0048] The equivalent second-order cone programming model construction calculation unit is used to construct an ordered guidance model according to the fairness index, the user selection model, the upper limit of the number of user recommendations and the upper limit of the recommendation of a single charging station, and to construct an equivalent second-order cone programming model based on the ordered guidance model to calculate the charging station recommendation set.

[0049] Furthermore, the fairness index f in the income fluctuation control unit is f=1-ρ, where is the volatility of the revenue of a single charging station. is the revenue of each charging station. is the average revenue per charging pile of charging stations in similar price areas, L is the total number of similar price areas, K l is the set of charging stations in area l, π i is the expected revenue of charging station i, C i is the number of charging piles at charging station i, K i is the set of charging stations in area i with similar price.

[0050] Furthermore, the multinomial Logit user selection model in the user selection unit meets the following conditions:

[0051]

[0052] Among them, r ni is the probability that electric vehicle user n chooses to go to charging station i, α is the initial utility of electric vehicle users for charging, β is the preference weight coefficient of electric vehicle users for driving distance, γ is the preference weight coefficient of electric vehicle users for charging price, d niis the distance between electric vehicle user n and charging station i, d n_max P is the maximum distance that electric vehicle user n is willing to travel to receive charging services, i is the charging price, p i_max is the maximum charging price, I n ={i∈I:d ni ≤d n_max} is the set of charging stations accessible to user n, x ni is a binary variable, x ni =0 means that user n selects charging station i, otherwise it does not.

[0053] Further, the equivalent second-order cone programming model in the equivalent second-order cone programming model construction calculation unit is:

[0054]

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[0058]

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[0066] Among them, r ni is the probability that user n chooses to go to charging station i, I n ={i∈I:d ni ≤d n_max} is the set of charging stations accessible to user n, α is the initial utility of electric vehicle users for charging, β is the preference weight coefficient of electric vehicle users for driving distance, γ is the preference weight coefficient of electric vehicle users for charging price, d ni is the distance between electric vehicle user n and charging station i, d n_maxP is the maximum distance that electric vehicle user n is willing to travel to receive charging services, i is the charging price, p i_max is the maximum charging price, π i is the expected revenue of charging station i, K i is the set of charging stations in area i with similar prices, C i is the number of available charging piles at charging station i, L is the total number of similar price areas, K l is the set of charging stations in area l, x ni is a binary variable, x ni =1 means that charging station i is recommended to user n, otherwise it is not recommended. maxF is the maximum selection probability of all users, E n is the charging demand of user n, r n0 is the probability that the user does not choose any charging station, B i is the number of available idle piles in charging station i, A n is the upper limit of the number of charging stations recommended to user n, C i is the number of charging stations at charging station i.

[0067] Furthermore, the guidance system further comprises:

[0068] The registration module is used by electric vehicle users and charging station operators to apply for digital certificates from government agencies to confirm the legitimacy of their identities when they join the charging platform. After obtaining the digital certificate, the charging station operators and electric vehicle users obtain a certified public-private key pair. The blockchain system automatically creates account information for accessing the platform in the state tree according to the agreement content. The account information includes the account information of the electric vehicle user and the account information of the charging station operator.

[0069] The account information of electric vehicle users includes balance, transaction times,

[0070] The charging station operator account information includes the number of idle charging piles in the station and the charging and connection prices.

[0071] The account information of electric vehicle users and charging station operators is stored in the blockchain system through a consensus mechanism;

[0072] The transaction module is used for electric vehicle users to initiate payment transactions through their personal mobile terminals after charging is completed. The transaction information is recorded in the blockchain system through a consensus mechanism.

[0073] The beneficial effects of the present invention are:

[0074] The present invention limits the volatility of the revenue of a single pile of charging station operators in similar price areas by introducing a fairness index. At the same time, the user selection model is used to describe the charging behavior of users at charging stations, and the equivalent second-order cone model of the ordered charging guidance model is used to provide users with a set of optional charging stations to reduce the volatility of the revenue of a single pile in similar price areas, ensure the fairness of the revenue distribution of charging station operators, and improve the charging utility of users.

[0075] The present invention can maximize the user's charging utility while ensuring the fairness of the revenue distribution of charging station operators through orderly charging scheduling. In addition, users can query all operators' charging stations through the blockchain unified charging platform. For a single operator, the user flow increases, the utilization rate of charging station resources is improved, and the revenue of each operator increases. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0077] Figure 1 This is a flow chart of the method for guiding orderly charging of electric vehicles based on blockchain smart contracts of the present invention;

[0078] Figure 2 A schematic diagram of the orderly guided model construction and solution process of the present invention;

[0079] Figure 3 This is a schematic diagram of the orderly charging guidance system for electric vehicles based on blockchain smart contracts of the present invention. DETAILED DESCRIPTION

[0080] The present invention is further described in detail below in conjunction with specific embodiments.

[0081] It should be noted that these embodiments are only used to illustrate the present invention rather than to limit the present invention. Simple improvements to the method based on the concept of the present invention all fall within the scope of protection claimed by the present invention.

[0082] See also Figure 1 , a method for guiding orderly charging of electric vehicles based on blockchain smart contracts, comprising:

[0083] (1) Electric vehicle users submit charging requests to the charging platform and submit charging demand parameters through their personal mobile terminals; the charging demand parameters submitted by electric vehicle users include charging demand, expected maximum driving distance, current remaining power, etc.

[0084] (2) The blockchain system obtains the distance between users and each charging station through the commercial map interface, forming a set of charging stations accessible to electric vehicle users and a distance matrix. The charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve the problem, obtain a recommended set of charging stations, and store the calculated data in the blockchain system through a consensus mechanism.

[0085] (3) Return the recommended charging station set to the electric vehicle user's personal mobile terminal.

[0086] See also Figure 2 , the charging station nodes take turns to call the ordered guidance model built in the smart contract to solve, and the recommended charging station set includes:

[0087] A. Establish a charging station in-station data set, which includes the location of the charging station, charging price, number of charging piles, number of idle charging piles, and the operator to which they belong; cluster the charging stations according to the charging price to form similar price areas, set a fairness index, and limit the fluctuation of the single pile revenue within the similar price area to no more than the fairness index;

[0088] In a specific embodiment, multiple similar price areas are clustered by using k-means clustering algorithm (k-Means), but the clustering algorithm is not limited thereto, and K-MEDOIDS algorithm, Clara algorithm and Clarans algorithm may also be used.

[0089] In one possible embodiment, the fairness index is constructed as follows:

[0090] After dividing into similar price areas, the revenue per charging station is Average single pile revenue per region Fairness index f = 1-ρ, where is the volatility of the revenue of a single charging station. is the revenue of each charging station. is the average revenue per charging pile of charging stations in similar price areas, L is the total number of similar price areas, K l is the set of charging stations in area l, π i is the expected revenue of charging station i, C i is the number of charging piles at charging station i, K i is the set of charging stations in area i with similar price.

[0091] B. Based on the multinomial Logit user selection model, the probability of the user charging at the charging station is calculated according to the driving distance from the user's demand point to the charging station and the charging rate; the upper limit of the number of recommendations for a single charging station is the number of idle charging piles in the station, and the upper limit of the number of user recommendations is determined by negotiation between the charging station operators.

[0092] The Logit model is the earliest discrete choice model and the most widely used model at present. It is an important tool for describing user choice behavior. The Logit model can take into account the user choice behavior model of various factors and derive a user choice model that fully reflects the user's choice. The probability of choosing which electric vehicle charging station to charge is based on each car owner's personal preferences and sensitivity to price and driving distance.

[0093] In a specific embodiment,

[0094] The process of constructing the user selection behavior model is as follows:

[0095] Considering that electric vehicle user n needs charging service, he will gather at accessible charging stations Choose a charging station from the list, or choose no charging station based on its utility. n_max The maximum distance that electric vehicle charging user n is willing to travel to receive charging services, so I n ={i∈I:d ni ≤d n_max}, d ni is the distance between user n and charging station i, and the user selection set is I n ∪{0}. V ni represents deterministic utility, ε ni represents the unobserved uncertain utility that affects user choices, U ni =V ni +ε ni is about the travel distance d ni and charging price P i The utility expression is as follows:

[0096]

[0097] Among them, α is the initial utility of user charging, β is the user's preference weight coefficient for driving distance, and γ is the user's preference weight coefficient for charging price. n0 It indicates the utility of the user not choosing to go to any charging station to charge after submitting the charging demand. Therefore, according to the user selection model and the principle of utility maximization, the probability of electric vehicle user n choosing charging station i∈I∪{0} can be obtained as:

[0098]

[0099] For simplicity of representation, for all charging stations i∈I∪{0}, define When the user cancels charging Therefore, the probability that user n chooses charging station i∈I∪{0} is:

[0100]

[0101] C. According to the fairness index, user selection model, the upper limit of user recommendation times and the upper limit of single charging station recommendation, an ordered guidance model is constructed. Based on the ordered guidance model, an equivalent second-order cone programming model is constructed to calculate the recommended charging station set.

[0102] As an implementation method, the ordered bootstrap model is as follows:

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[0112] make so is equivalent to:

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[0114] Same reason

[0115]

[0116] Due to the characteristics of the user selection model, that is, the probability that the user chooses to go to all recommended charging stations plus the probability that the user chooses not to charge is equal to 1, so the constraint is added:

[0117]

[0118] In summary, constraints (6) and (7) can be equivalent to:

[0119]

[0120]

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[0124] Substituting the equivalent second-order cone constraint into the model, the original model is equivalent to

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[0137] Among them, r ni is the probability that user n chooses to go to charging station i, I n ={i∈I:d ni ≤d n_max} is the set of charging stations accessible to user n, α is the initial utility of electric vehicle users for charging, β is the preference weight coefficient of electric vehicle users for driving distance, γ is the preference weight coefficient of electric vehicle users for charging price, d ni is the distance between electric vehicle user n and charging station i, d n_max P is the maximum distance that electric vehicle user n is willing to travel to receive charging services, i is the charging price, p i_max is the maximum charging price, π i is the expected revenue of charging station i, K i is the set of charging stations in area i with similar prices, C i is the number of available charging piles at charging station i, L is the total number of similar price areas, K l is the set of charging stations in area l, x ni is a binary variable, xni =1 means that charging station i is recommended to user n, otherwise it is not recommended. maxF is the maximum selection probability of all users, E n is the charging demand of user n, r n0 is the probability that the user does not choose any charging station, B i is the number of available idle piles in charging station i, A n is the upper limit of the number of charging stations recommended to user n, C i is the number of charging stations at charging station i.

[0138] The objective of objective function (14) is to maximize the probability of selection, which is calculated by the MNL model. Constraint (15) represents the expected revenue of charging station i. Constraints (16) and (17) are the probability of electric vehicle users choosing a charging station. Constraint (18) ensures that the average revenue fluctuation of charging piles in each similar price area does not exceed ρ. Constraint (19) indicates that the upper limit of the number of charging stations recommended to users does not exceed the number of idle charging piles at the station. Constraint (20) is the upper limit of the number of charging stations recommended for each user. Constraint (21) indicates that users cannot choose charging stations outside the accessible area. Constraint (22) indicates that the decision variable is 0-1 variable.

[0139] The equivalent second-order cone programming model is solved by Gurobi solver. Gurobi is a commercial solver and a new generation of large-scale mathematical programming optimizer developed by Gurobi, an American company. In the evaluation of third-party optimizers, it has shown faster optimization speed and accuracy.

[0140] The block header of the block where the smart contract account is located contains log information, which records any data related to the transactions in the block (including the results of smart contract execution), including the user and charging station information input of the smart contract, and the output of the recommended charging station collection results.

[0141] Therefore, blockchain operators can obtain the operation results of smart contracts by querying the log content in the blockchain, making the guidance strategy transparent and open. In summary, the information between different charging station operators is transparent, and the fairness of profit distribution is guaranteed by smart contracts. The construction of blockchain provides a credible decentralized platform for the electric vehicle charging market.

[0142] As a more detailed solution, before step (1), when electric vehicle users and charging station operators join the charging platform, they apply for digital certificates from government agencies to confirm the legitimacy of their identities. After obtaining the digital certificates, the charging station operators and electric vehicle users obtain authenticated public and private key pairs. The blockchain system automatically creates account information for accessing the platform in the state tree according to the agreement content. The account information includes the account information of the electric vehicle user and the account information of the charging station operator.

[0143] The account information of electric vehicle users includes balance and number of transactions;

[0144] The charging station operator account information includes the number of idle charging piles in the station and the charging and connection prices;

[0145] The account information of electric vehicle users and charging station operators is stored in the blockchain system through a consensus mechanism;

[0146] Step (3) also includes that after charging is completed, the electric vehicle user initiates a payment transaction through a personal mobile terminal, and the transaction information is recorded in the blockchain system through a consensus mechanism.

[0147] The government acts as a third-party certification authority in the blockchain. In the blockchain system, members must obtain permission to join the network.

[0148] As a blockchain node, the charging station operator is responsible for storing and maintaining transaction data on the blockchain and has the right to record accounts. The charging station operator account stores the number of idle charging piles and charging price information at the station. The operator can publish transactions and change the account status based on actual information;

[0149] The blockchain consists of node accounts and contract accounts, and transactions are verified through a consensus mechanism to change the account status. The contract account contains smart contracts, and nodes call smart contracts to complete related operations by issuing transactions.

[0150] See also Figure 3 , a guidance system for orderly charging of electric vehicles based on blockchain smart contracts, including:

[0151] The charging request submission module 200 is used for the electric vehicle user to submit a charging request to the charging platform and submit charging requirement parameters through a personal mobile terminal;

[0152] The charging station recommendation set calculation module 300 is used in the blockchain system to obtain the distance between the user and each charging station through the commercial map interface, and form a charging station set and distance matrix accessible to electric vehicle users. The charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve, obtain the charging station recommendation set, and store the calculated data in the blockchain system through the consensus mechanism;

[0153] The charging station recommendation set push module 400 is used to return the charging station recommendation set to the personal mobile terminal of the electric vehicle user;

[0154] The charging station recommendation set calculation module 300 includes:

[0155] The in-station data set establishing unit 310 is used to establish an in-station data set of the charging station;

[0156] A price clustering unit 320, used to cluster charging stations according to charging prices to form similar price areas;

[0157] The revenue fluctuation control unit 330 is used to set a fairness index to limit the fluctuation of the revenue of a single pile within a similar price range to not exceed the fairness index;

[0158] The user selection unit 340 calculates the probability of the user charging at the charging station based on the multinomial Logit user selection model and the driving distance from the user's demand point to the charging station and the charging rate;

[0159] Capacity setting unit 350, used to set the number of recommendations for a single charging station and the number of user recommendations. The upper limit of the number of recommendations for a single charging station is the number of idle charging piles in the station, and the upper limit of the number of user recommendations is determined by negotiation between the charging station operators;

[0160] The equivalent second-order cone programming model construction calculation unit 360 is used to construct an equivalent second-order cone programming model based on the ordered guidance model according to the fairness index, the user selection model, the upper limit of the number of user recommendations and the upper limit of the recommendation of a single charging station, and the ordered guidance model to calculate the charging station recommendation set.

[0161] The fairness index f in the income fluctuation control unit 330 is f=1-ρ, where is the volatility of the revenue of a single charging station. is the revenue of each charging station. is the average revenue per charging pile of charging stations in similar price areas, L is the total number of similar price areas, K l is the set of charging stations in area l, π i is the expected revenue of charging station i, C i is the number of charging piles at charging station i, K i is the set of charging stations in area i with similar price.

[0162] The multinomial Logit user selection model in the user selection unit 340 conforms to:

[0163]

[0164] Among them, r ni is the probability that electric vehicle user n chooses to go to charging station i, α is the initial utility of electric vehicle users for charging, β is the preference weight coefficient of electric vehicle users for driving distance, γ is the preference weight coefficient of electric vehicle users for charging price, d ni is the distance between electric vehicle user n and charging station i, d n_max P is the maximum distance that electric vehicle user n is willing to travel to receive charging services, i is the charging price, p i_maxis the maximum charging price, I n ={i∈I:d ni ≤d n_max} is the set of charging stations accessible to user n, x ni is a binary variable, x ni =0 means that user n selects charging station i, otherwise it does not.

[0165] The equivalent second-order cone programming model in the equivalent second-order cone programming model construction calculation unit 360 is:

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[0178] Among them, r ni is the probability that user n chooses to go to charging station i, I n ={i∈I:d ni ≤d n_max} is the set of charging stations accessible to user n, α is the initial utility of electric vehicle users for charging, β is the preference weight coefficient of electric vehicle users for driving distance, γ is the preference weight coefficient of electric vehicle users for charging price, d ni is the distance between electric vehicle user n and charging station i, d n_max P is the maximum distance that electric vehicle user n is willing to travel to receive charging services, i is the charging price, p i_max is the maximum charging price, π i is the expected revenue of charging station i, K iis the set of charging stations in area i with similar prices, C i is the number of available charging piles at charging station i, L is the total number of similar price areas, K l is the set of charging stations in area l, x ni is a binary variable, x ni =1 means that charging station i is recommended to user n, otherwise it is not recommended. maxF is the maximum selection probability of all users, E n is the charging demand of user n, r n0 is the probability that the user does not choose any charging station, B i is the number of available idle piles in charging station i, A n is the upper limit of the number of charging stations recommended to user n, C i is the number of charging stations at charging station i.

[0179] The boot system also includes:

[0180] The registration module 100 is used for electric vehicle users and charging station operators to apply for digital certificates from government agencies to confirm the legitimacy of their identities when joining the charging platform. After obtaining the digital certificates, the charging station operators and electric vehicle users obtain authenticated public and private key pairs. The blockchain system automatically creates account information for accessing the platform in the state tree according to the content of the agreement. The account information includes the account information of the electric vehicle user and the account information of the charging station operator.

[0181] The account information of electric vehicle users includes balance, transaction times,

[0182] The charging station operator account information includes the number of idle charging piles in the station and the charging and connection prices.

[0183] The account information of electric vehicle users and charging station operators is stored in the blockchain system through a consensus mechanism;

[0184] The transaction module 500 is used for the electric vehicle user to initiate a payment transaction through a personal mobile terminal after charging is completed, and the transaction information is recorded in the blockchain system through a consensus mechanism.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described with reference to the preferred embodiments of the present invention, it should be understood by those skilled in the art that various changes may be made in form and details without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for guiding orderly charging of electric vehicles based on blockchain smart contracts, characterized in that: include: (1) Electric vehicle users submit charging requests to the charging platform and submit charging demand parameters through their personal mobile terminals; (2) The blockchain system obtains the distance between the user and each charging station through the commercial map interface, forms a set of charging stations accessible to electric vehicle users and a distance matrix, and the charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve the problem, obtain a recommended set of charging stations, and store the calculated data in the blockchain system through a consensus mechanism; (3) Returning the recommended charging station set to the electric vehicle user's personal mobile terminal; In step (2), the charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve the problem, and the charging station recommendation set includes: A. Establish a charging station in-station data set, cluster the charging stations according to charging prices to form similar price areas, set a fairness index, and limit the fluctuation of single-pile revenue within the similar price area to not exceed the fairness index; B. Based on the multinomial Logit user selection model, the probability of an electric vehicle user charging at the charging station is calculated according to the driving distance from the electric vehicle user's demand point to the charging station and the charging rate; the upper limit of the number of recommendations for a single charging station is the number of idle charging piles in the station, and the upper limit of the number of recommendations for electric vehicle users is determined by negotiation between the charging station operators; C. constructing an ordered guidance model based on the fairness index, the user selection model, the upper limit of the number of electric vehicle user recommendations and the upper limit of the recommendation of a single charging station, and constructing an equivalent second-order cone programming model based on the ordered guidance model to calculate the recommended charging station set; The multinomial logit user choice model conforms to: ， in, For electric car users Select Go to Charging Station The probability of , is the initial charging utility of electric vehicle users, is the preference weight coefficient of electric vehicle users for driving distance, is the preference weight coefficient of electric vehicle users for charging prices, For electric car users With charging station The distance between For electric car users The maximum distance you are willing to travel to receive charging service, For charging price, is the highest charging price, For users A collection of accessible charging stations, is a binary variable, Representative User Select a charging station , otherwise it is not selected; The equivalent second-order cone programming model is: ， in, For users Select Go to Charging Station The probability of For users A collection of accessible charging stations, , is the initial charging utility of electric vehicle users, is the preference weight coefficient of electric vehicle users for driving distance, is the preference weight coefficient of electric vehicle users for charging prices, For electric car users With charging station The distance between For electric car users The maximum distance you are willing to travel to receive charging service, For charging price, is the highest charging price, It is a charging station The expected return, Similar price range A collection of charging stations, It is a charging station The number of available charging piles, L is the total number of similar price areas, K l is the set of charging stations in area l, is a binary variable, Represented by user Recommended charging stations Otherwise it is not recommended. is to maximize the selection probability of all users and, For users The charging demand, is the probability that the user does not choose any charging station, For charging stations The number of available idle piles in the station, It is for users The recommended upper limit of the number of charging stations, It is a charging station The number of charging stations.

2. The method for guiding orderly charging of electric vehicles based on blockchain smart contracts according to claim 1 is characterized in that: The fairness index ,in is the volatility of the revenue of a single charging station. is the revenue of each charging station. is the average revenue per charging pile of charging stations in similar price areas, L is the total number of similar price areas, is the set of charging stations in area l, It is a charging station The expected return, It is a charging station The number of charging stations, Similar price range Charging stations collection.

3. The method for guiding orderly charging of electric vehicles based on blockchain smart contracts according to claim 1 is characterized in that: When the electric vehicle user and the charging station operator join the charging platform before step (1), they apply for a digital certificate from the government agency to confirm the legitimacy of their identity. After obtaining the digital certificate, the charging station operator and the electric vehicle user obtain a certified public-private key pair. The blockchain system automatically creates account information for accessing the platform in the state tree according to the content of the agreement. The account information includes the account information of the electric vehicle user and the account information of the charging station operator. The account information of electric vehicle users includes balance and number of transactions; The charging station operator account information includes the number of idle charging piles in the station and the charging and connection prices; The account information of electric vehicle users and charging station operators is stored in the blockchain system through a consensus mechanism; The step (3) also includes that after charging is completed, the electric vehicle user initiates a payment transaction through a personal mobile terminal, and the transaction information is recorded in the blockchain system through a consensus mechanism.

4. An orderly charging guidance system for electric vehicles based on blockchain smart contracts, characterized in that: include: The charging request submission module is used by electric vehicle users to submit charging requests to the charging platform and submit charging demand parameters through personal mobile terminals; The charging station recommendation set calculation module is used in the blockchain system to obtain the distance between the user and each charging station through the commercial map interface, and form a charging station set and distance matrix accessible to electric vehicle users. The charging station nodes take turns to call the ordered guidance model constructed in the smart contract to solve the charging station recommendation set, and store the calculated data in the blockchain system through the consensus mechanism; A charging station recommendation set push module, used to return the charging station recommendation set to the personal mobile terminal of the electric vehicle user; The charging station recommendation set calculation module includes: An in-station data set establishing unit, used for establishing an in-station data set of a charging station; A price clustering unit, used for clustering the charging stations according to the charging prices to form similar price areas; An income fluctuation control unit, used to set a fairness index, and limit the fluctuation of the income of a single pile within the similar price range to not exceed the fairness index; A user selection unit, based on a multinomial Logit user selection model, calculates the probability of a user charging at the charging station according to the driving distance from the user's demand point to the charging station and the charging rate; A capacity setting unit, used to set the number of recommendations for a single charging station and the number of user recommendations. The upper limit of the number of recommendations for a single charging station is the number of idle charging piles in the station, and the upper limit of the number of user recommendations is determined by negotiation between the charging station operators; An equivalent second-order cone programming model construction calculation unit is used to construct an ordered guidance model according to the fairness index, the user selection model, the upper limit of the number of user recommendations and the upper limit of the recommendation of a single charging station, and to construct an equivalent second-order cone programming model based on the ordered guidance model to calculate the charging station recommendation set; The multinomial Logit user selection model in the user selection unit meets the following requirements: ， in, For electric car users Select Go to Charging Station The probability of , is the initial charging utility of electric vehicle users, is the preference weight coefficient of electric vehicle users for driving distance, is the preference weight coefficient of electric vehicle users for charging prices, For electric car users With charging station The distance between For electric car users The maximum distance you are willing to travel to receive charging service, For charging price, is the highest charging price, For users A collection of accessible charging stations, is a binary variable, Representative User Select a charging station , otherwise it is not selected; The equivalent second-order cone programming model in the equivalent second-order cone programming model construction calculation unit is: ， in, For users Select Go to Charging Station The probability of For users A collection of accessible charging stations, , is the initial charging utility of electric vehicle users, is the preference weight coefficient of electric vehicle users for driving distance, is the preference weight coefficient of electric vehicle users for charging prices, For electric car users With charging station The distance between For electric car users The maximum distance you are willing to travel to receive charging service, For charging price, is the highest charging price, It is a charging station The expected return, Similar price range A collection of charging stations, It is a charging station The number of available charging piles, L is the total number of similar price areas, K l is the set of charging stations in area l, is a binary variable, Represented by user Recommended charging stations Otherwise it is not recommended. is to maximize the selection probability of all users and, For users The charging demand, is the probability that the user does not choose any charging station, For charging stations The number of available idle piles in the station, It is for users The recommended upper limit of the number of charging stations, It is a charging station The number of charging stations.

5. According to claim 4, the orderly charging guidance system for electric vehicles based on blockchain smart contracts is characterized in that: The fairness index in the income fluctuation control unit ,in is the volatility of the revenue of a single charging station. is the revenue of each charging station. is the average revenue per charging pile of charging stations in similar price areas, L is the total number of similar price areas, is the set of charging stations in area l, It is a charging station The expected return, It is a charging station The number of charging stations, Similar price range Charging stations collection.

6. The electric vehicle orderly charging guidance system based on blockchain smart contract according to claim 4 is characterized in that: The guidance system further comprises: The registration module is used by electric vehicle users and charging station operators to apply for digital certificates from government agencies to confirm the legitimacy of their identities when they join the charging platform. After obtaining the digital certificate, the charging station operators and electric vehicle users obtain a certified public-private key pair. The blockchain system automatically creates account information for accessing the platform in the state tree according to the agreement content. The account information includes the account information of the electric vehicle user and the account information of the charging station operator. The account information of electric vehicle users includes balance, transaction times, The charging station operator account information includes the number of idle charging piles in the station and the charging and connection prices. The account information of electric vehicle users and charging station operators is stored in the blockchain system through a consensus mechanism; The transaction module is used for electric vehicle users to initiate payment transactions through their personal mobile terminals after charging is completed, and the transaction information is recorded in the blockchain system through a consensus mechanism.

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