Campus two-wheel electric vehicle charging pile re-layout optimization method and system

Abstract

The present invention relates to the field of charging pile layout optimization, and discloses a campus two-wheel electric vehicle charging pile re-layout optimization method and system, including: obtaining the number of two-wheel electric vehicles entering and leaving the campus every day, the frequency of entering and leaving the charging station, and the user's preference and demand distribution point data for selecting charging piles; pre-processing the acquired data and calibrating the data; analyzing the data to determine the constraints that the site selection plan needs to meet; formulating a site selection plan to construct an objective function with the minimum total investment cost and the minimum user charging cost; and using the walrus optimization algorithm to perform two rounds of optimization on the objective function while fully considering the user's preferences and needs, and obtaining the best electric vehicle charging pile layout optimization plan. The present invention ensures that the total cost of the optimization target is minimized, so that the optimized charging pile layout can meet the needs of users while balancing the interests of investors and users, and realizes reasonable parking and charging.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile layout optimization, and in particular to a method and system for optimizing the layout of charging piles for two-wheeled electric vehicles on campus. Background Art

[0002] With the popularity of two-wheeled electric vehicles on campus, the layout and construction of electric vehicle charging piles is particularly important. The layout of charging piles on many campuses mostly only considers the investment cost issue, and does not take into account the interests and charging needs of electric vehicle users. The layout of charging piles does not conform to the travel patterns and charging characteristics of users. The unreasonable layout of charging piles has a great impact on campus traffic and charging efficiency. Reasonable arrangement of the number and location of charging piles can maximize investment benefits, help teachers and students quickly find charging piles, reduce charging waiting time and improve charging efficiency, and avoid waste of charging resources.

[0003] Taking into account investment costs and user needs, optimizing the layout of two-wheeled electric vehicle charging piles on campus can help balance the interests of investors and users while meeting user needs and regulating campus traffic. By rationally planning the location of charging piles, electric vehicles can be guided to park and charge in an orderly manner, reducing random parking and improving campus road traffic efficiency. Summary of the invention

[0004] Purpose of the invention: In response to the problems pointed out in the background technology, the present invention discloses a method and system for optimizing the re-layout of two-wheel electric vehicle charging piles on campus. The objective function is to minimize the total investment cost and the user charging cost. The walrus optimization algorithm is used to perform two rounds of optimization on the objective function, so that the optimized site selection plan can meet the user needs while balancing the interests of investors and users.

[0005] Technical solution: The present invention discloses a method for re-layout optimization of charging piles for two-wheeled electric vehicles on campus, comprising:

[0006] Step (1) obtaining the number of two-wheeled electric vehicles entering and leaving the campus every day, the frequency of entering and leaving existing charging stations, and the location distribution data of charging piles used by users on campus;

[0007] Step (2) preprocessing and calibrating the acquired data;

[0008] Step (3) constructing an objective function with the minimum total investment cost and the minimum user charging cost, and determining the constraints of the site selection plan based on the preprocessed and calibrated data;

[0009] Step (4) Under the constraint conditions, the walrus optimization algorithm is used to perform two rounds of optimization with the total investment cost and the user charging cost as the objective function respectively. Under the condition of minimizing the total investment cost and the user charging cost, the optimal two-wheel electric vehicle charging pile layout plan is determined to obtain the final charging pile location and number.

[0010] Furthermore, the step (2) of preprocessing and calibrating the acquired data is specifically as follows:

[0011] The data obtained in step (1) is preprocessed by reading, filtering, and data format conversion, and the preprocessed data is calibrated using a multidimensional stability calibration algorithm. The calibrated data is subjected to exploratory analysis to formulate a charging pile site selection plan and determine the constraints that the site selection plan must meet. The constraints are the user's preferences and needs for selecting charging piles, including the location of the charging pile, the time it takes the user to find the target charging pile, and the time the user waits in line.

[0012] Furthermore, the objective function constructed with the minimum total investment cost in step (3) is specifically:

[0013] Including minimizing the investment cost of two-wheeled electric vehicle charging piles, minimizing the wiring cost of charging piles, and minimizing the subsequent maintenance and repair costs. The objective function is expressed as follows:

[0014] SP min =P 1 +P 2 +P 3

[0015] Where, SP min Indicates that the total investment cost is the minimum; P 1 represents the investment cost of charging pile; P 2 P represents the cost of charging pile wiring; 3 represents the subsequent repair and maintenance cost; P 1 Specifically, it includes the land investment cost for building a two-wheeled electric vehicle charging station, the investment cost based on the number of charging piles required, the investment cost of labor and materials required to build the charging piles, and the investment cost of electricity; 2 Specifically, it includes the cost of laying out the charging pile lines, including the investment cost of cables and wires; 3 Specifically, the later repair and maintenance costs include the cost of hiring labor, the cost of charging pile damage repair and regular maintenance, P 1 , P 2 , P 3 The specific expression is:

[0016]

[0017] P 2 =LS xian

[0018] P 3 =S p +S xiu +O

[0019] Where N is the total number of optimized charging piles; r is the depreciation rate; a is the service life of the charging pile; φ is the ratio coefficient of the area occupied by infrastructure, roads and other facilities in the electric vehicle charging station to the area of ​​the charging pile; S i is the area occupied by the i-th charging pile; Q i is the unit land price of the i-th charging pile; S pri is the purchase price of the charging pile; C is the labor and material cost required to build the charging pile; S ele is the electricity cost for a single charging pile; L is the total length of the required wire; S xian is the unit price of wire; S p S is the labor cost; xiu is the maintenance fee of the charging pile; O is the regular maintenance cost of the charging pile.

[0020] Furthermore, in step (3), the objective function constructed with the minimum user charging cost is specifically:

[0021] The objective function includes minimizing the time cost of users looking for the target charging pile, minimizing the user charging cost, and minimizing the waiting loss cost. The specific expression of the objective function is:

[0022] U min =w 1 U 1 +w 2 U 2 +w 3 U 3

[0023] Where U min Indicates that the user's charging cost is minimal; U 1 Indicates the time it takes for the user to find the target charging station; U 2 Represents the user's charging cost; U 3 represents the cost of user waiting in line; w 1 , w 2 , w 3 U 1 , U 2 , U 3 The corresponding weight is determined by the user's own needs;

[0024] The time U that the user spends searching for the target charging pile 1 The specific expression is as follows:

[0025]

[0026] Where, d m is the length of the road section m from the user to the target charging pile; M is the number of road sections; υ m is the average speed of the user on road section m; T useris the average travel time of users on road section m;

[0027] The user charging cost U 2 The specific expression is as follows:

[0028] U 2 =ψ h Tδ

[0029] In the formula, ψ h is the charging cost unit price of the charging pile; T is the charging time; δ is the number of times the user charges;

[0030] The user queues and waits for the loss cost U 3 The specific expression is as follows:

[0031]

[0032] Where H is the number of other users queuing when the user arrives at the target charging pile, T 前 is the charging time used by the h-th user in the queue; T h-1 The remaining time required for the user to use the charging pile when the user arrives at the target charging pile;

[0033] The weight calculation expression is as follows:

[0034] w 1 ,w 2 ,w 3 ∈{0,1},w 1 +w 2 +w 3 =1

[0035] The weight is determined by the user's own needs, that is, by the user's preferences and needs for selecting charging piles.

[0036] Furthermore, in step (4), the objective function is optimized and solved using the walrus optimization algorithm, specifically:

[0037] Step 5.1: Initialize the population and input parameter values, calculate the fitness and record the best position. The initialization and fitness function value expressions are as follows:

[0038]

[0039]

[0040] In the formula, X represents the population matrix; F represents the fitness function value; X i represents the i-th member; N represents the population size; m represents the dimension;

[0041] Step 5.2: When the danger signal is too high, that is, Dangersignal ≥ 1, the walrus population will migrate to a place more suitable for the survival of the population. The position update formula, danger signal and safety signal corresponding to the danger signal are as follows:

[0042]

[0043]

[0044] Safetysignal=y 3

[0045] In the formula, is the updated position of the i-th individual in the j-th dimension; is the current position; Migrationstep is the step length of the walrus movement; is the migration step length control factor corresponding to the two randomly selected vigilant positions in the population; A and G are risk factors; α decreases from 1 to 0 as the number of iterations increases; y 1 ,y 2 ,y 3 is a random number in [0,1];

[0046] Step 5.3: When the danger signal is low, that is, Dangersignal<1 and the safety signal Safetysignal is greater than 0.5, the walrus population will perform roosting behavior. Males will update their positions to expand the search range. Females and young walruses will update their current positions due to the influence of male walruses and natural enemies. The position update formula is as follows:

[0047]

[0048]

[0049]

[0050]

[0051] In the formula, For male walrus positions; is the position of the leading male walrus; I is the distress coefficient of the juvenile walrus, which is a random number in [0,1]; B is the reference safe position; LF is a random number vector based on Levy motion distribution;

[0052] Step 5.4: When the safety signal is less than 0.5, the walrus will be attacked by natural enemies while foraging. The walrus will take escape behavior and escape from the current activity area according to the danger signal sent by its companions, and update its position. This behavior occurs in the late iteration of the algorithm, which has a certain degree of disturbance to the population and is helpful for global search. The position update formula is as follows:

[0053]

[0054]

[0055]

[0056] In the formula, X 1 and X 2 are the two weights that affect the walrus's escape behavior; is the position of the second walrus in the current iteration; b, e are the escape coefficients; y 4 is a random number in [0,1];

[0057] Step 5.5: Update the position according to the above steps, find the global optimal solution, output the objective function value and determine whether it meets the termination conditions. If not, recalculate the fitness and return to step 5.1; if satisfied, output the optimal solution, which is the minimum investment cost and minimum user charging cost under the constraints. According to the minimum investment cost and the minimum user charging cost, further determine the final construction location and quantity of the charging piles to complete the layout optimization of the charging piles.

[0058] Furthermore, the input parameter values ​​in step 5.1 are specifically as follows:

[0059] To minimize the total investment cost, the input parameter value is: the investment cost of the charging pile P 1 , Charging pile line layout cost P 2 and subsequent maintenance and repair costs P 3 ; Its corresponding input data is P 1 , P 2 , P 3 The corresponding known data include the service life of the charging pile, the proportionality coefficient, the floor space of the charging pile, the unit land price of the charging pile, the purchase price of the charging pile, the labor and material cost, the electricity cost required for the charging pile, the total length of the wire, the unit price of the wire, the labor cost, the maintenance cost of the charging pile, and the regular maintenance cost of the charging pile; determine the total number of charging piles with the minimum total investment cost;

[0060] To minimize the user charging cost, the input parameters are: the time cost U of the user to find the target charging pile 1 、User charging cost U 2 and the waiting cost U3 ; Its corresponding input data is U 1 , U 2 and U 3 The corresponding known data include the average driving time of users on road section m, the number of road sections, the charging price of the charging pile, the charging time, the number of times users charge, the charging time used by the hth user in the queue, and the remaining time required for users who are charging to use the charging pile when the user arrives at the target charging pile; determine the total number of charging piles and the location of the charging piles under the condition of minimizing the total investment cost and minimizing the user's charging cost;

[0061] The present invention also discloses a system based on the above campus two-wheel electric vehicle charging pile re-layout optimization method, comprising a data monitoring and acquisition module, a data analysis and processing module and a layout optimization module;

[0062] The data monitoring and acquisition module is used to obtain the number of two-wheeled electric vehicles entering and leaving the campus every day, the frequency of entering and leaving the charging station, and the user's preference for selecting charging piles and the distribution point data of demand from the campus image monitor, time clock and access control device;

[0063] The data analysis and processing module is used to preprocess and calibrate the data acquired by the data monitoring and acquisition module, and to perform exploratory data analysis on the calibrated data to formulate a site selection plan;

[0064] The layout optimization module is used to construct the objective function with the minimum total investment cost and user charging cost respectively, taking full consideration of user selection preferences and needs, and determine the constraint conditions of the site selection plan according to the data analysis results, and use the walrus optimization algorithm to solve it, so as to realize the layout optimization of the two-wheel electric vehicle charging piles.

[0065] Beneficial effects:

[0066] 1. The present invention combines campus image monitors, time clocks and access control devices to collect and analyze data on daily parking spots and preferred spots of electric vehicle users, so that the optimized layout better caters to the interests and needs of users; the collected data is preprocessed and calibrated to ensure the quality of the data.

[0067] 2. The present invention constructs the objective function by minimizing the total investment cost and the user charging cost, and uses the optimization algorithm to perform two rounds of optimization on the objective function. The final site selection plan balances the interests between investors and users. The solution of the objective function uses the newer walrus optimization algorithm, which has good balancing ability and global search ability.

[0068] 3. The present invention can reasonably optimize the layout of charging piles for two-wheeled electric vehicles on campus, thereby achieving reasonable parking and charging of two-wheeled electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of the layout optimization module of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0071] This embodiment provides a method for optimizing the layout of charging piles for two-wheeled electric vehicles on campus, including:

[0072] (1) Obtain the number of two-wheeled electric vehicles entering and leaving the campus every day, the frequency of entering and exiting the charging station, and the user's preference and demand distribution point data for selecting charging piles from campus image monitors, time clocks, and access control devices.

[0073] (2) Preprocessing and calibrating the acquired data. The data acquired in step (1) is preprocessed through a series of processes, including reading, filtering, and data format conversion, and the preprocessed data is calibrated using a multidimensional stability calibration algorithm. An exploratory analysis is performed on the calibrated data to formulate a charging pile site selection plan and determine the constraints that the site selection plan must meet. The constraints are the user's preferences and requirements for selecting charging piles, including the location of the charging pile, the time it takes for the user to find the target charging pile, and the time it takes the user to wait in line.

[0074] The multi-dimensional stability calibration algorithm enhances the precision and accuracy of algorithm calibration by introducing stability parameters and accuracy parameters. In the process of data calibration, for time complexity optimization, the numerical integration method is used to convert continuous integration operations into discrete summation operations; for space complexity optimization, the data compression method is used to merge and store the data to realize the calibration of sensor and image data.

[0075] (3) Conduct exploratory data analysis on the calibrated data and formulate a site selection plan, taking into full account user preferences and needs, construct an objective function with the minimum total investment cost and the minimum user charging cost, and determine the constraints of the site selection plan based on the preprocessed and calibrated data.

[0076] The objective function constructed with the minimum total investment cost is as follows:

[0077] Including minimizing the investment cost of two-wheeled electric vehicle charging piles, minimizing the wiring cost of charging piles, and minimizing the subsequent maintenance and repair costs. The objective function is expressed as follows:

[0078] SP min =P 1 +P 2 +P 3

[0079] Where, SP min Indicates that the total investment cost is the minimum; P 1represents the investment cost of charging pile; P 2 P represents the cost of charging pile wiring; 3 Indicates the cost of subsequent repair and maintenance;

[0080] P 1 Specifically, it includes the land investment cost for building a two-wheeled electric vehicle charging station, the investment cost based on the number of charging piles required, the investment cost of labor and materials required to build the charging piles, and the investment cost of electricity; 2 Specifically, it includes the cost of laying out the charging pile lines, including the investment cost of cables and wires; 3 Specifically, the later repair and maintenance costs include the cost of hiring labor, the cost of charging pile damage repair and regular maintenance, P 1 , P 2 , P 3 The specific expression is:

[0081]

[0082] P 2 =LS xian

[0083] P 3 =S p +S xiu +O

[0084] Where N is the total number of charging piles after optimization; r is the depreciation rate; a is the service life of the charging pile; φ is the infrastructure, roads and other facilities in the electric vehicle charging station. The ratio coefficient of the area converted to the charging pile area; S i is the area occupied by the i-th charging pile; Q i is the unit land price of the i-th charging pile; S pri is the purchase price of the charging pile; C is the labor and material cost required to build the charging pile; S ele is the electricity cost for a single charging pile; L is the total length of the required wire; S xian is the unit price of wire; S p S is the labor cost; xiu is the maintenance fee of the charging pile; O is the regular maintenance cost of the charging pile.

[0085] The objective function constructed with the minimum user charging cost is as follows:

[0086] The objective function includes minimizing the time users spend searching for the target charging pile, minimizing the user charging cost, and minimizing the waiting cost. The specific expression of the objective function is:

[0087] U min =w 1 U 1 +w 2 U 2 +w3 U 3

[0088] Where U min Indicates that the user's charging cost is minimal; U 1 Indicates the time it takes for the user to find the target charging station; U 2 Represents the user's charging cost; U 3 represents the cost of user waiting in line; w 1 , w 2 , w 3 U 1 , U 2 , U 3 The corresponding weight is determined by the user's own needs;

[0089] The time U that the user spends searching for the target charging pile 1 The specific expression is as follows:

[0090]

[0091] Where, d m is the length of the road section m from the user to the target charging pile; M is the number of road sections; υ m is the average speed of the user on road section m; T user is the average travel time of users on road section m;

[0092] The user charging cost U 2 The specific expression is as follows:

[0093] U 2 =ψ h Tδ

[0094] In the formula, ψ h is the charging cost unit price of the charging pile; T is the charging time; δ is the number of times the user charges;

[0095] The user queues and waits for the loss cost U 3 The specific expression is as follows:

[0096]

[0097] Where H is the number of other users queuing when the user arrives at the target charging pile, T 前 is the charging time used by the h-th user in the queue; T h-1 The remaining time required for the user to use the charging pile when the user arrives at the target charging pile;

[0098] The weight calculation expression is as follows:

[0099] w 1 ,w 2,w 3 ∈{0,1},w 1 +w 2 +w 3 =1

[0100] The weight is determined by the user's own needs, that is, by the user's preferences and needs for selecting charging piles.

[0101] (4) Under the constraints, the walrus optimization algorithm is used to perform two rounds of optimization with the total investment cost and the user charging cost as the objective function. Under the conditions of minimizing the total investment cost and the user charging cost, the optimal two-wheel electric vehicle charging pile layout plan is determined to obtain the final charging pile location and number.

[0102] As attached Figure 1 As shown in the figure, the walrus optimization algorithm is used to optimize and solve the objective function, specifically:

[0103] The behavior of walrus populations is affected by danger signals and safety signals. The level of the signal determines whether the walrus is migrating, roosting, or escaping, thereby updating its current position and finding the global optimum. The solution process of the walrus optimization algorithm is as follows:

[0104] Step 1: Initialize the population and input parameter values, calculate the fitness and record the best position. The initialization and fitness function value expressions are as follows:

[0105]

[0106]

[0107] In the formula, X represents the population matrix; F represents the fitness function value; X i represents the i-th member; N represents the population size; m represents the dimension.

[0108] To minimize the total investment cost, the input parameter value is: the investment cost of the charging pile P 1 , Charging pile line layout cost P 2 and subsequent maintenance and repair costs P 3 ; Its corresponding input data is P 1 , P 2 , P 3 The corresponding known data include the service life of the charging pile, the proportional coefficient, the floor space of the charging pile, the unit land price of the charging pile, the purchase price of the charging pile, the labor and material cost, the electricity cost required for the charging pile, the total length of the wire, the unit price of the wire, the labor cost, the maintenance cost of the charging pile, and the regular maintenance cost of the charging pile; determine the total number of charging piles with the lowest total investment cost.

[0109] To minimize the user charging cost, the input parameters are: the time cost U of the user to find the target charging pile 1 、User charging cost U 2 and the waiting cost U 3 ; Its corresponding input data is U 1 , U 2 and U 3 The corresponding known data include the average driving time of users on road section m, the number of road sections, the unit price of charging fees of charging piles, charging time, the number of times users charge, the charging time used by the h-th user in the queue, and the remaining time required for users who are charging to use the charging pile when the user arrives at the target charging pile; determine the total number of charging piles and the locations of charging piles while satisfying the minimum total investment cost and the minimum user charging cost.

[0110] Step 2: When the danger signal is too high, that is, Dangersignal ≥ 1, the walrus population will migrate to a place more suitable for the survival of the population. The position update formula, danger signal and safety signal corresponding to the danger signal are expressed as follows:

[0111]

[0112]

[0113] Safetysignal=y 3

[0114] In the formula, is the updated position of the i-th individual in the j-th dimension; is the current position; Migrationstep is the step length of the walrus movement; is the migration step length control factor corresponding to the two randomly selected vigilant positions in the population; A and G are risk factors; α decreases from 1 to 0 as the number of iterations increases; y 1 ,y 2 ,y 3 is a random number in [0,1].

[0115] Step 3: When the danger signal is low, that is, Dangersignal<1 and the safety signal Safetysignal is greater than 0.5, the walrus population will perform roosting behavior. Males will update their positions to expand the search range. Females and young walruses will update their current positions due to the influence of male walruses and natural enemies. The position update formula is as follows:

[0116]

[0117]

[0118]

[0119]

[0120] In the formula, For male walrus positions; is the position of the leading male walrus; I is the distress coefficient of the juvenile walrus, which is a random number in [0,1]; B is the reference safe position; LF is a random number vector based on Levy motion distribution;

[0121] Step 4: When the safety signal is less than 0.5, the walrus will be attacked by natural enemies while foraging. The walrus will flee and escape from the current activity area according to the danger signal sent by its companions, and update its position. This behavior occurs in the late iteration of the algorithm, which has a certain degree of disturbance to the population and is helpful for global search. The position update formula is as follows:

[0122]

[0123]

[0124]

[0125] In the formula, X 1 and X 2 are the two weights that affect the walrus's escape behavior; is the position of the second walrus in the current iteration; b, e are the escape coefficients; y 4 is a random number in [0,1];

[0126] Step 5: Update the position according to the above steps, find the global optimal solution, output the objective function value and determine whether it meets the termination conditions. If not, recalculate the fitness and return to step 1; if satisfied, output the optimal solution. The optimal solution is the minimum investment cost and the minimum user charging cost under the constraints. According to the minimum investment cost and the minimum user charging cost, further determine the final construction location and quantity of charging piles to complete the layout optimization of charging piles.

[0127] The campus two-wheeled electric vehicle charging pile layout optimization system corresponding to the campus two-wheeled electric vehicle charging pile layout optimization method includes a data monitoring and acquisition module, a data analysis and processing module, and a layout optimization module. The modules exchange information through digital signals.

[0128] The data monitoring and acquisition module is used to obtain the number of two-wheeled electric vehicles entering and leaving the campus every day, the frequency of entering and exiting the charging station, and the user's preference and demand distribution point data for selecting charging piles from campus image monitors, time clocks and access control devices.

[0129] The data analysis and processing module is used to preprocess and calibrate the data acquired by the data monitoring and acquisition module, and to perform exploratory data analysis on the calibrated data to formulate a site selection plan.

[0130] The layout optimization module is used to construct the objective function with the minimum total investment cost and user charging cost respectively, taking full consideration of user selection preferences and needs, and determine the constraints of the site selection plan based on the data analysis results. It is solved using the walrus optimization algorithm to achieve layout optimization of two-wheeled electric vehicle charging piles.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for re-layout optimization of charging piles for two-wheeled electric vehicles on campus, characterized in that: include: Step (1) obtaining the number of two-wheeled electric vehicles entering and leaving the campus every day, the frequency of entering and leaving existing charging stations, and the location distribution data of charging piles used by users on campus; Step (2) preprocessing and calibrating the acquired data; The preprocessing and data calibration of the acquired data are as follows: The data acquired in step (1) is subjected to a preprocessing process of reading, filtering, and data format conversion, and the preprocessed data is calibrated using a multidimensional stability calibration algorithm; The multi-dimensional stability calibration algorithm enhances the precision and accuracy of algorithm calibration by introducing stability parameters and accuracy parameters. In the process of data calibration, for time complexity optimization, the numerical integration method is used to convert continuous integration operations into discrete summation operations; for space complexity optimization, the data compression method is used to merge and store data to achieve calibration of sensor and image data; an exploratory analysis is performed on the calibrated data to formulate a charging pile site selection plan and determine the constraints that the site selection plan must meet. The constraints are the user's preferences and needs for selecting charging piles, including the location of the charging pile, the time it takes for the user to find the target charging pile, and the waiting time for the user in line; Step (3) constructing an objective function with the minimum total investment cost and the minimum user charging cost, and determining the constraints of the site selection plan based on the preprocessed and calibrated data; The objective function constructed with the minimum total investment cost is as follows: Including minimizing the investment cost of two-wheeled electric vehicle charging piles, minimizing the wiring cost of charging piles, and minimizing the subsequent maintenance and repair costs. The objective function is expressed as follows: SP min =P1+P2+P3 Where, SP min Indicates the minimum total investment cost; P1 indicates the investment cost of charging piles; P2 indicates the cost of charging pile wiring; P3 indicates the cost of later repair and maintenance; P1 specifically includes the land investment cost for building a two-wheeled electric vehicle charging station, the investment cost based on the number of charging piles required, the investment cost of labor and materials required for building charging piles, and the investment cost of electricity; P2 specifically includes the cost of charging pile wiring, including the investment cost of cables and wires; P3 specifically includes the cost of later repair and maintenance, including the cost of hiring labor, charging pile damage repair and regular maintenance costs. The specific expressions of P1, P2, and P3 are: P2=LS xian P3=S p +S xiu +O Where N is the total number of charging piles after optimization; r is the depreciation rate; a is the service life of the charging pile; φ is the ratio coefficient of the area occupied by infrastructure, roads and other facilities in the electric vehicle charging station to the area of ​​the charging pile; S i is the area occupied by the i-th charging pile; Q i is the unit land price of the i-th charging pile; S pri is the purchase price of the charging pile; C is the labor and material cost required to build the charging pile; S ele is the electricity cost for a single charging pile; L is the total length of the required wire; S xian is the unit price of wire; S p S is the labor cost; xiu is the maintenance fee of the charging pile; O is the regular maintenance cost of the charging pile; The objective function constructed with the minimum user charging cost is as follows: The objective function includes minimizing the time cost of users looking for the target charging pile, minimizing the user charging cost, and minimizing the waiting loss cost. The specific expression of the objective function is: U min =w1U1+w2U2+w3U3 Where U min Indicates that the user's charging cost is minimal; U1 indicates the time it takes for the user to find the target charging pile; U2 represents the user's charging cost; U3 represents the user's waiting cost; w1, w2, w3 are the weights of U1, U2, U3 respectively, and the weights are determined by the user's own needs; The specific expression of the time U1 for the user to find the target charging pile is as follows: Where, d q is the length of the road section q from the user to the target charging pile; M is the number of road sections; υ q is the average speed of the user on road section q; T user is the average travel time of users on road section q; The specific expression of the user charging cost U2 is as follows: U2=ψ h Td In the formula, ψ h is the charging cost unit price of the charging pile; T is the charging time; δ is the number of times the user charges; The specific expression of the user queuing waiting loss cost U3 is as follows: Where H is the number of other users queuing when the user arrives at the target charging pile, T 前 is the charging time used by the hth user in the queue; T h-1 The remaining time required for the user to use the charging pile when the user arrives at the target charging pile; The weight calculation expression is as follows: w1,w2,w3∈{0,1},w1+w2+w3=1 The weight is determined by the user's own needs, that is, by the user's preferences and needs for selecting charging piles; Step (4) Under the constraint conditions, the Walrus optimization algorithm is used to perform two rounds of optimization with the total investment cost and the user charging cost as the objective function, respectively. Under the condition that the total investment cost is minimized and the user charging cost is minimized, the optimal two-wheel electric vehicle charging pile layout plan is determined to obtain the final charging pile location and number; In step (4), the objective function is optimized and solved using the walrus optimization algorithm, specifically: Step 4.1: Initialize the population and input parameter values, calculate the fitness and record the best position. The initialization and fitness function value expressions are as follows: In the formula, X represents the population matrix; F represents the fitness function value; X i represents the i-th member; N represents the population size; m represents the dimension; The input parameter values ​​in step 4.1 are as follows: With the minimum total investment cost, the input parameter values ​​are: the investment cost of the charging pile P1, the wiring cost of the charging pile P2 and the subsequent maintenance and repair cost P3; the corresponding input data are the known data corresponding to P1, P2 and P3, specifically the service life of the charging pile, the proportional coefficient, the floor area of ​​the charging pile, the unit land price of the charging pile, the purchase price of the charging pile, the labor and material cost, the electricity cost required for the charging pile, the total length of the wire, the unit price of the wire, the labor cost, the maintenance cost of the charging pile, and the regular maintenance cost of the charging pile; With the user charging cost minimized, the input parameters are: the user's time cost for finding the target charging pile U1, the user's charging cost U2, and the user's queuing and waiting loss cost U3; the corresponding input data are the known data corresponding to U1, U2, and U3, specifically the average driving time of the user on road section m, the number of road sections, the charging cost unit price of the charging pile, the charging time, the number of user charging times, the charging time used by the h-th user in the queue, and the remaining time for the user who is charging to use the charging pile when the user arrives at the target charging pile; Determine the total number of charging piles and their locations while minimizing the total investment cost and the user charging cost; Step 4.2: When the danger signal is too high, that is, Dangersignal ≥ 1, the walrus population will migrate to a place more suitable for the survival of the population. The position update formula, danger signal and safety signal corresponding to the danger signal are as follows: Safetysignal=y3 In the formula, is the updated position of the i-th individual in the j-th dimension; is the current position; Migrationstep is the step length of the walrus movement; is the migration step control factor corresponding to the two randomly selected vigilant positions in the population; A and G are risk factors; α decreases from 1 to 0 as the number of iterations increases; y1, y2, y3 are random numbers in [0,1]; Step 4.3: When the danger signal is low, that is, Dangersignal<1 and the safety signal Safetysignal is greater than 0.5, the walrus population will perform roosting behavior. Males will update their positions to expand the search range. Females and young walruses will update their current positions due to the influence of male walruses and natural enemies. The position update formula is as follows: In the formula, For male walrus positions; is the position of the leading male walrus; I is the distress coefficient of the juvenile walrus, which is a random number in [0,1]; B is the reference safe position; LF is a random number vector based on Levy motion distribution; Step 4.4: When the safety signal is less than 0.5, the walrus will be attacked by natural enemies while foraging. The walrus will flee and escape from the current activity area according to the danger signal sent by its companions, and update its position. This behavior occurs in the late iteration of the algorithm, which has a certain degree of disturbance to the population and is helpful for global search. The position update formula is as follows: In the formula, X1 and X2 are two weights that affect the walrus's escape behavior; is the position of the second walrus in the current iteration; b, e are escape coefficients; y4 is a random number in [0,1]; Step 4.5: Update the position according to the above steps, find the global optimal solution, output the objective function value and determine whether it meets the termination conditions. If not, recalculate the fitness and return to step 4.1; if satisfied, output the optimal solution, which is the minimum investment cost and minimum user charging cost under the constraints. According to the minimum investment cost and the minimum user charging cost, further determine the final construction location and quantity of the charging piles to complete the layout optimization of the charging piles.

2. A system based on the campus two-wheel electric vehicle charging pile re-layout optimization method according to claim 1, characterized in that: It includes data monitoring and acquisition module, data analysis and processing module and layout optimization module; The data monitoring and acquisition module is used to obtain the number of two-wheeled electric vehicles entering and leaving the campus every day, the frequency of entering and leaving the charging station, and the user's preference for selecting charging piles and the distribution point data of demand from the campus image monitor, time clock and access control device; The data analysis and processing module is used to preprocess and calibrate the data acquired by the data monitoring and acquisition module, and to perform exploratory data analysis on the calibrated data to formulate a site selection plan; The layout optimization module is used to construct an objective function with the minimum total investment cost and user charging cost while fully considering the user's selection preferences and needs, and to determine the constraints of the site selection plan based on the data analysis results, and to solve them using the walrus optimization algorithm to achieve layout optimization of two-wheeled electric vehicle charging piles.

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