Priority-driven hybrid shared parking and charging resource optimization method

Abstract

The invention discloses a priority-driven hybrid shared parking and charging resource optimization method, and aims to solve the problems of unreasonable resource allocation, unbalanced supply and demand of parking spaces and low utilization rate of charging parking spaces of an existing shared parking lot. The method comprises the following steps: firstly, constructing a priority matching mechanism of multiple types of parking spaces and parking demands, setting priorities according to parking demand types and parking space attributes, and preferentially meeting the matching of mixed demands and charging parking spaces; secondly, parking space and demand data of a parking lot are collected, an optimization model with operation profit maximization as a target is constructed, and target functions comprise profit, cost and penalty cost; the timeliness and rationality of supply and demand matching are ensured by introducing the time window constraint of the parking demand and the shared parking space supply; and establishing a constraint relationship between demand and supply, and optimizing the resource allocation efficiency. The parking and charging resource utilization efficiency can be remarkably improved, the user experience is improved, the parking problem is relieved, and popularization of the shared parking policy is facilitated.

Description

Technical Field

[0001] The present invention belongs to the field of traffic engineering and traffic information and control systems, and relates to the technical field of intelligent transportation systems. More specifically, it relates to a priority-driven hybrid shared parking and charging resource optimization method. Background Art

[0002] With the acceleration of urbanization and the continuous growth of the number of motor vehicles, the problem of parking difficulties has become increasingly prominent, especially in high-demand areas such as commercial centers, residential areas and universities, where the contradiction between supply and demand of parking resources has intensified. The introduction of the sharing economy model has provided a new solution to alleviate the parking problem, and the optimal allocation of shared parking resources has gradually become a research hotspot in academia and industry. Through the effective management and allocation of parking resources, it is possible to reduce congestion, improve parking efficiency, optimize parking utilization, and reduce resource waste. In existing research, most scholars focus on the allocation of parking resources for single functions, such as the allocation of shared parking spaces in residential areas, commercial areas and university parking lots. Chen Jun and Xie Kai proposed a two-layer dynamic shared allocation model based on the parking demand of universities, which takes into account the balance between vehicle demand and social demand (Chen Jun, Xie Kai. Dynamic allocation model and effect evaluation of shared parking spaces in universities in central urban areas [J]. China Journal of Highway and Transport, 2015, 28(11): 104-111.); Duan Manzhen proposed a dual-constraint shared parking space allocation model for residential areas, with walking distance and free parking space index as optimization targets (Duan Manzhen, Yang Zhaosheng, Zhang Lin, et al. Shared parking space allocation model for residential areas under personalized induction [J]. Journal of Northeastern University (Natural Science Edition), 2017, 38(02): 174-179.). Technologies such as weighted vertex graph model based on ant colony algorithm, particle swarm search algorithm and chance constrained optimization have also been used to solve the parking supply and demand matching problem, achieving good resource utilization effects. However, most of these studies are limited to the optimization of a single parking demand type and fail to cover the diversity and complexity of parking and charging demands in mixed parking lots. In recent years, with the popularity of electric vehicles, the layout of charging piles and the management of charging parking spaces have become new challenges for parking lot resource allocation. Some scholars have conducted research on the mixed parking and charging scenarios. For example, Xie et al. optimized the matching of parking demand and supply through a dynamic adjustment mechanism (Xie M, Lin S, Wu Z, et al. Optimal allocation and adjustment mechanism of shared parking slots considering combined parking resources [J]. Transportation Letters, 2023, 15 (7): 730-741.); Zhang et al. constructed a bilateral preference multi-objective optimization model to improve user satisfaction and parking space utilization (Zhang Y, Wen H, Zhao M, et al. How does bilateral preference affect shared parking in sharing economy? [J]. Mathematical Problems in Engineering, 2020, 2020.). However, current research mostly takes parking resources or charging resources as separate objects, and lacks a unified optimization method for the mixed parking and charging scenarios. In addition, most existing methods optimize resources from the user's perspective, while ignoring how backend managers can achieve optimal resource allocation through overall decision-making. In this context, how to build a priority-driven resource allocation model in a parking and charging hybrid parking lot, coordinate parking demand and charging demand through a dynamic matching mechanism, and improve the utilization efficiency of parking resources and charging facilities has become a key technical problem that needs to be solved. The present invention proposes a priority-driven hybrid shared parking and charging resource optimization method to meet diverse parking needs, achieve efficient resource allocation and comprehensively improve user experience. Summary of the invention

[0003] Technical problem: In view of the problems in existing parking resource allocation methods that ignore the characteristics of hybrid parking and charging demand, insufficient supply and demand matching mechanism, and inability to take into account both parking efficiency and user experience, the purpose of the present invention is to provide a priority-driven hybrid shared parking and charging resource optimization method. By introducing a priority-based dynamic matching mechanism, combined with collected data, time window constraints and multi-objective optimization models, the resource allocation of multiple types of parking spaces and parking and charging needs is coordinated to improve resource utilization efficiency and user satisfaction, and promote the intelligent and efficient management of shared parking lots.

[0004] Technical solution: To solve the above technical problems, a priority-driven hybrid shared parking and charging resource optimization method of the present invention comprises the following steps: Step 1: Establish a priority matching mechanism between various types of parking spaces and parking demands to achieve reasonable allocation of resources and maximize benefits. The matching of mixed demands with charging parking spaces has the highest priority. The platform obtains parking and charging benefits at the same time, giving priority to meeting mixed demands to avoid the failure to meet potential high-value demands due to insufficient resources. The matching of ordinary parking demands with ordinary parking spaces has the second highest priority to ensure the satisfaction of ordinary parking demands while avoiding ordinary demands occupying charging resources and ensuring the rational use of resources. The matching of ordinary parking demands with charging parking spaces has the lowest priority, only meeting parking demands and occupying charging parking space resources, which may cause a waste of charging resources and is only considered when resources are sufficient. Step 2: Collect parking space information and parking demand information, and build a model with the goal of maximizing the profit of the operator. The objective function includes three parts: benefit, cost, and penalty cost; Step 3: Construct the time window constraints between different parking demands, and the time window constraints between parking demands and parking sharing supply time; Step 4: Establish constraint relationships between variables based on demand and supply. The objective function construction in step 2 includes the following steps: Step 21: The objective function is shown in formula (1): max f=E1+E2+E3-C a -C b -C p1 -C p2 (1) Where: f is the profit of the operating manager; E1 and E3 represent the benefits obtained by matching parking demand with ordinary parking spaces and charging parking spaces, respectively; E2 represents the benefit obtained from mixed demand; C a and C b They represent the cost of renting ordinary parking spaces and charging parking spaces on the platform respectively; C p1 Indicates the penalty costs caused by sharing platforms refusing to rent parking spaces; C p2 It indicates the penalty cost caused by the sharing platform rejecting the demand; Step 22: The revenue E1 obtained by matching parking demand with ordinary parking spaces is calculated by formula (2): Where: P1 represents the parking fee paid by the parking demander for ordinary parking spaces, unit: yuan / hour; I represents the demand set of parking demanders, parking demand i, i∈I; N represents the set of ordinary parking spaces, ordinary parking spaces n, n∈N; Indicates the length of the idle time window of ordinary parking space n; x in is a 0-1 variable, x in =1 means parking demand i is allocated to ordinary parking space n, otherwise it is 0, i∈I,n∈N; The length of the idle time window of ordinary parking space n Calculated by formula (3): Where: and They represent the start time and end time when the ordinary parking space is vacant respectively; The benefit E2 obtained from mixed demand is calculated by formula (4): Where: H is the set of mixed demands for charging and parking, and each mixed demand h, h∈H; S represents the set of charging parking spaces, charging parking spaces s, s∈S; P2 represents the charging fee paid by the hybrid parking demander for the charging parking space, unit: yuan / kWh; Q h It represents the charging demand of mixed parking demanders, unit: kilowatt; x hs is a 0-1 variable, x hs =1 means that the mixed parking demand h is assigned to the charging parking space s, otherwise it is 0, h∈H,s∈S; The parking time window duration representing the mixed parking demand h is calculated by formula (4): Where: and They represent the start time and end time of the mixed demander respectively; The profit E3 obtained by matching parking demand with charging parking spaces is calculated by formula (6): Where: P3 represents the parking fee paid by mixed parking demanders for charging parking spaces, unit: yuan / hour; where: P3>P1; x is is a 0-1 variable, x is =1 means parking demand i is assigned to charging parking space s, otherwise it is 0, i∈I,s∈S; The parking time window duration of parking demand i is calculated by formula (7): Where: and They represent the start time and end time of parking for ordinary parking demanders respectively; The cost of renting a common parking space on the platform C a , calculated by formula (8): Where: C1 represents the cost of renting ordinary parking spaces on the sharing platform, unit: yuan / hour; y n is a 0-1 variable, y n =1 means that the shared platform rents ordinary parking space n, otherwise it is 0, n∈N; The cost of renting a charging parking space on the platform C b , calculated by formula (9): Where: C2 represents the cost of renting a charging parking space on a sharing platform, unit: RMB / hour; where: C2>C1; y s is a 0-1 variable, y s =1 means the sharing platform rents the charging parking space s, otherwise it is 0, s∈S; The duration of the idle time window of the charging parking space s Calculated by formula (10): Where: and They represent the start time and end time when the charging parking space is idle respectively; The penalty cost of refusing to rent a parking space by the sharing platform C p1 , calculated by formula (11): Where: θ1 represents the compensation cost paid by the sharing platform to the parking space provider when the ordinary parking space is not rented, unit: yuan / hour; θ2 represents the compensation cost paid by the sharing platform to the parking space provider when the charging parking space is not rented, unit: yuan / hour; The penalty cost C caused by the sharing platform rejecting demand p2 , calculated by formula (12): Where: τ represents the compensation cost paid by the sharing platform to ordinary parking demanders, unit: yuan / hour; γ represents the compensation cost paid by the sharing platform to mixed parking demanders, unit: yuan / hour. In step 3, the time window constraints between different parking demands and the time window constraints between parking demands and parking sharing supply time are constructed, including the following steps: Step 31: Time window constraints between different parking demands; When different parking demands conflict in parking time periods, they cannot be allocated to the same parking space; when parking demands i and j are allocated to ordinary parking spaces, the reservation time periods should not conflict, where: i, j∈I, i≠j; when parking demands i and e are allocated to charging parking spaces, the reservation time periods should not conflict, where: i, e∈I, i≠e; and when mixed parking demands h and f are allocated to charging parking spaces, the reservation time periods should not conflict, where: h, f∈S, h≠f; as shown in equations (13)-(15); Where: I represents the demand set of parking demanders, parking demand i, i∈I; S represents the set of charging parking spaces, charging parking spaces s, s∈S; and The time window length representing the mixed parking demands h and f; Respectively represent the parking time window duration of parking demand i, j, e; a ij 、a ie and a hf are all 0-1 variables, representing the conflicts among parking demands i, j, e, h, and f respectively; a ij =1 indicates that the reservation time period conflicts when parking demand i and j are allocated to ordinary parking spaces, otherwise it is 0, i,j∈I,i≠j; a ie =1 indicates that the reservation time period conflicts when parking demands i and e are allocated to charging parking spaces, otherwise it is 0, i,e∈I,i≠e; a hf =1 indicates that the reservation time period conflicts when the mixed parking demands h and f are allocated to the charging parking space, otherwise it is 0, h, f∈S, h≠f; Step 32: Time window constraints of parking demand and parking sharing supply time; The parking space supply user provides part of the idle time of his parking space. The demand allocated to the shared parking lot must comply with the shared time provided by the parking space. The parking period of parking demand i should not exceed the shared period of ordinary parking space n, the parking period of parking demand i should not exceed the shared period of charging parking space s, and the parking period of mixed parking demand h should not exceed the shared period of charging parking space s. The constraints are shown in equations (16)-(18); Where: N represents the set of ordinary parking spaces, ordinary parking spaces n, n∈N; S represents the set of charging parking spaces, charging parking spaces s, s∈S; I represents the demand set of parking demanders, parking demand i, i∈I; H is the set of mixed demands for charging and parking, and each mixed demand h, h∈H; g in , k is and l hs They represent the time window subordination between parking demand and ordinary parking space, parking demand and charging parking space, and mixed parking demand and charging parking space respectively; represents the time window length of parking demand i; Indicates the length of the idle time window of ordinary parking space n; Indicates the idle time window duration of the charging parking space s; The length of the parking time window for mixed parking demand h. In step 4, the constraint relationship between variables is established based on demand and supply, including the following steps: Step 41: In resource matching, there are only two possible results for the parking space demand proposed by the parking demander: acceptance or rejection; once the demand is accepted, a parking space is allocated to it; parking demand i is allocated to at most one ordinary parking space; the constraints are shown in formula (19); Where: N represents the set of ordinary parking spaces, ordinary parking spaces n, n∈N; S represents the set of charging parking spaces, charging parking spaces s, s∈S; x in is a 0-1 variable, x in =1 means parking demand i is allocated to ordinary parking space n, otherwise it is 0, i∈I,n∈N; x is is a 0-1 variable, x is=1 means parking demand i is assigned to charging parking space s, otherwise it is 0, i∈I,s∈S; The charging demand h is assigned to at most one charging parking space; the constraint is shown in formula (20); Where: H is the set of mixed demands for charging and parking, and each mixed demand h, h∈H; x hs is a 0-1 variable, x hs =1 means that the mixed parking demand h is assigned to the charging parking space s, otherwise it is 0, h∈H,s∈S; The parking times of parking demanders are mutually constrained; if parking demanders are assigned to the same parking space, in order to avoid time conflicts, the constraints are shown in equations (21)-(23); x in +a ij ·x jn ≤1,i,j∈I;i≠j;n∈N (21) x is +a ie ·x es ≤1,i,e∈I;i≠e;s∈S (22) x hs +a hf ·x fs ≤1,h,f∈H;h≠f;s∈S (23)Where: x jn is a 0-1 variable, x jn =1 means parking demand j is assigned to ordinary parking space n, otherwise it is 0, j∈I,n∈N; x es is a 0-1 variable, x es =1 means that parking demand e is allocated to charging parking space s, otherwise it is 0, e∈I,s∈S; x fs is a 0-1 variable, x fs =1 means that the mixed parking demand f is assigned to the charging parking space s, otherwise it is 0, f∈H,s∈S; a ij 、a ie and a hf They are all 0-1 variables, representing the conflicts among parking demands i, j, e, h, and f respectively; The parking time of the parking demander and the shared supply time of the parking space provider are mutually constrained; once the demander successfully obtains the parking space allocation, his parking time must strictly follow the time window constraint of the parking space, as shown in equations (24)-(26); x in ·gin =0,i∈I;n∈N (24) x is ·k is =0,i∈I;s∈S (25) x hs ·l hs =0,h∈H;s∈S (26) Where: g in , k is and l hs They represent the time window subordination between parking demand and ordinary parking space, parking demand and charging parking space, and mixed parking demand and charging parking space respectively; The occupancy time of all demands assigned to the same parking space should not exceed the total sharing time of the parking space. The constraints are shown in equations (27)-(29); Where: represents the time window length of parking demand i; Indicates the length of the idle time window of ordinary parking space n; Indicates the idle time window duration of the charging parking space s; The length of the parking time window for mixed parking demand h; y s is a 0-1 variable, y s =1 means the sharing platform rents the charging parking space s, otherwise it is 0, s∈S; y n is a 0-1 variable, y n =1 means that the shared platform rents ordinary parking space n, otherwise it is 0, n∈N; The charging power is less than the total power P in the parking lot. T-pow ;P h,e-pow represents the charging power occupied by the h-th hybrid demander, and the constraint is shown in formula (30); Ensure that the power provided by the charging parking space can meet the minimum demand of mixed demanders Q h , the constraint is shown in formula (31); Where P s,T-pow Represents the charging power of the sth charging parking space.

[0005] Beneficial effects: Compared with the prior art, the present invention has the following advantages

[0006] By constructing a priority matching mechanism based on demand type and parking space attributes, the conflict between parking and charging demands can be effectively resolved, resources can be rationally allocated, mixed demands can be met first, and resource utilization in the integrated parking and charging scenario can be improved. The introduction of real-time data acquisition and dynamic allocation models, combined with time window constraints and multi-objective optimization methods, can dynamically adjust the allocation plan, significantly improving matching efficiency and system adaptability. At the same time, an objective function with profit maximization as the core of the operator manager is constructed, taking into account user experience, resource utilization efficiency and social benefits, and balancing the interests of multiple parties. In view of the complexity of the mixed parking and charging scenario, the present invention provides an optimization method for coordinating parking and charging resources, which makes up for the lack of attention to mixed demand and resource matching in existing research, and is suitable for multi-functional parking lots. This method promotes the efficient use of electric vehicle charging facilities while alleviating parking problems, and has broad potential for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a flow chart of the method of the present invention;

[0008] Figure 2 A schematic diagram of the research object of the method of the present invention;

[0009] Figure 3 The result graph is shown in Figure 2. DETAILED DESCRIPTION

[0010] In conjunction with the accompanying drawings and embodiments, the technical solution of the present invention is described in detail as follows:

[0011] Step 1: Establish a priority matching mechanism between various types of parking spaces and parking demands to achieve reasonable allocation of resources and maximize benefits. The matching of mixed demands with charging parking spaces has the highest priority. The platform obtains parking and charging benefits at the same time, giving priority to meeting mixed demands to avoid the failure to meet potential high-value demands due to insufficient resources. The matching of ordinary parking demands with ordinary parking spaces has the second highest priority to ensure the satisfaction of ordinary parking demands while avoiding ordinary demands occupying charging resources and ensuring the rational use of resources. The matching of ordinary parking demands with charging parking spaces has the lowest priority, only meeting parking demands and occupying charging parking space resources, which may cause a waste of charging resources and is only considered when resources are sufficient. Step 2: Collect parking space information and parking demand information. The sharing platform has 8 parking spaces for rent, including 2 charging spaces and 6 ordinary parking spaces. 18 parking demand information, including 15 ordinary parking demands and 3 mixed parking and charging demands. The rental information schedule of different parking spaces is shown in Table 1. The different parking demand information schedule is shown in Table 2. Table 1 Different parking space rental information timetable Table 2 Timetable with different parking demand information The shared parking platform operates from 0:00 to 24:00. The parking price of ordinary parking spaces is P1 = 10 yuan / hour, the parking price of charging parking spaces is P3 = 15 yuan / hour, and the charging price of charging parking spaces is P2 = 0.8 yuan / kWh. The cost of renting an ordinary parking space is C1 = 5 yuan / hour, and the cost of renting a charging parking space is C2 = 8 yuan / hour. If rejected, the penalty cost is θ1 = 0.4 yuan / hour, θ2 = 0.5 yuan / hour, τ = 0.5 yuan / hour, and γ = 0.6 yuan / hour. The three charging users demand 30 degrees, 30 degrees, and 50 degrees of electricity respectively. For 18 different parking demands, the revenue obtained by the sharing platform, the cost of renting a parking space, and the penalty cost of rejecting the demand are shown in Table 3. Table 3 Platform benefits and costs There are 18 different parking demands. In order to meet the high-priority demands, demands 6 and 8 are rejected. Since there is no shared parking space that meets demand 10, demand 10 is rejected, and the profit is 332.15 yuan. The mixed parking and charging demands of No. 16, 17 and 18 are all met, which makes reasonable use of the resources of the charging parking spaces and prevents the charging resources from being occupied by the sharing platform in order to obtain more profits.

[0012] The present invention is not limited to this single example; any other changes, modifications, substitutions, combinations, simplifications that deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A priority-driven hybrid shared parking and charging resource optimization method, characterized in that: The method comprises the following steps: Step 1: Establish a priority matching mechanism between multiple types of parking spaces and parking demands; the matching of mixed demands with charging parking spaces has the highest priority. The platform obtains parking and charging benefits at the same time, giving priority to meeting mixed demands to avoid the failure to meet potential high-value demands due to insufficient resources; the matching of ordinary parking demands with ordinary parking spaces has the second highest priority to ensure the satisfaction of ordinary parking demands and avoid ordinary demands occupying charging resources to ensure the rational use of resources; the matching of ordinary parking demands with charging parking spaces has the lowest priority, only meeting parking demands and occupying charging parking space resources, which may cause a waste of charging resources and is only considered when resources are sufficient; Step 2: Collect parking space information and parking demand information, and build a model with the goal of maximizing the profit of the operator. The objective function includes three parts: benefit, cost, and penalty cost; Step 3: Construct the time window constraints between different parking demands, and the time window constraints between parking demands and parking sharing supply time; Step 4: Establish constraint relationships between variables based on demand and supply.

2. A priority-driven hybrid shared parking and charging resource optimization method according to claim 1, characterized in that: The objective function construction in step 2 includes the following steps: Step 21: The objective function is shown in formula (1): max f=E1+E2+E3-C a -C b -C p1 -C p2 (1) Where: f is the profit of the operating manager; E1 and E3 represent the benefits obtained by matching parking demand with ordinary parking spaces and charging parking spaces, respectively; E2 represents the benefit obtained from mixed demand; C a and C b They represent the cost of renting ordinary parking spaces and charging parking spaces on the platform respectively; C p1 Indicates the penalty costs caused by sharing platforms refusing to rent parking spaces; C p2 It indicates the penalty cost caused by the sharing platform rejecting the demand; Step 22: The revenue E1 obtained by matching parking demand with ordinary parking spaces is calculated by formula (2): Where: P1 represents the parking fee paid by the parking demander for ordinary parking spaces, unit: yuan / hour; I represents the demand set of parking demanders, parking demand i, i∈I; N represents the set of ordinary parking spaces, ordinary parking spaces n, n∈N; Indicates the length of the idle time window of ordinary parking space n; x in is a 0-1 variable, x in =1 means parking demand i is allocated to ordinary parking space n, otherwise it is 0, i∈I,n∈N; The length of the idle time window of ordinary parking space n Calculated by formula (3): Where: and They represent the start time and end time when the ordinary parking space is vacant respectively; The benefit E2 obtained from mixed demand is calculated by formula (4): Where: H is the set of mixed demands for charging and parking, and each mixed demand h, h∈H; S represents the set of charging parking spaces, charging parking spaces s, s∈S; P2 represents the charging fee paid by the hybrid parking demander for the charging parking space, unit: yuan / kWh; Q h It represents the charging demand of mixed parking demanders, unit: kilowatt; x hs is a 0-1 variable, x hs =1 means that the mixed parking demand h is assigned to the charging parking space s, otherwise it is 0, h∈H,s∈S; The parking time window duration representing the mixed parking demand h is calculated by formula (4): Where: and They represent the start time and end time of the mixed demander respectively; The profit E3 obtained by matching parking demand with charging parking spaces is calculated by formula (6): Where: P3 represents the parking fee paid by mixed parking demanders for charging parking spaces, unit: yuan / hour; Where: P3>P1; x is is a 0-1 variable, x is =1 means parking demand i is assigned to charging parking space s, otherwise it is 0, i∈I,s∈S; The parking time window duration of parking demand i is calculated by formula (7): Where: and They represent the start time and end time of parking for ordinary parking demanders respectively; The cost of renting a common parking space on the platform C a , calculated by formula (8): Where: C1 represents the cost of renting ordinary parking spaces on the sharing platform, unit: yuan / hour; y n is a 0-1 variable, y n =1 means that the shared platform rents ordinary parking space n, otherwise it is 0, n∈N; The cost of renting a charging parking space on the platform C b , calculated by formula (9): Where: C2 represents the cost of renting a charging parking space on a sharing platform, unit: yuan / hour; where: C2>W1; y s is a 0-1 variable, y s =1 means the sharing platform rents the charging parking space s, otherwise it is 0, s∈S; The duration of the idle time window of the charging parking space s Calculated by formula (10): Where: and They represent the start time and end time when the charging parking space is idle respectively; The penalty cost of refusing to rent a parking space by the sharing platform C p1 , calculated by formula (11): Where: θ1 represents the compensation cost paid by the sharing platform to the parking space provider when the ordinary parking space is not rented, unit: yuan / hour; θ2 represents the compensation cost paid by the sharing platform to the parking space provider when the charging parking space is not rented, unit: yuan / hour; The penalty cost C caused by the sharing platform rejecting demand p2 , calculated by formula (12): Where: τ represents the compensation cost paid by the sharing platform to ordinary parking demanders, unit: yuan / hour; γ represents the compensation cost paid by the sharing platform to mixed parking demanders, unit: yuan / hour.

3. A priority-driven hybrid shared parking and charging resource optimization method according to claim 1, characterized in that: The step 3 constructs the time window constraints between different parking demands and the time window constraints between parking demands and parking sharing supply time, including the following steps: Step 31: Time window constraints between different parking demands; When different parking demands conflict in parking periods, they cannot be allocated to the same parking space; When parking demands i and j are allocated to ordinary parking spaces, the reservation time periods should not conflict, where: i, j ∈ I, i ≠ j; when parking demands i and e are allocated to charging parking spaces, the reservation time periods should not conflict, where: i, e ∈ I, i ≠ e; and when mixed parking demands h and f are allocated to charging parking spaces, the reservation time periods should not conflict, where: h, f ∈ S, h ≠ f; as shown in equations (13)-(15); Where: I represents the demand set of parking demanders, parking demand i, i∈I; S represents the set of charging parking spaces, charging parking spaces s, s∈S; and The time window length representing the mixed parking demands h and f; Respectively represent the parking time window duration of parking demand i, j, e; a ij 、a ie and a hf are all 0-1 variables, representing the conflicts among parking demands i, j, e, h, and f respectively; a ij =1 indicates that the reservation time period conflicts when parking demand i and j are allocated to ordinary parking spaces, otherwise it is 0, i,j∈I,i≠j; a ie =1 indicates that the reservation time period conflicts when parking demands i and e are allocated to charging parking spaces, otherwise it is 0, i,e∈I,i≠e; a hf =1 indicates that the reservation time period conflicts when the mixed parking demands h and f are allocated to the charging parking space, otherwise it is 0, h, f∈S, h≠f; Step 32: Time window constraints of parking demand and parking sharing supply time; The parking space supply user provides part of the idle time of his parking space. The demand allocated to the shared parking lot must comply with the shared time provided by the parking space. The parking period of parking demand i should not exceed the shared period of ordinary parking space n, the parking period of parking demand i should not exceed the shared period of charging parking space s, and the parking period of mixed parking demand h should not exceed the shared period of charging parking space s. The constraints are shown in equations (16)-(18); Where: N represents the set of ordinary parking spaces, ordinary parking spaces n, n∈N; S represents the set of charging parking spaces, charging parking spaces s, s∈S; I represents the demand set of parking demanders, parking demand i, i∈I; H is the set of mixed demands for charging and parking, and each mixed demand h, h∈H; g in , k is and l hs They represent the time window subordination between parking demand and ordinary parking space, parking demand and charging parking space, and mixed parking demand and charging parking space respectively; represents the time window length of parking demand i; Indicates the length of the idle time window of ordinary parking space n; Indicates the idle time window duration of the charging parking space s; The length of the parking time window for mixed parking demand h.

4. A priority-driven hybrid shared parking and charging resource optimization method according to claim 1, characterized in that: In step 4, the constraint relationship between variables is established according to demand and supply, including the following steps: Step 41: In resource matching, there are only two possible results for the parking space demand proposed by the parking demander: acceptance or rejection; once the demand is accepted, a parking space is allocated to it; Parking demand i is assigned to at most one ordinary parking space; the constraints are shown in formula (19); Where: N represents the set of ordinary parking spaces, ordinary parking spaces n, n∈N; S represents the set of charging parking spaces, charging parking spaces s, s∈S; x in is a 0-1 variable, x in =1 means parking demand i is allocated to ordinary parking space n, otherwise it is 0, i∈I,n∈N; x is is a 0-1 variable, x is =1 means parking demand i is assigned to charging parking space s, otherwise it is 0, i∈I,s∈s; The charging demand h is assigned to at most one charging parking space; the constraint is shown in formula (20); Where: H is the set of mixed demands for charging and parking, and each mixed demand h, h∈H; x hs is a 0-1 variable, x hs =1 means that the mixed parking demand h is assigned to the charging parking space s, otherwise it is 0, h∈H,s∈S; The parking times of parking demanders are mutually constrained; if parking demanders are assigned to the same parking space, in order to avoid time conflicts, the constraints are shown in equations (21)-(23); x in +a ij ·x jn ≤1,i,j∈I;i≠j;n∈N (21) x is +a ie ·x es ≤1,i,e∈I;i≠e;s∈S (22) x hs +a hf ·x fs ≤1,h,f∈H;h≠f;s∈S (23) Where: x jn is a 0-1 variable, x jn =1 means parking demand j is assigned to ordinary parking space n, otherwise it is 0, j∈I,n∈N; x es is a 0-1 variable, x es =1 means that parking demand e is allocated to charging parking space s, otherwise it is 0, e∈I,s∈S; x fs is a 0-1 variable, x fs =1 means that the mixed parking demand f is assigned to the charging parking space s, otherwise it is 0, f∈H,s∈S; a ij 、a ie and a hf They are all 0-1 variables, representing the conflicts among parking demands i, j, e, h, and f respectively; The parking time of the parking demander and the shared supply time of the parking space provider are mutually constrained; once the demander successfully obtains the parking space allocation, his parking time must strictly follow the time window constraint of the parking space, as shown in equations (24)-(26); x in ·g in =0,i∈I;n∈N (24) x is ·k is =0,i∈I;s∈S (25) x hs ·l hs =0,h∈H;s∈S (26) Where: g in , k is and l hs They represent the time window subordination between parking demand and ordinary parking space, parking demand and charging parking space, and mixed parking demand and charging parking space respectively; The occupancy time of all demands assigned to the same parking space should not exceed the total sharing time of the parking space. The constraints are shown in equations (27)-(29); Where: represents the time window length of parking demand i; Indicates the length of the idle time window of ordinary parking space n; Indicates the idle time window duration of the charging parking space s; The length of the parking time window for mixed parking demand h; y s is a 0-1 variable, y s =1 means the sharing platform rents the charging parking space s, otherwise it is 0, s∈S; y n is a 0-1 variable, y n =1 means that the shared platform rents ordinary parking space n, otherwise it is 0, n∈N; The charging power is less than the total power P in the parking lot. T-pow ;P h,e-pow represents the charging power occupied by the jth hybrid demander, and the constraints are shown in formula (30); Ensure that the power provided by the charging parking space can meet the minimum demand of mixed demanders Q h , the constraint is shown in formula (31); Where P s,T-pow Represents the charging power of the sth charging parking space.

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