Parking resource allocation strategy determination method and device, equipment and storage medium
By constructing an integer linear programming model and dynamically adjusting the allocation ratio of parking resources, the problem of parking resource allocation failing to adapt to demand fluctuations is solved, achieving a balance between efficient resource utilization and user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 深圳市顺易通信息科技有限公司
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-21
AI Technical Summary
The existing parking lot resource allocation method cannot dynamically adapt to the fluctuations in demand for temporary parking and package users, resulting in insufficient supply during peak hours or idleness during off-peak hours, which reduces the efficiency of parking lot resource utilization.
By constructing a resource allocation optimization model based on integer linear programming and combining the revenue and cost factors of package and temporary parking, the resource allocation ratio parameters are dynamically adjusted to maximize the net revenue of the parking lot and balance user experience.
It improves the accuracy and flexibility of parking lot resource allocation, ensures the achievement of parking lot operation goals, and avoids the rigidity of traditional strategies and resource waste.
Smart Images
Figure CN121903285A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of smart parking, and more specifically, to a method, apparatus, device, and storage medium for determining parking resource allocation strategies. Background Technology
[0002] With the acceleration of urbanization and the continuous growth of motor vehicle ownership, parking lots, as a key component of urban transportation infrastructure, need to serve both package users and temporary parking users. These two types of users provide parking lots with stable basic income and high revenue per unit time, respectively. The rational allocation of parking space resources is directly related to the operational efficiency and user experience of parking lots.
[0003] Currently, the most widely used resource allocation method in the parking industry is a static allocation method based on a fixed ratio. This means that the operator pre-sets a fixed allocation ratio between package users and temporary parking spaces based on past experience. This ratio remains unchanged in long-term operation. For example, 60% of the total number of parking spaces are allocated to package users, and the remaining 40% are used as temporary parking spaces.
[0004] The existing technology has significant drawbacks: due to the obvious time-period fluctuations in temporary parking demand (such as the difference between weekdays and weekends, and between daytime and nighttime demand), the fixed allocation ratio cannot dynamically adapt to this change, resulting in insufficient supply of temporary parking spaces during peak hours and users being turned away, while a large number of temporary parking spaces are idle during off-peak hours. At the same time, there may be situations where temporary parking users cannot use the exclusive parking spaces for package deals when they are vacant, which seriously reduces the overall utilization efficiency of parking lot resources. Summary of the Invention
[0005] In view of this, the purpose of this application is to provide a parking resource allocation strategy method, apparatus, equipment and storage medium, which effectively improves the accuracy, targeting and flexibility of resource allocation.
[0006] In a first aspect, embodiments of this application provide a parking resource allocation strategy method, the method comprising: Receive the target operating parameters of the target parking lot from the front-end interface; The system receives the operational goals input from the front-end interface, inputs the target operational parameters into the corresponding resource allocation strategy based on the operational goals, and obtains the resource allocation ratio parameters for achieving the operational goals. The resource allocation ratio parameters are output to the front-end interface, and the confirmation result from the front-end interface is received.
[0007] Optionally, the resource allocation strategy is a matching strategy used to determine the scale of package issuance; The allocation strategy includes a resource allocation ratio parameter, which represents the ratio between the number of various types of parking packages issued and the total number of parking spaces in the parking lot.
[0008] Optionally, the step of inputting the target operational parameters into the corresponding resource allocation strategy according to the operational objective to obtain the resource allocation ratio parameters for achieving the operational objective includes: Construct a resource allocation optimization model based on the stated operational objectives and the stated operational parameters; Based on the resource allocation optimization model, the allocation ratio parameters of each resource are determined.
[0009] Optionally, the step of constructing a resource allocation optimization model based on the operational objectives and the target operational parameters includes: The resource allocation optimization model is constructed with the goal of maximizing the net profit function value. The net revenue function value is calculated based on the following four items: total package revenue based on the number of packages issued and the average daily fee, total temporary parking revenue based on the accepted temporary parking requests and the time period rate, total compensation cost based on the number of unmet package users and the unit compensation fee, and total penalty cost based on the number of rejected temporary parking requests and the unit penalty coefficient. The number of packages issued is determined based on the resource allocation ratio parameter; The constraint of the resource allocation optimization model is that, at any time during the operation cycle, the sum of the number of parking spaces allocated to package users and the number of parking spaces allocated to temporary parking users does not exceed the total number of parking spaces in the parking lot.
[0010] Optionally, determining the resource allocation ratio parameters based on the resource allocation optimization model includes: Based on the target operational parameters, multiple candidate resource allocation strategies are preset or generated. For each candidate allocation percentage parameter, the number of packages to be issued is determined based on the candidate allocation percentage parameter; Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, the parking space allocation and occupancy situation is simulated for at least one operating cycle. Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value. The resource allocation percentage parameters are determined based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0011] Optionally, determining the resource allocation ratio parameters based on the resource allocation optimization model includes: Based on the target operational parameters, multiple candidate resource allocation strategies are preset or generated. For each candidate allocation percentage parameter, the number of packages to be issued is determined based on the candidate allocation percentage parameter; Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, the parking space allocation and occupancy situation is simulated for at least one operating cycle. Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value. The resource allocation percentage parameters are determined based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0012] Optionally, after outputting the resource allocation ratio parameters to the front-end interface and receiving the confirmation result from the front-end interface, the method includes: In response to the update of the target operating parameters, the resource allocation ratio parameter is updated.
[0013] Secondly, embodiments of this application provide a parking resource allocation strategy determination device, the device comprising: The operation parameter acquisition module is used to receive the target operation parameters of the target parking lot input from the front-end interface; The percentage parameter determination module is used to receive the operation target input from the front-end interface, input the target operation parameters into the corresponding resource allocation strategy according to the operation target, and obtain the percentage parameters of each resource allocation to achieve the operation target. The confirmation result determination module is used to output the resource allocation ratio parameters to the front-end interface and receive the confirmation result from the front-end interface.
[0014] Optionally, the resource allocation strategy is a matching strategy used to determine the scale of package issuance; The allocation strategy includes a resource allocation ratio parameter, which represents the ratio between the number of various types of parking packages issued and the total number of parking spaces in the parking lot.
[0015] Optionally, the step of inputting the target operational parameters into the corresponding resource allocation strategy according to the operational objective to obtain the resource allocation ratio parameters for achieving the operational objective includes: Construct a resource allocation optimization model based on the stated operational objectives and the stated operational parameters; Based on the resource allocation optimization model, the allocation ratio parameters of each resource are determined.
[0016] Optionally, the step of constructing a resource allocation optimization model based on the operational objectives and the target operational parameters includes: The resource allocation optimization model is constructed with the goal of maximizing the net profit function value. The net revenue function value is calculated based on the following four items: total package revenue based on the number of packages issued and the average daily fee, total temporary parking revenue based on the accepted temporary parking requests and the time period rate, total compensation cost based on the number of unmet package users and the unit compensation fee, and total penalty cost based on the number of rejected temporary parking requests and the unit penalty coefficient. The number of packages issued is determined based on the resource allocation ratio parameter; The constraint of the resource allocation optimization model is that, at any time during the operation cycle, the sum of the number of parking spaces allocated to package users and the number of parking spaces allocated to temporary parking users does not exceed the total number of parking spaces in the parking lot.
[0017] Optionally, determining the resource allocation ratio parameters based on the resource allocation optimization model includes: Based on the target operational parameters, multiple candidate resource allocation strategies are preset or generated. For each candidate allocation percentage parameter, the number of packages to be issued is determined based on the candidate allocation percentage parameter; Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, the parking space allocation and occupancy situation is simulated for at least one operating cycle. Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value. The resource allocation percentage parameters are determined based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0018] Optionally, determining the resource allocation ratio parameters based on the resource allocation optimization model includes: Based on the target operational parameters, multiple candidate resource allocation strategies are preset or generated. For each candidate allocation percentage parameter, the number of packages to be issued is determined based on the candidate allocation percentage parameter; Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, the parking space allocation and occupancy situation is simulated for at least one operating cycle. Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value. The resource allocation percentage parameters are determined based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0019] Optionally, the device further includes an allocation ratio parameter update module, which, after outputting the resource allocation ratio parameters to the front-end interface and receiving the confirmation result from the front-end interface, updates the resource allocation ratio parameters in response to the update of the target operating parameters.
[0020] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the parking resource allocation strategy determination method described in any of the optional embodiments of the first aspect are performed.
[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the parking resource allocation strategy determination method described in any of the optional embodiments of the first aspect.
[0022] This application provides a method for determining parking resource allocation strategies. It receives target operational parameters of a target parking lot from a front-end interface and inputs these parameters into the corresponding resource allocation strategy. This yields the allocation ratio parameters for each resource within the operational target. This method standardizes and normalizes the collection of operational parameters, avoiding omissions or errors caused by traditional manual statistics and scattered recording. It ensures the accuracy and reliability of the data foundation upon which subsequent resource allocation strategy calculations rely, providing a prerequisite for scientific decision-making. Furthermore, it achieves precise matching between operational targets and resource allocation strategies, ensuring that resource allocation always revolves around the operational targets. This avoids the problem of traditional strategies being disconnected from actual operational goals, ensuring that the allocation results align with the core operational needs of the parking lot.
[0023] By outputting the resource allocation ratio parameters to the front-end interface and receiving the confirmation result from the front-end interface, a parameter feedback loop can be established, allowing operators to intuitively understand the resource allocation ratio. At the same time, parameters can be adjusted in a timely manner according to actual scenario needs, enhancing the flexibility and adaptability of resource allocation strategies and avoiding the strategy rigidity problem caused by the lack of confirmation links in traditional allocation methods.
[0024] In summary, this invention constructs a process-oriented parking resource allocation logic through standardized parameter input, goal-oriented strategy matching, and closed-loop result confirmation, effectively improving the accuracy, relevance, and flexibility of resource allocation and providing reliable support for the scientific operation of parking lots.
[0025] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 A flowchart of a parking resource allocation strategy determination method provided in Embodiment 1 of this application is shown; Figure 2 A flowchart of a method for determining a resource allocation ratio parameter provided in Embodiment 1 of this application is shown; Figure 3 A flowchart of the second method for determining the resource allocation ratio parameter provided in Embodiment 1 of this application is shown; Figure 4 A flowchart of the third method for determining the resource allocation ratio parameter provided in Embodiment 1 of this application is shown; Figure 5 A flowchart illustrating the determination of an optimal monthly card issuance ratio, provided in Embodiment 2 of this application, is shown. Figure 6 This paper shows a schematic diagram of the structure of a parking resource allocation strategy determination device provided in Embodiment 2 of this application; Figure 7 A schematic diagram of the structure of a computer device provided in Embodiment 3 of this application is shown. Detailed Implementation
[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0029] Example 1 To facilitate understanding of this application, the following is combined with... Figure 1 The flowchart illustrating a parking resource allocation strategy determination method provided in Embodiment 1 of this application will be used to describe Embodiment 1 of this application in detail.
[0030] See Figure 1 As shown, Figure 1 A flowchart of a parking resource allocation strategy determination method provided in Embodiment 1 of this application is shown, wherein the method includes steps S101 to S103: S101: Receives the target operation parameters of the target parking lot input from the front-end interface.
[0031] Specifically, the target operating parameters are the data foundation for model calculations, covering four major categories: parking lot physical parameters, operating parameters, demand parameters, and cost factors. These include, but are not limited to, the total number of parking spaces N, the number of time periods k divided into a day and the identifier for each time period, and the estimated total daily demand for temporary parking M.
[0032] This also includes, but is not limited to, demand matrices derived from historical parking data—the daily temporary parking user demand matrix Rak (an M-row, K-column 0-1 matrix representing the parking intentions of each temporary parking user at each time period) and the daily package user demand matrix Daxk (a Q-row, K-column 0-1 matrix generated subsequently based on the number of packages issued Q, representing the parking intentions of each package user at each time period).
[0033] It also includes pricing-related parameters: the fee standard P1 for temporary parking in each time period, and the equivalent cost P2 of the package fee spread over the day. It also involves two key cost parameters: the compensation fee P3 for failing to meet the parking needs of package users, and the implicit penalty coefficient F for refusing parking requests from temporary users. These two parameters are set independently by the parking lot operator based on their business strategy, reflecting the value assessment of user service guarantees and opportunity costs.
[0034] S102: Receive the operational target input from the front-end interface, input the target operational parameters into the corresponding resource allocation strategy according to the operational target, and obtain the resource allocation ratio parameters for achieving the operational target.
[0035] Specifically, the core operational objective is to maximize the overall net revenue of the parking lot, while balancing the service experience of package users with the acceptance rate of temporary parking users, and to solve the problem of neglecting long-term value factors such as user satisfaction and opportunity cost in traditional decision-making.
[0036] The corresponding resource allocation strategy is the "package temporary suspension ratio model". This model is a systematic method based on integer linear programming, which is different from the traditional experience-driven or static quota allocation method.
[0037] The core of the resource allocation ratio parameter is the resource allocation ratio parameter α, which is the ratio of the number of packages issued Q to the total number of parking spaces N in the parking lot. Q = α * N. The value of α can be flexibly set (such as 0.5 to 2.0). For example, α = 2.0 means that 10 parking spaces correspond to 20 packages. The optimal solution is found by iterating different values of α.
[0038] S103: Output the resource allocation ratio parameters to the front-end interface and receive the confirmation result from the front-end interface.
[0039] Specifically, the output includes not only each candidate α value, but also the expected net income corresponding to each α value. It can also generate a function graph of α and income (such as a line graph) to intuitively show the highest point of revenue and the sensitivity of income to changes in α.
[0040] The confirmation result on the front-end interface represents the operator's feedback on the optimal α value (α*), which can then be used to implement package issuance and berth allocation strategies.
[0041] The process also supports dynamic adjustment requirements. If the operator discovers changes in parameters, a recalculation can be triggered through the front end to ensure that the strategy is adapted to the actual operational scenario.
[0042] In one alternative implementation, the resource allocation strategy is a ratio strategy used to determine the scale of package issuance.
[0043] Specifically, this allocation strategy focuses on the dynamic allocation of core parking packages and temporary parking spaces in parking lots. The core objective is to solve two major business pain points: first, the reasonable division of parking spaces between package users and temporary parking users (the two are priced differently, and it is necessary to ensure a balance of rights); second, the delay between online pre-orders and offline parking lot data (to avoid security guards misjudging the status of parking spaces due to information asynchrony).
[0044] The strategy breaks free from the limitations of traditional experience-based decision-making, achieving a shift from "experience-driven" to "data-driven" approaches. By quantifying all influencing factors (including explicit revenue and implicit costs), it optimizes overall efficiency.
[0045] The allocation strategy includes a resource allocation ratio parameter, which represents the ratio between the number of various types of parking packages issued and the total number of parking spaces in the parking lot.
[0046] Specifically, the resource allocation ratio parameter is α (Q=α*N). The range of α needs to be preset based on the actual parking lot scenario. For example, it can be iterated from 0.5 (the number of packages is 50% of the total parking spaces) to 2.0 (the number of packages is twice the total parking spaces). The step size can be set to 0.1 to ensure coverage of the potential optimal solution range.
[0047] This ratio directly determines the basic proportion of parking space resources for package users and temporary parking users. If α is too high, package users may have "cards but no parking spaces," while if α is too low, parking spaces may be idle and stable income may be insufficient.
[0048] In an optional implementation, see Figure 2 As shown, Figure 2 The flowchart illustrates a method for determining resource allocation ratio parameters according to Embodiment 1 of this application. The step of inputting the target operation parameters into the corresponding resource allocation strategy based on the operation objective to obtain the resource allocation ratio parameters for achieving the operation objective includes steps S201-S202: S201: Construct a resource allocation optimization model based on the operational objectives and the target operational parameters.
[0049] Specifically, the operational objective here is to maximize the overall net revenue of the parking lot. The target operational parameters include core data such as the total number of parking spaces, the number of time periods, the temporary parking / package pricing standards, and user demand characteristics.
[0050] This step constructs an integer linear programming model, which contains three core elements: first, a binary decision variable (X representing whether to accept the temporary parking user's request). m y represents whether the needs of package users are met. q The first is the objective function of the overall net income (total revenue from integrated packages / temporary parking, compensation costs for unmet package needs, and penalty costs for refusing temporary parking). The second is the constraint (capacity constraint that the sum of berths occupied by packages and temporary parking in any given time period does not exceed the total number of berths, and type constraint that decision variables can only take 0 or 1). In this way, the operational objectives and actual parameters are transformed into a mathematical model that can be quantified and solved.
[0051] S202: Determine the resource allocation ratio parameters based on the resource allocation optimization model.
[0052] Specifically, the resource allocation ratio parameter here corresponds to the package issuance ratio α (the ratio of the number of packages to the total number of berths). This step iterates different α values (i.e., candidate values of the resource allocation ratio parameter) within a preset range, substitutes the number of packages corresponding to each α into the resource allocation optimization model, combines the target operation parameters to simulate berth allocation, calculates the corresponding net revenue, and finally obtains multiple sets of candidate α values that can achieve the operation goals. These candidate α values are the resource allocation ratio parameters.
[0053] In an optional implementation, the step of constructing a resource allocation optimization model based on the operational objectives and the target operational parameters includes: The resource allocation optimization model is constructed with the goal of maximizing the net profit function value.
[0054] Specifically, model building needs to integrate both revenue and cost dimensions. It should consider the direct revenue from packages and temporary parking, as well as the opportunity cost of refusing temporary parking and the compensation cost for not meeting package requirements, so as to achieve a balance between short-term cash flow and long-term user value.
[0055] The core of the model is to quantify soft indicators (such as user satisfaction and brand reputation) as hard data (F and P3) and incorporate them into the optimization calculation, so that the decision-making is closer to the real business environment and overcomes the limitation of traditional models that only focus on direct revenue.
[0056] This model is the core logic of the algorithm, rather than a simple time-sequence simulation process. It needs to be computed using a standard integer linear programming solver (such as Gurobi, CPLEX, or GNU Linear Programming Toolkit) to ensure that a globally optimal solution is found under constraints.
[0057] The model comprises three core elements: decision variables, objective function, and constraints. The decision variables are binary variables, specifically defined as follows: Decision variable 1: This indicates whether to accept the parking request of the m-th temporary parking user. =1 indicates acceptance. =0 indicates rejection.
[0058] Decision variable 2: This indicates whether the parking needs of the q-th package user are met. =1 indicates that the condition is met. =0 indicates that the condition is not met.
[0059] The objective function is to maximize the expected net revenue Z of the parking lot, and its mathematical expression is as follows: ; The unique definition of each character is: The maximization operator is used to indicate that the goal is to maximize the result of the subsequent expression. The core target variable represents the expected overall net revenue of the parking lot (net revenue after deducting compensation costs and hidden penalties). Temporary parking fees for each time period (one of the operational parameters); : Time period number (ranging from 1 to K), used to distinguish different time periods within a day; The total number of time periods divided within a day (one of the operational parameters); Time period The actual number of temporarily parked vehicles is expressed mathematically as follows: ,in For temporary parking user m, this is an indicator variable indicating whether there is demand during time period k. =1 indicates there is demand. =0 indicates no demand); Resource allocation ratio parameter (core resource allocation ratio parameter), which is the ratio of the number of packages issued Q to the total number of parking spaces N (Q= ); Total number of parking spaces in the parking lot (one of the physical parameters, a fixed value); The equivalent cost of the package is spread out to each day (one of the operational parameters); : Hidden penalty coefficient for refusing parking requests from users who request temporary parking (one of the cost factors, configurable by the operator); : Estimated total daily demand for temporary parking (one of the demand parameters); Temporary parking user ID (value range 1 to M), used to distinguish different temporary parking users; : Decision variable for whether to accept the m-th temporary parking user's request (binary variable, 0 or 1); Compensation fee to be paid when parking needs of package users cannot be met (one of the cost factors, which can be configured by the operator); The number of packages issued is determined by... and It is derived that (Q= ); Package user ID (ranging from 1 to Q), used to distinguish users of different packages; Does it satisfy the first condition? Indicator variables (binary variables, 0 or 1) for user demand for each package.
[0060] The net revenue function value is calculated based on the following four items: total package revenue based on the number of packages issued and the average daily fee; total temporary parking revenue based on the accepted temporary parking requests and the time period rate; total compensation cost based on the number of unmet package users and the unit compensation fee; and total penalty cost based on the number of rejected temporary parking requests and the unit penalty coefficient.
[0061] Total revenue from the package = Number of packages issued (Q) × Average daily equivalent cost of the package , where Q=α*N, is directly linked to the resource allocation ratio parameter α.
[0062] Total revenue from temporary parking = sum of fees for all accepted temporary parking users. Fee for a single temporary parking user = number of time periods occupied × temporary parking time period fee standard P1. The actual time periods occupied need to be calculated in conjunction with the temporary parking user demand matrix (Rak).
[0063] Total compensation cost = Number of times the needs of package users were not met × Unit compensation cost P3, Number of times the needs were not met = Number of packages issued Q - Number of package users whose needs were met ,Right now .
[0064] Total penalty cost = Number of times temporary suspension requests were rejected × Unit hidden penalty coefficient F, Number of rejections = Total temporary suspension demand M - Number of temporary suspension users accepted ,Right now .
[0065] The net revenue function is ultimately calculated as "total revenue from the package + total revenue from temporary parking - total compensation cost - total penalty cost", which is consistent with the objective function expression above.
[0066] The number of packages issued is determined based on the resource allocation ratio parameter.
[0067] Specifically, the number of packages issued, Q, is linearly related to the resource allocation ratio parameter α and the total number of berths, N, as expressed mathematically: Q = Each candidate α value corresponds to a unique Q value.
[0068] The value of Q directly affects the resource allocation for the two types of users: the larger Q is, the more parking spaces available to package users and the fewer remaining parking spaces available to temporary users, and vice versa. The model is needed to balance the interests of the two.
[0069] The constraint of the resource allocation optimization model is that, at any time during the operation cycle, the sum of the number of parking spaces allocated to package users and the number of parking spaces allocated to temporary parking users does not exceed the total number of parking spaces in the parking lot.
[0070] Specifically, the operating cycle is usually set to one day, which includes k divided time periods (such as morning, noon, evening peak and off-peak periods). The constraints must cover each time period to ensure that there is no overcapacity.
[0071] The mathematical expression for the constraint is: ; The unique definition of each character is: : An indicator variable indicating whether temporary parking user m has parking needs during time period k ( =1 indicates there is demand. =0 indicates no demand), taken from the temporary parking user demand matrix (Rak); : An indicator variable indicating whether package user q has parking needs during time period k ( =1 indicates there is demand. =0 indicates no demand), taken from the package user demand matrix (Daxk); : Logical symbol, representing "for all time periods k", ensuring that the constraint covers every time period within a day; The definitions of other characters are consistent with those in the target function.
[0072] This constraint can be further broken down into "number of temporarily parked vehicles in time period k". +Number of vehicles in time slot K package ≤ N”, where Let k be the number of packaged vehicles actually parked during time period k, expressed mathematically as follows: .
[0073] This constraint ensures that the total number of vehicles actually parked in the parking lot at any given time does not exceed the total number of parking spaces N, thus avoiding overloading of parking resources.
[0074] In an optional implementation, the resource allocation optimization model further includes variable type constraints, the mathematical expression of which is: ∈{0,1} m∈{1,2,...,M} ∈{0,1} q∈{1,2,...,Q} Specifically, variable type constraints clearly define the range of values for decision variables: (Decision on accepting temporary users) and (Indicator for fulfilling user needs of package) can only take 0 or 1, which is consistent with the binary scenario of "accept / reject" and "fulfill / not fulfill" in actual operation.
[0075] In an optional implementation, see Figure 3 As shown, Figure 3 The flowchart of the second method for determining resource allocation ratio parameters provided in Embodiment 1 of this application is shown, wherein determining each resource allocation ratio parameter based on the resource allocation optimization model includes steps S301 to S305: S301: Based on the target operation parameters, preset or generate multiple candidate resource allocation strategies.
[0076] Specifically, there are two ways to generate candidate resource allocation strategies: one is to preset a reasonable range based on historical parking lot operation data (such as past package issuance volume and peak temporary parking demand); the other is to automatically generate within a set range at fixed steps to ensure coverage of possible allocation ratios under different operation scenarios.
[0077] Each candidate resource allocation strategy corresponds to an α value. Subsequently, the overall net benefit of each strategy will be evaluated through model calculations to provide data support for the selection of the optimal strategy.
[0078] S302: For each candidate allocation percentage parameter, determine the number of packages to be issued based on the candidate allocation percentage parameter.
[0079] Specifically, the iteration range and step size of α need to be set first. For example, if α iterates from 0.5 to 2.0 with a step size of 0.1, 16 candidate allocation ratio parameter α values can be generated.
[0080] For each candidate allocation ratio parameter α, the number of packages issued is calculated by Q=α*N. For example, when N=100 and α=1.2, Q=120 packages, thus clarifying the user scale of the packages under this ratio.
[0081] S303: Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, simulate the parking space allocation and occupancy situation within at least one operating cycle.
[0082] Specifically, the simulation process is executed sequentially by time period, first processing the needs of package users: based on the package user demand matrix (Daxk)... The system uses variables to determine the number of parking spaces required by package subscribers at different times, pre-allocating corresponding parking spaces. Even if no package subscriber is using the space, it is considered occupied, thus protecting the rights of package subscribers. During the parking space allocation simulation, an allocation rule that prioritizes the parking needs of package subscribers is adopted.
[0083] If the demand from package subscribers exceeds the pre-allocated number of parking spaces, allocation will be based on a "first-come, first-served" principle, with equal rights for all subscribers within the same group. Package subscribers who do not receive a parking space will have their demand recorded as unmet. =0, cumulative compensation cost P3.
[0084] After fulfilling the needs of package subscribers, remaining parking spaces are allocated to temporary parking users: based on the temporary parking user demand matrix (Rak). Variables and berth availability for accepting temporary parking users, and the corresponding accepted users. =1, based on the number of time periods occupied ( Calculate P1 revenue, corresponding to requests rejected due to berths being full. =0, cumulative penalty coefficient F.
[0085] The simulation process strictly follows the constraints of the resource allocation optimization model. Ensure that the total number of parked vehicles does not exceed N at any given time, and record key data ( Values, Values, various revenues and costs).
[0086] S304: Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value.
[0087] Specifically, the simulation results include core data: Q (number of packages issued) and P2 (average daily cost of the packages). P1 (Total Revenue from Temporary Suspension) (Number of visits not met according to package requirements) (Number of times temporary suspension requests were rejected); Substitute the above data into the objective function. The expected comprehensive net return (Z value) corresponding to the candidate α value is calculated, providing a quantitative basis for subsequent optimal strategy selection.
[0088] S305: Determine the resource allocation percentage parameters based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0089] Specifically, the candidate allocation ratio parameter here corresponds to multiple pre-generated α values. Each α has obtained the corresponding expected comprehensive net benefit through the "parking simulation" and "net target value calculation" steps. This step will compare and sort the net benefit values corresponding to all candidate α values, and finally determine the α value corresponding to the expected comprehensive net benefit with the largest value as the final resource allocation ratio parameter. This parameter not only adapts to the actual operating parameters of the parking lot (such as the total number of parking spaces and user demand characteristics), but also takes into account the balance between comprehensive benefits and user experience.
[0090] In an optional implementation, see Figure 4 As shown, Figure 4 The flowchart of the third method for determining the resource allocation ratio parameter provided in Embodiment 1 of this application is shown. The step of determining the resource allocation ratio parameter based on the expected comprehensive net income corresponding to each resource allocation strategy includes steps S401-S402: S401: Compare the values of the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0091] Specifically, iterate through all candidate α values and their corresponding Z values (expected net return), sort all Z values by size, and clearly present the return differences of different allocation strategies.
[0092] During the comparison process, it is necessary to combine the functional relationship between α and Z and pay attention to the changing trend of the revenue curve (such as whether it is flat or the peak position) to provide a reference for the robustness of the operation strategy.
[0093] S402: The candidate allocation ratio parameter corresponding to the largest expected comprehensive net income is determined as the resource allocation ratio parameter.
[0094] Specifically, the core of the resource allocation ratio parameter is the optimal package ratio α*, which is the α value corresponding to the maximum Z value.
[0095] α* is the optimal solution that simultaneously maximizes parking lot revenue, optimizes parking space utilization, and balances user satisfaction under the current operating parameters. It can directly guide the implementation of package issuance and parking space allocation, and output the corresponding maximum Z value (expected optimal net revenue).
[0096] In an optional implementation, after outputting the resource allocation percentage parameters to the front-end interface and receiving confirmation from the front-end interface, the method includes: In response to the update of the target operating parameters, the resource allocation ratio parameter is updated.
[0097] Specifically, updates to operational parameters include fluctuations in temporary parking demand (M) (such as a surge in demand during holidays), adjustments to pricing standards (P1, P2), updates to the demand matrix (Rak, Daxk) due to changes in user behavior, and adjustments to strategies for P3 or F. After the parameters are updated, a T+1 offline analysis mode is adopted, and the optimal α* is recalculated daily to achieve dynamic adaptive management and ensure that the strategy always adapts to changes in market demand. The dynamic adjustment mechanism solves the problem that the traditional static management model cannot adapt to fluctuations in demand, allowing parking lot operations to flexibly respond to different demand differences in different scenarios such as weekdays and weekends, daytime and nighttime, and seasonality.
[0098] In an optional implementation, the method includes: Output the resource allocation ratio parameter and its corresponding expected net return.
[0099] Specifically, the output includes the exact value of α*, the corresponding expected maximum net profit, and a graph showing the relationship between α and Z. The graph uses α as the horizontal axis and Z as the vertical axis to visually display the change in revenue from α to its maximum value, marking the highest revenue point (α*, Z*), helping operators understand the impact of different allocation ratios on revenue and make more informed decisions.
[0100] In this application, the parking resource allocation strategy includes, but is not limited to, the monthly pass issuance ratio, the quarterly pass issuance ratio, and the annual pass issuance ratio. For a better explanation of the parking resource allocation strategy determination method provided in this application, please refer to [link to relevant documentation]. Figure 5 As shown, Figure 5 The flowchart for determining the optimal monthly card issuance ratio provided in Embodiment 2 of this application is shown. The process starts with the "Start" node and first enters the "Parameter Input and Initialization" stage, which corresponds to the step of "receiving the target operation parameters of the target parking lot input from the front-end interface" in the aforementioned technology, and completes the input and initialization of basic parameters such as the total number of parking spaces, time period division, and charging standard.
[0101] The process then proceeds to the "α value iteration loop?" judgment node. If the judgment result is "yes", then "calculate the number of monthly cards issued" is executed, corresponding to the rule in the aforementioned technology that "the number of packages issued is determined according to the resource allocation ratio parameter (α) (Q=α×total number of parking spaces)", to obtain the monthly card issuance quantity corresponding to the current α. Next, "parking simulation", "simulate monthly card users", and "simulate temporary parking users" are executed in sequence. Among them, "simulate monthly card users" and "simulate temporary parking users" correspond to the "parking space allocation rule that prioritizes meeting the parking needs of monthly card users" in the aforementioned technology. The needs of monthly card users are processed first, and then the remaining resources are allocated to temporary parking users. After that, "calculate the net target value" is executed, corresponding to the step of "calculating the expected comprehensive net income based on the net income function" in the aforementioned technology, to obtain the net income result corresponding to the current α. Then, the process returns to the "α value iteration loop?" node to continue the loop.
[0102] If the result of the judgment "α value iterative loop?" is "no", then "determine the optimal α" is executed, which corresponds to the step in the aforementioned technology of "comparing the expected comprehensive net income of each resource allocation strategy and determining the strategy with the largest value as the optimal one"; then "output the result" is executed, which corresponds to the step in the aforementioned technology of "outputting the resource allocation ratio parameter and its corresponding expected comprehensive net income to the front-end interface", and finally the entire process is completed with the "end" node.
[0103] The parking resource allocation strategy method provided in this application is suitable for the operation scenarios of parking lots with fluctuating demand, such as commercial districts, office buildings, and mixed-use commercial and residential areas. Commercial districts need to match the difference between the demand for weekend temporary parking peaks and weekday monthly passes. Office buildings need to balance the resource occupation of monthly passes during the morning peak and temporary parking during off-peak hours. Mixed-use commercial and residential areas need to match the day and night demand of residents' nighttime monthly passes and commercial daytime temporary parking. The flowchart accurately adapts to the resource allocation requirements of different scenarios through α value iteration, user demand simulation and other steps.
[0104] Once implemented, this process can achieve multiple benefits: by iteratively cycling the alpha value and calculating the net target value, it can adapt to demand fluctuations and avoid rigid strategies, while also balancing revenue and hidden costs; prioritizing the simulation of package users ensures a consistent user experience, while the parking simulation process reduces idle parking spaces; and finally, by replacing experience-based decision-making with data-driven approaches, it achieves a synergistic improvement in overall parking lot revenue, user retention, and resource utilization.
[0105] Example 2 See Figure 6 As shown, Figure 6 This illustration shows a schematic diagram of a parking resource allocation strategy determination device according to Embodiment 2 of this application, wherein the device includes: The operation parameter acquisition module 601 is used to receive the target operation parameters of the target parking lot input from the front-end interface; The proportion parameter determination module 602 is used to receive the operation target input from the front-end interface, input the target operation parameters into the corresponding resource allocation strategy according to the operation target, and obtain the proportion parameters of each resource allocation to achieve the operation target. The confirmation result determination module 603 is used to output the resource allocation ratio parameters to the front-end interface and receive the confirmation result from the front-end interface.
[0106] In one optional implementation, the resource allocation strategy is a matching strategy used to determine the scale of package issuance; The allocation strategy includes a resource allocation ratio parameter, which represents the ratio between the number of various types of parking packages issued and the total number of parking spaces in the parking lot.
[0107] In an optional implementation, the device further includes: The resource allocation strategy generation module is used to preset or generate multiple candidate resource allocation ratio parameters as preset multiple resource allocation strategies based on the parking lot operation parameters.
[0108] In an optional implementation, the step of inputting the target operational parameters into the corresponding resource allocation strategy according to the operational objective to obtain the resource allocation ratio parameters for achieving the operational objective includes: Construct a resource allocation optimization model based on the stated operational objectives and the stated operational parameters; Based on the resource allocation optimization model, the allocation ratio parameters of each resource are determined.
[0109] In an optional implementation, the step of constructing a resource allocation optimization model based on the operational objectives and the target operational parameters includes: The resource allocation optimization model is constructed with the goal of maximizing the net profit function value. The net revenue function value is calculated based on the following four items: total package revenue based on the number of packages issued and the average daily fee, total temporary parking revenue based on the accepted temporary parking requests and the time period rate, total compensation cost based on the number of unmet package users and the unit compensation fee, and total penalty cost based on the number of rejected temporary parking requests and the unit penalty coefficient. The number of packages issued is determined based on the resource allocation ratio parameter; The constraint of the resource allocation optimization model is that, at any time during the operation cycle, the sum of the number of parking spaces allocated to package users and the number of parking spaces allocated to temporary parking users does not exceed the total number of parking spaces in the parking lot.
[0110] In an optional implementation, determining the resource allocation ratio parameters based on the resource allocation optimization model includes: Based on the target operational parameters, multiple candidate resource allocation strategies are preset or generated. For each candidate allocation percentage parameter, the number of packages to be issued is determined based on the candidate allocation percentage parameter; Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, the parking space allocation and occupancy situation is simulated for at least one operating cycle. Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value. The resource allocation percentage parameters are determined based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0111] In an optional implementation, determining the resource allocation ratio parameters based on the resource allocation optimization model includes: Based on the target operational parameters, multiple candidate resource allocation strategies are preset or generated. For each candidate allocation percentage parameter, the number of packages to be issued is determined based on the candidate allocation percentage parameter; Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, the parking space allocation and occupancy situation is simulated for at least one operating cycle. Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value. The resource allocation percentage parameters are determined based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
[0112] In an optional implementation, the device further includes an allocation ratio parameter update module, which, after outputting the resource allocation ratio parameters to the front-end interface and receiving the confirmation result from the front-end interface, updates the resource allocation ratio parameters in response to the update of the target operating parameters.
[0113] Example 3 Based on the same application concept, see [link / reference] Figure 7 As shown, Figure 7This illustration shows a structural schematic diagram of a computer device provided in Embodiment 3 of this application, wherein, as shown... Figure 7 As shown, the computer device 700 provided in Embodiment 3 of this application includes: The computer device 700 includes a processor 701, a memory 702, and a bus 703. The memory 702 stores machine-readable instructions that can be executed by the processor 701. When the computer device 700 is running, the processor 701 communicates with the memory 702 through the bus 703. When the machine-readable instructions are executed by the processor 701, the steps of the parking resource allocation strategy determination method shown in Embodiment 1 above are performed.
[0114] Example 4 Based on the same concept, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the parking resource allocation strategy determination method described in any of the above embodiments.
[0115] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0116] The computer program product for determining parking resource allocation strategies provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.
[0117] The parking resource allocation strategy determination device provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.
[0118] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
[0119] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0120] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0121] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0122] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0123] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.
Claims
1. A method for determining parking resource allocation strategies, characterized in that, The method includes: Receive the target operating parameters of the target parking lot from the front-end interface; The system receives the operational goals input from the front-end interface, inputs the target operational parameters into the corresponding resource allocation strategy based on the operational goals, and obtains the resource allocation ratio parameters for achieving the operational goals. The resource allocation ratio parameters are output to the front-end interface, and the confirmation result from the front-end interface is received.
2. The method according to claim 1, characterized in that, The resource allocation strategy is a ratio strategy used to determine the scale of package issuance; The allocation strategy includes a resource allocation ratio parameter, which represents the ratio between the number of various types of parking packages issued and the total number of parking spaces in the parking lot.
3. The method according to claim 1, characterized in that, The step of inputting the target operational parameters into the corresponding resource allocation strategy according to the operational objective to obtain the resource allocation ratio parameters for achieving the operational objective includes: Construct a resource allocation optimization model based on the stated operational objectives and the stated operational parameters; Based on the resource allocation optimization model, the allocation ratio parameters of each resource are determined.
4. The method according to claim 3, characterized in that, The construction of the resource allocation optimization model based on the operational objectives and the target operational parameters includes: The resource allocation optimization model is constructed with the goal of maximizing the net profit function value. The net revenue function value is calculated based on the following four items: total package revenue based on the number of packages issued and the average daily fee, total temporary parking revenue based on the accepted temporary parking requests and the time period rate, total compensation cost based on the number of unmet package users and the unit compensation fee, and total penalty cost based on the number of rejected temporary parking requests and the unit penalty coefficient. The number of packages issued is determined based on the resource allocation ratio parameter; The constraint of the resource allocation optimization model is that, at any time during the operation cycle, the sum of the number of parking spaces allocated to package users and the number of parking spaces allocated to temporary parking users does not exceed the total number of parking spaces in the parking lot.
5. The method according to claim 4, characterized in that, The determination of the resource allocation ratio parameters based on the resource allocation optimization model includes: Based on the target operational parameters, multiple candidate resource allocation strategies are preset or generated. For each candidate allocation percentage parameter, the number of packages to be issued is determined based on the candidate allocation percentage parameter; Based on the number of packages issued and the parking lot operation parameters, and under the constraints of the resource allocation optimization model, the parking space allocation and occupancy situation is simulated for at least one operating cycle. Based on the simulation results of the berth allocation and the occupancy status, the expected comprehensive net income corresponding to the candidate allocation ratio parameter is calculated based on the net income function value. The resource allocation percentage parameters are determined based on the expected comprehensive net income corresponding to each candidate allocation percentage parameter.
6. The method according to claim 5, characterized in that, The process of determining the resource allocation ratio parameters based on the expected comprehensive net income corresponding to each candidate allocation ratio parameter includes: Compare the values of the expected comprehensive net income corresponding to the allocation ratio parameters of each candidate; The candidate allocation percentage parameter corresponding to the largest expected comprehensive net income is determined as the resource allocation percentage parameter.
7. The method according to claim 1, characterized in that, After outputting the resource allocation ratio parameters to the front-end interface and receiving the confirmation result from the front-end interface, the method includes: In response to the update of the target operating parameters, the resource allocation ratio parameter is updated.
8. A device for determining parking resource allocation strategy, characterized in that, The device includes: The operation parameter acquisition module is used to receive the target operation parameters of the target parking lot input from the front-end interface; The percentage parameter determination module is used to receive the operation target input from the front-end interface, input the target operation parameters into the corresponding resource allocation strategy according to the operation target, and obtain the percentage parameters of each resource allocation to achieve the operation target. The confirmation result determination module is used to output the resource allocation ratio parameters to the front-end interface and receive the confirmation result from the front-end interface.
9. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the parking resource allocation strategy determination method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the parking resource allocation strategy determination method as described in any one of claims 1 to 7.