An optimal parking space solving method and parking guidance system for indoor parking lot

Abstract

This invention relates to the field of parking management technology and provides a method for finding the optimal parking space in an indoor parking lot. The method includes the following steps: Step 1, collecting relevant data; Step 2, sending and saving parking information form data to a cloud server; Step 3, sending a parking request to the cloud server; Step 4, the cloud server accepts the parking request and calls the central controller; Step 5, the central controller solves for the optimal parking space and plans the optimal route; Step 6, transmitting the obtained optimal parking space and route to the front-end functional processing board; Step 7, starting parking based on the obtained optimal parking space and route information. This invention provides a method for finding the optimal parking space in an indoor parking lot and a parking guidance system that, by establishing an optimal parking space allocation model based on multi-factor decision-making, comprehensively considers the individual needs of car owners and the system utilization rate of the parking lot, maximizing car owner parking satisfaction and improving parking efficiency.

Description

Technical Field

[0001] This invention belongs to the field of parking management technology, and in particular relates to a method for finding the optimal parking space and a parking guidance system for indoor parking lots. Background Technology

[0002] With the continuous development and progress of society and the economy, and the gradual improvement of people's living standards, private cars have become commonplace. While bringing convenience to people's lives, the increasing number of vehicles has also created many problems that urgently need to be addressed, such as traffic congestion and environmental pollution. Among these, parking problems, as one of the important causes of traffic pressure, are becoming increasingly serious and particularly prominent.

[0003] To address the pressing issue of "parking difficulties," the construction and management of parking lots are crucial. In reality, when drivers are unaware of parking information near their destination, they typically resort to the traditional method of searching for a parking space by scouting. This unguided parking process consumes a significant amount of their time. Common indoor underground parking garages, with their enclosed spaces and limited landmarks, easily disorient drivers, increasing the difficulty of scouting and causing frustration, resulting in a less comfortable parking experience.

[0004] However, many current intelligent parking lot guidance systems assign spaces randomly, have unclear control strategies, low system utilization, and often conflict between individual and system efficiency. There is a need to build an intelligent parking space allocation model with clear control strategies, high efficiency, and the ability to truly meet the parking needs of car owners. This model should provide car owners with path guidance from the parking lot entrance to their assigned parking space to solve the practical problem of intelligent parking guidance in indoor parking lots, thereby improving the travel experience of car owners, addressing environmental issues, and increasing the management efficiency of parking lots.

[0005] Therefore, in view of the above situation, there is an urgent need to develop an optimal parking space solution method and parking guidance system for indoor parking lots to overcome the shortcomings in current practical applications. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for finding the optimal parking space in an indoor parking lot and a parking guidance system to solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for finding the optimal parking space in an indoor parking lot, the method comprising the following steps:

[0009] Step 1: Provide personal information management functions for car owners, and collect parking data of car owners and relevant data of parking lots;

[0010] Step 2: Send and save the car owner's parking information form data to the cloud server;

[0011] Step 3: The car owner sends a parking request to the cloud server according to their personal needs;

[0012] Step 4: The cloud server accepts the parking request and calls the central controller;

[0013] Step 5: The central controller first selects the appropriate parking control strategy based on the density of vehicles waiting to park, then calls the optimal parking space allocation model to solve for the optimal parking space, and finally plans the corresponding optimal path based on the solved optimal parking space.

[0014] Step 6: After the central controller call ends, the cloud server will transmit the obtained optimal berth and path to the front-end function processing board;

[0015] Step 7: The car owner obtains the best parking space and route information through the front-end function processing board and begins parking.

[0016] As a further technical solution of the present invention, in step one, the relevant data are vehicle data and parking lot data;

[0017] Vehicle data refers to the parking information registered by the vehicle owner through the front-end function processing board. The parking information includes static information and dynamic information. Static information includes the vehicle owner's name, valid document number, driver's license information, license plate number, and contact information. Dynamic information includes vehicle size, driving proficiency, maximum acceptable walking distance, and intended destination.

[0018] Parking data includes parking space types, parking lot entrances, elevator exits, vertical mapping locations of destinations, parking space allocation in each zone of the parking lot, parking space occupancy rates in each zone, parking flow control thresholds, and a topology diagram of the parking lot.

[0019] As a further technical solution of the present invention, in step four, the cloud server takes the parking application as input, the optimal parking space and the globally optimal planned path as output, and provides the static data and dynamic data of the car owner to the central controller, waiting for the allocation result of the central controller.

[0020] As a further technical solution of the present invention, in step five, the parking control strategy includes a first-come, first-served greedy control strategy and a multi-objective simultaneous optimization control strategy. The central controller calculates the density of vehicles waiting to park by checking the number of queuing requests in the waiting queue.

[0021] When the density of vehicles waiting to park does not exceed the predetermined threshold, it is handled as if the traffic flow is low, and a greedy control strategy of first-come, first-served is adopted.

[0022] When the density of vehicles waiting to park exceeds a predetermined threshold, the system is treated as a case of high traffic volume, and a multi-objective simultaneous optimization control strategy is adopted.

[0023] As a further technical solution of the present invention, the first-come-first-served greedy control strategy refers to sequentially calling the optimal parking space allocation model for each vehicle applying for a parking space to solve for the optimal parking space that meets its individual needs. The input and output have a one-to-one relationship.

[0024] The multi-objective simultaneous optimization control strategy refers to optimizing multiple waiting vehicles simultaneously through a combination of optimization algorithms. It adopts the simulated annealing algorithm, calls the optimal parking space allocation model once, and solves for the optimal parking space that meets the needs of multiple car owners at the same time. The input and output have a many-to-many relationship.

[0025] As a further technical solution of the present invention, in step five, the optimal berth allocation model is based on multivariate factor decision-making, and the optimal berth allocation model includes user factors and system factors.

[0026] User factors are used to meet the individual needs of users, including vehicle size, driving proficiency, selected destination, and acceptable maximum walking distance. Qualitative indicators for vehicle size and driving proficiency are transformed into quantitative descriptions using fuzzy evaluation methods.

[0027] System factors include the driving distance of each parking space from the parking entrance, the walking distance of each parking space from the elevator exit, parking difficulty, the utilization rate of parking spaces in each zone of the parking lot, and the congestion coefficient of local road sections.

[0028] As a further technical solution of the present invention, the optimal berth obtained by the optimal berth allocation model refers to the optimal result obtained by weighted summation evaluation after normalizing each evaluation index through the fitness function of the berth. The fitness function of the berth is a comprehensive consideration of user factors and system factors.

[0029] As a further technical solution of the present invention, in step five, the optimal path refers to the central controller taking the entrance as the starting point and the optimal parking space as the ending point, applying Dijkstra's algorithm in the parking lot topology map to obtain the globally optimal planned path.

[0030] A parking guidance system, applied to the above-mentioned method for finding the optimal parking space in an indoor parking lot, the parking guidance system comprising:

[0031] The data acquisition module is used for vehicle owners to register their personal information and collect user factors required for the parking space allocation model.

[0032] The data storage module is used to store vehicle owner data and parking lot-related data;

[0033] The data transmission module is used to transmit the parking request issued by the user to the cloud server, and then send the parking space allocation result and the planned route back to the user; and

[0034] Data processing module.

[0035] As a further technical solution of the present invention, the data processing module includes:

[0036] The density estimation module is used to estimate the density of vehicles requesting parking within a short period of time;

[0037] The strategy selection module is used to compare the traffic flow density with a predetermined threshold and select the appropriate parking control strategy.

[0038] The parking space solution module, based on the established parking control strategy, solves for the optimal parking space and plans the optimal route through optimal parking space allocation.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention establishes an optimal parking space allocation model based on multi-factor decision-making, which comprehensively considers the individual needs of car owners and the system utilization rate of parking lots. It maximizes car owners' parking satisfaction and improves parking efficiency. At the same time, it prevents the situation where parking spaces in some areas are concentrated while other areas have an excess of empty parking spaces. The system utilization rate is improved, and the utilization and allocation of parking space resources are more reasonable.

[0041] Furthermore, different parking control strategies are adopted by estimating traffic density. When traffic volume is low, a first-come, first-served greedy strategy is used to allocate parking spaces to waiting vehicles in sequence, ensuring parking satisfaction for each driver. When traffic volume is high, a multi-objective simultaneous optimization strategy is adopted to simultaneously allocate the best parking space to multiple waiting vehicles within a short period of time. This strategy can effectively solve the parking space allocation problem under different traffic volume conditions. Modules responsible for different functions are established, with clear division of labor among the modules. Through coordination, a complete parking guidance system is established. For the user end, the operation is simple, easy to use, and highly efficient. For the server end, the control strategy is clear, and the calculation method is efficient and reasonable.

[0042] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0043] Figure 1 This is a floor plan illustration of the indoor parking lot simulation test environment for this invention.

[0044] Figure 2 This is a schematic diagram illustrating the influencing factors considered in the optimal berth allocation model of this invention.

[0045] Figure 3 This is a flowchart of the first-come, first-served greedy control strategy described in this invention.

[0046] Figure 4 This is a flowchart of the multi-objective simultaneous optimization control strategy described in this invention.

[0047] Figure 5 This is a flowchart illustrating the solution process for the initial berth allocation scheme in the multi-objective simultaneous optimization control strategy described in this invention.

[0048] Figure 6 A solution flowchart is generated for the new berth allocation scheme in the multi-objective simultaneous optimization control strategy described in this invention.

[0049] Figure 7 This is a flowchart illustrating the parking control strategy selection method of the present invention.

[0050] Figure 8 This is a flowchart illustrating the relationships between the various modules of the parking guidance system of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0052] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0053] As an embodiment of the present invention, a method for finding the optimal parking space in an indoor parking lot is provided, the method comprising the following steps:

[0054] Step 1: Provide personal information management functions for car owners, and collect parking data of car owners and relevant data of parking lots;

[0055] Step 2: Send and save the car owner's parking information form data to the cloud server;

[0056] Step 3: The car owner sends a parking request to the cloud server according to their personal needs;

[0057] Step 4: The cloud server accepts the parking request and calls the central controller;

[0058] Step 5: The central controller first selects the appropriate parking control strategy based on the density of vehicles waiting to park, then calls the optimal parking space allocation model to solve for the optimal parking space, and finally plans the corresponding optimal path based on the solved optimal parking space.

[0059] Step 6: After the central controller call ends, the cloud server will transmit the obtained optimal berth and path to the front-end function processing board;

[0060] Step 7: The car owner obtains the best parking space and route information through the front-end function processing board and begins parking.

[0061] In a preferred embodiment of the present invention, in step one, the relevant data includes vehicle data and parking lot data;

[0062] Vehicle data refers to the parking information registered by the vehicle owner through the front-end function processing board. The parking information includes static information and dynamic information. Static information includes the vehicle owner's name, valid document number, driver's license information, license plate number, and contact information, while dynamic information includes vehicle size, driving proficiency, maximum acceptable walking distance, and intended destination.

[0063] The front-end function processing board can be a parking APP, which manages all users entering the parking lot. Car owners need to register and record information to use the parking lot.

[0064] Parking data includes parking space types, parking lot entrances, elevator exits, vertical mapping locations of destinations, parking space allocation in each zone of the parking lot, parking space occupancy rates in each zone, parking flow control thresholds, and a topology diagram of the parking lot.

[0065] In a preferred embodiment of the present invention, in step four, the cloud server takes the parking application as input, the optimal parking space and the globally optimal planned route as output, and provides the vehicle owner's static data and dynamic data to the central controller, waiting for the allocation result from the central controller.

[0066] In a preferred embodiment of the present invention, in step five, the parking control strategy includes a first-come, first-served greedy control strategy and a multi-objective simultaneous optimization control strategy. The central controller calculates the density of vehicles waiting to park by checking the number of queuing requests in the waiting queue.

[0067] When the density of vehicles waiting to park does not exceed the predetermined threshold, it is handled as if the traffic flow is low, and a greedy control strategy of first-come, first-served is adopted.

[0068] When the density of vehicles waiting to park exceeds a predetermined threshold, the system is treated as a case of high traffic volume, and a multi-objective simultaneous optimization control strategy is adopted.

[0069] As a preferred embodiment of the present invention, the first-come-first-served greedy control strategy refers to sequentially calling the optimal parking space allocation model for each vehicle applying for a parking space to solve for the optimal parking space that meets its individual needs. The input and output have a one-to-one relationship.

[0070] The multi-objective simultaneous optimization control strategy refers to optimizing multiple waiting vehicles simultaneously through a combination of optimization algorithms. It adopts the simulated annealing algorithm, calls the optimal parking space allocation model once, and solves for the optimal parking space that meets the needs of multiple car owners at the same time. The input and output have a many-to-many relationship.

[0071] In a preferred embodiment of the present invention, in step five, the optimal berth allocation model is based on multivariate factor decision-making, and the optimal berth allocation model includes user factors and system factors.

[0072] User factors are used to meet the individual needs of users, including vehicle size, driving proficiency, selected destination, and acceptable maximum walking distance. Qualitative indicators for vehicle size and driving proficiency are transformed into quantitative descriptions using fuzzy evaluation methods.

[0073] System factors include the driving distance of each parking space from the parking entrance, the walking distance of each parking space from the elevator exit, parking difficulty, the utilization rate of parking spaces in each zone of the parking lot, and the congestion coefficient of local road sections.

[0074] In a preferred embodiment of the present invention, the optimal berth obtained by the optimal berth allocation model refers to the optimal result obtained by weighted summation of various evaluation indicators after normalizing them through the fitness function of the berth. The fitness function of the berth is a comprehensive consideration of user factors and system factors.

[0075] In a preferred embodiment of the present invention, in step five, the optimal path refers to the path that the central controller obtains globally optimal by applying Dijkstra's algorithm to the parking lot topology map, with the entrance as the starting point and the best parking space as the ending point.

[0076] A parking guidance system, applied to the above-mentioned method for finding the optimal parking space in an indoor parking lot, the parking guidance system comprising:

[0077] The data acquisition module, located on the client side, is used for users to register and record their personal information and to collect user factors required for the berth allocation model.

[0078] The data storage module, located on the cloud server, is used to store users' static data, dynamic data, and parking lot-related data.

[0079] The data transmission module connects the client and server, transmitting user parking requests and dynamic data from the client to the server, and transmitting the optimal parking space and globally optimal route from the server to the client for user use; and

[0080] The data processing module, located on the cloud server, is mainly used by the central controller to select a suitable parking control strategy. It takes the user's dynamic data and parking lot-related data as input, calls the optimal parking space allocation method, obtains the optimal parking space and the globally optimal path, and outputs both.

[0081] In a preferred embodiment of the present invention, the data processing module includes:

[0082] The density estimation module is used to estimate the density of vehicles requesting parking within a short period of time;

[0083] The strategy selection module is used to compare the traffic flow density with a predetermined threshold and select the appropriate parking control strategy.

[0084] The parking space solution module, based on the established parking control strategy, solves for the optimal parking space and plans the optimal route through optimal parking space allocation.

[0085] like Figure 1 As mentioned above, 1 in the figure refers to the entrance of the parking lot, that is, the starting point for vehicles waiting to park;

[0086] In the diagram, 2 refers to a single-level parking space, and the test environment has a total of 52 single-level parking spaces;

[0087] In the figure, 3 refers to a double-layer parking space. Each double-layer parking space can park 5 cars. The test environment has a total of 16 double-layer parking spaces, which can park a total of 80 vehicles.

[0088] In the diagram, 4, 5, 6 and 7 all refer to elevator exits, also known as pedestrian exits, which are the exits that car owners walk to after parking their cars in designated parking spaces.

[0089] In the diagram, 8, 9, and 10 all refer to destinations. These destinations are not actually located inside the parking lot, but rather are vertical mappings of the actual destination. For example, this parking lot design could be applied to an underground parking lot in a commercial district. Destination 9 corresponds to a movie theater. From the driver's perspective, if they want to get to the movie theater as quickly as possible, the best option would be to find a suitable parking space near the elevator exit marked 5. Obviously, such a parking space would be more satisfactory to the driver than a parking space selected solely based on the shortest driving distance.

[0090] In the diagram, 11 and 12 refer to the two designated parking areas. All other parking spaces constitute a third parking area. The parking space management adopts the principle of zoned management to ensure that the utilization rate of parking spaces in each area remains balanced throughout the operation of the entire parking lot.

[0091] like Figure 2The present invention model divides the important factors affecting berth allocation into two categories: user factors and system factors. User factors can adapt to different types of user needs. At the same time, user factors play their role by influencing system factors. Therefore, the two types of factors are not independent of each other, but are logically classified.

[0092] For system factors, describe them in detail using the following factors:

[0093] Travel time T(s',j): This represents the time it takes for a vehicle to travel from entrance s' to parking space j and complete parking. It includes the following components:

[0094] The driving distance D(s',j) represents the shortest distance a vehicle travels from entrance s' to parking space j, denoted by v. c Represents vehicle speed;

[0095] Turning time t ci , where represents the turning time of the i-th vehicle, and x is the number of turns;

[0096] Parking time t pi , where is the time it takes for the i-th vehicle to directly enter the parking space;

[0097] Scheduling time t mj , represents the additional time cost required for the j-th parking space to be dispatched through the double-layer parking space, and η represents whether the parking space requires additional dispatch cost, with a value of 0 or 1;

[0098] Based on the above, the formula for calculating the travel time T(s',j) is given as shown in (1).

[0099]

[0100] Walking distance D(j,e) k ), indicating the distance of parking space j from elevator exit e. k The shortest distance, in the actual calculation of this invention, is the distance from the parking space j to the elevator exit e. k The value is represented by the Euclidean distance.

[0101] Parking difficulty r affects the time it takes for car owners to drive and park in a parking lot. The result is obtained through fuzzy evaluation method and is affected by user factors. The specific calculation method will be described in detail in the user factors section later.

[0102] Local road congestion coefficient P z , is used to describe the congestion in a local section of the parking lot caused by parking guidance, and the calculation formula is shown in (2):

[0103]

[0104] In formula (2), Indicated by d z The total number of vehicles waiting to park at the destination. Indicates at destination d z The number of available berths within a certain range, obviously, P z This can intuitively reflect the reason for heading to destination d. z Traffic congestion in some sections of the road caused by guiding vehicles waiting to park. z The smaller the value, the more time-consuming the destination d is. z The more vacant parking spaces there are within a certain range, and the fewer vehicles waiting to park, the greater the probability that these parking spaces will be allocated.

[0105] User factors are described in detail using the following factors:

[0106] Model size M i : Directly affects the time a car owner spends driving in the parking lot and the time it takes to park, and is a qualitative indicator;

[0107] Driving Level L i : Directly affects the time a car owner spends driving in the parking lot and the time it takes to park, and is a qualitative indicator;

[0108] Destination d z The destination chosen by the car owner based on their own needs directly affects the walking distance D(j,e). k The value of ) depends on the different destinations d. z This will inevitably lead to different pedestrian exits. k The corresponding walking distance D(j,e) k ) will also change accordingly;

[0109] Acceptable maximum walking distance D MAX This indicates the degree to which car owners are willing to compromise on parking space satisfaction; generally, car owners hope for D... MAX The goal is to obtain parking spaces with the shortest walking distance, thus enabling faster arrival at the destination. The minimum value of this metric increases as the number of available parking spaces (m) decreases.

[0110] For vehicle size M i And driving level L i The fuzzy evaluation method is used for grade evaluation. The specific evaluation method is shown in Table 1. The integer values ​​in parentheses in the figure represent the quantitative representation of the attribute value.

[0111]

[0112] Table 1

[0113] like Figure 3As mentioned above, the vehicle size M is set. i And driving level L i The weights are w1 and w2 respectively, and the calculation formula for the fuzzy evaluation result r, i.e. the berthing difficulty, is shown in formula (3):

[0114] r = w1Mx + w2L i (0≤w1<1,0≤w2<1) (3)

[0115] In summary, the evaluation function for berth j can be defined as follows, as shown in formula (4):

[0116] F(s',j)=αT(s',j) * +βD(j,e k ) * +γP z * (4)

[0117] Regarding formula (4), it should be noted that:

[0118] T(s',j) * D(j,e) k ) * and P z * They are T(s',j) and D(j,e) respectively. k ) and P z The result after normalization;

[0119] αβ and γ are both weighting factors, with values ​​ranging from (0,1), where α=λr (λ>0), meaning α is proportional to the berthing difficulty evaluation value r.

[0120] like Figure 3 As shown, this document details how to find the optimal parking space for any vehicle waiting to park:

[0121] 1. The cloud server transmits the relevant attribute values ​​of the vehicles waiting to be parked to the central controller;

[0122] 2. The central controller first sets the current number of available parking spaces S in the parking lot. m To facilitate future use, and to optimize parking system utilization, all available parking spaces will be managed by partitioned areas. These three partitions will each correspond to a set of parking spaces, S. m1 S m2 and S m3 ;

[0123] 3. Obtain the parking space occupancy rate of the three parking space sets. The calculation method is the ratio of the number of currently occupied parking spaces to the total number of parking spaces in that zone. The parking space occupancy rates of the three zones are rate1, rate2 and rate3 respectively.

[0124] 4. Find the minimum value among rate1, rate2, and rate3, and denote it as rate;

[0125] 5. Based on the calculated rate, select the corresponding set of berths S. mk ;

[0126] 6. Regarding S mk For all available berths, calculate their evaluation function value F(s',j), and find the berth j that minimizes the evaluation function value. * This will serve as the proposed optimal berth.

[0127] 7. From S mk Delete j * ;

[0128] 8. Compare and determine the optimal berth j * Walking distance D(j) * ,e k ) and the maximum walking distance D acceptable to the car owner MAX ;

[0129] 9. If D(j) * ,e k ) <D MAX Then determine the optimal berth j * To find the optimal parking space that meets the needs of car owners, the central controller uses Dijkstra's algorithm, starting from the entrance s' and finding the optimal parking space j. * Calculate the globally optimal path Path(s',j) with the destination as the endpoint. * );

[0130] 10. The central controller returns the calculation results, including the optimal berth j. * The globally optimal path Path(s',j) * And the current minimum acceptable maximum walking distance D MAX .

[0131] 11. If D(j) * ,e k )>D MAX That is, to determine the optimal berth. * This cannot meet the car owner's needs and requires recalculation.

[0132] 12. If If the current number of available parking spaces is 0, it means that all available parking spaces in the current parking lot cannot meet the needs of car owners. Therefore, D needs to be moved to another parking space. MAX Increase the size, and simultaneously use the copied set of available berths from step 2 to restore S. mk ;

[0133] After the above first-come, first-served greedy control strategy has finished running, the cloud server will assign the best berth j * The globally optimal path Path(s',j) * The information is returned to the front-end information panel, which is then displayed on the parking app. Drivers can follow the route guidance to find a parking spot. At the same time, the cloud server updates the maximum walking distance D in the back-end database. MAX The value is raised to a new D. MAX This ensures that subsequent parking requests from car owners can also find the best parking space.

[0134] like Figure 4 The aforementioned multi-objective simultaneous optimization control strategy aims to address situations with high parking volume. It sacrifices a small portion of user satisfaction to improve the overall parking space utilization rate, thereby reducing congestion within the parking lot. Therefore, under this strategy, the acceptable maximum walking distance value D will be adjusted. MAX Not to be considered; the following will be combined with Figure 4 This section details how to allocate the best parking space to multiple vehicles that simultaneously apply for parking spaces within a short period of time.

[0135] 1. The cloud server transmits the relevant attribute values ​​of all vehicles waiting to be parked to the central controller;

[0136] 2. The central controller employs a simulated annealing algorithm for simultaneous multi-objective optimization, with an initial temperature T set. start and termination temperature T end The number of iterations is set at each temperature, and the cooling control strategy uses the temperature update method in formula (5):

[0137] T curr =ρT pre (5)

[0138] In formula (5), T pre T represents the temperature value from the previous iteration. curr The temperature value for the next iteration is ρ = 0.95.

[0139] 3. Select a suitable allocation scheme A0 from the set of available parking spaces. A0 contains n parking spaces and serves as the initial solution for iterative optimization. Note that the selection of parking spaces in A0 must also ensure the balance of parking space utilization across different zones of the parking lot. The initial parking space selection scheme of this invention is as follows: Figure 5 As shown:

[0140] 4. Generate a new berth allocation scheme NA k The specific generation method is as follows: Figure 5 As shown:

[0141] 5. Calculate berth allocation scheme Ak-1 and NA k Their respective evaluation function values ​​F(A) k-1 ) and F(NA k );

[0142] 6. Determine if the program currently meets the termination conditions. Two termination conditions are used to control the program's termination:

[0143] If |F(NA) k )-F(A k-1 If | < θ, the termination condition is satisfied, where θ is a predetermined threshold of the system;

[0144] If T start ≤T end The termination condition is met.

[0145] 7. If the program meets any of the termination conditions, it means that the optimal berth allocation scheme NA has been obtained. k Execute A k =NA k ;

[0146] 8. The central controller invokes Dijkstra's algorithm to sequentially process the optimal berth allocation scheme A. k For each berth in the algorithm, the globally optimal path is calculated to obtain the set of globally optimal paths, Path(A). k );

[0147] 9. The central controller will assign the optimal berth allocation scheme A. k and the global optimal path set Path(A k Return them together;

[0148] 10. If the program does not meet any of the termination conditions, it indicates that allocation scheme A is not working. k-1 This is not the optimal solution; we need to determine the appropriate solution for A. k-1 And the newly generated scheme NA k The magnitude relationship of the evaluation function values;

[0149] 11. If F(NA) k ) <F(A k-1 If the result is NA, then it indicates that the newly generated scheme is NA. k It is superior to Ax -1 Therefore, the current solution A will be... k Assigned the value NA k This indicates acceptance of the new proposal;

[0150] 12. Proceed to the next iteration at the current temperature;

[0151] 13. If F(NA) k )>F(A k-1If the result is positive, it means that the newly generated solution is better than A. k-1 Even worse, to prevent the program from getting stuck in a local minimum, this worse solution is accepted as the current solution with a certain probability p. Otherwise, the current solution remains unchanged, and the program proceeds to the next iteration calculation at the current temperature. The probability p is calculated using formula (6):

[0152]

[0153] Where, ΔF=F(NA) k )-F(A k-1 ).

[0154] Figure 7 The flowchart for selecting the parking control strategy described in this invention is as follows: Figure 7 As shown:

[0155] 1. The central controller periodically checks the number of waiting berth requests in the request queue and records this value as size;

[0156] 2. Set the traffic flow threshold to δ, for example, set δ = 10;

[0157] 3. If size > δ, it means that the number of vehicles waiting to park is greater than 10, which is a case of large traffic flow. The central controller will retrieve all requests in the request queue at once and adopt a multi-objective simultaneous optimization control strategy.

[0158] 4. If size < δ, it means that the number of vehicles waiting to park does not exceed 10, which is a case of low traffic volume. The central controller will process the requests at the head of the request queue in sequence, and adopt a first-come, first-served control strategy.

[0159] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimal parking space solving for indoor parking lots, characterized in that, The method includes the following steps: Step 1: Provide personal information management functions for car owners, collecting their parking data and related data from parking lots. This related data includes vehicle data and parking lot data. Vehicle data refers to the parking information registered by the car owner through the front-end processing panel. Parking information includes static and dynamic information. Static information includes the car owner's name, valid ID number, driver's license information, license plate number, and contact information. Dynamic information includes vehicle size, driving proficiency, maximum acceptable walking distance, and intended destination. Parking lot data includes parking space types, parking lot entrance, elevator exit, vertical mapping location of the destination, parking space allocation in each zone of the parking lot, parking space occupancy rate in each zone, parking flow control thresholds, and a topology diagram of the parking lot. Step 2: Send and save the car owner's parking information form data to the cloud server; Step 3: The car owner sends a parking request to the cloud server according to their personal needs; Step 4: The cloud server accepts the parking request and calls the central controller; Step 5: The central controller first selects the appropriate parking control strategy based on the density of vehicles waiting to park. Then, it calls the optimal parking space allocation model to solve for the optimal parking space. Finally, it plans the corresponding optimal path based on the solved optimal parking space. The parking control strategy includes a first-come, first-served greedy control strategy and a multi-objective simultaneous optimization control strategy. The central controller calculates the density of vehicles waiting to park by checking the number of queued requests in the waiting queue. When the density of vehicles waiting to park does not exceed a predetermined threshold, it is treated as a case of low traffic volume, adopting a first-come, first-served greedy control strategy. When the density of vehicles waiting to park exceeds a certain threshold... When the traffic volume exceeds a predetermined threshold, the system is handled according to the case of high traffic volume, and a multi-objective simultaneous optimization control strategy is adopted. The first-come-first-served greedy control strategy means that the optimal parking space allocation model is called individually for each vehicle applying for a parking space to solve for the optimal parking space that meets its individual needs. The input and output have a one-to-one relationship. The multi-objective simultaneous optimization control strategy means that multiple vehicles waiting to park are optimized simultaneously through a combination optimization algorithm. The simulated annealing algorithm is used to call the optimal parking space allocation model once and solve for the optimal parking space that meets the needs of multiple car owners. The input and output have a many-to-many relationship. The optimal berth allocation model is based on multivariate factor decision-making, which includes user factors and system factors. User factors are designed to meet individual user needs, including vehicle size, driving skill level, chosen destination, and acceptable maximum walking distance. Qualitative indicators for vehicle size and driving skill are transformed into quantitative descriptions using fuzzy evaluation. System factors include the driving distance from each parking space to the parking entrance, the walking distance from each parking space to the elevator exit, parking difficulty, parking space utilization rate of each zone in the parking lot, and local road congestion coefficients. The optimal parking space allocation model solves for the best parking space by performing a weighted summation evaluation after normalizing each evaluation indicator using the parking space fitness function. The parking space fitness function is a comprehensive consideration of user factors and system factors. Step 6: After the central controller call ends, the cloud server will transmit the obtained optimal berth and path to the front-end function processing board; Step 7: The car owner obtains the best parking space and route information through the front-end function processing board and begins parking.

2. The optimal parking space solving method for indoor parking lots according to claim 1, characterized in that, In step four, the cloud server takes the parking application as input, the optimal parking space and the globally optimal planned route as output, and provides the vehicle owner's static and dynamic data to the central controller, waiting for the allocation result from the central controller.

3. The method for finding the optimal parking space in an indoor parking lot according to claim 1, characterized in that, In step five, the optimal path refers to the path that the central controller uses as the starting point at the entrance and as the ending point at the best parking space, applying Dijkstra's algorithm to the parking lot topology map to obtain the globally optimal planned path.

4. A parking guidance system, applied to the optimal parking space solution method for indoor parking lots as described in any one of claims 1 to 3, characterized in that, The parking guidance system includes: The data acquisition module is used for vehicle owners to register their personal information and collect user factors required for the parking space allocation model. The data storage module is used to store vehicle owner data and parking lot-related data; The data transmission module is used to transmit the parking request issued by the user to the cloud server, and then send the parking space allocation result and the planned route back to the user; and The data processing module includes: The density estimation module is used to estimate the density of vehicles requesting parking within a short period of time; The strategy selection module is used to compare the traffic flow density with a predetermined threshold and select the appropriate parking control strategy. The parking space solution module, based on the established parking control strategy, solves for the optimal parking space and plans the optimal route through optimal parking space allocation.

Images