Diversified intelligent management method and system for parking lot based on intelligent parking

Abstract

The invention relates to the technical field of parking lot traffic control, and discloses a parking lot diversified intelligent management method and system based on intelligent parking, and the system comprises a vehicle information collection module, a parking lot parking space matching module, and a parking lot parking space searching module. According to the invention, accurate matching of parking lot parking space feature information required by a target vehicle is carried out based on vehicle model feature text information in combination with an intelligent search algorithm and scientifically preset parking lot parking space feature information, so that accurate matching of a proper parking space object based on a vehicle model specification is realized; the nearest parking stall object required by a target vehicle is intelligently matched based on vehicle position coordinate information, target parking lot parking stall position coordinate information, an intelligent identification algorithm and scientifically stored parking lot space three-dimensional model information, and the optimal parking lot parking stall object is intelligently screened based on vehicle model information and parking distance information. And the intelligence and applicability of parking traffic management of the parking lot are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of parking lot traffic control, and specifically to a diversified intelligent management method and system for a parking lot based on intelligent parking. Background Art

[0002] A parking lot management system is a network system built through computers, network devices, and lane management devices for managing vehicle access in the parking lot, guiding the traffic flow inside the lot, and collecting parking fees. It is an essential tool for professional parking lot management companies. It realizes the dynamic and static comprehensive management of vehicle access and vehicles inside the lot by collecting and recording vehicle access records and positions inside the lot. In the early stage, the system generally uses radio frequency identification cards as carriers, and widely uses optical digital lens license plate recognition methods to replace traditional radio frequency card billing. It records vehicle entry and exit information through the identification card, and completes functions such as charging strategy implementation, toll accounting management, and lane equipment control through management software. The existing parking lot management systems cannot achieve intelligent and accurate matching of parking spaces based on vehicle information and parking lot space information, nor can they provide an intuitive and intelligent parking space search service for vehicle owners.

[0003] The Chinese invention patent with the publication number CN106373427B discloses a parking management method and a parking management system, which use a first positioning method and a second positioning method to realize intelligent judgment of the vehicle change state in the parking area; at the same time, when obtaining the first position information of the vehicle or the parking space by combining the first positioning method, it is judged whether the second position information of any vehicle corresponding to the first position information is obtained by the second positioning method, so as to realize the charging management of vehicles by flexibly using different positioning methods; however, the above technical solutions cannot achieve accurate matching of parking spaces for vehicles based on the vehicle type information. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] To solve the problem that the existing parking lot management system cannot achieve intelligent and accurate matching of parking spaces based on vehicle information and parking lot space information, nor can it provide an intuitive and intelligent parking space search service for vehicle owners, and to achieve the purpose of accurately collecting vehicle characteristic information, scientifically and intelligently matching the optimal parking space information, intelligently planning the parking space search path, and providing an intuitive and visual parking space search service.

[0006] (2) Technical Solutions

[0007] The present invention is realized through the following technical solutions: A diversified intelligent management method for a parking lot based on intelligent parking, the method comprising the following steps:

[0008] S1. Collect vehicle license plate text data, vehicle appearance image data, and vehicle position coordinate data;

[0009] S2. Identify and process the vehicle model feature information based on the vehicle appearance image data to generate vehicle model feature text data;

[0010] S3. Search and process the parking lot space feature information required by the target vehicle based on the vehicle model feature text data and the parking lot space feature text data to construct the target parking lot space feature text data;

[0011] S4. Match the position coordinate information of the target parking lot space based on the target parking lot space feature text data and the parking lot space position coordinate data to construct the target parking lot space position coordinate data;

[0012] S5. Perform the optimal parking lot space object matching process required by the target vehicle based on the vehicle position coordinate data, the target parking lot space position coordinate data, and the three-dimensional model data of the parking lot space to construct the optimal parking lot space text data;

[0013] S6. Construct the optimal parking lot space position coordinate data and the vehicle position coordinate data to perform the path planning process for the target vehicle to find the optimal parking lot space, and generate the optimal parking lot space search path data;

[0014] S7. Construct the target vehicle parking lot space management data and perform the parking lot space search feedback operation.

[0015] Preferably, the operation steps for collecting the vehicle license plate text data, vehicle appearance image data, and vehicle position coordinate data are as follows:

[0016] S11. Online collect the license plate number text information of the target vehicle through the parking lot gate system and generate the vehicle license plate text data P;

[0017] Online capture the overall appearance feature image information of the target vehicle through the parking lot gate system and generate the vehicle appearance image data U;

[0018] Online collect the spatial position coordinate information of the target vehicle at the parking lot entrance through the position sensor carried by the parking lot gate system and generate the vehicle position coordinate data O. The vehicle position coordinate data includes the longitude, latitude, and altitude of the parking lot entrance where the target vehicle is located.

[0019] The present invention realizes the efficient and accurate collection of the vehicle license plate information, appearance information, and vehicle parking lot entrance position information through the parking lot gate system combined with the position sensor, achieving the effect of providing real data support for subsequent scientific and intelligent identification of vehicle model information and scientific matching of vehicle parking lot spaces.

[0020] Preferably, the operation steps for identifying and processing vehicle model feature information based on the vehicle appearance image data to generate vehicle model feature text data are as follows:

[0021] S21. Use the K-D tree nearest neighbor search algorithm to search for the vehicle model feature text information of the target vehicle in the Internet platform for the vehicle appearance image data U, and generate vehicle model feature text data U chexing , where the vehicle model feature text data includes any one of small car model information, compact car model information, medium-sized car model information, large car model information, MPV model information, small SUV model information, compact SUV model information, medium-sized SUV model, and large SUV model information; the Internet platform includes any one of Sina, NetEase, Sohu, and Tencent.

[0022] The present invention achieves the effect of intelligent analysis of vehicle model feature information by intelligently identifying the vehicle model information of the target vehicle in the Internet of Things platform based on the vehicle appearance image information in combination with the K-D tree nearest neighbor search algorithm.

[0023] Preferably, the operation steps for searching for the parking lot space feature information required by the target vehicle based on the vehicle model feature text data and the parking lot space feature text data to construct the target parking lot space feature text data are as follows:

[0024] S31. Establish a parking lot space feature text data set A = (a 1 , …, a x , …, a β ), where x = 1, 2, 3, …, β; where a x represents the parking lot space feature text data corresponding to the x-th parking lot space, β represents the maximum value of the number of parking lot spaces, and the parking lot space feature text data includes the parking lot space number, the use status of the parking lot space, and the parked vehicle model type; the use status of the parking lot space includes parked vehicle and non-parked vehicle;

[0025] S32. Use the KMP search algorithm to search in the parking lot space feature text data set A according to the vehicle model feature text data U chexing to find the parking lot space feature text data a chexing corresponding to the parking lot space whose vehicle model information is the same as that of the vehicle model feature text data U x and the use status of the parking lot space is a non-parked vehicle, and construct a target parking lot space feature text data set A' = (a' x1 , …, a' x2 ), where 1 ≤ x1 ≤ x ≤ x2 ≤ β; where a′ x1 represents the x1-th target parking lot space feature text data, a′x2 Represents the text data of the x2-th target parking lot space feature, and the text data of the target parking lot space feature represents the text information of the parking lot space feature that meets the parking requirements of the target vehicle.

[0026] In the present invention, by combining the text information of the vehicle model feature with the KMP search algorithm and the scientifically preset parking lot space feature information, the accurate matching of the parking lot space feature information required by the target vehicle is achieved, and the effect of accurately matching the appropriate parking space object based on the vehicle model specification is achieved.

[0027] Preferably, according to the text data of the target parking lot space feature and the parking lot space position coordinate data, the matching process of the position coordinate information of the target parking lot space is carried out, and the operation steps for constructing the position coordinate data of the target parking lot space are as follows:

[0028] S41. Establish a set B=(b 1 ,…,b x ,…,b β ) of the parking lot space position coordinate data, where b x represents the parking lot space position coordinate data corresponding to the x-th parking lot space, and the parking lot space position coordinate data includes the longitude, latitude and altitude of the parking lot space;

[0029] S42. Use the Aho-Corasick search algorithm to match the text data a′ x1 to a′ x2 of the target parking lot space feature in the set A' of the text data of the target parking lot space feature with the parking lot space position coordinate data b x in the set B of the parking lot space position coordinate data, search for the text data a' x1 to a' x2 and the corresponding parking lot space position coordinate data b x , and generate a set B'=(b' x1 ,…,b' x2 ) of the target parking lot space position coordinate data through data identification, where b' x1 represents the x1-th target parking lot space position coordinate data, and b' x2 represents the x2-th target parking lot space feature text data, and the target parking lot space position coordinate data represents the parking lot space position coordinate information that meets the parking requirements of the target vehicle.

[0030] The present invention autonomously and efficiently retrieves the position coordinate information of the parking space for the target vehicle by combining the characteristic information of the parking spaces in the target parking lot with the Aho-Corasick search algorithm and the position coordinate information of the parking spaces in the parking lot set by the standard, achieving the effect of dynamically collecting the position information of the appropriate parking spaces for the vehicle.

[0031] Preferably, the operation steps for performing the optimal parking space object matching process for the target vehicle based on the vehicle position coordinate data, the target parking space position coordinate data, and the three-dimensional model data of the parking lot space, and constructing the optimal parking space text data are as follows:

[0032] S51. Establish the three-dimensional model data Q of the parking lot space, where the three-dimensional model data of the parking lot space represents the three-dimensional entity model data of the geographical area of the parking lot where the target vehicle is located;

[0033] S52. In the search space of the three-dimensional model data Q of the parking lot space, analyze and measure the path length from the vehicle position coordinate data O to the target parking space position coordinate data b' in the target parking space position coordinate data set B' x1 to b' x2 of the target parking space position coordinate data corresponding to the shortest path length, and construct the optimal parking space text data B duixiang , and perform the specific operation steps for generating the optimal parking space text data B duixiang as follows:

[0034] S521. Initialize and update the number N of the pigeon population for parking space identification and the maximum number of iterations T;

[0035] S522. Phase 1: When the number of iterations is within the range of [0, t 1 , it is Phase 1, where represents a random number in the range of (0.6, 0.96). First, according to the pigeon for parking space identification, analyze and measure the path length from the vehicle position coordinate data O to the target parking space position coordinate data b' x1 to b' x2 of the target parking space position coordinate data corresponding to the shortest path length in the search space of the three-dimensional model data Q of the parking lot space, calculate the speed of the pigeon for parking space identification in the search space of the three-dimensional model data Q of the parking lot space, and add the speed to the current position of the pigeon for parking space identification in the search space of the three-dimensional model data Q of the parking lot space to obtain a new position in the search space of the three-dimensional model data Q of the parking lot space. The speed calculation formula of the pigeon for parking space identification is as follows:

[0036] Among them represents the new velocity of the parking space identification pigeon i in the j - dimensional search space of the three - dimensional model data Q of the parking lot; represents the original velocity of the parking space identification pigeon i in the j - dimensional search space of the three - dimensional model data Q of the parking lot; is a constant with a value of 0.2, t represents the current iteration number of the algorithm, represents the exponential function with the base e and the exponent of rand represents a random number in the range [0, 1], represents that the parking space identification pigeon measures the path length from the vehicle position coordinate data O to the target parking space position coordinate data b' in the j - dimensional search space of the three - dimensional model data Q of the parking lot x1 to b' x2 The best position of the target parking space position coordinate data corresponding to the shortest path length; W i j represents that the parking space identification pigeon i measures the path length from the vehicle position coordinate data O to the target parking space position coordinate data b' in the j - dimensional search space of the three - dimensional model data Q of the parking lot x1 to b' x2 The original position of the target parking space position coordinate data corresponding to the shortest path length; The formula for the new position of the parking space identification pigeon is as follows: Among them represents the new position of the target parking space position coordinate data corresponding to the shortest path length from the vehicle position coordinate data O to the target parking space position coordinate data b' measured by the parking space identification pigeon i in the j - dimensional search space of the three - dimensional model data Q of the parking lot x1 to b' x2 ;

[0037] S523. Phase two: When the iteration number is in the range of [t 1 , T], it is phase two. First, sort the group of parking space identification pigeons in the search space of the three - dimensional model data Q of the parking lot, divide the group of parking space identification pigeons into two equal groups. The group of parking space identification pigeons with a larger fitness value that searches out the target parking space position coordinate data b' with the shortest path length from the vehicle position coordinate data O x1 to b' x2 keeps its position unchanged, and at the same time provides its position in the search space of the three - dimensional model data Q of the parking lot to assist the parking space identification pigeons in searching out the target parking space position coordinate data b' with the shortest path length from the vehicle position coordinate data O x1 to b'x2 The group of parking space recognition pigeons with smaller fitness values determines their new positions in the search space of the three-dimensional model data Q of the parking lot space. The calculation formula for the new positions of the parking space recognition pigeons with smaller fitness values is as follows:

[0038] Where represents the new position of the target parking lot space position coordinate data corresponding to the shortest path length measured from the vehicle position coordinate data O to the target parking lot space position coordinate data b' in the j - dimensional search space of the three - dimensional model data Q of the parking lot space by the parking space recognition pigeon i with a smaller fitness value in the second stage x1 to b' x2 ; represents the centroid position of the group of parking space recognition pigeons with larger fitness values in the j - dimensional search space of the three - dimensional model data Q of the parking lot space;

[0039] S524. When the algorithm meets the maximum number of iterations, output the target parking lot space position coordinate data corresponding to the shortest path length from the vehicle position coordinate data O to the target parking lot space position coordinate data b' x1 to b' x2 ;

[0040] S525. Use the parking lot space number information corresponding to the target parking lot space position coordinate data output in step S524, and construct the optimal parking lot space text data B duixiang .

[0041] The present invention performs intelligent matching of the target vehicle's required nearest parking space object by combining vehicle position coordinate information, target parking lot space position coordinate information, an artificial intelligence pigeon flock algorithm, and scientifically stored three - dimensional model information of the parking lot space, achieving the effect of intelligently screening out the optimal parking lot space object based on vehicle model information and parking distance information.

[0042] Preferably, the operation steps for performing path planning processing on the optimal parking lot space position coordinate data and the vehicle position coordinate data to find the path from the target vehicle to the optimal parking lot space and generating the optimal parking lot space search path data are as follows:

[0043] S61. Use the KMP search algorithm to search for the optimal parking lot space text data B duixiang in the target parking lot space position coordinate data set B' with the target parking lot space position coordinate data b' x1 to b' x2 according to the parking lot space number character search to find the optimal parking lot space text data B duixiangThe corresponding target parking lot space position coordinate data, and construct the optimal parking lot space position coordinate data b zuiyou ;

[0044] S62. Input the vehicle position coordinate data O and the optimal parking lot space position coordinate data b zuiyou into the start dialog box and the end dialog box of the vehicle navigation platform respectively. The vehicle navigation platform measures and generates the optimal parking space search path information from the vehicle position coordinate data O to the optimal parking lot space position coordinate data b zuiyou for the target vehicle to the parking lot space, and generates the optimal parking lot space search path data H. When the generation of the optimal parking lot space search path data H is not completed, continue to execute the operation instruction for generating the optimal parking lot space search path data H. The vehicle navigation platform includes any one of the Gaode Navigation Platform, Baidu Navigation Platform, and Tencent Navigation Platform.

[0045] The present invention accurately searches for the optimal parking lot space position coordinate information through the KMP search algorithm, combines the vehicle navigation platform with the vehicle position coordinate information, and performs dynamic and efficient planning processing on the search path from the target vehicle to the optimal parking lot space, achieving the effect of scientifically and accurately planning the path of the vehicle to the most suitable parking space.

[0046] Preferably, the operation steps of constructing the target vehicle parking lot space management data and performing the parking lot space search feedback operation are as follows:

[0047] S71. When the generation of the optimal parking lot space search path data H is completed, combine the vehicle license plate text data P, the optimal parking lot space text data B duixiang , and the optimal parking lot space search path data H to construct the target vehicle parking lot space management data F, where F = (P, B duixiang , H);

[0048] S72. Push and feedback the target vehicle parking lot space management data F through the parking lot display screen to perform the parking lot space search feedback operation.

[0049] The present invention scientifically constructs the target vehicle parking lot space management information by combining the vehicle license plate information, the optimal parking lot space information, and the parking lot space search path information through data combination, and autonomously visualizes and executes the parking lot space search feedback operation in combination with the display screen, achieving the effect of intelligent parking space matching and diversified management of parking space search in the parking lot.

[0050] A diversified intelligent management system for a parking lot based on intelligent parking, used to implement the described diversified intelligent management method for a parking lot based on intelligent parking. The system includes a vehicle information collection module, a parking lot space matching module, and a parking lot space search module;

[0051] The vehicle information collection module includes a vehicle license plate information collection unit, a vehicle appearance image collection unit, a vehicle position coordinate information collection unit, and a vehicle model information search unit;

[0052] The vehicle license plate information collection unit collects vehicle license plate text data through the parking lot gate system; the vehicle appearance image collection unit collects vehicle appearance image data through the parking lot gate system; the vehicle position coordinate information collection unit collects vehicle position coordinate data through a position sensor carried by the parking lot gate system; the vehicle model information search unit performs vehicle model feature information recognition processing based on the vehicle appearance image data in combination with the Internet of Things platform to generate vehicle model feature text data;

[0053] The parking lot space matching module includes a parking lot space feature information storage unit, a target parking lot space feature information search unit, a parking lot space position coordinate storage unit, a target parking lot space position coordinate search unit, a three-dimensional parking lot space model storage unit, and an optimal parking lot space matching unit;

[0054] The parking lot space feature information storage unit is used to store parking lot space feature text data; the target parking lot space feature information search unit performs target vehicle required parking lot space feature information search processing based on the vehicle model feature text data and the parking lot space feature text data to construct target parking lot space feature text data; the parking lot space position coordinate storage unit is used to store parking lot space position coordinate data; the target parking lot space position coordinate search unit performs position coordinate information matching processing of the target parking lot space according to the target parking lot space feature text data and the parking lot space position coordinate data to construct target parking lot space position coordinate data; the three-dimensional parking lot space model storage unit is used to store three-dimensional parking lot space model data; the optimal parking lot space matching unit performs optimal parking lot space object matching processing required by the target vehicle based on the vehicle position coordinate data, the target parking lot space position coordinate data, and the three-dimensional parking lot space model data to construct optimal parking lot space text data;

[0055] The parking lot space searching module includes an optimal parking lot space position coordinate search unit, an optimal parking lot space searching path generation unit, and a vehicle parking lot space searching unit;

[0056] The optimal parking lot space position coordinate search unit constructs optimal parking lot space position coordinate data based on the optimal parking lot space object information and the target parking lot space position coordinate information; the optimal parking lot space search path generation unit performs target vehicle to optimal parking lot space search path planning processing according to the optimal parking lot space position coordinate data and the vehicle position coordinate data, and generates optimal parking lot space search path data; the vehicle parking lot space search unit is used to construct target vehicle parking lot space management data and perform parking lot space search feedback operations in combination with a display screen.

[0057] (III) Advantageous effects

[0058] The present invention provides a diversified intelligent management method and system for a parking lot based on intelligent parking. It has the following advantageous effects:

[0059] First, through the combination of the parking lot gate system and position sensors, efficient and accurate acquisition of vehicle license plate information, appearance information, and vehicle parking lot entrance position information is achieved, providing real data support for subsequent scientific and intelligent identification of vehicle model information and scientific matching of vehicle parking lot spaces, improving the efficiency of parking lot traffic management; based on vehicle appearance image information and an intelligent search algorithm, intelligent identification of target vehicle model information is carried out on the Internet of Things platform, realizing intelligent analysis of vehicle model feature information and improving the accuracy of parking lot space matching.

[0060] Second, through the combination of vehicle model feature text information and an intelligent search algorithm with scientifically preset parking lot space feature information, accurate matching of the target vehicle's required parking lot space feature information is achieved, realizing accurate matching of suitable parking space objects based on vehicle model specifications, and improving the scientific nature of parking lot space traffic management; according to the target parking lot space feature information, an intelligent search algorithm, and standard-set parking lot space position coordinate information, autonomous and efficient retrieval of the position coordinate information of the target vehicle's parking lot space is carried out, realizing dynamic acquisition of the position information of the vehicle's suitable parking space, and improving the quality of parking lot space traffic management; based on vehicle position coordinate information, target parking lot space position coordinate information, an intelligent identification algorithm, and scientifically stored three-dimensional parking lot space model information, intelligent matching of the target vehicle's required nearest parking space object is carried out, realizing intelligent screening of the optimal parking lot space object based on vehicle model information and parking distance information, and improving the intelligence and applicability of parking lot management.

[0061] III. By accurately searching for the optimal parking space location coordinate information based on data analysis, combining the vehicle navigation platform with the vehicle position coordinate information, and dynamically and efficiently planning the path for the target vehicle to find the optimal parking space, the scientific and accurate path planning for the vehicle to the most suitable parking space is realized, and the parking operation time of vehicles in the parking lot is shortened; based on the vehicle license plate information, the optimal parking space information, and the parking space search path information, a target vehicle parking space management information is scientifically constructed by combining data, and the parking space search feedback operation is autonomously visualized and executed through a display screen, realizing intelligent parking space matching and diversified management of parking space search in the parking lot, and improving the user satisfaction and reliability of parking space traffic management in the parking lot. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 FIG. is a schematic diagram of modules of a diversified intelligent management system for a parking lot based on intelligent parking provided by the present invention;

[0063] Figure 2 FIG. is a flowchart of a diversified intelligent management method for a parking lot based on intelligent parking provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] Embodiments of the diversified intelligent management method and system for a parking lot based on intelligent parking are as follows:

[0066] Embodiment 1:

[0067] Please refer to Figure 1 - Figure 2 , a diversified intelligent management method for a parking lot based on intelligent parking, the method includes the following steps:

[0068] S1. Collect vehicle license plate text data, vehicle appearance image data, and vehicle position coordinate data;

[0069] S2. Identify and process vehicle model feature information based on the vehicle appearance image data to generate vehicle model feature text data;

[0070] S3. Search and process the parking space feature information required by the target vehicle based on the vehicle model feature text data and the parking space feature text data of the parking lot to construct the target parking space feature text data;

[0071] S4. Match and process the position coordinate information of the target parking lot spaces based on the target parking lot space feature text data and the parking lot space position coordinate data to construct the target parking lot space position coordinate data;

[0072] S5. Perform matching processing on the optimal parking lot space object required by the target vehicle based on the vehicle position coordinate data, the target parking lot space position coordinate data, and the three-dimensional model data of the parking lot space to construct the optimal parking lot space text data;

[0073] S6. Construct the optimal parking lot space position coordinate data and the vehicle position coordinate data to perform path planning processing for the target vehicle to find the optimal parking lot space, and generate the optimal parking lot space search path data;

[0074] S7. Construct the target vehicle parking lot space management data and execute the parking lot space search feedback operation.

[0075] Further, please refer to Figure 1 - Figure 2 The operation steps for collecting the vehicle license plate text data, vehicle appearance image data, and vehicle position coordinate data are as follows:

[0076] S11. Online collect the license plate number text information of the target vehicle through the parking lot gate system and generate the vehicle license plate text data P;

[0077] Online capture the overall appearance feature image information of the target vehicle through the parking lot gate system and generate the vehicle appearance image data U;

[0078] Online collect the spatial position coordinate information of the target vehicle at the parking lot entrance through the position sensor carried by the parking lot gate system and generate the vehicle position coordinate data O. The vehicle position coordinate data includes the longitude, latitude, and altitude of the parking lot entrance where the target vehicle is located.

[0079] The operation steps for identifying the vehicle model feature information based on the vehicle appearance image data to generate the vehicle model feature text data are as follows:

[0080] S21. Use the K-D tree nearest neighbor search algorithm to search for the vehicle model feature text information of the target vehicle in the Internet platform for the vehicle appearance image data U, and generate the vehicle model feature text data U chexing , the vehicle model feature text data includes any one of small sedan model information, compact sedan model information, medium sedan model information, large sedan model information, MPV model information, small SUV model information, compact SUV model information, medium SUV model, and large SUV model information; the Internet platform includes any one of Sina, NetEase, Sohu, and Tencent.

[0081] Through the mutual cooperation among the vehicle license plate information acquisition unit, the vehicle appearance image acquisition unit, and the vehicle position coordinate information acquisition unit, the parking lot gate system combined with position sensors is used to efficiently and accurately collect the license plate information, appearance information, and vehicle parking lot entrance position information of the vehicle, providing real data support for subsequent scientific and intelligent identification of vehicle models and scientific matching of vehicle parking spaces in the parking lot, and improving the efficiency of parking traffic management in the parking lot; the vehicle model information search unit intelligently identifies the vehicle model information of the target vehicle on the Internet of Things platform based on the vehicle appearance image information combined with intelligent search algorithms, realizes intelligent analysis of vehicle model feature information, and improves the accuracy of parking space matching in the parking lot.

[0082] Further, please refer to Figure 1 - Figure 2 , and the operating steps for searching and processing the parking lot space feature information required for the target vehicle based on the vehicle model feature text data and the parking lot space feature text data to construct the target parking lot space feature text data are as follows:

[0083] S31. Establish a parking lot space feature text data set A = (a 1 ,…,a x ,…,a β ), where x = 1, 2, 3,…,β; where a x represents the parking lot space feature text data corresponding to the xth parking lot space, β represents the maximum value of the number of parking lot spaces, and the parking lot space feature text data includes the parking lot space number, the parking lot space usage status, and the parked vehicle model type of the parking lot space; the parking lot space usage status includes parked vehicles and non-parked vehicles;

[0084] S32. Use the KMP search algorithm to search in the parking lot space feature text data set A for the parking lot space feature text data a chexing corresponding to the parking lot space whose vehicle model feature text data U chexing is consistent with the vehicle model information of the vehicle and the parking lot space usage status is non-parked, and construct a target parking lot space feature text data set A' = (a' x ,…,a' x1 ,…,a' x2 ), where 1 ≤ x1 ≤ x ≤ x2 ≤ β; where a' x1 represents the x1th target parking lot space feature text data, and a′ x2 represents the x2th target parking lot space feature text data, and the target parking lot space feature text data represents the parking lot space feature text information that meets the parking requirements of the target vehicle.

[0085] Perform the matching process of the position coordinate information of the target parking lot spaces based on the target parking lot space feature text data and the parking lot space position coordinate data, and the operation steps for constructing the target parking lot space position coordinate data are as follows:

[0086] S41. Establish a set B of parking lot space position coordinate data B = (b 1 , …, b x , …, b β ), where b x represents the parking lot space position coordinate data corresponding to the x-th parking lot space, and the parking lot space position coordinate data includes the longitude, latitude, and altitude of the parking lot space;

[0087] S42. Use the Aho-Corasick search algorithm to match the target parking lot space feature text data a′ x1 to a′ x2 in the target parking lot space feature text data set A' with the parking lot space position coordinate data b x in the parking lot space position coordinate data set B for the character matching of the parking lot space quantity number, and search out the target parking lot space feature text data a' x1 to a' x2 and the corresponding parking lot space position coordinate data b x , and generate a target parking lot space position coordinate data set B' = (b' x1 , …, b' x2 ) through data identification, where b' x1 represents the x1-th target parking lot space position coordinate data, and b' x2 represents the x2-th target parking lot space feature text data, and the target parking lot space position coordinate data represents the parking lot space position coordinate information that meets the parking requirements of the target vehicle.

[0088] Perform the matching process of the optimal parking lot space object required by the target vehicle based on the vehicle position coordinate data, the target parking lot space position coordinate data, and the parking lot space three-dimensional model data, and the operation steps for constructing the optimal parking lot space text data are as follows:

[0089] S51. Establish a parking lot space three-dimensional model data Q, and the parking lot space three-dimensional model data represents the spatial three-dimensional entity model data of the geographical area where the target vehicle is located;

[0090] S52. In the search space of the parking lot space three-dimensional model data Q, measure the vehicle position coordinate data O to the target parking lot space position coordinate data b' x1 to b' x2The parking space number information corresponding to the target parking space position coordinate data with the shortest path length, and construct the optimal parking space text data B duixiang , execute to generate the optimal parking space text data B duixiang The specific operation steps are as follows:

[0091] S521. Initialize, update the number N of pigeons for parking space recognition and the maximum number of iterations T;

[0092] S522. Phase 1, when the number of iterations is in the range of [0, t 1 , it is Phase 1, where represents a random number in the range (0.6, 0.96). First, according to the pigeons for parking space recognition, analyze and measure the vehicle position coordinate data O to the target parking space position coordinate data b' in the search space of the three-dimensional model data Q of the parking lot space according to the path length value x1 to b' x2 corresponding to the target parking space position coordinate data with the shortest path length, calculate the speed of the pigeons for parking space recognition in the search space of the three-dimensional model data Q of the parking lot space, and add the speed to the current position of the pigeons for parking space recognition in the search space of the three-dimensional model data Q of the parking lot space to obtain a new position in the search space of the three-dimensional model data Q of the parking lot space. The speed calculation formula of the pigeons for parking space recognition is as follows: where represents the new speed of the pigeon for parking space recognition i in the j - dimensional search space of the three - dimensional model data Q of the parking lot space; represents the original speed of the pigeon for parking space recognition i in the j - dimensional search space of the three - dimensional model data Q of the parking lot space; is a constant and its value is 0.2, t represents the current iteration number of the algorithm, represents the exponential function with the base e and the exponent of , rand represents a random number in the range [0, 1], represents the best position of the target parking space position coordinate data corresponding to the shortest path length from the vehicle position coordinate data O to the target parking space position coordinate data b' analyzed and measured by the pigeon for parking space recognition i in the j - dimensional search space of the three - dimensional model data Q of the parking lot space x1 to b' x2 ; W i j represents the target parking space position coordinate data corresponding to the shortest path length from the vehicle position coordinate data O to the target parking space position coordinate data b' analyzed and measured by the pigeon for parking space recognition i in the j - dimensional search space of the three - dimensional model data Q of the parking lot space x1 to b' x2The original position of the target parking space position coordinate data corresponding to the shortest path length; the new position calculation formula for the parking space recognition pigeon is as follows: Where represents that the parking space recognition pigeon i measures the vehicle position coordinate data O to the target parking space position coordinate data b' in the three-dimensional model data Q search space of the j-dimensional parking lot space according to the path length value analysis x1 to b' x2 The new position of the target parking space position coordinate data corresponding to the shortest path length;

[0093] S523. Stage 2. When the number of iterations is in the range of [t 1 , T], it is stage 2. First, sort the parking space recognition pigeon group in the three-dimensional model data Q search space of the parking lot space, divide the parking space recognition pigeon group into two groups evenly, and the parking space recognition pigeons search for the target parking space position coordinate data b' with the shortest path length from the vehicle position coordinate data O x1 to b' x2 The group of parking space recognition pigeons with the larger fitness value keeps its position unchanged, and at the same time provides its position in the three-dimensional model data Q search space of the parking lot space to assist the parking space recognition pigeons in searching for the target parking space position coordinate data b' with the shortest path length from the vehicle position coordinate data O x1 to b' x2 The group of parking space recognition pigeons with the smaller fitness value determines their new positions in the three-dimensional model data Q search space of the parking lot space. The new position calculation formula for the parking space recognition pigeon group with the smaller fitness value is as follows: Where represents that the parking space recognition pigeon i with the smaller fitness value measures the vehicle position coordinate data O to the target parking space position coordinate data b' in the three-dimensional model data Q search space of the j-dimensional parking lot space in stage 2 according to the path length value analysis x1 to b' x2 The new position of the target parking space position coordinate data corresponding to the shortest path length; represents the centroid position of the group of parking space recognition pigeons with the larger fitness value in the three-dimensional model data Q search space of the j-dimensional parking lot space;

[0094] S524. When the algorithm meets the maximum number of iterations, output the target parking space position coordinate data corresponding to the shortest path length from the vehicle position coordinate data O x1 to b' x2 ;

[0095] S525. Obtain the parking space quantity number information corresponding to the target parking lot space position coordinate data output in step S524, and construct the optimal parking lot space text data B duixiang 。

[0096] Through the target parking lot space feature information search unit, based on the vehicle model feature text information, combined with the intelligent search algorithm and the scientifically preset parking lot space feature information, accurately match the target vehicle's required parking lot space feature information, realize the accurate matching of suitable parking space objects based on the vehicle model specifications, and improve the scientific nature of parking lot space traffic management; the target parking lot space position coordinate search unit, according to the target parking lot space feature information, combined with the intelligent search algorithm and the standard-set parking lot space position coordinate information, independently and efficiently retrieve the position coordinate information of the target vehicle's parking space in the parking lot, realize the dynamic acquisition of the vehicle's suitable parking space position information, and improve the quality of parking lot space traffic management; the optimal parking lot space matching unit, based on the vehicle position coordinate information, the target parking lot space position coordinate information, combined with the intelligent recognition algorithm and the scientifically stored three-dimensional parking lot space model information, intelligently match the target vehicle's required nearest parking space object, realize the intelligent screening of the optimal parking lot space object based on the vehicle model information and the parking distance information, and improve the intelligence and applicability of parking lot parking management.

[0097] Further, please refer to Figure 1 - Figure 2 , the operation steps for constructing the optimal parking lot space position coordinate data and the vehicle position coordinate data to perform the path planning process for finding the optimal parking lot space for the target vehicle and generating the optimal parking lot space finding path data are as follows:

[0098] S61. Use the KMP search algorithm to search for the optimal parking lot space text data B duixiang from the target parking lot space position coordinate data b' in the target parking lot space position coordinate data set B' x1 to b' x2 According to the parking space quantity number characters, search for the optimal parking lot space text data B duixiang corresponding target parking lot space position coordinate data, and construct the optimal parking lot space position coordinate data b zuiyou ;

[0099] S62. Input the vehicle position coordinate data O and the optimal parking lot space position coordinate data b zuiyou into the start dialog box and the end dialog box of the vehicle navigation platform respectively. The vehicle navigation platform measures and generates the vehicle position coordinate data O to the optimal parking lot space position coordinate data b zuiyouOptimal parking space search path information of the target vehicle to the parking lot, and generate optimal parking lot parking space search path data H. When the generation of the optimal parking lot parking space search path data H is not completed, continue to execute the operation instruction for generating the optimal parking lot parking space search path data H. The vehicle navigation platform includes any one of the Amap navigation platform, Baidu navigation platform, and Tencent navigation platform.

[0100] The operation steps for constructing the target vehicle parking lot parking space management data and performing the parking space search feedback operation are as follows:

[0101] S71. When the generation of the optimal parking lot parking space search path data H is completed, combine the vehicle license plate text data P, the optimal parking lot parking space text data B duixiang , and the optimal parking lot parking space search path data H for data combination to construct the target vehicle parking lot parking space management data F, where F = (P, B duixiang , H);

[0102] S72. Push the target vehicle parking lot parking space management data F online through the parking lot display screen to perform the parking space search feedback operation.

[0103] Through the mutual cooperation of the optimal parking lot parking space position coordinate search unit and the optimal parking lot parking space search path generation unit, accurately search for the optimal parking lot parking space position coordinate information based on data analysis, and perform dynamic and efficient planning processing on the target vehicle to the optimal parking lot parking space search path in combination with the vehicle navigation platform and the vehicle position coordinate information, realizing scientific and accurate planning of the vehicle to the most suitable parking space path, and shortening the parking operation time of the vehicles in the parking lot; The vehicle parking lot parking space search unit, based on the vehicle license plate information, the optimal parking lot parking space information, and the parking lot parking space search path information, combines data to scientifically construct the target vehicle parking lot parking space management information, and independently visualizes and executes the parking space search feedback operation in combination with the display screen, realizing intelligent parking space matching and diversified management of parking space search in the parking lot, and improving the user satisfaction and reliability of the parking lot parking space traffic management.

[0104] Embodiment 2:

[0105] Please refer to Figure 1 - Figure 2 , a diversified intelligent management system for a parking lot based on intelligent parking, used to implement a diversified intelligent management method for a parking lot based on intelligent parking. The system includes a vehicle information collection module, a parking lot parking space matching module, and a parking lot parking space search module;

[0106] The vehicle information collection module includes a vehicle license plate information collection unit, a vehicle appearance image collection unit, a vehicle position coordinate information collection unit, and a vehicle model information search unit;

[0107] The vehicle license plate information collection unit collects vehicle license plate text data through the parking lot barrier system; the vehicle appearance image collection unit collects vehicle appearance image data through the parking lot barrier system; the vehicle position coordinate information collection unit collects vehicle position coordinate data through the position sensor carried by the parking lot barrier system; the vehicle model information search unit performs vehicle model feature information recognition processing based on the vehicle appearance image data in combination with the Internet of Things platform to generate vehicle model feature text data;

[0108] The parking lot space matching module includes a parking lot space feature information storage unit, a target parking lot space feature information search unit, a parking lot space position coordinate storage unit, a target parking lot space position coordinate search unit, a parking lot space three-dimensional model storage unit, and an optimal parking lot space matching unit;

[0109] The parking lot space feature information storage unit is used to store parking lot space feature text data; the target parking lot space feature information search unit performs search processing on the parking lot space feature information required by the target vehicle based on the vehicle model feature text data and the parking lot space feature text data to construct the target parking lot space feature text data; the parking lot space position coordinate storage unit is used to store parking lot space position coordinate data; the target parking lot space position coordinate search unit performs position coordinate information matching processing on the target parking lot space based on the target parking lot space feature text data and the parking lot space position coordinate data to construct the target parking lot space position coordinate data; the parking lot space three-dimensional model storage unit is used to store parking lot space three-dimensional model data; the optimal parking lot space matching unit performs optimal parking lot space object matching processing required by the target vehicle based on the vehicle position coordinate data, the target parking lot space position coordinate data, and the parking lot space three-dimensional model data to construct the optimal parking lot space text data;

[0110] The parking lot space search module includes an optimal parking lot space position coordinate search unit, an optimal parking lot space search path generation unit, and a vehicle parking lot space search unit;

[0111] The optimal parking lot space position coordinate search unit constructs the optimal parking lot space position coordinate data based on the optimal parking lot space object information and the target parking lot space position coordinate information; the optimal parking lot space search path generation unit performs target vehicle to optimal parking lot space search path planning processing based on the optimal parking lot space position coordinate data and the vehicle position coordinate data to generate the optimal parking lot space search path data; the vehicle parking lot space search unit is used to construct the target vehicle parking lot space management data and perform the parking lot space search feedback operation in combination with the display screen.

[0112] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A diversified intelligent management method for parking lots based on smart parking, characterized in that: The method comprises the following steps: S1, collecting vehicle license plate text data, vehicle appearance image data and vehicle position coordinate data; S2, performing vehicle model feature information recognition processing based on the vehicle appearance image data to generate vehicle model feature text data; S3, performing a search process for parking space feature information required by the target vehicle based on the vehicle model feature text data and the parking space feature text data, and constructing the parking space feature text data of the target parking lot; S4, performing a matching process on the position coordinate information of the parking spaces in the target parking lot according to the parking space feature text data of the target parking lot and the parking space position coordinate data of the parking lot, and constructing the parking space position coordinate data of the target parking lot; S5, performing an optimal parking lot parking space object matching process required by the target vehicle according to the vehicle position coordinate data, the target parking lot parking space position coordinate data and the parking lot space three-dimensional model data, and constructing optimal parking lot parking space text data; S6, constructing the optimal parking lot parking space position coordinate data and the vehicle position coordinate data to perform path planning processing for the target vehicle to find the optimal parking space, and generating the optimal parking lot parking space finding path data; S7: Construct parking space management data of the target vehicle parking lot and execute parking space search feedback operation.

2. According to claim 1, a parking lot diversified intelligent management method based on smart parking is characterized by: The S1 comprises the following steps: S11, collecting the license plate number text information of the target vehicle online through the parking lot gate system, and generating vehicle license plate text data P; The parking lot gate system is used to capture the overall appearance feature image information of the target vehicle online, and the vehicle appearance image data U is generated; The spatial position coordinate information of the target vehicle at the entrance of the parking lot is collected online through the position sensor installed in the parking lot gate system, and the vehicle position coordinate data O is generated.

3. A parking lot diversified intelligent management method based on smart parking according to claim 2, characterized in that: The S2 comprises the following steps: S21, using the KD tree nearest neighbor search algorithm to search and process the target vehicle model feature text information U on the Internet platform to generate vehicle model feature text data U chexing .

4. The method for diversified intelligent management of parking lots based on smart parking according to claim 3 is characterized by: The S3 comprises the following steps: S31, establish parking lot parking space feature text data set A = (a1, ..., a x ,…,a β ), x=1,2,3,…,β; where a x represents the parking space feature text data corresponding to the x-th parking space, and β represents the maximum number of parking spaces in the parking lot; S32, using KMP search algorithm according to the U chexing Search A for the chexing The vehicle model information is consistent and the parking space usage status is a corresponding to the parking space without a parked vehicle x , and construct the target parking lot parking space feature text data set A'=(a' x1 ,…,a' x2 ), 1≤x1≤x≤x2≤β; where a' x1 represents the feature text data of the x1th target parking lot, a′ x2 It represents the parking space feature text data of the x2th target parking lot, and the parking space feature text data of the target parking lot represents the parking space feature text information of the parking lot that meets the parking demand of the target vehicle.

5. A parking lot diversified intelligent management method based on smart parking according to claim 4, characterized in that: The S4 comprises the following steps: S41, establish parking lot parking space location coordinate data set B = (b1, ..., b x ,…,b β ), where b x Indicates the parking lot space location coordinate data corresponding to the x-th parking lot space; S42, using the Aho-Corasick search algorithm to find the a' in the A' x1 to a' x2 with the b in the b x Perform a character match on the parking lot number and search for the a' x1 to a' x2 The corresponding b x , and generate the target parking lot parking space location coordinate data set B'=(b' x1 ,…,b' x2 ), where b' x1 represents the x1th target parking lot parking space location coordinate data, b' x2 Represents the feature text data of the x2th target parking lot space.

6. A parking lot diversified intelligent management method based on smart parking according to claim 5, characterized in that: The S5 comprises the following steps: S51, establishing parking lot space three-dimensional model data Q, wherein the parking lot space three-dimensional model data represents the space three-dimensional entity model data of the geographical area of ​​the parking lot where the target vehicle is located; S52, in the Q search space, according to the path length numerical analysis, measure the b' from O to B' x1 to b' x2 The path length is the shortest, and the parking space number information of the target parking lot corresponding to the parking space position coordinate data is constructed to obtain the optimal parking space text data B duixiang , execute to generate the B duixiang The specific steps are as follows: S521, initialization, updating the number of pigeon populations N and the maximum number of iterations T for parking space recognition; S522, stage 1, the number of iterations in the range [0, t1] is stage 1, where represents a random number in the range of (0.6, 0.96). First, according to the parking space identification pigeon in the Q search space, the path length is numerically analyzed and measured from O to b' x1 to b' x2 The target parking lot parking space position coordinate data target corresponding to the shortest path length is calculated, and the speed of the parking space recognition pigeon in the Q search space is calculated. The current position of the parking space recognition pigeon in the Q search space plus the speed is obtained to obtain the new position in the Q search space; S523, stage 2, the number of iterations within the range of [t1, T] is stage 2, first sort the parking space recognition pigeon population in the Q search space, divide the parking space recognition pigeon population into two groups, and search for the parking space recognition pigeon b' with the shortest path length between O and x1 to b' x2 The group of parking space identification pigeons with a large fitness value keeps its position unchanged and provides its position in the Q search space to assist the parking space identification pigeons in searching for the b' with the shortest path length between O and x1 to b' x2 The parking space recognition pigeon group with a small fitness value determines its new position in the Q search space; S524, when the algorithm meets the maximum number of iterations, output the O to the b' x1 to b' x2 The target parking lot parking space position coordinate data corresponding to the shortest path length; S525, the parking lot parking space number information corresponding to the target parking lot parking space position coordinate data output in step S524 is used to construct the optimal parking lot parking space text data B duixiang .

7. A parking lot diversified intelligent management method based on smart parking according to claim 6, characterized in that: The S6 comprises the following steps: S61, using KMP search algorithm to duixiang and B' in b' x1 to b' x2 Search for the B according to the number of parking spaces in the parking lot duixiang The corresponding target parking lot parking space position coordinate data, and construct the optimal parking lot parking space position coordinate data b zuiyou ; S62, the O and the b zuiyou Enter the start dialog box and the end dialog box of the vehicle navigation platform respectively, and the vehicle navigation platform measures and generates the O to the b zuiyou The target vehicle searches for path information of the optimal parking space in the parking lot and generates H. When the generation of H is not completed, the H generation operation instruction continues to be executed.

8. A parking lot diversified intelligent management method based on smart parking according to claim 7, characterized in that: The S7 comprises the following steps: S71, when the H is generated, the P, the B duixiang , the H performs data combination to construct parking space management data F of the target vehicle parking lot; S72, pushing the F online through the parking lot display screen to feedback the parking lot parking space search operation.

9. A parking lot diversified intelligent management system based on smart parking, used to implement a parking lot diversified intelligent management method based on smart parking as described in any one of claims 1 to 8, characterized in that: The system includes a vehicle information collection module, a parking lot space matching module, and a parking lot space searching module.

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