Smart parking lot self-adaptive intelligent lighting method and system based on Internet of Things

Abstract

The invention relates to the technical field of parking lighting, and discloses a smart parking lot self-adaptive intelligent lighting method and system based on the Internet of Things. According to the method, corresponding illuminating lamps are installed based on the position of a parking lot, corresponding Internet of Things control equipment is installed for each illuminating lamp, meanwhile, parking space condition data in the parking lot are collected, a parking space data matrix of the parking lot is constructed based on the collected parking space condition data, and after construction is completed, parking demands of users are collected in real time. The method comprises the steps of constructing a parking lot parking space data matrix, distributing parking spaces for users based on the constructed parking lot parking space data matrix, planning a user parking route based on a path guiding algorithm after the user parking space distribution is completed, guiding the users according to the planned parking route, and adaptively controlling lighting lamps of a parking lot based on a user guiding process. And the self-adaptive intelligent lighting efficiency of the intelligent parking lot is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of parking lighting, and in particular to an adaptive intelligent lighting method and system for a smart parking lot based on the Internet of Things. Background Art

[0002] Existing parking lots mostly adopt a single parking method of "first come, first served", and the lighting method of the parking lot mostly adopts a timed opening method. The parking area cannot be divided according to the parking needs of users. There is still room for improvement in the technical level of providing users with differentiated and customized parking services.

[0003] The existing open patent application CN114973753A uses the induction lamp and guide lamp above the parking space in the parking lot. The induction lamp is equipped with an ultrasonic detection module, a Bluetooth wireless ad hoc network communication module and a power module, and the guide lamp is equipped with a traffic light module, a Bluetooth wireless ad hoc network communication module and a power module. The ultrasonic detection module of the induction lamp determines whether the current parking space is occupied, and guides through the traffic light module of the guide lamp. However, due to ignoring the traffic flow in the parking lot and the real-time use of the parking space, it is easy to cause guidance errors, which has certain limitations. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides an adaptive intelligent lighting method and system for a smart parking lot based on the Internet of Things, which has the advantages of accuracy, real-time and high efficiency, and solves the problems of single parking and lighting control in parking lots.

[0006] (II) Technical solution

[0007] In order to solve the technical problems of single parking and lighting control in the above-mentioned parking lot, the present invention provides the following technical solutions:

[0008] The present invention discloses an adaptive intelligent lighting method for a smart parking lot based on the Internet of Things, which specifically comprises the following steps:

[0009] S1. Install corresponding lighting lamps based on the parking lot location, and install corresponding IoT control devices for each lighting lamp;

[0010] S2, collecting parking space situation data in the parking lot, and constructing a parking space data matrix of the parking lot based on the collected parking space situation data;

[0011] S3, collect user parking needs in real time, and allocate parking spaces to users based on the constructed parking lot parking space data matrix;

[0012] S31. Process the user's parking demand data collected in real time to obtain the processed parking demand data;

[0013] S32. Update the parking lot space data matrix in real time, import the processed parking demand data, and allocate parking spaces for users based on the import results;

[0014] S4. After the user's parking space is allocated, plan the user's parking route based on the path guidance algorithm and guide the user according to the planned parking route;

[0015] S5. Based on the process of guiding the user, adaptively control the lighting in the parking lot;

[0016] S51. When the user drives into the parking lot, match the parking space data based on the user's vehicle information;

[0017] S52. After successful matching, real-time monitor the user's location based on the installed Internet of Things control device;

[0018] S53. Adaptively control the lighting in the parking lot based on the real-time monitored user location information.

[0019] In the present invention, corresponding lighting fixtures are installed based on the location of the parking lot, and corresponding Internet of Things control devices are installed for each lighting fixture. At the same time, the data on the parking space situation in the parking lot is collected, and a parking lot space data matrix is constructed based on the collected parking space situation data. After the construction is completed, the user's parking demand is collected in real time, and parking spaces are allocated for the user based on the constructed parking lot space data matrix. After the user's parking space is allocated, the user's parking route is planned based on the path guidance algorithm and the user is guided according to the planned parking route. At the same time, based on the process of guiding the user, the lighting in the parking lot is adaptively controlled, improving the efficiency of adaptive intelligent lighting in the smart parking lot.

[0020] Preferably, the step of collecting the data on the parking space situation in the parking lot and constructing a parking lot space data matrix based on the collected parking space situation data includes the following steps:

[0021] The real-time collection of the data on the parking space situation in the parking lot includes: road section data and parking space data;

[0022] Set the set of road section data in the parking lot as: E = {r 1 , r 2 ,..., r m};

[0023] Among them, E represents the set of road section data in the parking lot, and r m represents the m-th road section data in the parking lot;

[0024] The road section data r m consists of a quadruple {l m, b m , z m , c m} represents, where l m represents the road segment identifier of the road segment data, b m represents the starting point of the road segment data, z m represents the end point of the road segment data, c m represents the distance of the road segment;

[0025] Set the parking space data set: T = {tr 1 , tr 2 ,..., tr α};

[0026] Among them, T represents the parking space data set, and tr α represents the α-th group of parking space data in the parking lot;

[0027] Set the length of a single parking space as a node, and represent the road segment data and parking space data in the parking lot based on the set node;

[0028] Set 1 to represent road segment data, 10 to represent vacant parking spaces, and 100 to represent occupied parking spaces;

[0029] Summarize the road segment data and parking space data in the parking lot to construct a parking lot parking space data matrix.

[0030] The present invention improves the data of intelligent parking lot management by collecting and processing the parking space situation data in the parking lot in real time, and at the same time, by setting the unit length and constructing a parking lot parking space data matrix based on the set unit length and corresponding data.

[0031] Preferably, the processing of the real-time collected user parking demand data to obtain the processed parking demand data includes the following steps:

[0032] The user parking demand data includes: estimated parking time, estimated arrival time, vehicle license plate, and parking exit position;

[0033] Set that any parking demand data must include the estimated parking time, estimated arrival time, vehicle license plate, and parking exit position;

[0034] Delete the parking demand data with missing content;

[0035] Summarize the processed parking demand data to obtain the processed parking demand data.

[0036] Preferably, the real-time update of the parking lot parking space data matrix, importing the processed parking demand data, and allocating parking spaces for users based on the import result includes the following steps:

[0037] Allocate parking spaces for users based on the parking exit positions in the user parking demand data and the parking space data matrix of the parking lot;

[0038] Set A(x 1 ,y 1 ), B(x i ,y i ), where A(x 1 ,y 1 ) is the planar coordinate of the pedestrian exit of the intelligent parking lot, and B(x i ,y i ) is the planar coordinate of the position of any parking space within the planar area of the intelligent parking lot;

[0039]

[0040] Calculate the behavior path D = |AB|, divide the distance value of |AB| according to size to obtain multiple ranges; the set of parking spaces corresponding to the same range is divided into a parking area;

[0041] Judge the vacant parking spaces in the divided parking area according to the parking space data matrix of the parking lot, and allocate parking spaces for users in sequence according to the parking exit position.

[0042] By processing the real-time collected user parking demand data, deleting the missing demand data, and dividing the parking lot based on the parking exit positions in the user parking demand data and the parking space data matrix, and at the same time allocating parking spaces for each user according to the divided parking areas and user demands, the real-time performance of parking space allocation is improved.

[0043] Preferably, after the user parking space allocation is completed, planning the user parking route based on the path guidance algorithm and guiding the user according to the planned parking route includes the following steps:

[0044] S41. Calculate the shortest distance from the user to the allocated parking space based on the ant colony algorithm;

[0045] S42. Plan the user parking route based on the path guidance algorithm.

[0046] Preferably, calculating the shortest distance from the user to the allocated parking space based on the ant colony algorithm includes the following steps:

[0047] Set the number of ants in the ant colony to h, and set the pheromone concentration on the connection path between the parking space node e and the parking space node f in the parking lot at time t to τ ef (t);

[0048] At the initial moment, the pheromone concentrations on the connection paths between each parking space node in the parking lot are the same, τ ij (t) = τ 0, τ 0 is the pheromone concentration on the connection path between parking space nodes in each parking lot at the initial moment;

[0049] Ant q (q = 1, 2,..., h) determines the next visited node according to the pheromone concentration on the connection path between parking space nodes in each parking lot. Let represent the probability that ant q moves from parking space node e to parking space node f in the parking lot at time t. The transfer probability formula is as follows:

[0050]

[0051] where η ef (t) is the heuristic function, represents the expected degree of the ant moving from parking space node e to parking space node f in the parking lot, and d ef represents the distance between parking space node e and parking space node f in the parking lot. η es (t) is the heuristic function, represents the expected degree of the ant moving from parking space node e to parking space node s in the parking lot, and d es represents the distance between parking space node e and parking space node s in the parking lot. allow q represents the set of parking space nodes in the parking lot that ant q is to visit. α is the pheromone importance factor, β is the heuristic function importance factor, and τ es (t) represents the pheromone concentration between parking space node e and parking space node s in the parking lot at time t;

[0052] Based on the transfer probability formula, a solution space is constructed. Each ant is randomly placed at a different parking space node in the parking lot. For each ant q (q = 1, 2,..., h), its next parking space node to be visited in the parking lot is calculated based on the transfer probability formula until all ants have visited all parking space nodes in the parking lot, and at the same time, the path lengths passed by each ant are calculated;

[0053] Set the number of iterations of the algorithm, and at the same time update the pheromone concentration on the connection path between each parking space node;

[0054] After reaching the set number of iterations, terminate the calculation and output the shortest path of the connection path between each parking space node in the parking lot.

[0055] Preferably, the user parking route planning based on the path guidance algorithm includes the following steps:

[0056] The path guidance algorithm formula is as follows:

[0057]

[0058] Among them, Q n is the passing weight of the nth parking area after division, and K n represents the number of available parking spaces in the nth parking area after division, and U n represents the number of cars passing through in the nth parking area after division, and W n represents the lane width in the nth parking area after division;

[0059] Based on the calculated passing weight, it is judged whether the user turns or goes straight at the next intersection, and the user is guided according to the judgment result.

[0060] In the present invention, by using the ant colony algorithm, with the same initial pheromone concentration set, through the random behaviors of different ants and the transition probability formula, a solution space of the shortest distance from the user to the assigned parking space is constructed, and the shortest distance is determined by continuous iteration. After determining the shortest distance, based on the path guidance algorithm, the user's parking route is planned, and the traveling direction of the user at the next intersection is judged by calculating the passing weight, which improves the accuracy of path guidance.

[0061] Preferably, when the user drives into the parking lot, based on the user vehicle information, the steps for matching the parking space data include:

[0062] When the user arrives at the intelligent parking lot, the parking lot will query the user's parking demand data according to the vehicle license plate, detect whether the reserved parking space of the user is idle. If it is not idle, re-match the parking space and transmit the new parking space information to the user. If it is idle, the user will be directly released.

[0063] After successful matching, based on the entrance position where the user drives into the parking lot, as well as the matching parking space data and the shortest path of the connection path between the parking space nodes calculated in the parking lot, the user's position is initially estimated, and at the same time, the user's position is accurately determined in real time through the installed infrared monitoring device;

[0064] Preferably, the steps for adaptively controlling the lighting lamps in the parking lot based on the real-time monitored user position information include:

[0065] Set the control switch of the lighting lamps in the parking lot based on the user position and the position of the lighting lamps;

[0066] Set the response distance threshold of the lighting lamp switch. When the user position is less than or equal to the set response distance threshold, control the corresponding lighting lamp to turn on; when the user position is greater than the set response distance threshold, control the corresponding lighting lamp to turn off.

[0067] When the user drives into the parking lot, based on the user's vehicle information, the present invention performs parking space data matching. After successful matching, it real-time monitors the user's position based on the installed Internet of Things control device, and adaptively controls the lighting in the parking lot based on the real-time monitored user position information, improving the accuracy of parking lot lighting.

[0068] The present invention also discloses an Internet of Things-based intelligent parking lot adaptive intelligent lighting system for implementing the Internet of Things-based intelligent parking lot adaptive intelligent lighting method. The system includes: a demand acquisition module, a parking space data planning module, a parking space allocation module, a path guidance module, and a lighting control module;

[0069] The demand acquisition module is used for the real-time demand acquisition module and processes the user's demands;

[0070] The parking space data planning module is used to collect the parking space situation data in the parking lot and make a plan;

[0071] The parking space allocation module is used to allocate parking spaces for users according to the user's demands and the planning results;

[0072] The path guidance module is used to calculate the user's parking route and guide;

[0073] The lighting control module is used to control the lighting in the parking lot according to the user's position.

[0074] (III) Beneficial effects

[0075] Compared with the prior art, the present invention provides an Internet of Things-based intelligent parking lot adaptive intelligent lighting method and system, having the following beneficial effects:

[0076] 1. The invention installs corresponding lighting fixtures based on the parking lot location, installs corresponding Internet of Things control devices for each lighting fixture, simultaneously collects the parking space situation data in the parking lot, constructs a parking lot parking space data matrix based on the collected parking space situation data. After construction, it real-time collects the user's parking demands, allocates parking spaces for users based on the constructed parking lot parking space data matrix. After the user's parking space allocation is completed, it plans the user's parking route based on the path guidance algorithm and guides the user according to the planned parking route. At the same time, based on the guiding process of the user, it adaptively controls the lighting in the parking lot, improving the efficiency of the Internet of Things-based intelligent parking lot adaptive intelligent lighting.

[0077] 2. The invention real-time collects and processes the parking space situation data in the parking lot, and at the same time sets a unit length and constructs a parking lot parking space data matrix based on the set unit length and corresponding data, improving the data nature of the Internet of Things-based intelligent parking lot management.

[0078] 3. The invention processes the user parking demand data collected in real time, deletes the missing demand data, divides the parking lot into regions based on the parking exit positions in the user parking demand data and the parking lot space data matrix, and allocates parking spaces for each user according to the divided parking areas and user demands, improving the real-time performance of parking space allocation.

[0079] 4. The invention uses the ant colony algorithm. By setting the initial pheromone concentration to be the same, through the random behaviors of different ants and the transition probability formula, it constructs the solution space of the shortest distance from the user to the allocated parking space, and determines the shortest distance through continuous iteration. After determining the shortest distance, it plans the user parking route based on the path guidance algorithm, and judges the traveling direction of the user at the next intersection by calculating the weight, improving the accuracy of path guidance.

[0080] 5. When the user drives into the parking lot, the invention matches the parking space data based on the user vehicle information. After successful matching, it real-time monitors the user's position based on the installed Internet of Things control device, and adaptively controls the lighting of the parking lot based on the real-time monitored user position information, improving the accuracy of parking lot lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic structural diagram of the adaptive intelligent lighting process of the intelligent parking lot of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0082] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the 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.

[0083] Embodiment 1

[0084] Please refer to Figure 1 , this embodiment discloses an Internet of Things-based intelligent parking lot adaptive intelligent lighting method, which specifically includes the following steps:

[0085] S1. Install corresponding lighting lamps based on the parking lot location, and install corresponding Internet of Things control devices for each lighting lamp;

[0086] S2. Collect the parking space situation data in the parking lot, and construct a parking lot space data matrix based on the collected parking space situation data;

[0087] S3. Real-time collect user parking demands, and allocate parking spaces for users based on the constructed parking lot space data matrix;

[0088] S31. Process the user parking demand data collected in real time to obtain the processed parking demand data;

[0089] S32. Update the parking lot space data matrix in real time, import the processed parking demand data, and allocate parking spaces for users based on the import results;

[0090] S4. After the user parking space allocation is completed, plan the user's parking route based on the path guidance algorithm and guide the user according to the planned parking route;

[0091] S5. Based on the process of guiding the user, adaptively control the lighting of the parking lot;

[0092] S51. When the user drives into the parking lot, match the space data based on the user vehicle information;

[0093] S52. After successful matching, monitor the user's location in real time based on the installed Internet of Things control device;

[0094] S53. Adaptively control the lighting of the parking lot based on the real-time monitored user location information;

[0095] Furthermore, please refer to Figure 1 , collect the data on the situation of parking spaces in the parking lot, and construct the parking lot space data matrix based on the collected data on the situation of parking spaces, including the following steps:

[0096] The data on the situation of parking spaces in the parking lot collected in real time includes: road section data and space data;

[0097] Set the set of road section data in the parking lot as: E = {r 1 , r 2 ,..., r m};

[0098] Among them, E represents the set of road section data in the parking lot, and r m represents the m-th road section data in the parking lot;

[0099] The road section data r m is represented by a quadruple {l m , b m , z m , c m}, where l m represents the road section identifier of this road section data, b m represents the starting point of this road section data, z m represents the end point of this road section data, and c m represents the distance of this road section;

[0100] Set the set of space data: T = {tr 1 , tr 2,...,tr α};

[0101] Among them, T represents the set of parking space data, and tr α represents the α-th group of parking space data in the parking lot;

[0102] Furthermore, set the length of a single parking space as a node, and represent the road section data and parking space data in the parking lot based on the set node;

[0103] Set 1 to represent road section data, 10 to represent vacant parking spaces, and 100 to represent occupied parking spaces;

[0104] Summarize the road section data and parking space data in the parking lot to construct a parking lot parking space data matrix;

[0105] Furthermore, please refer to Figure 1 , real-time collection of users' parking demands, and allocating parking spaces for users based on the constructed parking lot parking space data matrix includes the following steps:

[0106] S31. Process the real-time collected users' parking demand data to obtain the processed parking demand data;

[0107] The users' parking demand data includes: estimated parking time, estimated arrival time, vehicle license plate, and parking exit location;

[0108] Set that any parking demand data must include the estimated parking time, estimated arrival time, vehicle license plate, and parking exit location;

[0109] Furthermore, delete the parking demand data with missing content;

[0110] Summarize the processed parking demand data to obtain the processed parking demand data;

[0111] S32. Real-time update the parking lot parking space data matrix, import the processed parking demand data, and allocate parking spaces for users based on the import result;

[0112] Allocate parking spaces for users based on the parking exit location in the users' parking demand data and the parking lot parking space data matrix;

[0113] Set A(x 1 , y 1 ), B(x i , y i ) where A(x 1 , y 1 ) is the plane coordinate of the pedestrian exit of the intelligent parking lot, and B(x i , y i ) is the plane coordinate of the location of any parking space within the plane area of the intelligent parking lot;

[0114]

[0115] By calculating the behavior path D = |AB|, the distance values of |AB| are divided according to their magnitudes to obtain multiple ranges; the set of parking spaces corresponding to the same range is divided into a parking area;

[0116] Based on the parking lot space data matrix, the available spaces in the divided parking area are judged, and parking spaces are allocated to users in sequence according to the parking exit positions;

[0117] Further, please refer to Figure 1 , after the user's parking space allocation is completed, based on the path guidance algorithm, the user's parking route is planned, and the user is guided according to the planned parking route, including the following steps:

[0118] S41. Calculate the shortest distance from the user to the allocated parking space based on the ant colony algorithm;

[0119] Set the number of ants in the ant colony to h, and set the pheromone concentration on the connection path between the parking space node e and the parking space node f in the parking lot at time t as τ ef (t);

[0120] At the initial moment, the pheromone concentrations on the connection paths between the parking space nodes in each parking lot are the same, τ ij (t) = τ 0 , τ 0 is the pheromone concentration on the connection paths between the parking space nodes in each parking lot at the initial moment;

[0121] For ant q (q = 1, 2,..., h), determine the next visited node according to the pheromone concentration on the connection path between the parking space nodes in each parking lot. Let represent the probability that ant q moves from the parking space node e to the parking space node f in the parking lot at time t. The transition probability formula is as follows:

[0122]

[0123] where η ef (t) is the heuristic function, represents the expected degree of the ant moving from the parking space node e to the parking space node f in the parking lot, d ef represents the distance between the parking space node e and the parking space node f in the parking lot, η es (t) is the heuristic function, represents the expected degree of the ant moving from the parking space node e to the parking space node s in the parking lot, d es represents the distance between the parking space node e and the parking space node s in the parking lot, allow qdenotes the set of parking space nodes in the parking lot to be visited by ant q. α is the importance factor of pheromone, β is the importance factor of the heuristic function, and τ es (t) represents the pheromone concentration between parking space node e and parking space node s in the parking lot at time t;

[0124] Construct a solution space based on the transition probability formula, and randomly place each ant at different parking space nodes in the parking lot. For each ant q (q = 1, 2,..., h), calculate the next parking space node to be visited in the parking lot based on the transition probability formula until all ants have visited all parking space nodes in the parking lot, and at the same time calculate the path lengths passed by each ant;

[0125] Set the number of iterations of the algorithm, and at the same time update the pheromone concentration on the connection paths between parking space nodes;

[0126] After reaching the set number of iterations, terminate the calculation and output the shortest path of the connection paths between parking space nodes;

[0127] S42. Plan the user's parking route based on the path guidance algorithm;

[0128] The formula of the path guidance algorithm is as follows:

[0129]

[0130] Among them, Q n is the passing weight of the nth parking area after division, K n represents the number of available parking spaces in the nth parking area after division, U n represents the number of cars passing through in the nth parking area after division, W n represents the lane width in the nth parking area after division;

[0131] Furthermore, based on the calculated passing weight, judge whether the user turns or goes straight at the next intersection, and guide the user according to the judgment result;

[0132] Furthermore, based on the guiding process of the user, the adaptive control of the lighting lamps in the parking lot includes the following steps:

[0133] S51. When the user drives into the parking lot, match the parking space data based on the user's vehicle information;

[0134] When the user arrives at the intelligent parking lot, the parking lot will query the user's parking demand data according to the vehicle license plate, detect whether the reserved parking space of the user is idle. If it is not idle, re-match the parking space and transmit the new parking space information to the user. If it is idle, directly release;

[0135] S52. After successful matching, the user's location is monitored in real time based on the installed Internet of Things control device;

[0136] Based on the entrance location where the user drives into the parking lot, as well as the matching parking space data and the shortest path of the connection path between the parking space nodes calculated in the parking lot, the user's location is initially estimated, and at the same time, the user's location is accurately determined in real time through the installed infrared monitoring device;

[0137] S53. The lighting in the parking lot is adaptively controlled based on the user's location information monitored in real time;

[0138] The control switch of the lighting in the parking lot is set based on the user's location and the location of the lighting;

[0139] Set the response distance threshold of the lighting switch. When the user's location is less than or equal to the set response distance threshold, control the corresponding lighting to turn on; when the user's location is greater than the set response distance threshold, control the corresponding lighting to turn off;

[0140] Embodiment 2

[0141] Please refer to Figure 1 , this embodiment also discloses an Internet of Things-based intelligent parking lot adaptive intelligent lighting system for implementing the Internet of Things-based intelligent parking lot adaptive intelligent lighting method. The system includes: a demand acquisition module, a parking space data planning module, a parking space allocation module, a path guidance module, and a lighting control module;

[0142] The demand acquisition module is used for the real-time demand acquisition module and processes the user's demands;

[0143] The parking space data planning module is used to collect the parking space situation data in the parking lot and make plans;

[0144] The parking space allocation module is used to allocate parking spaces for users according to the user's demands and the planning results;

[0145] The path guidance module is used to calculate the user's parking route and guide;

[0146] The lighting control module is used to control the lighting in the parking lot according to the user's location.

[0147] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive intelligent lighting method for a smart parking lot based on the Internet of Things, characterized in that: The following steps are involved: S1. Install corresponding lighting lamps based on the parking lot location, and install corresponding IoT control devices for each lighting lamp; S2, collecting parking space situation data in the parking lot, and constructing a parking space data matrix of the parking lot based on the collected parking space situation data; S3, collect user parking needs in real time, and allocate parking spaces to users based on the constructed parking lot parking space data matrix; S31, processing the user parking demand data collected in real time to obtain processed parking demand data; S32, updating the parking space data matrix of the parking lot in real time, importing the processed parking demand data, and allocating parking spaces to users based on the import results; S4. After the user parking space is allocated, the user parking route is planned based on the path guidance algorithm, and the user is guided according to the planned parking route; S5. Based on the user guidance process, adaptively control the parking lot lighting; S51, when the user enters the parking lot, matching parking space data based on the user's vehicle information; S52, after successful matching, real-time monitoring of the user's location based on the installed IoT control device; S53: adaptively control the parking lot lighting based on the real-time monitored user location information.

2. According to the method of claim 1, the method is characterized in that: The method of collecting parking space situation data in the parking lot and constructing a parking space data matrix of the parking lot based on the collected parking space situation data comprises the following steps: Real-time collection of parking space data in the parking lot includes: road section data and parking space data; The road segment data set in the parking lot is set as: E = {r1, r2, ..., r m }; Among them, E represents the road segment data set in the parking lot, r m Represents the mth road section data in the parking lot; Road section data m A four-tuple {l m ,b m ,z m ,c m } means that l m Indicates the road segment identifier of the road segment data, b m Indicates the starting point of the road section data, z m Indicates the end point of the road section data, c m Indicates the distance of the road segment; Set the parking space data set: T = {tr1,tr2,...,tr α }; Among them, T represents the parking space data set, tr α Represents the αth group of parking space data in the parking lot; The length of a single parking space is set as a node, and the road section data and parking space data in the parking lot are represented based on the set node; Set 1 to represent road segment data, 10 to represent vacant parking spaces, and 100 to represent occupied parking spaces; The road section data and parking space data in the parking lot are aggregated to construct a parking lot parking space data matrix.

3. According to the method of adaptive intelligent lighting for smart parking lot based on Internet of Things in claim 1, it is characterized in that: The processing of the user parking demand data collected in real time to obtain the processed parking demand data comprises the following steps: User parking demand data includes: estimated parking time, estimated arrival time, vehicle license plate, and parking exit location; Any parking demand data must include estimated parking time, estimated arrival time, vehicle license plate, and parking exit location; Deleting parking demand data with missing content; The processed parking demand data are aggregated to obtain processed parking demand data.

4. According to the method of claim 1, the method is characterized in that: The real-time updating of the parking lot parking space data matrix, importing the processed parking demand data, and allocating parking spaces to users based on the import results comprises the following steps: Allocate parking spaces for users based on the parking exit positions in the user's parking demand data and the parking lot parking space data matrix; Let A(x1,y1), B(x i ,y i ) where A(x1,y1) is the plane coordinate of the pedestrian exit of the smart parking lot, B(x i ,y i ) is the plane coordinate of the position of any parking space in the plane area of ​​the intelligent parking lot; By calculating the behavior path D = |AB|, the distance value of |AB| is divided according to size to obtain multiple ranges; the set of corresponding parking spaces in the same range is divided into a parking area; The available parking spaces in the parking area are determined based on the parking space data matrix, and parking spaces are allocated to users in sequence based on the parking and exit locations.

5. According to the method of adaptive intelligent lighting for smart parking lot based on Internet of Things in claim 1, it is characterized in that: After the user parking space allocation is completed, the user parking route is planned based on the path guidance algorithm, and the user is guided according to the planned parking route, including the following steps: S41, calculating the shortest distance from the user to the allocated parking space based on the ant colony algorithm; S42: Plan the user's parking route based on a path guidance algorithm.

6. The method for adaptive intelligent lighting of a smart parking lot based on the Internet of Things according to claim 5 is characterized in that: The method of calculating the shortest distance from the user to the allocated parking space based on the ant colony algorithm includes the following steps: Set the number of ants in the ant colony to h, and set the pheromone concentration on the connection path between the parking space node e in the parking lot and the parking space node f in the parking lot at time t to τ ef (t); Assume that at the initial moment, the pheromone concentration on the connection path between the parking nodes in each parking lot is the same, τ ij (t) = τ0, τ0 is the pheromone concentration on the connection path between parking space nodes in each parking lot at the initial moment; Ant q (q = 1, 2, ..., h) decides the next node to visit based on the pheromone concentration on the connection path between parking nodes in each parking lot. It represents the probability of ant q moving from parking space node e to parking space node f at time t. The transfer probability formula is as follows: Among them, η ef (t) is the heuristic function, represents the ant's expectation degree from the parking space node e in the parking lot to the parking space node f in the parking lot, d ef represents the distance between parking space node e and parking space node f in the parking lot, η es (t) is the heuristic function, represents the expectation degree of the ant from the parking space node e in the parking lot to the parking space node s in the parking lot, d es Indicates the distance between parking space node e and parking space node s in the parking lot, allow q represents the set of parking space nodes in the parking lot to be visited by ant q, α is the pheromone importance factor, β is the heuristic function importance factor, τ es (t) represents the pheromone concentration between parking space node e and parking space node s in the parking lot at time t; Based on the transition probability formula, the solution space is constructed, and each ant is randomly placed in different parking space nodes in the parking lot. For each ant q (q = 1, 2, ..., h), the next parking space node to be visited in the parking lot is calculated based on the transition probability formula until all ants have visited all parking space nodes in the parking lot. At the same time, the path length of each ant is calculated; Set the number of iterations of the algorithm and update the pheromone concentration on the connection path between parking nodes in each parking lot; After reaching the set number of iterations, the calculation is terminated and the shortest path connecting the parking space nodes in each parking lot is output.

7. The method for adaptive intelligent lighting of a smart parking lot based on the Internet of Things according to claim 5 is characterized in that: The method of planning a user's parking route based on a path guidance algorithm comprises the following steps: The path guidance algorithm formula is as follows: Among them, Q n is the passing weight of the nth parking area after division, K n It indicates the number of available parking spaces in the nth parking area after division, U n W represents the number of cars passing through the nth parking area after division. n Indicates the lane width in the nth parking area after division; Based on the calculated passing weight, it is determined whether the user will turn or go straight at the next intersection, and the user is guided according to the judgment result.

8. The method for adaptive intelligent lighting of a smart parking lot based on the Internet of Things according to claim 1 is characterized in that: When the user enters the parking lot, matching the parking space data based on the user's vehicle information includes the following steps: When a user arrives at the smart parking lot, the parking lot will query the user's parking demand data based on the vehicle license plate, and detect whether the parking space reserved by the user is vacant. If not, the parking space will be re-matched and the new parking space information will be transmitted to the user. If it is vacant, the user will be released directly.

9. The method for adaptive intelligent lighting of a smart parking lot based on the Internet of Things according to claim 1 is characterized in that: The method of adaptively controlling the parking lot lighting based on real-time monitored user location information comprises the following steps: Setting the control switch of the parking lot lighting based on the user's location and the location of the lighting; Set the response distance threshold of the lighting switch. When the user position is less than or equal to the set response distance threshold, the corresponding lighting is controlled to turn on; when the user position is greater than the set response distance threshold, the corresponding lighting is controlled to turn off.

10. A system for implementing the method for adaptive intelligent lighting of a smart parking lot based on the Internet of Things according to any one of claims 1 to 9, characterized in that: include: Demand collection module, parking space data planning module, parking space allocation module, path guidance module and lighting control module; The demand collection module is used for real-time demand collection and processing of user demands; The parking space data planning module is used to collect parking space situation data in the parking lot and plan; The parking space allocation module is used to allocate parking spaces to users according to user needs and planning results; The path guidance module is used to calculate the user's parking route and guide the user; The lighting control module is used to control the lighting in the parking lot according to the user's location.

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