Parking lot navigation method based on directed graph, and system and storage medium

Abstract

The present invention relates to the technical field of traffic navigation. Disclosed are a parking lot navigation method based on a directed graph, and a system and a storage medium. The method comprises: constructing a basic navigation graph of a parking lot; acquiring license plate photographs and capture times of vehicles passing through section nodes, and vehicle occupancy information of parking spaces; for any two adjacent section nodes, determining a vehicle speed weighted average value of a road between the adjacent section nodes on the basis of an average vehicle speed and a traffic flow; on the basis of the vehicle speed weighted average values, lengths, free-flow vehicle speeds and the number of lanes of roads between all adjacent section nodes, determining a traffic congestion index of the parking lot; on the basis of the traffic congestion index, optimizing the basic navigation graph, so as to obtain a weighted graph of navigation time costs; and on the basis of the weighted graph of navigation time costs and the basic navigation graph, using Dijkstra's algorithm to determine an entrance path and an exit path of the vehicles, and generating a corresponding navigation path two-dimensional code. The present invention can plan a rational traveling route for vehicles.

Description

A parking lot navigation method, system and storage medium based on directed graph

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 11, 2024, with application number 202410275305.7 and invention name “A parking lot navigation method, system and storage medium based on directed graph”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present invention relates to the technical field of traffic navigation, and in particular to a parking lot navigation method, system and storage medium based on a directed graph. Background Art

[0003] Existing parking lot management platforms offer relatively limited functionality, primarily focused on vehicle billing and management. Parking lot navigation typically uses fixed signage to indicate routes for vehicles entering or leaving the lot, leaving drivers to subjectively determine their routes. This approach suffers from a lack of real-time awareness and processing of parking lot traffic conditions. During peak hours, this can easily lead to traffic congestion and congestion within the parking lot, making it impossible to plan reasonable routes for vehicles.

[0004] With the increasing popularity of mobile internet, parking management platforms with wireless positioning capabilities have emerged. These platforms use wireless LAN terminals with wireless positioning capabilities to collect vehicle positioning data, calculate the collected data using a relational database, and provide route guidance to drivers via traffic guidance displays. While this approach eliminates the need for drivers to subjectively judge their routes, it still cannot accurately plan reasonable routes based on real-time traffic conditions within the parking lot to avoid traffic congestion. Furthermore, these parking management platforms require a large number of sensor devices and the maintenance costs of traffic guidance displays are high, which undoubtedly increases the cost of parking management platforms.

[0005] Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a directed graph-based parking lot navigation method, system, and storage medium, which can plan a reasonable driving route for vehicles according to the real-time road conditions in the parking lot, while reducing the cost of the parking lot management platform.

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

[0008] A parking lot navigation method based on a directed graph, comprising:

[0009] Constructing a basic navigation map for the parking lot; the basic navigation map is a directed graph determined based on the parking lot map; the directed graph includes section nodes and auxiliary nodes; the section nodes include entry nodes, exit nodes, and intersection nodes; a road is formed between each adjacent section node; at least one parking space is provided along the road between each adjacent intersection node; the auxiliary nodes are intersection points formed by drawing a perpendicular line from the center line of the parking space to the center line of the road where the parking space is located;

[0010] Obtain license plate photos and capture times of vehicles passing through the cross-section nodes, as well as vehicle occupancy information of parking spaces between adjacent intersection nodes;

[0011] For adjacent entry nodes and intersection nodes, the average vehicle speed and traffic volume of the road between the entry node and the intersection node are determined based on the length of the road between the entry node and the intersection node, the license plate photo, and the capture time;

[0012] For adjacent exit nodes and intersection nodes, the average vehicle speed and traffic volume on the road between the exit node and the intersection node are determined based on the length of the road between the exit node and the intersection node, the license plate photo, and the capture time;

[0013] For any two adjacent intersection nodes, determine the average vehicle speed and traffic volume on the road between the adjacent intersection nodes based on the length of the road between the adjacent intersection nodes, the license plate photo, the capture time, vehicle occupancy information, and the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side;

[0014] For any two adjacent section nodes, a weighted average of vehicle speeds on the road between the adjacent section nodes is determined based on the average vehicle speed and traffic volume;

[0015] The traffic congestion index of the parking lot is determined based on the weighted average speed, length, free flow speed and number of lanes of the roads between all adjacent section nodes;

[0016] Optimizing the navigation basic graph based on the traffic congestion index to obtain a navigation time cost weighted graph;

[0017] Based on the navigation time cost weighted graph and the navigation basic graph, the Dijkstra algorithm is used to determine the vehicle's approach path and departure path, and a navigation path QR code is obtained based on the approach path and the departure path; the navigation path QR code is used to generate an optimal vehicle driving route on the user side.

[0018] Optionally, for any two adjacent intersection nodes, based on the length of the road between the adjacent intersection nodes, the license plate photo, the capture time, the vehicle occupancy information, and the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side, the average vehicle speed and traffic volume of the road between the adjacent intersection nodes are determined, specifically including:

[0019] Determine the average speed of the road between the adjacent intersection nodes according to the length of the road between the adjacent intersection nodes and the capture time;

[0020] Determine the initial traffic flow of the road between adjacent intersection nodes according to the number of license plate photos;

[0021] Determine the compensation traffic flow of the road between the adjacent intersection nodes according to the number of license plate photos and vehicle occupancy information;

[0022] The traffic flow of the road between the adjacent intersection nodes is determined based on the initial traffic flow, the compensated traffic flow, the length between the adjacent intersection nodes, the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side, and the traffic flow compensation formula; the traffic flow compensation formula is:

[0023] Where tv is the traffic flow, s is the length between the auxiliary node and the intersection node on the entrance side, S is the length between adjacent intersection nodes, tv1 is the initial traffic flow, and tv2 is the compensation traffic flow.

[0024] Optionally, for any two adjacent section nodes, a weighted average of vehicle speeds of the road between the adjacent section nodes is determined based on the average vehicle speed and traffic volume, specifically including:

[0025] The average vehicle speed of the road between adjacent section nodes in the current month is determined based on the average vehicle speed, traffic volume, and a first calculation formula; the first calculation formula is:

[0026] Where Mv is the average vehicle speed of the month, tv T is the traffic volume in the counting period T, v T is the average vehicle speed within the counting period T, where the duration from T1 to T2 is one month;

[0027] The average vehicle speed of the road between adjacent section nodes in the previous hour is determined based on the average vehicle speed, traffic volume, and a second calculation formula; the second calculation formula is:

[0028] Where Hv is the average vehicle speed in the previous hour, and the duration from T3 to T4 is one hour;

[0029] The weighted average speed of the road between adjacent section nodes is determined based on the average speed of the current month, the average speed of the previous hour, and a third calculation formula; the third calculation formula is:

[0030] Pv=(Mv×A1+Hv×A2)÷(A1+A2);

[0031] Where Pv is the weighted average vehicle speed, A1 is the first coefficient, A2 is the second coefficient, and A1>A2.

[0032] Optionally, the traffic congestion index of the parking lot is determined based on the weighted average of the speed, length, free flow speed, and number of lanes of the roads between all adjacent section nodes, specifically:

[0033] Where, TSI is the traffic congestion index, K i is the number of lanes on road i, L i is the length of road i, V f is the free flow speed, Pv i is the weighted average speed of road i, and d is the total number of roads.

[0034] Optionally, constructing a basic navigation map of the parking lot specifically includes:

[0035] Determine the target node and node auxiliary information based on the parking lot map; the target node includes the section node and the auxiliary node; the node auxiliary information is the name of the corresponding node;

[0036] Determining directed edges and directed edge auxiliary information based on adjacent target nodes; the directed edge auxiliary information includes the length of the directed edge and the direction of the directed edge; the length of the directed edge is the length between adjacent target nodes; the direction of the directed edge is the driving direction of the vehicle;

[0037] A basic navigation map of the parking lot is obtained based on the target node, the node auxiliary information, the directed edges and the directed edge auxiliary information.

[0038] Optionally, optimizing the navigation basic graph based on the traffic congestion index to obtain a navigation time cost weighted graph specifically includes:

[0039] Determining an expected vehicle speed in the parking lot based on the traffic congestion index and an interval expected speed table; the interval expected speed table is a table recording different traffic congestion index intervals and the expected vehicle speeds corresponding to the different traffic congestion index intervals;

[0040] determining an average time cost for each road based on the length of all roads and the expected vehicle speed;

[0041] The lengths of all directed edges in the navigation basic graph are replaced by corresponding average time costs to obtain a navigation time cost weighted graph.

[0042] Optionally, based on the navigation time cost weighted graph and the navigation basic graph, the Dijkstra algorithm is used to determine the vehicle's approach path and departure path, and a navigation path QR code is obtained according to the approach path and the departure path, specifically including:

[0043] Taking the parking space specified by the user as the target, the entry node and the exit node as the source, the navigation time cost weighted graph and the navigation basic graph as the base graph, and using the Dijkstra algorithm to trace the vehicle's approach path;

[0044] Taking the entry node and the exit node as the target, the user-specified parking space as the source, the navigation time cost weighted graph and the navigation basic graph as the base graph, and using the Dijkstra algorithm to trace the exit path of the vehicle;

[0045] The entry path and the exit path are converted into navigation path QR codes using QR code generation software.

[0046] A computer device system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement steps of a directed graph-based parking lot navigation method.

[0047] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a parking lot navigation method based on a directed graph.

[0048] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0049] The parking lot navigation method provided by the embodiment of the present invention does not require the installation of a large number of fixed signboards or traffic guidance display screens. It only requires the user to scan the navigation path QR code generated by the system to obtain the optimal vehicle driving route. The user only needs to drive according to the route displayed by the QR code to reach the target parking space. In addition, the generation process of the approach route and the departure route in the vehicle driving route is not determined solely based on the positioning of the vehicle, but takes into account the real-time road conditions in the parking lot. The average speed and traffic volume of each road are determined based on the real-time road conditions. The traffic congestion index of the parking lot is further determined using the average speed and traffic volume of each road. The traffic congestion index is used to optimize the navigation base map of the parking lot to facilitate the selection of the optimal vehicle driving route. This method plans a reasonable driving route for the vehicle in combination with the real-time road conditions in the parking lot, while also reducing the cost of the parking lot management platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] FIG1 is a first flow chart of a directed graph-based parking lot navigation method according to an embodiment of the present invention;

[0052] FIG2 is a second flow chart of a directed graph-based parking lot navigation method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] The purpose of the present invention is to provide a parking lot navigation method, system and storage medium based on a directed graph, which can plan a reasonable driving route for vehicles according to the real-time road conditions in the parking lot, while reducing the cost of the parking lot management platform.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Example 1

[0057] FIG1 shows a specific process of a parking lot navigation method based on a directed graph.

[0058] Referring to FIG1 , a parking lot navigation method based on a directed graph specifically includes:

[0059] Step S1: Construct a basic navigation map of the parking lot; the basic navigation map is a directed graph determined based on the parking lot map; the directed graph includes section nodes and auxiliary nodes; the section nodes include entry nodes, exit nodes, and intersection nodes; a road is formed between each adjacent section node; at least one parking space is set up next to the road between each adjacent intersection node; the auxiliary node is the intersection formed by drawing a perpendicular line from the center line of the parking space to the center line of the road where the parking space is located.

[0060] Construct a basic navigation map for the parking lot, including:

[0061] Step S11: determining a target node and node auxiliary information based on the parking lot map; the target node includes a section node and an auxiliary node; the node auxiliary information is the name of the corresponding node.

[0062] Step S12: Determine directed edges and directed edge auxiliary information based on adjacent target nodes; the directed edge auxiliary information includes the length of the directed edge and the direction of the directed edge; the length of the directed edge is the length between adjacent target nodes; the direction of the directed edge is the driving direction of the vehicle.

[0063] Step S13: Obtaining a basic navigation map of the parking lot based on the target node, node auxiliary information, directed edges, and directed edge auxiliary information.

[0064] The navigation base graph is a data structure based on a directed graph. Nodes are all parking spaces in a parking lot (measured by the midpoint of the parking line for that space), entrances, exits, intersections (measured by the intersection of the centerline of the road connected to the intersection), auxiliary nodes, stairwells, and other locations. Bidirectional roads connect each node with a directed edge. Node auxiliary information records the node's name, such as "Entrance and Exit 1" or "Parking Space A001." Directed edge auxiliary information records the node number of the connected node, the length of the road, and the driver's action from the starting node to the ending node of the directed edge, such as "turn left," "turn right," "enter the parking space," or "go straight."

[0065] In one example, the process of building a navigation base graph is as follows:

[0066] (1) Read in the electronic drawing of the parking lot.

[0067] (2) Obtain the center position coordinates and name details of all node elements (such as parking spaces, exits, entrances, intersections, etc.) in the electronic drawing.

[0068] (3) Construct the nodes of the directed graph according to the center position coordinates of the node elements, and store the name details in the auxiliary information of the node.

[0069] (4) Obtain the starting position coordinates, ending position coordinates and directions (such as north-south, east-west, etc.) of all roads in the electronic drawings.

[0070] (5) With the node element corresponding to the starting position coordinates of the road as the starting point and the node element corresponding to the ending position coordinates as the end point, a directed edge is constructed based on the starting and end points. The geometric distance between the starting and end points is calculated and converted to a certain scale. The converted geometric distance is used as the length of the directed edge, and the line between the two points is used as the centerline of the road. The direction of the road is used as the direction of the directed edge, and the names of the starting and end points are recorded as the element names of the corresponding nodes in the auxiliary information of the edge. The length of the directed edge is also recorded as the node element distance in the auxiliary information of the edge.

[0071] (6) Determine the auxiliary node based on the center position coordinates of the parking space, the starting position coordinates of the road, and the ending position coordinates of the road.

[0072] (7) Construct two directed edges with the beginning of the road as the starting point and the auxiliary node as the end point, and two directed edges with the auxiliary node as the starting point and the end point of the road as the end point. After the construction is completed, delete the directed edge constructed with the original road.

[0073] (8) Construct a directed edge with the center coordinates of the parking space as the starting point and the center coordinates of the auxiliary node as the end point. The element name of the starting point is the parking space name, and the element name of the end point is the auxiliary node name, connecting the parking space and the auxiliary node.

[0074] Repeat (2) to (3), (4) to (5), and (6) to (7) until all node elements and roads in the electronic drawing are processed.

[0075] Step S2: Obtain license plate photos and capture times of vehicles passing through the cross-section nodes, as well as vehicle occupancy information of parking spaces between adjacent intersection nodes.

[0076] In this embodiment, the traffic data collection of the parking lot mainly includes:

[0077] Capturing vehicle information: The parking lot security monitoring system's checkpoint network cameras capture video surveillance footage at parking lot entrances, exits, and intersections. They capture real-time photos of vehicles as they appear, extracting license plate numbers, transit time, location, and instantaneous speed, and transferring these data to a relational database. In addition to capturing video images, the checkpoint network cameras can also automatically capture photos of moving vehicles on the road, recording vehicle information such as license plate numbers, and transmitting these data back to the backend via a dedicated local area network.

[0078] Collect parking space occupancy information: The occupancy status of all parking spaces is collected through the parking space occupancy sensors installed on the parking spaces and transmitted to the relational database.

[0079] Parking space collection: Through manual input by the driver or parking lot staff, the license plate, parking space number, parking start time and parking end time of all vehicles in the parking lot are obtained.

[0080] Furthermore, after completing parking lot traffic data collection, it is necessary to regularly extract vehicle characteristics and path features. Every minute, stored vehicle snapshot information is extracted from the relational database and converted into the corresponding node element name based on location. Furthermore, the changed parking space number, license plate number, change time, and whether the vehicle is parked in the space are determined based on parking space occupancy and parking status.

[0081] Step S3: For adjacent entry nodes and intersection nodes, determine the average vehicle speed and traffic volume of the road between the entry node and the intersection node based on the length of the road between the entry node and the intersection node, the license plate photo and the capture time.

[0082] In this embodiment, since there are no parking spaces between adjacent entry nodes and intersection nodes, there is no vehicle entry or exit. The average speed on the road between adjacent entry nodes and intersection nodes = the length of the road between the entry node and the intersection node / the difference in the capture time between the entry node and the intersection node. The traffic volume on the road between the entry node and the intersection node is determined by the number of license plate photos at the two nodes.

[0083] Step S4: For adjacent exit nodes and intersection nodes, determine the average vehicle speed and traffic volume of the road between the exit node and the intersection node based on the length of the road between the exit node and the intersection node, the license plate photo, and the capture time.

[0084] In this embodiment, since there are no parking spaces between adjacent exit nodes and intersection nodes, there is no vehicle entry or exit. The average vehicle speed on the road between adjacent exit nodes and intersection nodes = the length of the road between the exit node and the intersection node / the difference in the capture time between the exit node and the intersection node. The traffic volume on the road between the exit node and the intersection node is determined by the number of license plate photos at both nodes.

[0085] Step S5: For any two adjacent intersection nodes, the average vehicle speed and traffic volume of the road between the adjacent intersection nodes are determined based on the length of the road between the adjacent intersection nodes, the license plate photo, the capture time, the vehicle occupancy information, and the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side.

[0086] In this embodiment, since there are multiple parking spaces between two adjacent intersection nodes, there are situations where vehicles enter and exit the parking space. If the number of license plate photos at the two nodes is still used to determine the number of parking spaces, it is obviously not accurate. Therefore, the change of vehicles in the parking spaces needs to be considered. Step S5 specifically includes:

[0087] Step S51: determining the average vehicle speed of the road between the adjacent intersection nodes according to the length of the road between the adjacent intersection nodes and the capture time.

[0088] Step S52: Determine the initial traffic volume of the road between the adjacent intersection nodes according to the number of license plate photos.

[0089] Step S53: Determine the compensated traffic flow of the road between the adjacent intersection nodes according to the number of license plate photos and vehicle occupancy information.

[0090] Step S54: Determine the traffic volume of the road between the adjacent intersection nodes based on the initial traffic volume, the compensated traffic volume, the length between the adjacent intersection nodes, the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side, and the traffic volume compensation formula; the traffic volume compensation formula is:

[0091] Where tv is the traffic flow, s is the length between the auxiliary node and the intersection node on the entrance side, S is the length between adjacent intersection nodes, tv1 is the initial traffic flow, and tv2 is the compensation traffic flow.

[0092] Step S6: For any two adjacent section nodes, determine the weighted average vehicle speed of the road between the adjacent section nodes based on the average vehicle speed and traffic volume.

[0093] In this embodiment, step S6 specifically includes:

[0094] Step S61: Determine the monthly average vehicle speed of the road between adjacent section nodes based on the average vehicle speed, vehicle flow, and a first calculation formula; the first calculation formula is:

[0095] Where Mv is the average vehicle speed of the month, tv T is the traffic volume in the counting period T, v T is the average vehicle speed within the counting period T, and the duration from T1 to T2 is one month (for example, T1 is 00:00 on December 1, 2023, and T2 is 00:00 on January 1, 2024).

[0096] Step S62: Based on the average vehicle speed, traffic volume, and a second calculation formula, determine the average vehicle speed of the road between adjacent section nodes in the previous hour; the second calculation formula is:

[0097] Where Hv is the average vehicle speed over the previous hour, and the duration from T3 to T4 is one hour (e.g., T3 is 9:00 on December 29, 2023, and T4 is 10:00 on December 29, 2023).

[0098] Step S63: Determine the weighted average speed of the road between adjacent section nodes based on the average speed of the current month, the average speed of the previous hour, and a third calculation formula; the third calculation formula is:

[0099] Pv=(Mv×A1+Hv×A2)÷(A1+A2)

[0100] Where Pv is the weighted average vehicle speed, A1 is the first coefficient, A2 is the second coefficient, and A1>A2.

[0101] In this embodiment, parking lots are divided into residential parking lots and public building parking lots. For the weighted average speed of residential parking lots, the following is obtained:

[0102] Pv=(Mv×7+Hv×3)÷10

[0103] For the weighted average speed of public building parking lots, we have:

[0104] Pv=(Mv×6+Hv×4)÷10

[0105] Step S7: Determine the traffic congestion index of the parking lot based on the weighted average speed, length, free flow speed, and number of lanes of the roads between all adjacent section nodes, that is, satisfying:

[0106] Where, TSI is the traffic congestion index, K i is the number of lanes on road i (usually a constant of 2), L i is the length of road i, V f is the free flow speed (3km / h is often used in parking lots with a speed limit of 5km / h), Pv i is the weighted average speed of road i, and d is the total number of roads.

[0107] Step S8: Optimize the navigation basic graph based on the traffic congestion index to obtain a navigation time cost weighted graph.

[0108] In this embodiment, step S8 specifically includes:

[0109] Step S81: Determine the expected vehicle speed in the parking lot based on the traffic congestion index and the interval expected speed table; the interval expected speed table is a table that records different traffic congestion index intervals and the expected vehicle speeds corresponding to the different traffic congestion index intervals.

[0110] Table 1 shows the relationship between different traffic congestion index intervals and the expected vehicle speed in a parking lot with a speed limit of 5 km / h.

[0111] Table 1 Expected speed table for interval

[0112] As shown in Table 1, the TSI is divided into four intervals according to its numerical value: when the TSI is less than or equal to 0.2, it corresponds to interval 1; when the TSI is greater than 0.2 and less than or equal to 0.4, it corresponds to interval 2; when the TSI is greater than 0.4 and less than or equal to 0.6, it corresponds to interval 3; and when the TSI is greater than 0.6, it corresponds to interval 4.

[0113] Step S82: Determine the average time cost of each road based on the length of all roads and the expected vehicle speed.

[0114] In one example, the ASP.NET analysis service platform performs a division operation with two decimal places of precision using the road length between two adjacent facility nodes and the expected vehicle speed corresponding to the current moment to obtain the average time cost, and uses the calculated average time cost as the weighted path length of the directed edge in the navigation base graph until all roads in the navigation base graph are processed and stored in the database as a weighted navigation time cost graph.

[0115] Step S83: Replace the lengths of all directed edges in the navigation basic graph with the corresponding average time costs to obtain a navigation time cost weighted graph.

[0116] Step S9: Based on the navigation time cost weighted graph and the navigation basic graph, the Dijkstra algorithm is used to determine the vehicle's approach path and departure path, and a navigation path QR code is obtained based on the approach path and departure path; the navigation path QR code is used to generate the optimal vehicle driving route on the user side.

[0117] In this embodiment, step S9 specifically includes:

[0118] Step S91: Using the user-specified parking space as the target, the entry node and the exit node as the source, and the navigation time cost weighted graph and the navigation basic graph as the base map, the Dijkstra algorithm is used to trace the vehicle's approach path.

[0119] Step S92: Using the entry node and exit node as targets, the user-specified parking space as the source, and the navigation time cost weighted graph and the navigation basic graph as the base graph, the Dijkstra algorithm is used to trace the exit path of the vehicle.

[0120] Step S93: Use QR code generation software to generate a navigation path QR code for the entry path and exit path.

[0121] Among them, the Dijkstra algorithm calculates the shortest path between two nodes in a directed or undirected graph without negatively weighted edges. Starting from the initial node, the algorithm selects the shortest edge from the edges that have been extended to the unextended node each time, and ends when all nodes have been extended. This solution adopts the Dijkstra algorithm, using the navigation time cost weighted graph and the navigation base map as the base map input, the designated parking space as the target, and the entrance and exit as the source. Based on the parking spaces and entrances and exits specified by the user, the road with the minimum weighted road length is calculated, and the sequence of nodes and directed edges it passes through is returned as the approach road. Without changing the base map input, the entrance and exit with the minimum weighted road length is used as the target, the designated parking space as the source, and the sequence of nodes and directed edges it passes through is calculated and saved as the departure road in the database.

[0122] After determining the user's entry and exit roads, the ASP.NET application service program converts the sequence of nodes and directed edges into a navigation solution based on the additional node information and directed edge information recorded in the navigation base map and sends it to the web user interface. The web user interface generates a QR code and displays it on the parking machine for the driver to scan. The navigation solution conversion process is carried out as follows:

[0123] (1) From the sequence of the navigation base graph, the nodes and directed edge sequences that only pass through the entrance, exit, intersection, and parking space are merged. Specifically, all nodes from the start node to the end node are deleted in sequence, and the two directed edges passing through the node are merged into one directed edge, until only the start node, the end node, and one directed edge remain in the sequence.

[0124] (2) Replace the sequence of other facility nodes with the node element names stored in the auxiliary information of the navigation base map.

[0125] (3) Replace the sequence of directed edges with the length of the directed edges and the corresponding actions stored in the auxiliary information.

[0126] (4) Add parking space reminder before the navigation plan.

[0127] In one example, a navigation solution encoded in a QR code is scanned and the following prompt is displayed:

[0128] "Your parking space is: A007".

[0129] "To reach your parking space: enter the garage, please pay attention to the downhill and sharp turns, turn left at the intersection, go straight for 4 parking spaces, the target parking space A007 is on the left."

[0130] "Exit your parking space: The target parking space A007 is on the left. Go straight for 4 parking spaces and turn right. Exit the garage door at the intersection. Please pay attention to the uphill and sharp turns."

[0131] As shown in Figure 2, the present invention collects parking lot traffic data through the parking lot's video surveillance system, extracts vehicle characteristics and vehicle path characteristics, uses the parking management platform to obtain target parking spaces, and analyzes these characteristics to achieve real-time traffic characteristic data prediction. By generating daily characteristic data and using historical data to assist decision-making, the parking lot's navigation base map is continuously optimized. This optimized navigation base map is used to match the starting and ending points to calculate the navigation path, and the corresponding navigation solution is encoded as a QR code. Drivers can view the navigation solution by simply scanning the QR code.

[0132] Example 2

[0133] A computer device system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement steps of a directed graph-based parking lot navigation method.

[0134] Example 3

[0135] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a parking lot navigation method based on a directed graph.

[0136] In summary, the present invention reduces the need for drivers to understand the distribution of parking spaces within a parking lot, thus avoiding traffic congestion caused by a large number of drivers choosing to park along specific routes. Drivers enter and exit the parking lot according to the routes specified by the system, without having to plan their own entry and exit routes. Drivers only need to save the navigation QR code and access the navigation plan at any time. Furthermore, the present invention eliminates the need for wireless LAN access terminals with wireless positioning capabilities, reducing the use of sensors and traffic guidance display screens, significantly reducing costs. Furthermore, the present invention's data calculation method is real-time and highly accurate.

[0137] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0138] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A parking lot navigation method based on a directed graph, characterized in that: include: Construct a basic navigation map for the parking lot; The navigation base graph is a directed graph determined according to the parking lot map; The directed graph includes section nodes and auxiliary nodes; The section nodes include entry nodes, exit nodes, and intersection nodes; a road is formed between each adjacent section node; at least one parking space is provided beside the road between each adjacent intersection node; the auxiliary node is an intersection formed by drawing a perpendicular line from the center line of the parking space to the center line of the road where the parking space is located; Obtain license plate photos and capture times of vehicles passing through the cross-section nodes, as well as vehicle occupancy information of parking spaces between adjacent intersection nodes; For adjacent entry nodes and intersection nodes, the average vehicle speed and traffic volume of the road between the entry node and the intersection node are determined based on the length of the road between the entry node and the intersection node, the license plate photo, and the capture time; For adjacent exit nodes and intersection nodes, the average vehicle speed and traffic volume on the road between the exit node and the intersection node are determined based on the length of the road between the exit node and the intersection node, the license plate photo, and the capture time; For any two adjacent intersection nodes, determine the average vehicle speed and traffic volume on the road between the adjacent intersection nodes based on the length of the road between the adjacent intersection nodes, the license plate photo, the capture time, vehicle occupancy information, and the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side; For any two adjacent section nodes, a weighted average of vehicle speeds on the road between the adjacent section nodes is determined based on the average vehicle speed and traffic volume; The traffic congestion index of the parking lot is determined based on the weighted average speed, length, free flow speed and number of lanes of the roads between all adjacent section nodes; Optimizing the navigation basic graph based on the traffic congestion index to obtain a navigation time cost weighted graph; Based on the navigation time cost weighted graph and the navigation basic graph, the Dijkstra algorithm is used to determine the vehicle's approach path and departure path, and a navigation path QR code is obtained based on the approach path and the departure path; the navigation path QR code is used to generate an optimal vehicle driving route on the user side.

2. The parking lot navigation method based on directed graph according to claim 1, characterized in that: For any two adjacent intersection nodes, the average vehicle speed and traffic volume on the road between the adjacent intersection nodes are determined based on the length of the road between the adjacent intersection nodes, the license plate photo, the capture time, the vehicle occupancy information, and the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side. Specifically, the following methods are used: Determine the average speed of the road between the adjacent intersection nodes according to the length of the road between the adjacent intersection nodes and the capture time; Determine the initial traffic flow of the road between adjacent intersection nodes according to the number of license plate photos; Determine the compensation traffic flow of the road between the adjacent intersection nodes according to the number of license plate photos and vehicle occupancy information; The traffic flow of the road between the adjacent intersection nodes is determined based on the initial traffic flow, the compensated traffic flow, the length between the adjacent intersection nodes, the length between the auxiliary node in the adjacent intersection node and the intersection node on the entrance side, and the traffic flow compensation formula; the traffic flow compensation formula is: Where tv is the traffic flow, s is the length between the auxiliary node and the intersection node on the entrance side, S is the length between adjacent intersection nodes, tv1 is the initial traffic flow, and tv2 is the compensation traffic flow.

3. The parking lot navigation method based on directed graph according to claim 1, characterized in that: For any two adjacent section nodes, the weighted average vehicle speed of the road between the adjacent section nodes is determined based on the average vehicle speed and traffic volume, specifically including: The average vehicle speed of the road between adjacent section nodes in the current month is determined based on the average vehicle speed, traffic volume, and a first calculation formula; the first calculation formula is: Where Mv is the average vehicle speed of the month, tv T is the traffic volume in the counting period T, v T is the average vehicle speed within the counting period T, where the duration from T1 to T2 is one month; The average vehicle speed of the road between adjacent section nodes in the previous hour is determined based on the average vehicle speed, traffic volume, and a second calculation formula; the second calculation formula is: Where Hv is the average vehicle speed in the previous hour, and the duration from T3 to T4 is one hour; The weighted average speed of the road between adjacent section nodes is determined based on the average speed of the current month, the average speed of the previous hour, and a third calculation formula; the third calculation formula is: Pv=(Mv×A1+Hv×A2)÷(A1+A2); Where Pv is the weighted average vehicle speed, A1 is the first coefficient, A2 is the second coefficient, and A1>A2.

4. The parking lot navigation method based on directed graph according to claim 1, characterized in that: The traffic congestion index of the parking lot is determined based on the weighted average speed, length, free flow speed and number of lanes of the roads between all adjacent section nodes. Specifically: Where, TSI is the traffic congestion index, K i is the number of lanes on road i, L i is the length of road i, V f is the free flow speed, Pv i is the weighted average speed of road i, and d is the total number of roads.

5. The parking lot navigation method based on directed graph according to claim 1, characterized in that: The construction of the basic navigation map of the parking lot specifically includes: Determine the target node and node auxiliary information based on the parking lot map; the target node includes the section node and the auxiliary node; the node auxiliary information is the name of the corresponding node; Determining directed edges and directed edge auxiliary information based on adjacent target nodes; the directed edge auxiliary information includes the length of the directed edge and the direction of the directed edge; the length of the directed edge is the length between adjacent target nodes; the direction of the directed edge is the driving direction of the vehicle; A basic navigation map of the parking lot is obtained based on the target node, the node auxiliary information, the directed edges and the directed edge auxiliary information.

6. The parking lot navigation method based on directed graph according to claim 5, characterized in that: The navigation basic graph is optimized based on the traffic congestion index to obtain a navigation time cost weighted graph, specifically including: Determining an expected vehicle speed in the parking lot based on the traffic congestion index and an interval expected speed table; the interval expected speed table is a table recording different traffic congestion index intervals and the expected vehicle speeds corresponding to the different traffic congestion index intervals; determining an average time cost for each road based on the length of all roads and the expected vehicle speed; The lengths of all directed edges in the navigation basic graph are replaced by corresponding average time costs to obtain a navigation time cost weighted graph.

7. The parking lot navigation method based on directed graph according to claim 1, characterized in that: Based on the navigation time cost weighted graph and the navigation basic graph, the Dijkstra algorithm is used to determine the vehicle's approach path and departure path, and a navigation path QR code is obtained according to the approach path and the departure path, specifically including: Taking the parking space specified by the user as the target, the entry node and the exit node as the source, the navigation time cost weighted graph and the navigation basic graph as the base graph, and using the Dijkstra algorithm to trace the vehicle's approach path; Taking the entry node and the exit node as the target, the user-specified parking space as the source, the navigation time cost weighted graph and the navigation basic graph as the base graph, and using the Dijkstra algorithm to trace the exit path of the vehicle; The entry path and the exit path are converted into navigation path QR codes using QR code generation software.

8. A computer device system comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the directed graph-based parking lot navigation method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the directed graph-based parking lot navigation method according to any one of claims 1 to 7 are implemented.

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