Urban intelligent parking management system, methods, equipment and storage media

Abstract

This invention relates to an urban smart parking management system, method, device, and storage medium, comprising the following steps: collecting video surveillance data within a parking lot and identifying parking space occupancy in the video surveillance data to obtain parking space occupancy status information; mapping the parking space distribution based on the occupancy status information to obtain an electronic map of parking space distribution, and dividing the electronic map of parking space distribution into areas with location relationships; collecting vehicle entry and exit records of vehicles entering and exiting the parking lot through a license plate recognition device, and calculating the parking turnover rate of the parking areas based on the vehicle entry and exit records; analyzing the traffic status of the internal passages of the parking lot based on the location relationships of the parking areas and the parking turnover rate, and uploading the traffic status to the corresponding platform to guide vehicles by area, thereby solving the technical problem of uneven parking space utilization in many parking lots.

Description

Technical Field

[0001] This invention relates to the field of parking management technology, and in particular to urban smart parking management systems, methods, devices and storage media. Background Technology

[0002] With the acceleration of urbanization, the number of motor vehicles in cities has exploded, while the construction of parking lots has failed to keep pace. The "parking difficulty" problem has become a major bottleneck restricting smooth urban traffic flow and improving residents' quality of life. During morning and evening rush hours, large numbers of vehicles circle around parking lots searching for empty spaces, exacerbating traffic congestion, wasting fuel, increasing exhaust emissions, and placing additional pressure on the urban environment. At the same time, many parking lots suffer from uneven utilization of parking spaces, with some areas consistently saturated while others remain vacant. This irrational allocation of resources further highlights the contradiction between parking supply and demand. Traditional parking management methods relying on manual inspections and paper records are no longer sufficient to meet the demand for efficient use of parking resources. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a smart urban parking management system, comprising: The acquisition module is used to acquire video surveillance data in the parking lot and to identify parking space occupancy from the video surveillance data to obtain parking space occupancy status information. The mapping module is used to map the parking space distribution of the parking lot based on the occupancy status information to obtain an electronic map of parking space distribution, and to divide the electronic map of parking space distribution into areas to obtain parking areas with location relationships. The recording module is used to collect vehicle entry and exit records of the parking lot through license plate recognition equipment, and to calculate the parking turnover rate of the parking area based on the vehicle entry and exit records; The analysis module is used to analyze the traffic status of the internal passages of the parking lot based on the location relationship of the parking areas and the parking turnover rate, and upload the traffic status to the corresponding platform to guide vehicles by area.

[0004] This invention also provides a smart parking management method for cities, comprising: Collect video surveillance data in the parking lot, and identify parking space occupancy from the video surveillance data to obtain parking space occupancy status information; Based on the occupancy status information, the parking lot is mapped to obtain an electronic map of parking space distribution. The electronic map of parking space distribution is then divided into regions to obtain parking areas with location relationships. The system collects vehicle entry and exit records of vehicles entering and exiting the parking lot using license plate recognition equipment, and calculates the parking turnover rate of the parking area based on these records. The system analyzes the traffic status of the internal passageways of the parking lot based on the location relationship of the parking areas and the parking turnover rate, and uploads the traffic status to the corresponding platform to guide vehicles by area.

[0005] Furthermore, parking space occupancy identification is performed on the video surveillance data to obtain parking space occupancy status information, including: The parking space images in the video surveillance data are extracted frame by frame. The parking space images are images of each parking space at different times. The parking space images are then processed to grayscale to obtain grayscale parking space images. Parking space occupancy is identified based on the grayscale parking space image to obtain parking space occupancy status information.

[0006] Furthermore, based on the occupancy status information, the parking lot is mapped to obtain an electronic map of parking space distribution, including: The occupancy status information is associated with parking space codes, and the occupancy status information of each parking space is bound to a corresponding unique preset parking space code to obtain parking space code parameters, and the parking space coordinates are determined based on the parking space code parameters; The spatial arrangement of all parking spaces in the parking lot is displayed in advance to obtain a preliminary parking space distribution map; The parking space status map is marked with status indicators based on the parking space coordinates. According to the parking space coordinates corresponding to the occupied or vacant status of the parking space, the parking spaces on the preliminary parking space distribution map are marked with corresponding colors or symbols to obtain the parking space status distribution map. Add geographic information elements such as parking lot boundaries, passages, and entrances / exits to the parking space distribution map to obtain an electronic map of parking space distribution.

[0007] Furthermore, the electronic map of parking space distribution is divided into regions to obtain parking areas with locational relationships, including: Identify the features and connections of the aisle lines in the electronic map of parking space distribution to obtain aisle location connection information, and mark the boundaries of the aisle location connection information to obtain an aisle boundary map; A channel association analysis is performed on the parking spaces in the channel boundary map. By determining the distance and relative position relationship between the parking space and the channel, the parking space is assigned to the area associated with the nearest channel, thus obtaining parking space association area data. The parking space associated area data is aggregated to merge the areas associated with adjacent parking spaces that have similar characteristics, thus obtaining a preliminary parking area. The adjacency relationships between the areas in the initial parking area are determined to identify the adjacency relationships between the areas. Based on the adjacency relationships, arrows or lines are used on the electronic map of parking space distribution to represent the spatial relationships between the areas, thus obtaining a parking area distribution map with positional relationships.

[0008] Furthermore, based on the vehicle entry and exit records, the parking turnover rate of the parking area is calculated, including: The vehicle entry and exit records are matched for regional affiliation. By analyzing the license plate information and entry and exit time in the vehicle entry and exit records, and combining them with the electronic map of parking space distribution, regional vehicle entry and exit data is obtained. Based on the vehicle entry and exit data of the area, the turnover rate of each parking area is analyzed. The parking turnover rate is calculated by dividing the cumulative number of vehicles entering and leaving in each time period by the total number of parking spaces in the corresponding parking area.

[0009] Furthermore, based on the locational relationships of parking areas and the parking turnover rate, the traffic status of the internal passageways of the parking lot is analyzed, including: The location relationship of parking areas is analyzed by channel connection analysis. By calculating the channel length and width parameters between adjacent areas, channel capacity data is obtained. Based on the channel capacity data, the traffic capacity of each channel is evaluated to obtain the channel capacity threshold. The vehicle flow density of the parking lot’s internal passage is calculated based on the parking turnover rate, and the vehicle flow density is subjected to time-period fluctuation analysis to obtain the vehicle flow fluctuation trend. The passage capacity threshold and the traffic flow fluctuation trend are used to determine the traffic status of the parking lot's internal passages.

[0010] Furthermore, the traffic status is uploaded to the corresponding platform to guide vehicles by area, including: The traffic status is used to calculate the channel guidance weight, and the guidance weight value of each channel is obtained. Based on the guidance weight value, the accessibility of the parking area is analyzed to obtain the area guidance priority. Based on the area guidance priority, vehicles entering the parking lot are assigned target areas to obtain a vehicle zoning guidance scheme, which is then uploaded to the corresponding platform to guide vehicles by area.

[0011] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.

[0012] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above methods.

[0013] This invention provides a smart parking management method for cities, comprising the following steps: collecting video surveillance data within a parking lot and identifying parking space occupancy in the video surveillance data to obtain parking space occupancy status information; mapping the parking space distribution based on the occupancy status information to obtain an electronic map of parking space distribution, and dividing the electronic map of parking space distribution into areas to obtain parking areas with location relationships; collecting vehicle entry and exit records of vehicles entering and exiting the parking lot through license plate recognition equipment, and calculating the parking turnover rate of the parking areas based on the vehicle entry and exit records; analyzing the traffic status of the internal passages of the parking lot based on the location relationships of the parking areas and the parking turnover rate, and uploading the traffic status to the corresponding platform to guide vehicles by area. This method solves the technical problem of uneven parking space utilization in many parking lots, helps managers to promptly identify resource shortages in high-turnover areas and idle conditions in low-turnover areas, provides data support for optimizing parking space resource allocation, and improves the overall utilization rate of parking resources. Attached Figure Description

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating the steps of the urban smart parking management method in an embodiment of the present invention. Figure 2 This is a structural block diagram of the urban intelligent parking management system in an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0015] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0018] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0019] In the description of this invention, unless otherwise explicitly defined, terms such as "setting," "installing," and "connecting" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0020] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0021] Reference Figure 1 This invention provides a smart parking management method for cities, comprising: Step S1: Collect video surveillance data in the parking lot and identify parking space occupancy in the video surveillance data to obtain parking space occupancy status information.

[0022] Specifically, firstly, high-definition network cameras are deployed in the monitoring areas corresponding to each parking space in the parking lot to ensure that each parking space is fully covered, thereby collecting real-time video monitoring data of the parking lot. For example, in corner parking spaces in underground parking lots, the camera angle is adjusted to avoid obstruction by pillars, ensuring that the presence of a vehicle in the parking space can be clearly captured. Next, the collected video monitoring data is used to identify parking space occupancy. Typically, the video frames are preprocessed to remove interference factors such as changes in light and shadows. Then, image segmentation technology is used to extract each parking space area from the video frame separately. The difference between the baseline image when the parking space is vacant and the real-time video image is compared. If a set of pixels matching the outline features of a vehicle appears in the parking space area in the real-time image, it is determined that the parking space is occupied, thus obtaining the occupancy status information of the parking space. For example, when an SUV parkes in a standard parking space, the system can accurately identify it by comparing the outline size, without misjudging due to differences in vehicle size.

[0023] Step S2: Based on the occupancy status information, the parking lot is mapped to obtain an electronic map of parking space distribution. The electronic map of parking space distribution is then divided into regions to obtain parking areas with location relationships.

[0024] Specifically, the occupancy status information of each parking space is first associated with the actual physical layout data of the parking lot. For example, the "vacant" status of parking space A01 and the "occupied" status of parking space A02 are mapped to the coordinates of each parking space in the parking lot CAD drawing. Geographic information coding technology is used to transform this data into a visualized electronic map of parking space distribution. Different colors are used to mark vacant and occupied parking spaces on the map, such as green for vacant and red for occupied. Then, the electronic map of parking space distribution is divided into areas according to the direction of the passageways, the location of the entrances and exits, and the density of parking spaces. For example, parking spaces 1-20, which are close to the main entrance and exit and distributed along the main passageway, are divided into parking area 1, and parking spaces 21-40, which are located inside the parking lot and around the secondary passageway, are divided into parking area 2. The adjacency relationship between each area is marked during the division to ensure that parking areas with positional relationships are obtained.

[0025] Step S3: Collect vehicle entry and exit records of vehicles entering and exiting the parking lot through license plate recognition equipment, and calculate the parking turnover rate of the parking area based on the vehicle entry and exit records.

[0026] Specifically, license plate recognition cameras are installed at the entrances and exits of parking lots. When a vehicle approaches, the camera automatically captures the license plate image and parses the characters, simultaneously recording the time of entry or exit and the license plate number, forming a single vehicle entry and exit record. For example, when a vehicle enters at 8:00 AM, the record is "8:00, Zhejiang A12345, enters," and when it exits at 12:00 PM, the record is "12:00, Zhejiang A12345, exits." These records are then categorized and organized by parking area. The total number of parking times in each area within a set time period (e.g., 1 hour) is calculated, and then divided by the total number of parking spaces in that area to obtain the parking turnover rate. For example, if area 1 has 20 parking spaces and 30 vehicles parked there in 1 hour, its parking turnover rate is 30 ÷ 20 = 1.5.

[0027] Step S4: Analyze the traffic status of the internal passages of the parking lot based on the location relationship of the parking areas and the parking turnover rate, and upload the traffic status to the corresponding platform to guide vehicles by area.

[0028] Specifically, first, analyze the adjacent locations and connections of each parking area to the main access road. For example, Area 1 is adjacent to the main access road, and Area 2 is connected to the main access road via a side access road. Then, combine the parking turnover rates of the two areas. If the turnover rate of Area 1 is consistently higher than that of Area 2, it indicates that the traffic flow from the main access road to Area 1 is greater. Therefore, it is judged that the section of the main access road near Area 1 may experience slow traffic. Then, based on this traffic status, mark the guidance screen at the parking lot entrance to allow vehicles heading to Area 1 to use the outer lane of the main access road first, while guiding some vehicles to turn to the side access road to enter Area 2, so as to avoid congestion on a single section of the main access road.

[0029] In a specific embodiment, parking space occupancy identification is performed on the video surveillance data to obtain parking space occupancy status information, including: The parking space images in the video surveillance data are extracted frame by frame. The parking space images are images of each parking space at different times. The parking space images are then processed to grayscale to obtain grayscale parking space images. Parking space occupancy is identified based on the grayscale parking space image to obtain parking space occupancy status information.

[0030] Specifically, the parking space images are extracted frame by frame from the video surveillance data in chronological order. These images completely record the actual scene of each parking space at different times. For example, images of a parking space at consecutive time points such as 9:01 AM and 9:02 AM can be extracted individually, ensuring that no subtle changes in the parking space status are missed. Next, the extracted parking space images are processed into grayscale. By weighting the RGB components of each pixel in the image according to a fixed ratio, they are converted into a single grayscale value. For example, a weighting coefficient of 0.299 for the R channel, 0.587 for the G channel, and 0.114 for the B channel is used. This transforms the originally colorful parking space images into grayscale parking space images containing only black, white, and gray gradients. This effectively removes color interference and simplifies the complexity of subsequent recognition calculations.

[0031] When identifying parking space occupancy based on the obtained grayscale parking space images, the grayscale reference image of each parking space in an vacant state is first retrieved. This reference image is acquired when there are no vehicles parked and the lighting is stable, and includes fixed features such as parking space line outlines and ground textures. Then, the real-time extracted grayscale parking space images are compared pixel by pixel with the corresponding grayscale reference images, and the grayscale difference matrix between the two is calculated. If the grayscale difference of a certain area exceeds a preset threshold (e.g., 30), and the difference of that area remains above the threshold in 5 consecutive frames, and the outline of the difference area conforms to the shape proportions of common vehicles (e.g., aspect ratio between 1.5 and 2.5), then it is determined that the parking space is currently occupied.

[0032] For example, when a car parks in a parking space, the grayscale value of the area covered by the vehicle in the real-time grayscale parking space image will be significantly different from that in the reference image, and the difference area will show a rectangular vehicle outline feature. The system will then record the occupancy status information of the parking space as "occupied". If the grayscale difference between the real-time grayscale parking space image and the reference image is less than a preset threshold, and no consecutive frames show a difference area that matches the vehicle outline, then the parking space is determined to be in an vacant state, and the corresponding "vacant" occupancy status information is recorded. The entire process accurately captures the actual usage of the parking space by comparing the features of the grayscale image.

[0033] In a specific embodiment, the parking lot is mapped based on the occupancy status information to obtain an electronic map of parking space distribution, including: The occupancy status information is associated with parking space codes, and the occupancy status information of each parking space is bound to a corresponding unique preset parking space code to obtain parking space code parameters, and the parking space coordinates are determined based on the parking space code parameters; The spatial arrangement of all parking spaces in the parking lot is displayed in advance to obtain a preliminary parking space distribution map; The parking space status map is marked with status indicators based on the parking space coordinates. According to the parking space coordinates corresponding to the occupied or vacant status of the parking space, the parking spaces on the preliminary parking space distribution map are marked with corresponding colors or symbols to obtain the parking space status distribution map. Add geographic information elements such as parking lot boundaries, passages, and entrances / exits to the parking space distribution map to obtain an electronic map of parking space distribution.

[0034] Specifically, the acquired occupancy status information is first associated with parking space codes, assigning a unique preset parking space code to each parking space in the parking lot. For example, the first parking space in the first row near the main entrance is coded as C0101, the second one in the first row as C0102, and so on to form an ordered coding system. Then, the "occupied" or "vacant" status information of each parking space is bound to the corresponding preset parking space code one by one, generating parking space code parameters that include the code and status association. At the same time, based on the actual physical location corresponding to the preset parking space code and combined with the parking lot's plane coordinate system, the parking space coordinates corresponding to each parking space code parameter are determined. For example, the coordinates corresponding to C0101 can be set as (X10, Y5) to ensure that the coordinates accurately correspond to the actual spatial location of the parking space.

[0035] Next, the overall spatial layout data of the parking lot is collected in advance, including the arrangement and physical spacing of all parking spaces. This data is then transformed into a visual preliminary distribution map of parking spaces using graphic drawing technology. This map clearly shows the spatial arrangement of all parking spaces in the parking lot, such as which parking spaces are distributed along the main passage and which parking spaces are concentrated on the inner side of the parking lot, making the overall layout of the parking spaces clear at a glance.

[0036] Then, based on the determined parking space coordinates, status markers are added to the preliminary parking space distribution map. According to preset rules, if the occupancy status information corresponding to the parking space code parameter is "occupied", a solid red dot is marked at the corresponding position on the distribution map. If it is "vacant", a hollow green dot is marked. For example, if the status of parking space C0101 corresponding to coordinates (X10, Y5) is "vacant", a hollow green dot is marked at the corresponding position on the distribution map. In this way, the status marking of all parking spaces is completed, and a parking space status distribution map that can intuitively distinguish the occupancy status of parking spaces is obtained.

[0037] Finally, the parking space distribution map is supplemented with geographic information elements such as parking lot boundaries, passages, and entrances / exits. Boundary elements are drawn according to the actual perimeter of the parking lot's walls or fences. Passage elements indicate the width and direction of the main and secondary passages. Entrance / exit elements clearly indicate the specific location of vehicles entering and exiting and mark arrows indicating the direction. For example, a 5-meter-wide main entrance / exit is marked on the right side of the distribution map, with a blue arrow indicating the direction of vehicle entry, and a 3-meter-wide secondary entrance / exit is marked on the left side, indicating the direction of exit. By integrating these geographic information elements, a complete electronic map of parking space distribution is finally formed, which includes the location and status information of parking spaces and clearly shows the overall geographical layout of the parking lot.

[0038] In a specific embodiment, the electronic map of parking space distribution is divided into regions to obtain parking areas with locational relationships, including: Identify the features and connections of the aisle lines in the electronic map of parking space distribution to obtain aisle location connection information, and mark the boundaries of the aisle location connection information to obtain an aisle boundary map; A channel association analysis is performed on the parking spaces in the channel boundary map. By determining the distance and relative position relationship between the parking space and the channel, the parking space is assigned to the area associated with the nearest channel, thus obtaining parking space association area data. The parking space associated area data is aggregated to merge the areas associated with adjacent parking spaces that have similar characteristics, thus obtaining a preliminary parking area. The adjacency relationships between the areas in the initial parking area are determined to identify the adjacency relationships between the areas. Based on the adjacency relationships, arrows or lines are used on the electronic map of parking space distribution to represent the spatial relationships between the areas, thus obtaining a parking area distribution map with positional relationships.

[0039] Specifically, the process begins by using image recognition technology to capture straight or broken lines of uniform width without parking space markers on the electronic map of parking spaces. These lines represent the characteristics of the passageways. Simultaneously, the intersections and connections between different passageways are recorded to form passageway location connection information. For example, a main passageway, 4 meters wide and running east-west, is identified on the map, along with three secondary passageways, each 2.5 meters wide and perpendicularly connected to the main passageway. After clarifying the connection nodes between the main passageway and each secondary passageway, the edge contours of all passageways are marked on the map with dashed lines, resulting in a passageway boundary map. Next, for each parking space on the passageway boundary map, its straight-line distance to each passageway is measured, and its orientation relative to the passageway is determined. For instance, if a parking space is 3 meters from the north secondary passageway and 8 meters from the main passageway, it is assigned to the area associated with the north secondary passageway. After all parking spaces have undergone this determination, parking space association area data, including the area to which each parking space belongs, is generated. Next, the parking space association area data is examined, and adjacent areas with the same passage type are merged. For example, the areas associated with two adjacent secondary passages on the west side are merged into one large area because both passages are secondary and the parking spaces are arranged in the same way, forming a preliminary parking area. Finally, the edges of each area in the preliminary parking area are checked one by one to see if there are any overlapping or adjacent boundaries. If two areas share a passage boundary, they are determined to be adjacent. Then, on the electronic map of parking space distribution, orange arrows are used to point from one area to the adjacent area, with the arrow direction set along the passage. For example, an arrow is drawn from the merged western area to the area associated with the main passage, thus indicating the spatial relationship between the areas, and finally, a parking area distribution map with positional relationships is obtained.

[0040] In a specific embodiment, calculating the parking turnover rate of the parking area based on the vehicle entry and exit records includes: The vehicle entry and exit records are matched for regional affiliation. By analyzing the license plate information and entry and exit time in the vehicle entry and exit records, and combining them with the electronic map of parking space distribution, regional vehicle entry and exit data is obtained. Based on the vehicle entry and exit data of the area, the turnover rate of each parking area is analyzed. The parking turnover rate is calculated by dividing the cumulative number of vehicles entering and leaving in each time period by the total number of parking spaces in the corresponding parking area.

[0041] Specifically, first, perform regional attribution matching on the collected vehicle access records. First, extract the license plate information and access time from each record. For example, a certain record shows that the license plate is "粤B8X219", the entry time is 9:15 am, and the exit time is 11:40 am. Then, retrieve the electronic map of the parking space distribution. Through the parking space code corresponding to the parked vehicle with this license plate recorded in the map, determine the parking area to which this parking space belongs. Assuming that the parking space code corresponds to Parking Area 2, then bind this vehicle access record to Parking Area 2. In the same way, match all vehicle access records with the corresponding parking areas respectively, and finally summarize and form regional vehicle access data containing the vehicle entry and exit information of each parking area. For example, there are 35 vehicle access records in Parking Area 1 from 9 am to 12 pm, and 28 records in Parking Area 2 during the same period, etc.

[0042] After that, based on the obtained regional vehicle access data, perform turnover analysis on each parking area. First, determine the statistical time period. For example, taking 1 hour as a statistical unit, respectively count the number of vehicles entering and leaving each parking area within each time period. For example, in Parking Area 1 from 10 am to 11 am, 12 vehicles enter and 10 vehicles leave, and the total cumulative vehicle flow volume within this time period is 22 vehicles (the sum of the number of entering and leaving vehicles). Then, query the total number of parking spaces in Parking Area 1. Assuming that there are 15 parking spaces in this area, and then divide the total cumulative vehicle flow volume by the total number of parking spaces, that is, 22÷15≈1.47, to obtain the parking turnover rate of Parking Area 1 from 10 am to 11 am; according to the same calculation method, respectively count the vehicle flow volume and the total number of parking spaces in other time periods and other parking areas, and calculate the parking turnover rate of each parking area in different time periods in turn. For example, Parking Area 2 has 12 parking spaces, 8 vehicles enter and 7 vehicles leave from 10 am to 11 am, with a cumulative of 15 vehicles, and its parking turnover rate is 15÷12 = 1.25. Through such a calculation process, complete the statistics of the parking turnover rates of all parking areas.

[0043] In a specific embodiment, based on the positional relationship of the parking areas and the above-mentioned parking turnover rate, analyze the traffic status of the internal channels of the parking lot, including: Perform channel connection analysis on the positional relationship of the parking areas. By calculating the channel length and width parameters between adjacent areas of the parking areas, obtain channel capacity data, and based on the channel capacity data, evaluate the traffic capacity of each channel to obtain the channel capacity threshold; Calculate the traffic flow density of the internal channels of the parking lot based on the above-mentioned parking turnover rate, and perform time period fluctuation analysis on the traffic flow density to obtain the traffic flow fluctuation trend; Determine the traffic status of the internal channels of the parking lot based on the channel capacity threshold and the traffic flow fluctuation trend.

[0044] Specifically, the first step is to analyze the location relationships of parking areas through a channel connection analysis. On a map showing the distribution of parking areas with location relationships, the channels between adjacent parking areas are identified. The actual length and width of these channels are measured using measuring tools. For example, the channel between parking area 1 and parking area 2 is measured to be 50 meters long and 4 meters wide. These data are recorded to form channel capacity data. Next, the traffic capacity is assessed based on this data. Referring to the standard of vehicles that can pass per meter of channel width per hour (e.g., approximately 100 vehicles per meter of channel width per hour), the theoretical traffic volume per hour for that channel is calculated based on the channel width. Then, considering the space required for vehicle movement based on the channel length, the theoretical traffic volume is adjusted to obtain the final channel capacity threshold. For example, a 50-meter long and 4-meter wide channel has a theoretical traffic volume of 4 × 100 = 400 vehicles / hour. After adjusting for the length factor, the channel capacity threshold is determined to be 380 vehicles / hour.

[0045] Then, based on the parking turnover rate, the traffic density of the parking lot's internal passages is calculated. First, the number of vehicles leaving each parking area within a unit of time (e.g., 1 hour) is counted. These leaving vehicles will enter adjacent passages. For example, if 200 vehicles leave area 1 in 1 hour and 180 vehicles leave area 2 in 1 hour, the total traffic flow in the shared passage is 380 vehicles. Then, the total traffic flow is divided by the passage length to obtain the traffic density (380 vehicles ÷ 50 meters = 7.6 vehicles / meter). After that, it is calculated in 15-minute intervals. The traffic density of the channel is calculated for each time period, for example, 6.2 vehicles / meter from 9:00 to 9:15, 7.8 vehicles / meter from 9:15 to 9:30, 8.1 vehicles / meter from 9:30 to 9:45, and 7.5 vehicles / meter from 9:45 to 10:00. These data are arranged in chronological order and their changes are observed to obtain the traffic flow fluctuation trend. For example, the traffic density of the channel reaches its peak from 9:30 to 9:45, and the overall trend shows a fluctuation of first rising and then falling.

[0046] Finally, the previously obtained channel capacity threshold is combined with the traffic flow fluctuation trend. If the actual traffic volume corresponding to the traffic density in a certain period is close to or exceeds the channel capacity threshold, and the traffic flow fluctuation trend indicates that this state will continue, the channel traffic status is determined to be "congested". If the actual traffic volume corresponding to the traffic density is much lower than the channel capacity threshold, and the fluctuation trend is stable, it is determined to be "smooth". For example, if the channel capacity threshold is 380 vehicles / hour, and the actual traffic volume corresponding to the traffic flow in the period from 9:30 to 9:45 is about 384 vehicles / hour, which is close to the threshold and has been on an upward trend in the previous period, the channel traffic status in that period is determined to be "congested".

[0047] In a specific embodiment, the traffic status is uploaded to the corresponding platform to guide vehicles by area, including: The traffic status is used to calculate the channel guidance weight, and the guidance weight value of each channel is obtained. Based on the guidance weight value, the accessibility of the parking area is analyzed to obtain the area guidance priority. Based on the area guidance priority, vehicles entering the parking lot are assigned target areas to obtain a vehicle zoning guidance scheme, which is then uploaded to the corresponding platform to guide vehicles by area.

[0048] Specifically, the guidance weight of each lane is first calculated based on its traffic status. If the traffic status of a lane is "smooth," it means that the current traffic pressure in that lane is low, so a higher guidance weight value is set. For example, the guidance weight value of a main lane with a traffic efficiency of over 90% is set to 0.8. If the traffic status of a lane is "slow," with a traffic efficiency between 60% and 90%, the guidance weight value is set to 0.5. If the traffic status of a lane is "congested," with a traffic efficiency below 60%, the guidance weight value is set to 0.2. The guidance weight value of each lane is obtained in this way. Next, based on these guidance weight values, an accessibility analysis is performed on the parking areas to determine which routes can reach each parking area. Then, the average guidance weight values ​​of each route leading to the area are taken. The higher the average value, the easier the area is to reach, and the higher the area guidance priority. For example, parking area 3 can be reached through the main route with a guidance weight value of 0.8 and the secondary route with a guidance weight value of 0.5, with an average weight value of 0.65. Parking area 4 can only be reached through the congested route with a guidance weight value of 0.2, with an average weight value of 0.2. Therefore, the area guidance priority of parking area 3 is higher than that of parking area 4.

[0049] Then, based on the area guidance priority, vehicles entering the parking lot are assigned to target areas. When a vehicle is identified at the parking lot entrance, the system first queries the number of available parking spaces in each parking area, and then allocates them according to the area guidance priority. For example, at 10:00 AM, parking area 1 (guidance priority 0.7, 8 available parking spaces), parking area 2 (guidance priority 0.6, 5 available parking spaces), and parking area 5 (guidance priority 0.3, 10 available parking spaces) all have available parking spaces. At this time, the vehicle is given priority to be assigned to parking area 1, which has a higher guidance priority. If there are not enough available parking spaces in area 1, the vehicle is then assigned to area 2 and then area 5 in turn. The target parking area is determined for each entering vehicle according to this rule, forming a vehicle zoning guidance scheme. Finally, based on the vehicle zoning guidance scheme, vehicles are guided to different zones. The target zone and recommended driving route for each vehicle are displayed on the guidance screen at the parking lot entrance. At the same time, the guidance information is updated on the passage signs inside the parking lot. For example, for vehicles assigned to Zone 1, the message "Go straight for 300 meters along the main passage and turn left to enter the Zone 1 entrance" is displayed, and for vehicles assigned to Zone 2, the message "Go straight for 200 meters along the main passage and turn right to enter the Zone 2 entrance" is displayed. This ensures that vehicles can accurately go to the target parking area according to the guidance information and complete the zoned guidance operation.

[0050] The urban smart parking management method in the embodiments of the present invention has been described above. The urban smart parking management system in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the smart parking management system for cities in this invention includes: The acquisition module 21 is used to acquire video surveillance data in the parking lot and perform parking space occupancy identification on the video surveillance data to obtain parking space occupancy status information. The mapping module 22 is used to map the parking space distribution of the parking lot based on the occupancy status information to obtain an electronic map of parking space distribution, and to divide the electronic map of parking space distribution into areas to obtain parking areas with location relationships. The recording module 23 is used to collect vehicle entry and exit records of vehicles entering and exiting the parking lot through the license plate recognition device, and to calculate the parking turnover rate of the parking area based on the vehicle entry and exit records; Analysis module 24 is used to analyze the traffic status of the internal passages of the parking lot based on the location relationship of the parking areas and the parking turnover rate, and upload the traffic status to the corresponding platform to guide vehicles by area.

[0051] In this embodiment, the specific implementation of each unit in the above system embodiment is the same as that in the above method embodiment, and will not be repeated here.

[0052] like Figure 3As shown in the diagram, this embodiment of the invention provides a structural schematic block diagram of a computer device, including: At least one processor; At least one memory for storing at least one program; When at least one program is executed by at least one processor, the at least one processor implements the above-described smart parking management method for cities.

[0053] It is evident that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0054] Furthermore, this application also discloses a computer program product or computer program stored in a computer-readable storage medium. The processor of a computer device can read the computer program from the computer-readable storage medium and execute the computer program, causing the computer device to perform the aforementioned smart parking management method. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0055] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A smart urban parking management system, characterized in that, include: The acquisition module is used to acquire video surveillance data in the parking lot and to identify parking space occupancy from the video surveillance data to obtain parking space occupancy status information. The mapping module is used to map the parking space distribution of the parking lot based on the occupancy status information to obtain an electronic map of parking space distribution, and to divide the electronic map of parking space distribution into areas to obtain parking areas with location relationships. The recording module is used to collect vehicle entry and exit records of the parking lot through license plate recognition equipment, and to calculate the parking turnover rate of the parking area based on the vehicle entry and exit records; The analysis module is used to analyze the traffic status of the internal passages of the parking lot based on the location relationship of the parking areas and the parking turnover rate, and upload the traffic status to the corresponding platform to guide vehicles by area.

2. A smart parking management method for cities, used to execute the smart parking management system of claim 1, comprising: Collect video surveillance data in the parking lot, and identify parking space occupancy from the video surveillance data to obtain parking space occupancy status information; Based on the occupancy status information, the parking lot is mapped to obtain an electronic map of parking space distribution. The electronic map of parking space distribution is then divided into regions to obtain parking areas with location relationships. The system collects vehicle entry and exit records of vehicles entering and exiting the parking lot using license plate recognition equipment, and calculates the parking turnover rate of the parking area based on these records. The system analyzes the traffic status of the internal passageways of the parking lot based on the location relationship of the parking areas and the parking turnover rate, and uploads the traffic status to the corresponding platform to guide vehicles by area.

3. The urban smart parking management method according to claim 2, characterized in that, The video surveillance data is used to identify parking space occupancy to obtain parking space occupancy status information, including: The parking space images in the video surveillance data are extracted frame by frame. The parking space images are images of each parking space at different times. The parking space images are then processed to grayscale to obtain grayscale parking space images. Parking space occupancy is identified based on the grayscale parking space image to obtain parking space occupancy status information.

4. The urban intelligent parking management method according to claim 2, characterized in that, Based on the occupancy status information, the parking lot is mapped to obtain an electronic map of parking space distribution, including: The occupancy status information is associated with parking space codes, and the occupancy status information of each parking space is bound to a corresponding unique preset parking space code to obtain parking space code parameters, and the parking space coordinates are determined based on the parking space code parameters; The spatial arrangement of all parking spaces in the parking lot is displayed in advance to obtain a preliminary parking space distribution map; The parking space status map is marked with status indicators based on the parking space coordinates. According to the parking space coordinates corresponding to the occupied or vacant status of the parking space, the parking spaces on the preliminary parking space distribution map are marked with corresponding colors or symbols to obtain the parking space status distribution map. Add geographic information elements such as parking lot boundaries, passages, and entrances / exits to the parking space distribution map to obtain an electronic map of parking space distribution.

5. The urban intelligent parking management method according to claim 4, characterized in that, The electronic map of parking space distribution is divided into regions to obtain parking areas with locational relationships, including: Identify the features and connections of the aisle lines in the electronic map of parking space distribution to obtain aisle location connection information, and mark the boundaries of the aisle location connection information to obtain an aisle boundary map; A channel association analysis is performed on the parking spaces in the channel boundary map. By determining the distance and relative position relationship between the parking space and the channel, the parking space is assigned to the area associated with the nearest channel, thus obtaining parking space association area data. The parking space associated area data is aggregated to merge the areas associated with adjacent parking spaces that have similar characteristics, thus obtaining a preliminary parking area. The adjacency relationships between the areas in the initial parking area are determined to identify the adjacency relationships between the areas. Based on the adjacency relationships, arrows or lines are used on the electronic map of parking space distribution to represent the spatial relationships between the areas, thus obtaining a parking area distribution map with positional relationships.

6. The urban intelligent parking management method according to claim 4, characterized in that, Based on the vehicle entry and exit records, the parking turnover rate of the parking area is calculated, including: The vehicle entry and exit records are matched for regional affiliation. By analyzing the license plate information and entry and exit time in the vehicle entry and exit records, and combining them with the electronic map of parking space distribution, regional vehicle entry and exit data is obtained. Based on the vehicle entry and exit data of the area, the turnover rate of each parking area is analyzed. The parking turnover rate is calculated by dividing the cumulative number of vehicles entering and leaving in each time period by the total number of parking spaces in the corresponding parking area.

7. The urban intelligent parking management method according to claim 2, characterized in that, Based on the location relationships of parking areas and the parking turnover rate, the traffic status of the internal passageways of the parking lot is analyzed, including: The location relationship of parking areas is analyzed by channel connection analysis. By calculating the channel length and width parameters between adjacent areas, channel capacity data is obtained. Based on the channel capacity data, the traffic capacity of each channel is evaluated to obtain the channel capacity threshold. The vehicle flow density of the parking lot’s internal passage is calculated based on the parking turnover rate, and the vehicle flow density is subjected to time-period fluctuation analysis to obtain the vehicle flow fluctuation trend. The passage capacity threshold and the traffic flow fluctuation trend are used to determine the traffic status of the parking lot's internal passages.

8. The urban intelligent parking management method according to claim 2, characterized in that, The traffic status is uploaded to the corresponding platform to guide vehicles by area, including: The traffic status is used to calculate the channel guidance weight, and the guidance weight value of each channel is obtained. Based on the guidance weight value, the accessibility of the parking area is analyzed to obtain the area guidance priority. Based on the area guidance priority, vehicles entering the parking lot are assigned target areas to obtain a vehicle zoning guidance scheme, which is then uploaded to the corresponding platform to guide vehicles by area.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, The processor executes the steps of any one of claims 2 to 8 when executing a computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When a computer program is executed by a processor, it implements the steps of the method of any one of claims 2 to 8.

Images