CN116884266A - Smart city parking management method and management system based on big data - Google Patents
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/148—Management of a network of parking areas
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- G—PHYSICS
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Abstract
The invention discloses a smart city parking management method and a management system based on big data, and relates to the technical field of city parking management.A driver can acquire real-time parking space information and a navigation route by monitoring parking space data in real time and analyzing the parking space data to acquire a parking space utilization rate Ly and a street congestion index Yd and matching with an intelligent navigation system, so that the time and trouble for searching for a parking space are reduced, convenient parking service and navigation guidance are provided, and the parking experience and satisfaction of the driver can be improved; abnormal vehicles in the image, such as electric vehicles, bicycles and the like, can be identified through the target detection model and the texture feature extraction; through dislocation sign and timely take measures, can improve the effect of parking management, reduce the emergence of improper parking action, improve the utilization condition in parking stall, promote whole parking management level.
Description
Technical Field
The invention relates to the technical field of urban parking management, in particular to a smart urban parking management method and system based on big data.
Background
Smart city parking management is a process in which pointers plan, organize, and implement a series of measures and policies for parking requirements and parking management problems within a city. Urban parking management aims to reasonably utilize limited parking resources, provide convenient parking service, reduce parking time and congestion, and improve urban traffic mobility and resident life quality.
The existing intelligent parking management method based on big data is generally used for managing a fixed parking lot in the intelligent city process, the parking lot is convenient for placing various parking marks and is guided by management work, however, the intelligent parking management method is not applicable to the street, the street situation is complex, the area is large, staff is employed to guide the driver to park, the difficulty is large, the occupancy of a parking space on the road surface is not systematically recommended, more time is wasted in the process of blindly searching the parking space by some drivers, and even traffic jam is caused due to parking difficulty.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a smart city parking management method and a management system based on big data, which can quickly find available parking spaces by monitoring the parking space data and intelligent navigation in real time, reduce the time for searching the parking spaces, thereby reducing the traffic jam degree, acquire the parking space utilization rate Ly and the street jam index Yd by analyzing the parking data, and cooperate with the intelligent navigation system, so that a driver can acquire real-time parking space information and navigation routes, quickly find nearby available parking spaces, reduce the time and trouble for searching the parking spaces, provide convenient parking service and navigation guidance, and improve the parking experience and satisfaction of the driver; abnormal vehicles in the image, such as electric vehicles, bicycles and the like, can be identified through the target detection model and the texture feature extraction; through dislocation sign and timely take measures, can improve the effect of parking management, reduce the emergence of improper parking action, improve the utilization condition in parking stall, promote whole parking management level.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a smart city parking management method based on big data comprises the following steps:
s1, a sensor network is established on an urban street, the sensor network comprises a parking space detector and camera equipment, and real-time monitoring parking space data is collected;
s2, processing and analyzing the collected real-time monitored parking space data through a big data analysis technology to obtain a parking space utilization rate Ly and a street congestion index Yd;
s3, acquiring the real-time GPS position of a target vehicle owner, calculating to obtain a parking space close to the GPS position of the target vehicle owner according to the parking space utilization rate Ly and the street congestion index Yd, determining at least three parking spaces close to the GPS position of the target vehicle owner, and acquiring the latest parking space information and navigation route through a navigation system;
three parking stall distances that are close to the GPS position of the target car owner include first parking stall, second parking stall and third parking stall, first parking stall is less than 50 meters from the GPS position of the target car owner in, first parking stall sets up to be 50 meters to 80 meters from the GPS position distance of the target car owner in, third parking stall sets up to be greater than 80 meters from the GPS position distance of the target car owner, sets up first parking stall as first priority, and under the condition that first parking stall is not discerned, automatic recommendation second parking stall is second priority under the condition that first parking stall and second parking stall are not discerned, automatic recommendation third parking stall is third priority.
Preferably, the step S1 specifically includes:
s11, determining a street area to be monitored, planning and designing a sensor network layout, and setting the installation position and the camera position of a parking space sensor by considering the length of a street and the distribution of parking spaces;
the parking space sensor is arranged as one or more of a geomagnetic sensor, a pressure sensor or a photoelectric sensor;
s12, installing the planned layout, namely burying a parking space sensor into the ground or installing the sensor above a parking space representation, and installing a camera on a street lamp post position to ensure that equipment is stably installed and the state of the parking space and the flow of vehicles can be monitored;
and S13, connecting the parking space sensor equipment and the camera to a central server or a cloud platform, and carrying out data transmission and storage to acquire real-time monitoring parking space data.
Preferably, the step S2 specifically includes:
s21, acquiring real-time monitoring parking space data, wherein the real-time monitoring parking space data comprises image data and sensor data, and performing data processing on the sensor data, including data cleaning, anomaly monitoring and data aggregation;
preprocessing image data, including adjusting the image size, cropping and removing noise;
s22, extracting features of the image data and the sensor data, extracting edge information in the street image by using an edge detection algorithm, analyzing the road structure and the vehicle position, and calculating to obtain the parking space utilization Ly and the street congestion index Yd.
Preferably, the parking space utilization Ly and the street congestion index Yd are obtained by the following formula:
wherein: zy is expressed as the number of occupied parking spaces, ztc is expressed as the total number of parking spaces, cl is expressed as the vehicle flow, and T is expressed as the average time for a vehicle to park into a parking space; alpha represents a correction constant.
Preferably, the distance between the parking spaces is calculated, and for each parking space on the street surface, the distance between the parking space and the GPS position of the target vehicle owner is calculated, and the Euclidean distance measurement method is used for calculating the distance;
after the parking space utilization rate Ly and the street congestion index Yd are subjected to normalization processing, a comprehensive index I is calculated to measure the priority of the parking space, and a specific calculation formula can be obtained according to actual requirements:
I＝w1*(1-Ly)+w2*(1-Yd)
wherein: i is identified as a comprehensive evaluation value and is used for measuring and sequencing the priority and the applicability of different parking spaces; w1 and w2 are weights for adjusting the relative importance of the parking space utilization and the street congestion index in the comprehensive index; the influence degree of different factors on the comprehensive index is controlled by adjusting the weight;
under a given condition, the comprehensive index I is calculated according to the parking space utilization rate Ly and the street congestion index Yd; by defining a proper calculation formula and weight, the comprehensive index can comprehensively consider the two factors and provide an evaluation value for each parking space.
Preferably, according to the distance between parking spaces and the comprehensive index, three parking spaces closest to the GPS position of the target vehicle owner and highest in comprehensive index are selected as nearby parking spaces;
the parking space distance and the GPS position of the target car owner are set to be less than 50 meters, and are set to be a first priority and recommended to be a first parking space;
50. setting the distance between the parking space and the GPS position of the target car owner to be equal to or less than 80 meters as a second priority, and recommending the distance to be a second parking space;
setting the distance between the parking space and the GPS position of the target car owner as a third priority level which is recommended as a third parking space, wherein the distance between the parking space and the GPS position of the target car owner is more than 80 meters;
and recommending the target car owner by the navigation system according to the first priority, the second priority and the third priority.
Preferably, the step S21 is configured to extract texture features in the road image by a texture analysis method, set to one of a local binary pattern LBP or a gray level co-occurrence matrix GLCM, extract texture features in the road image, and analyze the condition of a street road surface parking space:
and (3) monitoring and classifying the image targets, establishing a target detection model, identifying and classifying vehicles in the image, identifying different types of vehicles through training the model, and carrying out dislocation identification on abnormal conditions in the image according to requirements when detecting and identifying the conditions of the electric vehicle and the bicycle in a parking space area of the motor vehicle, and adding a frame and a label on the image to identify improper parking.
Preferably, the result of the dislocation identification is transmitted to a cloud platform, and the cloud platform sends a notice, an alarm or a report to related personnel so as to take necessary measures in time;
meanwhile, the cloud platform sends feedback information to the parking management system to update and improve the result of the dislocation mark.
A smart city parking management system based on big data comprises a sensor monitoring module, wherein a sensor network is established on a parking lot and a street in a city, and the use condition of the parking lot, the vehicle flow and the stay time data are monitored and collected in real time by installing a parking space detector, a camera and other devices;
the data storage module is used for transmitting the collected data in the real-time monitoring module to the central server through wireless transmission or wired network for real-time processing and storage; the monitoring data comprise parking space state, vehicle flow, residence time and payment information;
and a data analysis module: processing and analyzing the collected data by utilizing a big data analysis technology, extracting useful information, and calculating to obtain the utilization rate Ly of a parking space and the congestion index Yd of a street;
and a real-time navigation module: providing real-time parking space navigation and recommendation for a target car owner driver through smart phone application and a navigation system;
and a prediction module: based on the parking space utilization rate Ly and the street congestion index Yd, calculating to obtain a first parking space, a second parking space and a third parking space, and according to the analysis result, indicating a target vehicle owner driver to go to the idle parking space or reserve the parking space by the system, so that the time for searching the parking space and the congestion condition are reduced;
and the cloud platform is used for storing the data of the modules.
(III) beneficial effects
The invention provides a smart city parking management method and system based on big data. The beneficial effects are as follows:
(1) According to the smart city parking management method and system based on big data, through real-time monitoring of parking space data and intelligent navigation, a driver can quickly find available parking spaces, and time for searching the parking spaces is reduced, so that the traffic jam degree is reduced, the utilization condition of the parking spaces can be known through analysis of the parking data, allocation and scheduling of the parking spaces are optimized, and the utilization efficiency of parking resources is improved; S1-S3, a driver can acquire real-time parking space information and navigation routes, quickly find nearby available parking spaces, reduce the time and trouble of finding the parking spaces, provide convenient parking service and navigation guidance, and improve the parking experience and satisfaction of the driver.
(2) According to the smart city parking management method and system based on big data, the utilization rate Ly of the parking space is calculated, the utilization condition of the parking space is reflected, the utilization rate of parking resources and the supply and demand balance condition of the parking space are helped to be evaluated, the distribution and management of the parking resources can be optimized through the evaluation of the utilization rate Ly of the parking space, the supply and demand of the parking space are reasonably planned, and the utilization rate of the parking resources is improved; the street congestion index Yd evaluates the traffic congestion degree of the street according to the traffic flow and the number of parking spaces, provides a reference for traffic planning and management, and can monitor and regulate the traffic flow according to the evaluation of the street congestion index Yd so as to reduce the traffic congestion and improve the road traffic capacity; by evaluating and analyzing the parking space utilization Ly and the street congestion index Yd, guidance and support are provided for street parking management and traffic optimization.
(3) According to the smart city parking management method and system based on big data, according to the calculation of the parking space distance and the comprehensive index, the parking space closest to the GPS position of the target vehicle owner and highest in comprehensive index can be selected, and the service of nearby parking selection is provided; through calculation of comprehensive indexes, the parking space utilization rate Ly and the street congestion index Yd are comprehensively considered, a user is helped to select the most suitable parking space, and parking experience and efficiency are improved; according to the priority order of the parking spaces, the parking spaces with the closer distances are recommended to be set to be the first priority, which means that a target vehicle owner has higher possibility of finding available parking spaces in a shorter time, and therefore the trouble of parking time and loitering to find the parking spaces is reduced.
(4) According to the smart city parking management method and system based on big data, abnormal vehicles in images, such as electric vehicles and bicycles, can be identified through the target detection model and texture feature extraction. The method is helpful for finding and identifying improper parking behaviors and improving the reasonable utilization of the parking space; the improper parking condition is marked in a dislocation manner on the image, and the abnormal condition can be intuitively displayed by adding the frame and the label, so that related personnel are reminded to take corresponding measures; the result of the dislocation identification is transmitted to related personnel through the cloud platform, and parking management personnel or law enforcement departments can be timely informed to prompt the personnel to take necessary actions. Meanwhile, feedback information is sent to the parking management system, so that the result of the dislocation mark can be optimized and improved continuously, and the accuracy and effect of the system are improved. Through dislocation sign and timely take measures, can improve the effect of parking management, reduce the emergence of improper parking action, improve the utilization condition in parking stall, promote whole parking management level.
Drawings
FIG. 1 is a schematic diagram of steps of a smart city parking management method based on big data according to the present invention;
FIG. 2 is a schematic diagram of a smart city parking management system based on big data according to the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Smart city parking management is a process in which pointers plan, organize, and implement a series of measures and policies for parking requirements and parking management problems within a city. Urban parking management aims to reasonably utilize limited parking resources, provide convenient parking service, reduce parking time and congestion, and improve urban traffic mobility and resident life quality.
The existing intelligent parking management method based on big data is generally used for managing a fixed parking lot in the intelligent city process, the parking lot is convenient for placing various parking marks and is guided by management work, however, the intelligent parking management method is not applicable to the street, the street situation is complex, the area is large, staff is employed to guide the driver to park, the difficulty is large, the occupancy of a parking space on the road surface is not systematically recommended, more time is wasted in the process of blindly searching the parking space by some drivers, and even traffic jam is caused due to parking difficulty.
The invention provides a smart city parking management method based on big data, referring to fig. 1, comprising the following steps:
s1, a sensor network is established on an urban street, the sensor network comprises a parking space detector and camera equipment, and real-time monitoring parking space data is collected;
s2, processing and analyzing the collected real-time monitored parking space data through a big data analysis technology to obtain a parking space utilization rate Ly and a street congestion index Yd;
s3, acquiring the real-time GPS position of a target vehicle owner, calculating to obtain a parking space close to the GPS position of the target vehicle owner according to the parking space utilization rate Ly and the street congestion index Yd, determining at least three parking spaces close to the GPS position of the target vehicle owner, and acquiring the latest parking space information and navigation route through a navigation system;
three parking stall distances that are close to the GPS position of the target car owner include first parking stall, second parking stall and third parking stall, first parking stall is less than 50 meters from the GPS position of the target car owner in, first parking stall sets up to be 50 meters to 80 meters from the GPS position distance of the target car owner in, third parking stall sets up to be greater than 80 meters from the GPS position distance of the target car owner, sets up first parking stall as first priority, and under the condition that first parking stall is not discerned, automatic recommendation second parking stall is second priority under the condition that first parking stall and second parking stall are not discerned, automatic recommendation third parking stall is third priority.
In this embodiment, through real-time monitoring parking space data and intelligent navigation, the driver can find available parking space fast, reduce the time on seeking the parking space to reduce the traffic jam degree, through analysis parking data, obtain parking space utilization Ly and street congestion index Yd, and cooperate intelligent navigation system, make the driver can obtain real-time parking space information and navigation route, find available parking space nearby fast, reduce the time and the puzzlement of seeking the parking space, provide convenient parking service and navigation guidance, can improve driver's parking experience and satisfaction.
Example 2
The present embodiment is an explanation made in embodiment 1, specifically, the step S1 specifically includes:
s11, determining a street area to be monitored, planning and designing a sensor network layout, and setting the installation position and the camera position of a parking space sensor by considering the length of a street and the distribution of parking spaces;
the parking space sensor is arranged as one or more of a geomagnetic sensor, a pressure sensor or a photoelectric sensor;
s12, installing the planned layout, namely burying a parking space sensor into the ground or installing the sensor above a parking space representation, and installing a camera on a street lamp post position to ensure that equipment is stably installed and the state of the parking space and the flow of vehicles can be monitored;
and S13, connecting the parking space sensor equipment and the camera to a central server or a cloud platform, and carrying out data transmission and storage to acquire real-time monitoring parking space data.
In this embodiment, real-time parking space data including information such as occupancy, idle state and vehicle flow of a parking space can be obtained through monitoring of a parking space sensor and a camera, and analysis and evaluation of the utilization rate of the parking space can be performed based on the collected real-time parking space data. The utilization condition of the parking spaces can be known by analyzing the utilization condition of the parking spaces, the allocation and management of the parking spaces are optimized, and the utilization efficiency of parking resources is improved.
Example 3
The present embodiment is the explanation in embodiment 1, specifically, the step S2 specifically includes:
s21, acquiring real-time monitoring parking space data, wherein the real-time monitoring parking space data comprises image data and sensor data, and performing data processing on the sensor data, including data cleaning, anomaly monitoring and data aggregation; the parking space data monitored in real time are cleaned and aggregated through data processing, so that accurate parking space utilization conditions and vehicle occupation states can be obtained;
preprocessing image data, including adjusting the image size, cropping and removing noise; and the accuracy and effect of the subsequent feature extraction are improved.
S22, extracting features of the image data and the sensor data, extracting edge information in the street image by using an edge detection algorithm, analyzing the road structure and the vehicle position, and calculating to obtain the parking space utilization Ly and the street congestion index Yd. The parking space utilization Ly may be calculated from the number of occupied parking spaces and the total number of parking spaces. The street congestion index Yd can be calculated according to the data of the vehicle flow, the stay time and the like, and is used for evaluating the congestion degree of the street.
The embodiment can extract useful characteristic information from the real-time monitoring data, and support and guide are provided for subsequent parking space selection and traffic management decision.
Example 4
The present embodiment is explained in embodiment 3, specifically, the parking space utilization Ly and the street congestion index Yd are obtained by the following formula:
wherein: zy is expressed as the number of occupied parking spaces, ztc is expressed as the total number of parking spaces, cl is expressed as the vehicle flow, and T is expressed as the average time for a vehicle to park into a parking space; alpha represents a correction constant.
In the embodiment, the utilization rate Ly of the parking space is calculated, so that the utilization condition of the parking space is reflected, the utilization rate of the parking resource and the supply and demand balance condition of the parking space are helped to be evaluated, the distribution and management of the parking resource can be optimized through the evaluation of the utilization rate Ly of the parking space, the supply and demand of the parking space are reasonably planned, and the utilization rate of the parking resource is improved; the street congestion index Yd evaluates the traffic congestion degree of the street according to the traffic flow and the number of parking spaces, provides a reference for traffic planning and management, and can monitor and regulate the traffic flow according to the evaluation of the street congestion index Yd so as to reduce the traffic congestion and improve the road traffic capacity; by evaluating and analyzing the parking space utilization Ly and the street congestion index Yd, guidance and support are provided for street parking management and traffic optimization.
Example 5
The present embodiment is the explanation made in embodiment 4, specifically, the distance of the parking spaces is calculated, the distance between each parking space on the road surface of the street and the GPS position of the target vehicle owner is calculated, and the euclidean distance measuring method is used to calculate the distance; for each parking space, the Euclidean distance metric method is used to calculate its distance from the GPS location of the target vehicle owner. Assuming that the GPS position of the target car owner is (x_target, y_target), and the GPS position of the parking space is (x_parking, y_parking), the parking space distance calculation formula is: distance=sqrt ((x_target-x_working)/(2+ (y_target-y_working)/(2));
after the parking space utilization rate Ly and the street congestion index Yd are normalized, the comprehensive index I can be calculated through the parking space utilization rate Ly and the street congestion index Yd. Before calculation, ly and Yd are normalized, and the value ranges of Ly and Yd are mapped between [0,1 ]. The specific normalization method can be selected according to the actual requirements and the value range of specific indexes. The comprehensive index calculation formula is as follows: score=w1×ly_normalized+w2×yd_normalized; calculating a comprehensive index I to measure the priority of the parking space, wherein a specific calculation formula can be obtained according to actual requirements:
I＝w1*(1-Ly)+w2*(1-Yd)
wherein: i is identified as a comprehensive evaluation value and is used for measuring and sequencing the priority and the applicability of different parking spaces; w1 and w2 are weights for adjusting the relative importance of the parking space utilization and the street congestion index in the comprehensive index; the influence degree of different factors on the comprehensive index is controlled by adjusting the weight;
under a given condition, the comprehensive index I is calculated according to the parking space utilization rate Ly and the street congestion index Yd; by defining a proper calculation formula and weight, the comprehensive index can comprehensively consider the two factors and provide an evaluation value for each parking space.
According to the distance between parking spaces and the comprehensive index, selecting three parking spaces closest to the GPS position of the target vehicle owner and highest in comprehensive index as nearby parking spaces;
the parking space distance and the GPS position of the target car owner are set to be less than 50 meters, and are set to be a first priority and recommended to be a first parking space;
50. setting the distance between the parking space and the GPS position of the target car owner to be equal to or less than 80 meters as a second priority, and recommending the distance to be a second parking space;
setting the distance between the parking space and the GPS position of the target car owner as a third priority level which is recommended as a third parking space, wherein the distance between the parking space and the GPS position of the target car owner is more than 80 meters;
and recommending the target car owner by the navigation system according to the first priority, the second priority and the third priority.
In this embodiment, according to the calculation of the parking space distance and the comprehensive index, the parking space closest to the GPS position of the target vehicle owner and having the highest comprehensive index can be selected, and the service of nearby parking selection is provided; through calculation of comprehensive indexes, the parking space utilization rate Ly and the street congestion index Yd are comprehensively considered, a user is helped to select the most suitable parking space, and parking experience and efficiency are improved; according to the priority order of the parking spaces, the parking spaces with the closer distances are recommended to be set to be the first priority, which means that a target vehicle owner has higher possibility of finding available parking spaces in a shorter time, and therefore the trouble of parking time and loitering to find the parking spaces is reduced.
Example 6
The embodiment is explained in embodiment 3, specifically, the S21 extracts the texture feature in the road image by using a texture analysis method, sets the texture feature in the road image to be one of a local binary pattern LBP or a gray level co-occurrence matrix GLCM, and analyzes the condition of the street road surface parking space:
and (3) monitoring and classifying the image targets, establishing a target detection model, identifying and classifying vehicles in the image, identifying different types of vehicles through training the model, and carrying out dislocation identification on abnormal conditions in the image according to requirements when detecting and identifying the conditions of the electric vehicle and the bicycle in a parking space area of the motor vehicle, and adding a frame and a label on the image to identify improper parking.
The result of the dislocation identification is transmitted to a cloud platform, and the cloud platform sends a notice, an alarm or a report to related personnel so as to take necessary measures in time; the sending of notifications, alarms or reports may help take necessary actions in time, such as alerting or fines for improper parking activities, etc.;
meanwhile, the cloud platform sends feedback information to the parking management system to update and improve the result of the dislocation mark; this may facilitate continued optimization and improvement of the system, increasing the accuracy and effectiveness of parking management.
In this embodiment, by the object detection model and the texture feature extraction, an abnormal vehicle in an image, such as an electric vehicle, a bicycle, or the like, can be identified. The method is helpful for finding and identifying improper parking behaviors and improving the reasonable utilization of the parking space; the improper parking condition is marked in a dislocation manner on the image, and the abnormal condition can be intuitively displayed by adding the frame and the label, so that related personnel are reminded to take corresponding measures; the result of the dislocation identification is transmitted to related personnel through the cloud platform, and parking management personnel or law enforcement departments can be timely informed to prompt the personnel to take necessary actions. Meanwhile, feedback information is sent to the parking management system, so that the result of the dislocation mark can be optimized and improved continuously, and the accuracy and effect of the system are improved. Through dislocation sign and timely take measures, can improve the effect of parking management, reduce the emergence of improper parking action, improve the utilization condition in parking stall, promote whole parking management level.
Referring to fig. 2, the smart city parking management system based on big data comprises a sensor monitoring module, wherein a sensor network is established on a parking lot and a street in a city, and the use condition, the vehicle flow and the residence time data of a parking space are monitored and collected in real time by installing a parking space detector, a camera and other devices;
the data storage module is used for transmitting the collected data in the real-time monitoring module to the central server through wireless transmission or wired network for real-time processing and storage; the monitoring data comprise parking space state, vehicle flow, residence time and payment information;
and a data analysis module: processing and analyzing the collected data by utilizing a big data analysis technology, extracting useful information, and calculating to obtain the utilization rate Ly of a parking space and the congestion index Yd of a street;
and a real-time navigation module: providing real-time parking space navigation and recommendation for a target car owner driver through smart phone application and a navigation system;
and a prediction module: based on the parking space utilization rate Ly and the street congestion index Yd, calculating to obtain a first parking space, a second parking space and a third parking space, and according to the analysis result, indicating a target vehicle owner driver to go to the idle parking space or reserve the parking space by the system, so that the time for searching the parking space and the congestion condition are reduced;
and the cloud platform is used for storing the data of the modules.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A smart city parking management method based on big data is characterized in that: the method comprises the following steps:
s1, a sensor network is established on an urban street, the sensor network comprises a parking space detector and camera equipment, and real-time monitoring parking space data is collected;
s2, processing and analyzing the collected real-time monitored parking space data through a big data analysis technology to obtain a parking space utilization rate Ly and a street congestion index Yd;
s3, acquiring the real-time GPS position of a target vehicle owner, calculating to obtain a parking space close to the GPS position of the target vehicle owner according to the parking space utilization rate Ly and the street congestion index Yd, determining at least three parking spaces close to the GPS position of the target vehicle owner, and acquiring the latest parking space information and navigation route through a navigation system;
three parking stall distances that are close to the GPS position of the target car owner include first parking stall, second parking stall and third parking stall, first parking stall is less than 50 meters from the GPS position of the target car owner in, first parking stall sets up to be 50 meters to 80 meters from the GPS position distance of the target car owner in, third parking stall sets up to be greater than 80 meters from the GPS position distance of the target car owner, sets up first parking stall as first priority, and under the condition that first parking stall is not discerned, automatic recommendation second parking stall is second priority under the condition that first parking stall and second parking stall are not discerned, automatic recommendation third parking stall is third priority.
2. The smart city parking management method based on big data as claimed in claim 1, wherein: the step S1 specifically comprises the following steps:
s11, determining a street area to be monitored, planning and designing a sensor network layout, and setting the installation position and the camera position of a parking space sensor by considering the length of a street and the distribution of parking spaces;
the parking space sensor is arranged as one or more of a geomagnetic sensor, a pressure sensor or a photoelectric sensor;
s12, installing the planned layout, namely burying a parking space sensor into the ground or installing the sensor above a parking space representation, and installing a camera on a street lamp post position to ensure that equipment is stably installed and the state of the parking space and the flow of vehicles can be monitored;
and S13, connecting the parking space sensor equipment and the camera to a central server or a cloud platform, and carrying out data transmission and storage to acquire real-time monitoring parking space data.
3. The smart city parking management method based on big data as claimed in claim 1, wherein: the step S2 specifically includes:
s21, acquiring real-time monitoring parking space data, wherein the real-time monitoring parking space data comprises image data and sensor data, and performing data processing on the sensor data, including data cleaning, anomaly monitoring and data aggregation;
preprocessing image data, including adjusting the image size, cropping and removing noise;
s22, extracting features of the image data and the sensor data, extracting edge information in the street image by using an edge detection algorithm, analyzing the road structure and the vehicle position, and calculating to obtain the parking space utilization Ly and the street congestion index Yd.
4. A smart city parking management method based on big data as claimed in claim 3, wherein: the parking space utilization Ly and the street congestion index Yd are obtained through the following formula:
wherein: zy is expressed as the number of occupied parking spaces, ztc is expressed as the total number of parking spaces, cl is expressed as the vehicle flow, and T is expressed as the average time for a vehicle to park into a parking space; alpha represents a correction constant.
5. The smart city parking management method based on big data as claimed in claim 4, wherein: calculating the distance between each parking space on the street road surface, calculating the distance between each parking space and the GPS position of the target vehicle owner, and calculating the distance by using a Euclidean distance measurement method;
after the parking space utilization rate Ly and the street congestion index Yd are subjected to normalization processing, a comprehensive index I is calculated to measure the priority of the parking space, and a specific calculation formula can be obtained according to actual requirements:
I＝w1*(1-Ly)+w2*(1-Yd)
wherein: i is identified as a comprehensive evaluation value and is used for measuring and sequencing the priority and the applicability of different parking spaces; w1 and w2 are weights for adjusting the relative importance of the parking space utilization and the street congestion index in the comprehensive index; the influence degree of different factors on the comprehensive index is controlled by adjusting the weight;
under a given condition, the comprehensive index I is calculated according to the parking space utilization rate Ly and the street congestion index Yd; by defining a proper calculation formula and weight, the comprehensive index can comprehensively consider the two factors and provide an evaluation value for each parking space.
6. The smart city parking management method based on big data as claimed in claim 1, wherein: according to the distance between parking spaces and the comprehensive index, selecting three parking spaces closest to the GPS position of the target vehicle owner and highest in comprehensive index as nearby parking spaces;
the parking space distance and the GPS position of the target car owner are set to be less than 50 meters, and are set to be a first priority and recommended to be a first parking space;
50. setting the distance between the parking space and the GPS position of the target car owner to be equal to or less than 80 meters as a second priority, and recommending the distance to be a second parking space;
setting the distance between the parking space and the GPS position of the target car owner as a third priority level which is recommended as a third parking space, wherein the distance between the parking space and the GPS position of the target car owner is more than 80 meters;
and recommending the target car owner by the navigation system according to the first priority, the second priority and the third priority.
7. A smart city parking management method based on big data as claimed in claim 3, wherein: and S21, extracting texture features in the road image by a texture analysis method, setting the texture features as one of a local binary pattern LBP or a gray level co-occurrence matrix GLCM, extracting the texture features in the road image, and analyzing the condition of a street road surface parking space:
and (3) monitoring and classifying the image targets, establishing a target detection model, identifying and classifying vehicles in the image, identifying different types of vehicles through training the model, and carrying out dislocation identification on abnormal conditions in the image according to requirements when detecting and identifying the conditions of the electric vehicle and the bicycle in a parking space area of the motor vehicle, and adding a frame and a label on the image to identify improper parking.
8. The smart city parking management method based on big data of claim 7, wherein: the result of the dislocation identification is transmitted to a cloud platform, and the cloud platform sends a notice, an alarm or a report to related personnel so as to take necessary measures in time;
meanwhile, the cloud platform sends feedback information to the parking management system to update and improve the result of the dislocation mark.
9. A smart city parking management system based on big data is characterized in that: the system comprises a sensor monitoring module, wherein a sensor network is established on a parking lot and a street in a city, and the use condition, the vehicle flow and the stay time data of a parking space are monitored and collected in real time by installing a parking space detector, a camera and other devices;
the data storage module is used for transmitting the collected data in the real-time monitoring module to the central server through wireless transmission or wired network for real-time processing and storage; the monitoring data comprise parking space state, vehicle flow, residence time and payment information;
and a data analysis module: processing and analyzing the collected data by utilizing a big data analysis technology, extracting useful information, and calculating to obtain the utilization rate Ly of a parking space and the congestion index Yd of a street;
and a real-time navigation module: providing real-time parking space navigation and recommendation for a target car owner driver through smart phone application and a navigation system;
and a prediction module: based on the parking space utilization rate Ly and the street congestion index Yd, calculating to obtain a first parking space, a second parking space and a third parking space, and according to the analysis result, indicating a target vehicle owner driver to go to the idle parking space or reserve the parking space by the system, so that the time for searching the parking space and the congestion condition are reduced;
and the cloud platform is used for storing the data of the modules.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117789504A (en) * | 2024-02-28 | 2024-03-29 | 苏州申亿通智慧运营管理有限公司 | Intelligent commanding and dispatching method and system for urban tunnel traffic |
| CN117831338A (en) * | 2023-12-26 | 2024-04-05 | 武汉理工大学 | Data collaborative sharing method based on intelligent guidance terminal of parking lot |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117831338A (en) * | 2023-12-26 | 2024-04-05 | 武汉理工大学 | Data collaborative sharing method based on intelligent guidance terminal of parking lot |
| CN117789504A (en) * | 2024-02-28 | 2024-03-29 | 苏州申亿通智慧运营管理有限公司 | Intelligent commanding and dispatching method and system for urban tunnel traffic |
| CN117789504B (en) * | 2024-02-28 | 2024-05-03 | 苏州申亿通智慧运营管理有限公司 | Intelligent commanding and dispatching method and system for urban tunnel traffic |
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