CN112687110A - Parking space level navigation method and system based on big data analysis - Google Patents
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Abstract
The invention discloses a parking space level navigation method and a system thereof based on big data analysis, comprising the following steps: acquiring daily vehicle in-out data, wherein the vehicle in-out-in data at least comprises license plate numbers, entry time, departure time, parking cost and parking space numbers; performing modeling analysis on the daily vehicle in-out database; and scheduling the vehicle parking according to the modeling analysis result. According to the invention, by means of big data, daily parking data are systematically analyzed, the number of schedulable vacant parking spaces is dynamically submitted to the parking space scheduling system, and redundant vacant parking spaces are externally allocated under the condition of ensuring the sufficiency of daily parking spaces, so that the overall use efficiency of the garage is improved. The parking space dispatching system can perform manual intervention, does not perform parking space distribution in a certain time period, deals with emergency situations, improves management flexibility, achieves reasonable dispatching of vehicles, improves the utilization rate of parked vehicles, and facilitates parking of vehicle owners.
Description
Technical Field
The invention relates to the technical field of big data analysis and processing, in particular to a parking space level navigation method and system based on big data analysis.
Background
The parking lot is a place for parking vehicles. The parking lot has a simple parking lot without management and charge by drawing parking spaces, and also has a charge parking lot with entrance and exit gates, a parking manager and a time-keeping cashier. Modern parking lots often have automated time-based charging systems, closed-circuit televisions, and video recorder systems. The legal responsibilities of the parking lot owner and the administrator are usually only provided for the driving person to park the vehicle, the damage and the vehicle loss responsibilities are not guaranteed, and generally, the legal responsibilities are attached to the exemption terms outside the parking lot gate and are referred by the owner.
However, the existing parking lot can not reasonably schedule vehicles by combining parking data (license plate number, entrance time, departure time, parking duration, parking cost and parking space number) in the parking lot so as to improve the utilization rate of the parked vehicles and facilitate the parking of car owners. For example, the use conditions of the garage in daytime and at night every day are analyzed according to big data, and under the condition that the vacant parking spaces in the garage are redundant, the system automatically allocates parking spaces with a certain proportion to be used for outside through the result of parking space analysis, so that the use efficiency is maximized. The parking spaces in a certain proportion can be analyzed by daily parking data, a small part of parking spaces are reserved under the condition that the daily parking spaces are enough for parking, and other parking spaces are automatically distributed by the system for external use.
Disclosure of Invention
In view of the above-mentioned defects in the prior art, the technical problem to be solved by the present invention is to provide a parking space level navigation method and system based on big data analysis, so as to solve the deficiencies in the prior art.
In order to achieve the purpose, the invention provides a parking space level navigation method based on big data analysis, which comprises the following steps: acquiring daily vehicle in-out data, wherein the vehicle in-out-in data comprises license plate numbers, entry time, departure time, parking cost and parking space numbers; performing modeling analysis on the daily vehicle in-out database; according to the modeling analysis result, scheduling the vehicle parking; when the parking spaces are scheduled to guide the vehicle to park, an optimal navigation line is drawn on the map by using a shortest path algorithm on the map according to the current position of the mobile terminal and the target parking space.
The modeling analysis of the daily vehicle in-out-of-warehouse data is modeled from the following dimensions: dimension of parking duration: counting the parking time of each vehicle, and classifying the data according to the parking time to obtain the proportion of the parking time of the user in each stage and the average parking time; dimension of parking period: counting the number of vehicles scattered in 24 hours by taking the entering time and the leaving time as analysis bases, and analyzing the time point of the largest number of parked vehicles in one day; meanwhile, the parking amount of a specific date is analyzed to be compared with the usual parking amount, and the data change of the specific date is analyzed, wherein the specific date is weekend or holiday; parking vehicle identity dimension: analyzing the attribution of the vehicle according to the license plate number; dimension of parking space utilization rate: according to the parking records, the utilization rate of the parking spaces in the time periods is analyzed by counting the parking spaces in the time periods of one year, half year, one month and one day.
Scheduling the vehicle parking, including manual intervention allocation and automatic allocation of vacant parking spaces;
the manual intervention allocation of the vacant parking spaces is to manually allocate the redundant vacant parking spaces to the car owners within a certain time period, or not to manually allocate some parking spaces to the car owners within a certain time period so as to reserve emergency standby;
the automatic allocation of the vacant parking spaces is the allocation of optimal distances to all the vacant parking spaces, and when the license plate number of a user is scanned at the entrance of the parking lot, one vacant parking space closest to the entrance is automatically allocated.
When the license plate number of the user is scanned at the entrance of the parking lot, a vacant parking space closest to the entrance is automatically allocated, and the method specifically comprises the following steps: a rectangular coordinate system is established according to the position of the vehicle, then the coordinates (0, 0) of the vehicle are set, the linear distance from the vehicle to each vacant parking space is set to be L1 and L2 … … LN, the coordinates (X1 and Y1) and (X2 and Y2) … … (XN and YN), and then according to the Pythagorean theorem:
L1＝sqrt(X12+Y12)，L2＝sqrt(X22+Y22)……LN＝sqrt(XN2+YN2)，
and then comparing the L1 with the L2 … … LN, and recommending the minimum distance to the user.
The data of the vacant parking spaces are collected through parking space cameras; and the vehicle in-out and in-out data is acquired through an RFID (radio frequency identification) identifier or an electric railing at the vehicle entrance and exit.
And recognizing and collecting the license plate through the parking space camera, and when the parking space automatically distributed by the system is occupied by other vehicles, the system recommends a new parking space for the current vehicle owner according to a scheduling method and provides an indoor navigation line.
The guide user goes to the target parking stall, and at the in-process of guide, the target parking stall is occupied, and a vacant parking stall is recommended again to the system, catches through the camera on the parking stall, and when the camera was found the parking stall state and changed, can transmit the server through the network with information, and the server can revise the state of current parking stall, recommends a vacant parking stall simultaneously to through the propelling movement agreement notice of removing the end at present mobile terminal who navigates to, change the route.
Drawing a parking lot by using a map, regarding each fork road as a vertex, regarding a road between the fork roads as an edge, and regarding the length of the road as the weight of the edge; if the path is a one-way path, drawing a directed edge between the two vertexes; if the road is a double-row road, drawing two edges with different directions between two vertexes, and abstracting the whole map into a directed weighted graph; the shortest path algorithm is specifically as follows:
step 1: regarding the initial point on the graph as a set S, regarding other points as another set;
step 2: according to the initial point, finding the distance d [ i ] from other points to the initial point; if adjacent, d [ i ]
Is the edge weight value; if not, then di is infinite;
and step 3: selecting the smallest di and recording as d [ x ], recording the point corresponding to the di side as x, adding the set S, wherein the d [ x ] value of the point added to the set is the shortest distance from the point to the initial point;
and 4, step 4: and updating the value of d [ y ] of the point y adjacent to the x according to the x: d [ y ] ═ min { d [ y ], d [ x ] + edge weight w [ x ] [ y ] }, this update operation is called a relaxation operation because it is possible to turn the distance down;
and 5: and repeating the step 3 and the step 4 until the target point is added with the set, wherein the d [ i ] corresponding to the target point is the shortest path length.
A system for dispatching parking spaces based on big data analysis comprises
The system comprises a parking data acquisition module, a parking data acquisition module and a parking management module, wherein the parking data acquisition module is used for acquiring daily vehicle in-out data, and the vehicle in-out data comprises license plate numbers, entry time, departure time, parking cost and parking space numbers;
the parking data analysis module is used for carrying out modeling analysis on the vehicle in-out database data every day;
the parking scheduling module is used for scheduling the vehicle parking according to the modeling analysis result;
and the optimal navigation line generation module is used for drawing an optimal navigation line on the map by using a shortest path algorithm on the map according to the current position of the mobile terminal and the target parking space when the parking space is scheduled to guide the vehicle to park.
The parking data acquisition module is an RFID recognizer arranged at the entrance and the exit of each parking garage or an electric railing arranged at the entrance and the exit of a vehicle, and a plurality of cameras arranged in the parking garage.
The invention has the beneficial effects that:
according to the invention, the daily parking data is systematically analyzed in a big data mode, the operation condition of the parking lot parking space number in each time period is counted through data modeling, the parking space vacancy data is submitted to the parking space scheduling system, the holiday and each time period can be analyzed, and the schedulable vacancy parking space number is dynamically submitted to the parking space scheduling system. The parking space dispatching system is provided with a set of distribution mode for the empty parking spaces, under the condition that the daily parking spaces are enough, the unnecessary empty parking spaces are distributed outwards, and the overall use efficiency of the garage is improved. The parking space dispatching system can perform manual intervention, does not perform parking space distribution in a certain time period, deals with emergency situations, improves management flexibility, achieves reasonable dispatching of vehicles, improves the utilization rate of parked vehicles, and facilitates parking of vehicle owners.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a schematic view of a parking space site configuration of the present invention;
FIG. 2 is a functional block diagram of the parking control system of the present invention;
FIG. 3 is a system framework diagram of the present invention;
FIG. 4 is a flow chart of a method of the present invention;
fig. 5 is an optimal navigation route diagram.
Detailed Description
As shown in fig. 4, the invention provides a parking space level navigation method based on big data analysis, which comprises the following steps:
acquiring daily vehicle in-out database data, wherein the vehicle in-out database data at least comprises license plate numbers, entry time, departure time, parking duration, parking cost and parking space numbers;
carrying out modeling analysis on daily vehicle in-out database data;
according to the modeling analysis result, scheduling the vehicle parking;
when the parking spaces are scheduled to guide the vehicle to park, an optimal navigation line is drawn on the map by using a shortest path algorithm on the map according to the current position of the mobile terminal and the target parking space.
In this embodiment, modeling analysis is performed on daily vehicle in and out-of-warehouse data to model from the following dimensions:
dimension of parking duration: counting the parking time of each vehicle, and classifying the data according to the parking time to obtain the proportion of the parking time of the user in each stage and the average parking time;
dimension of parking period: counting the number of vehicles scattered in 24 hours by taking the entering time and the leaving time as analysis bases, and analyzing the time point of the largest number of parked vehicles in one day; meanwhile, the parking amount of a specific date is analyzed and compared with the parking amount at ordinary times, and the data change of the specific date is analyzed, wherein the specific date is weekend or holiday;
parking vehicle identity dimension: analyzing the attribution of the vehicle according to the license plate number;
dimension of parking space utilization rate: according to the parking records, the utilization rate in the parking space time period is analyzed by counting the parking spaces in the time periods of one year, half year, one month and one day respectively.
In the embodiment, the vehicle parking is scheduled, and the scheduling comprises manual intervention allocation and automatic allocation of vacant parking spaces;
manually intervening and allocating the vacant parking spaces, namely manually allocating the redundant vacant parking spaces to the car owners within a certain time period, or not manually allocating some parking spaces to the car owners within a certain time period to reserve emergency standby;
the automatic allocation of the vacant parking spaces is the allocation of optimal distances to all the vacant parking spaces, and when the license plate number of a user is scanned at the entrance of the parking lot, one vacant parking space closest to the entrance is automatically allocated.
In this embodiment, when the license plate number of the user is scanned at the entrance of the parking lot, a vacant parking space closest to the entrance is automatically allocated, specifically according to the following algorithm: a rectangular coordinate system is established according to the position of the vehicle, then the coordinates (0, 0) of the vehicle are set, the linear distance from the vehicle to each vacant parking space is set to be L1 and L2 … … LN, the coordinates (X1 and Y1) and (X2 and Y2) … … (XN and YN), and then according to the Pythagorean theorem:
L1＝sqrt(X12+Y12)，L2＝sqrt(X22+Y22)……LN＝sqrt(XN2+YN2)，
and then comparing the L1 with the L2 … … LN, and recommending the minimum distance to the user.
In this embodiment, the data of vacant parking stall is gathered through the camera.
In this embodiment, the vehicle warehouse entry and exit data is collected through an RFID identifier or an electric railing at the vehicle entrance and exit.
Referring to fig. 5, a parking lot is mapped, each intersection is regarded as a vertex, a road between each intersection is regarded as an edge, and the length of the road is the weight of the edge; if the path is a one-way path, drawing a directed edge between the two vertexes; if the road is a double-row road, drawing two edges with different directions between two vertexes, and abstracting the whole map into a directed weighted graph; the shortest path algorithm is specifically as follows:
1. regarding the initial point on the graph as a set S, regarding other points as another set;
2. according to the initial point, finding the distance d [ i ] from other points to the initial point (if adjacent, d [ i ] is the side weight, if not, d [ i ] is infinite);
3. selecting the smallest di (marked as d [ x ]), and adding the corresponding point (marked as x) of the di edge into the set S (actually, the d [ x ] value of the point added into the set is the shortest distance from the point to the initial point);
4. and updating the value of d [ y ] of the point y adjacent to the x according to the x: d [ y ] ═ min { d [ y ], d [ x ] + edge weight w [ x ] [ y ] }, this update operation is called a relaxation operation because it is possible to turn the distance down;
5. repeating the steps 3 and 4 until the target point is added into the set, wherein the d [ i ] corresponding to the target point is the shortest path length.
As shown in fig. 1, 2 and 3, the invention also provides a system for scheduling parking spaces based on big data analysis, which comprises
The parking data acquisition module is used for acquiring daily vehicle in-out warehouse data, and the vehicle in-out warehouse data at least comprises license plate numbers, entrance time, departure time, parking cost and parking space numbers;
the parking data analysis module is used for carrying out modeling analysis on daily vehicle in-out database data;
the parking scheduling module is used for scheduling the vehicle parking according to the modeling analysis result;
and the optimal navigation line generation module is used for drawing an optimal navigation line on the map by using a shortest path algorithm on the map according to the current position of the mobile terminal and the target parking space when the parking space is scheduled to guide the vehicle to park.
The parking data acquisition module is an RFID recognizer arranged at the entrance and the exit of each parking garage or an electric railing arranged at the entrance and the exit of a vehicle, and a plurality of cameras arranged in the parking garage. As shown in fig. 2, the RFID recognizer or the electric railing at the entrance and exit of each parking garage, and set up a plurality of cameras in the parking garage, the vehicle output of collection is transmitted for backend server through network transmission module (such as switch or wireless AP) and is carried out vehicle data modeling, vehicle scheduling, then backend server's dispatch data can be sent for car owner's user client (special mobile phone APP) through the network in real time, this APP can show each parking stall parking circumstances in parking garage, can recommend the car owner to be from the nearest parking stall of vehicle simultaneously, realize that the car owner is high-efficient convenient to park.
In conclusion, the daily parking data are systematically analyzed in a big data mode, the operation condition of the number of parking spaces in the parking lot in each time period is counted through data modeling, the vacant parking space data are submitted to the parking space scheduling system, holidays and each time period can be analyzed, and the number of the vacant parking spaces which can be scheduled is dynamically submitted to the parking space scheduling system. The parking space dispatching system is provided with a set of distribution mode for the empty parking spaces, under the condition that the daily parking spaces are enough, the unnecessary empty parking spaces are distributed outwards, and the overall use efficiency of the garage is improved. The parking space dispatching system can perform manual intervention, does not perform parking space distribution in a certain time period, deals with emergency situations, improves management flexibility, achieves reasonable dispatching of vehicles, improves the utilization rate of parked vehicles, and facilitates parking of vehicle owners.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A parking space level navigation method based on big data analysis is characterized by comprising the following steps:
acquiring daily vehicle in-out data, wherein the vehicle in-out-in data comprises license plate numbers, entry time, departure time, parking cost and parking space numbers;
performing modeling analysis on the daily vehicle in-out database;
according to the modeling analysis result, scheduling the vehicle parking;
when the parking spaces are scheduled to guide the vehicle to park, an optimal navigation line is drawn on the map by using a shortest path algorithm on the map according to the current position of the mobile terminal and the target parking space.
2. The parking space level navigation method based on big data analysis as claimed in claim 1, wherein the modeling analysis of the daily vehicle entrance and exit data is modeled from the following dimensions:
dimension of parking duration: counting the parking time of each vehicle, and classifying the data according to the parking time to obtain the proportion of the parking time of the user in each stage and the average parking time;
dimension of parking period: counting the number of vehicles scattered in 24 hours by taking the entering time and the leaving time as analysis bases, and analyzing the time point of the largest number of parked vehicles in one day; meanwhile, the parking amount of a specific date is analyzed to be compared with the usual parking amount, and the data change of the specific date is analyzed, wherein the specific date is weekend or holiday;
parking vehicle identity dimension: analyzing the attribution of the vehicle according to the license plate number;
dimension of parking space utilization rate: according to the parking records, the utilization rate of the parking spaces in the time periods is analyzed by counting the parking spaces in the time periods of one year, half year, one month and one day.
3. The parking space level navigation method based on big data analysis as claimed in claim 1, wherein the vehicle parking is scheduled, including manual intervention allocation and automatic allocation of vacant parking spaces;
the manual intervention allocation of the vacant parking spaces is to manually allocate the redundant vacant parking spaces to the car owners within a certain time period, or not to manually allocate some parking spaces to the car owners within a certain time period so as to reserve emergency standby;
the automatic allocation of the vacant parking spaces is the allocation of optimal distances to all the vacant parking spaces, and when the license plate number of a user is scanned at the entrance of the parking lot, one vacant parking space closest to the entrance is automatically allocated.
4. The parking space level navigation method based on big data analysis as claimed in claim 3, wherein when the license plate number of the user is scanned at the entrance of the parking lot, a vacant parking space nearest to the entrance is automatically allocated, specifically according to the following algorithm: a rectangular coordinate system is established according to the position of the vehicle, then the coordinates (0, 0) of the vehicle are set, the linear distance from the vehicle to each vacant parking space is set to be L1 and L2 … … LN, the coordinates (X1 and Y1) and (X2 and Y2) … … (XN and YN), and then according to the Pythagorean theorem:
L1＝sqrt(X12+Y12)，L2＝sqrt(X22+Y22)……LN＝sqrt(XN2+YN2) Then, L1 and L2 … … LN are compared, and the minimum distance is recommended to the user.
5. The parking space level navigation method based on big data analysis as claimed in claim 3, characterized in that the data of the vacant parking spaces are collected by a parking space camera; and the vehicle in-out and in-out data is acquired through an RFID (radio frequency identification) identifier or an electric railing at the vehicle entrance and exit.
6. The parking space level navigation method based on big data analysis as claimed in claim 5, characterized in that, through the recognition and collection of the parking space cameras, when the parking space automatically allocated by the system is occupied by other vehicles, the system recommends a new parking space for the current vehicle owner according to the scheduling method and gives an indoor navigation route.
7. The parking space level navigation method based on big data analysis as claimed in claim 1, characterized in that, guiding the user to go to the target parking space, in the guiding process, the target parking space is occupied, the system recommends an empty parking space again, capturing is performed through a camera in the parking space, when the camera finds that the parking space state changes, information is transmitted to the server through the network, the server modifies the state of the current parking space, recommends an empty parking space at the same time, and notifies the mobile terminal currently being navigated to through the push protocol of the mobile terminal to change the route.
8. The parking space level navigation method based on big data analysis as claimed in claim 1, characterized in that the parking lot is mapped, each intersection is regarded as a vertex, the road between the intersections is regarded as an edge, and the length of the road is the weight of the edge; if the path is a one-way path, drawing a directed edge between the two vertexes; if the road is a double-row road, drawing two edges with different directions between two vertexes, and abstracting the whole map into a directed weighted graph; the shortest path algorithm is specifically as follows:
step 1: regarding the initial point on the graph as a set S, regarding other points as another set;
step 2: according to the initial point, finding the distance d [ i ] from other points to the initial point; if adjacent, d [ i ] is the edge weight; if not, then di is infinite;
and step 3: selecting the smallest di and recording as d [ x ], recording the point corresponding to the di side as x, adding the set S, wherein the d [ x ] value of the point added to the set is the shortest distance from the point to the initial point;
and 4, step 4: and updating the value of d [ y ] of the point y adjacent to the x according to the x: d [ y ] ═ min { d [ y ], d [ x ] + edge weight w [ x ] [ y ] }, this update operation is called a relaxation operation because it is possible to turn the distance down;
and 5: and repeating the step 3 and the step 4 until the target point is added with the set, wherein the d [ i ] corresponding to the target point is the shortest path length.
9. A parking space level navigation system based on big data analysis is characterized by comprising
The system comprises a parking data acquisition module, a parking data acquisition module and a parking management module, wherein the parking data acquisition module is used for acquiring daily vehicle in-out data, and the vehicle in-out data comprises license plate numbers, entry time, departure time, parking cost and parking space numbers;
the parking data analysis module is used for carrying out modeling analysis on the vehicle in-out database data every day;
the parking scheduling module is used for scheduling the vehicle parking according to the modeling analysis result;
and the optimal navigation line generation module is used for drawing an optimal navigation line on the map by using a shortest path algorithm on the map according to the current position of the mobile terminal and the target parking space when the parking space is scheduled to guide the vehicle to park.
10. The big data analysis-based parking space level navigation system according to claim 9, wherein the parking data collection module is an RFID identifier disposed at an entrance of each parking garage or an electric railing at an entrance of a vehicle, and a plurality of cameras disposed in the parking garage.
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| CN115985131A (en) * | 2022-12-22 | 2023-04-18 | 厦门科拓通讯技术股份有限公司 | Parking lot parking space reservation method and system |
| CN117523906A (en) * | 2023-10-18 | 2024-02-06 | 上海软杰智能设备有限公司 | Intelligent parking space guiding management system and method based on Internet of things |
| CN118014404A (en) * | 2024-04-08 | 2024-05-10 | 绿城科技产业服务集团有限公司 | Intelligent optimization method and system based on parking system |
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