CN110111599B - Parking guidance method based on big data, terminal equipment and storage medium - Google Patents
Parking guidance method based on big data, terminal equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
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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
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
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Abstract
The invention relates to a parking guidance method based on big data, a terminal device and a storage medium, wherein in the method, firstly, a parking space point location information model is established, and parking space condition data of each parking lot are collected and stored; secondly, when a parking request sent by a vehicle owner is received, determining the current point position of the vehicle owner, searching corresponding parking lots according to a preset radiation range, namely calculating the matching degree of each parking lot, and screening out the parking lots larger than a screening threshold; and finally, calculating the expected value of the free parking space according to the screened parking lot, and sending the parking space serial number and the travel route of the free parking space with the maximum expected value to the vehicle owner. The invention realizes the effective utilization of the vacant parking spaces in the parking lot and solves the problem of difficult parking of the car owner.
Description
Technical Field
The invention relates to the field of big data analysis and analysis, in particular to a parking guidance method based on big data, terminal equipment and a storage medium.
Background
In recent years, the urbanization process is gradually accelerated, and the car is an indispensable part for the travel of residents. However, the increasingly large volume of cars also causes the problem of difficult parking, and some areas even have a 'one-off' step. The insufficient parking space causes the traffic phenomenon of illegal parking, and influences the smoothness of the road and the image of the city.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a parking guidance method, a terminal device and a storage medium based on big data, so as to effectively utilize the vacant parking spaces in the parking lot and solve the problem of difficult parking of the car owner.
The specific scheme is as follows:
a parking guidance method based on big data comprises the following steps:
s1: establishing a parking space point location information model, wherein the parking space point location information model comprises entrance point location information of each parking lot and point location information of each parking space in each parking lot, and the point location information comprises longitude and latitude coordinates of corresponding positions and serial numbers of point locations;
s2: collecting and storing the parking space condition data of each parking lot;
the parking space condition data comprises the daily parking number of the parking lot, the maximum value, the minimum value and the average value of daily idle parking spaces, the vehicle entrance rate and the vehicle exit rate of each parking space in each time period, the vehicle flow rate near the parking lot in each time period, the number of parking requests initiated by vehicle owners in each time period and the number of idle parking spaces in the current time period;
s3: when a parking request sent by a vehicle owner is received, determining the current vehicle owner point position;
s4: in the parking space point location model, according to a preset radiation range, searching entry point locations of all parking lots within the radiation range of the current vehicle owner point location, and calculating matching degrees of the found entry point locations of all parking lots;
s5: setting a parking lot screening threshold s (l) { N (o) }, wherein N (o) represents the number of parking lots radiated by the vehicle owner o, and setting the parking lots corresponding to the entry points of the parking lots larger than or equal to the parking lot screening threshold as primary screening parking lots according to the calculated matching degrees of the entry points of all the parking lots;
s6: calculating the expected values of the idle parking spaces of all the primary screening parking lots;
s7: obtaining a final expected value of the idle parking spaces through a weighting algorithm according to the expected value of the idle parking spaces of the primary screening parking lot;
s8: and according to the final expected value, sending the parking space serial number and the travel route of the idle parking space with the highest final expected value to the vehicle owner.
Further, the owner point v (o) is calculated by the following formula:
V(o)＝{lat(o)，lng(o)}
where o is the serial number of the owner's point, lat () represents longitude, and lng () represents latitude.
Further, the calculation formula of the matching degree s (p) is as follows:
s(p)＝{V(o)，V(p)，R(p)，N(p)}
wherein p is the serial number of the parking lot, v (p) is an entry point calculation function of the parking lot p, r (p) represents the traffic flow near the parking lot p in the time period corresponding to the current time period, and n (p) represents the number of the free parking spaces in the current time period of the parking lot.
Further, the calculation formula of the expected value w (i) of the vacant parking space is as follows:
w(i)＝{InAvg(i)，OutAvg(i)，InMax(i)，OutMax(i)，Inf(t)}
wherein i is the serial number of the parking space, InAvg () represents the average value of the daily input amount of the parking space, OutAvg () represents the average value of the daily output amount of the parking space, InMax () represents the maximum value of the daily input amount of the parking space, OutMax () represents the maximum value of the daily output amount of the parking space, and inf (t) represents the factor influencing the access frequency of the vehicle in the parking space in the time period t.
Further, in step S7, the weighting algorithm is a matching degree of the parking lot corresponding to the final expected value b + a, where the weight b is determined according to the occupancy rate of the parking space and the frequency of entering and exiting the parking space, and the weight a is determined according to the number of free parking spaces in the parking lot and the distance between the free parking space and the entrance of the parking lot.
Further, when n car owners send parking requests in the same time period and the calculated primary screening parking lots of the n car owners are the same, allocating parking spaces for the n car owners by using the betting rotation principle p (i) ═ E (i)/E (sum)), wherein p (i) is the probability that a parking space is allocated to the ith car owner, E (i) is the final expected value of the ith car owner for the parking space, and E (sum) ═ E (1) + E (2) + E (3) + … + E (n)) is the sum of all the final expected values of the n car owners.
The parking guidance terminal device based on the big data comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to an embodiment of the invention as described above.
According to the technical scheme, the parking lot combination distance, the parking number and other traffic flow influences radiated at the periphery are preliminarily screened, the traffic flow of entering and exiting of parking places in the parking lot in each time period is collected, the mass data of the parking sites are analyzed by using a big data technology, and the matching calculation is carried out on different parking places by combining the combination of different algorithms, so that the optimal parking site group and route scheme of an owner is given, the effective utilization of the vacant parking places in the parking lot can be better realized, and the problem of difficult parking of the owner is solved.
Drawings
Fig. 1 is a schematic diagram of a first embodiment of the invention.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
referring to fig. 1, the present invention provides a big data-based parking guidance method, including the following steps:
s1: and establishing a parking space point location information model, wherein the parking space point location information model comprises entrance point location information of each parking lot and point location information of each parking space in each parking lot.
The point location information comprises longitude and latitude coordinates of corresponding positions and serial numbers of point locations.
The latitude and longitude coordinates can be acquired by the existing common coordinate positioning method, such as GPS positioning.
It should be noted that the entry point location information of the parking lot includes longitude and latitude coordinates of an entry and a serial number corresponding to the parking lot entry, for example, in fig. 1, the serial number corresponding to the parking lot a is 32, and the serial number corresponding to the parking lot B is 19. The needs here can be set at the discretion of the skilled person.
In the point location information of each parking space, because the parking space is usually rectangular, one point adjacent to the road can be selected as the point location of the parking space, and the serial number can be a preset serial number in each parking lot, such as 1028.
The entrance coordinates of each parking lot and the entrance coordinates of each parking space in the parking lot are recorded by establishing a model, so that the distance between each parking lot entrance and the vehicle owner can be judged according to the coordinates on one hand, and on the other hand, a proper route can be planned for the vehicle owner when the parking lot is selected.
S2: and collecting and storing the parking space condition data of each parking lot.
The parking space condition data comprises the daily parking number of the parking lot, the maximum value, the minimum value and the average value of the daily idle parking spaces, the vehicle entrance rate and the vehicle exit rate of each parking space in each time period, the vehicle flow rate near the parking lot in each time period, the number of parking requests initiated by vehicle owners in each time period and the number of the idle parking spaces in the current time period.
The parking space condition data can be acquired through a camera or a geomagnetic sensor and the like arranged in the parking lot.
Further, in order to extract valid data from all the acquired parking space condition data, data screening is performed after the parking space condition data of each parking lot is acquired in step S2, and repeated data and invalid data are removed. The embodiment specifically comprises the following steps: and screening the times of the parking requests repeatedly initiated by the vehicle owner in each time period, simultaneously eliminating data error report caused by local server delay and fault reasons, and extracting effective data of normal operation of geomagnetic equipment and normal report of local data for storage.
In this embodiment, an analysis platform of big data is established, and the analysis platform specifically includes: the method comprises the steps of building a SpringCloud distributed service, configuring an Eureka registration center, building a Feign client component for parameter transmission, and configuring Ribbon load balancing and Hystrix fusing to realize service stability.
In this embodiment, the parking space condition data may be stored in a relational database such as Oracle, and in other embodiments, may also be stored in a local database such as other MySQL or a cloud database, which is not limited herein.
Furthermore, data with high reading rate for parking space condition data can be efficiently stored and accessed by adopting a Redis database, such as the number of idle parking spaces in the current time period.
S3: when a parking request sent by a vehicle owner is received, the current vehicle owner point V (o) is determined.
The owner point v (o) is calculated by the following formula:
V(o)＝{lat(o)，lng(o)}
where o is the serial number of the owner's point, lat () represents longitude, and lng () represents latitude.
S4: in the parking space point location model, according to a preset radiation range r, searching the entrance point locations of all parking lots within the radiation range r of the current vehicle owner point location, and calculating the matching degrees s (p) of the searched entrance point locations of all parking lots.
The calculation formula of the matching degree s (p) is as follows:
s(p)＝{V(o)，V(p)，R(p)，N(p)}
wherein p is the serial number of the parking lot, v (p) is an entry point calculation function of the parking lot p, r (p) represents the traffic flow near the parking lot p in the time period corresponding to the current time period, and n (p) represents the number of the free parking spaces in the current time period of the parking lot.
S5: setting a parking lot screening threshold s (l) { n (o) }, wherein n (o) represents the number of parking lots irradiated by the vehicle owner o, and setting parking lots corresponding to the entry points of the parking lots larger than or equal to the parking lot screening threshold s (l) as preliminary screening parking lots according to the calculated matching degrees of the entry points of all the parking lots. Table 1 shows a parking lot preliminary screening indication table.
TABLE 1
| Parking lot | Degree of matching s | Screening results |
| A | 25.52 | By passing |
| B | 20.19 | By passing |
| C | 0.09 | Is eliminated |
| D | 1.46 | Is eliminated |
| E | 3.26 | Is eliminated |
S6: calculating the expected value w (i) of the free parking spaces of all the preliminary screening parking lots:
w(i)＝{InAvg(i)，OutAvg(i)，InMax(i)，OutMax(i)，Inf(t)}
wherein i is the serial number of the parking space, InAvg () represents the average value of the daily input amount of the parking space, OutAvg () represents the average value of the daily output amount of the parking space, InMax () represents the maximum value of the daily input amount of the parking space, OutMax () represents the maximum value of the daily output amount of the parking space, and inf (t) represents the factor influencing the access frequency of the vehicle in the parking space in the time period t.
S7: according to the expected value of the free parking spaces of the primary screening parking lot, the final expected value E of the free parking spaces is obtained through a weighting algorithm E (i) ═ a(s) (p) + b (w) (i), wherein the weighting value a mainly considers the number of the free parking spaces in the parking lot and the distance between the free parking spaces and the entrance of the parking lot, and the weighting value b mainly considers the occupation rate of the parking spaces and the access frequency of the parking spaces.
Table 2 shows a parking space matching expectation diagram.
TABLE 2
S8: and according to the final expected value, sending the parking space serial number and the travel route of the idle parking space with the highest final expected value to the vehicle owner.
When n car owners in the same area simultaneously initiate requests in the same time period, a parking lot is allocated to the n car owners according to the final expected value E calculated in S7 by using the betting round principle p (i) ═ E (i)/E (sum), where p (i) is the probability that the parking space is allocated to the ith car owner, E (i) is the final expected value of the ith car owner for the parking space, and E (sum) ═ E (1) + E (2) + E (3) + … + E (n) is the sum of all the final expected values.
According to the embodiment of the invention, the combination distance and the parking quantity of the parking lot radiated by the periphery and the influence of other traffic flows are primarily screened, the traffic flow of the parking lot entering and exiting the parking lot in each time period is collected, the mass data of the parking sites are analyzed by using a big data technology, and the matching calculation is carried out on different parking places by combining the combination of different algorithms, so that the optimal parking site group and route scheme of a vehicle owner is provided, the effective utilization of the idle parking places in the parking lot can be better realized, and the problem of difficult parking of the vehicle owner is solved.
Example two:
the invention also provides parking guidance terminal equipment based on big data, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the parking guidance terminal device based on big data may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The big data based parking guidance terminal device may include, but is not limited to, a processor, a memory. It will be understood by those skilled in the art that the above-mentioned composition structure of the big data-based parking guidance terminal device is only an example of the big data-based parking guidance terminal device, and does not constitute a limitation on the big data-based parking guidance terminal device, and may include more or less components than the above-mentioned one, or combine some components, or different components, for example, the big data-based parking guidance terminal device may further include an input-output device, a network access device, a bus, etc., which is not limited in this embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor is a control center of the big data based parking guidance terminal device, and various interfaces and lines are used to connect various parts of the whole big data based parking guidance terminal device.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the big-data based parking guidance terminal device by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The big data-based parking guidance terminal device integrated module/unit may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM ), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A parking guidance method based on big data is characterized by comprising the following steps:
s1: establishing a parking space point location information model, wherein the parking space point location information model comprises entrance point location information of each parking lot and point location information of each parking space in each parking lot, and the point location information comprises longitude and latitude coordinates of corresponding positions and serial numbers of point locations;
s2: collecting and storing the parking space condition data of each parking lot;
the parking space condition data comprises the daily parking number of the parking lot, the maximum value, the minimum value and the average value of daily idle parking spaces, the vehicle entrance rate and the vehicle exit rate of each parking space in each time period, the vehicle flow rate near the parking lot in each time period, the number of parking requests initiated by vehicle owners in each time period and the number of idle parking spaces in the current time period;
s3: when a parking request sent by a vehicle owner is received, determining the current vehicle owner point position V (o);
the owner point v (o) is calculated by the following formula:
V(o)＝{lat(o)，lng(o)}
wherein o is the serial number of the owner point V (o), lat () represents longitude, and lng () represents latitude;
s4: in the parking space point location model, according to a preset radiation range, searching the entrance point locations of all parking lots in the radiation range of the current vehicle owner point location V (o), and calculating the matching degrees s (p) of the searched entrance point locations of all parking lots;
the calculation formula of the matching degree s (p) is as follows:
s(p)＝{V(o)，V(p)，R(p)，N(p)}
wherein p is the serial number of the parking lot, V (p) is an entry point calculation function of the parking lot p, R (p) represents the traffic flow near the parking lot p in the time period corresponding to the current time period, and N (p) represents the number of the idle parking spaces in the current time period of the parking lot;
s5: setting a parking lot screening threshold s (l) { N (o) }, wherein N (o) represents the number of parking lots radiated by the vehicle owner o, and setting parking lots corresponding to the entry points of the parking lots larger than or equal to the parking lot screening threshold as primary screening parking lots according to the calculated matching degrees s (p) of the entry points of all the parking lots;
s6: calculating the expected values w (i) of the idle parking spaces of all the primary screening parking lots;
the calculation formula of the expected value w (i) of the free parking space is as follows:
w(i)＝{InAvg(i)，OutAvg(i)，InMax(i)，OutMax(i)，Inf(t)}
wherein i is the serial number of the parking space, InAvg () represents the average value of daily driving amount of the parking space, OutAvg () represents the average value of daily driving amount of the parking space, InMax () represents the maximum value of daily driving amount of the parking space, OutMax () represents the maximum value of daily driving amount of the parking space, and inf (t) represents the factor influencing the frequency of the vehicles in the parking space in the time period t;
s7: obtaining a final expected value of the idle parking spaces through a weighting algorithm E (i) according to the expected value w (i) of the idle parking spaces of the primary screening parking lot;
the weighting algorithm E (i) ═ b (w) (i) + a(s) (p), wherein b is determined according to the occupation rate of the parking spaces and the access frequency of the parking spaces, and a is determined according to the number of the free parking spaces in the parking lot and the distance between the free parking spaces and the entrance of the parking lot;
s8: according to the final expected value, when n car owners send parking requests in the same time period and the calculated primary screening parking lots of the n car owners are the same, allocating parking spaces for the n car owners by using a rotation betting principle p (i) ═ E (i)/E (sum)), wherein p (i) is the probability that a parking space is allocated to the ith car owner, E (i) is the final expected value of the ith car owner for the parking space, and E (sum) ═ E (1) + E (2) + E (3) + … + E (n)) is the sum of all the final expected values of the n car owners; and sending the parking space serial number and the travel route of the idle parking space with the highest final expected value to the vehicle owner.
2. The utility model provides a parking inducement terminal equipment based on big data which characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, which computer program when executed by the processor carries out the steps of the method as claimed in claim 1.
3. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method as claimed in claim 1.
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| CN111932930A (en) * | 2020-07-20 | 2020-11-13 | 清华-伯克利深圳学院筹备办公室 | Parking space allocation method, equipment and storage medium |
| CN111932931A (en) * | 2020-07-27 | 2020-11-13 | 苏州中科先进技术研究院有限公司 | Shared parking space scheduling method, system, terminal and storage medium |
| CN112802358B (en) * | 2020-12-28 | 2023-09-01 | 平安科技（深圳）有限公司 | Parking space navigation method and device based on artificial intelligence, terminal equipment and medium |
| CN112885134B (en) * | 2021-01-24 | 2023-05-16 | 陕西合友网络科技有限公司 | Smart city traffic management method based on big data |
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