CN114255596B - Parking lot parking space recommendation system and method based on big data - Google Patents

Parking lot parking space recommendation system and method based on big data Download PDF

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CN114255596B
CN114255596B CN202210192487.2A CN202210192487A CN114255596B CN 114255596 B CN114255596 B CN 114255596B CN 202210192487 A CN202210192487 A CN 202210192487A CN 114255596 B CN114255596 B CN 114255596B
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parking space
space
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CN114255596A (en
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王小强
汪雪钟
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Jiangsu Ninebit Information System Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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Abstract

The invention discloses a parking lot parking space recommendation system and method based on big data, and belongs to the technical field of parking space recommendation. The system comprises an instruction information acquisition module, a three-dimensional calibration module, a multi-source data analysis module, a parking space change prediction module and a recommendation module; the output end of the instruction information acquisition module is connected with the input end of the three-dimensional calibration module; the output end of the three-dimensional checking module is connected with the input end of the multi-source data analysis module; the output end of the multi-source data analysis module is connected with the input end of the parking space change prediction module; the output end of the parking space change prediction module is connected with the input end of the recommendation module. The invention further provides a parking lot parking space recommendation method based on the big data, which is used for specific analysis. The parking space recommendation method and device can realize accurate recommendation of the parking space, meet the current digital and refined development requirements, and further meet the requirements of car owners.

Description

Parking lot parking space recommendation system and method based on big data
Technical Field
The invention relates to the technical field of parking space recommendation, in particular to a parking space recommendation system and method based on big data for a parking lot.
Background
With the rapid development of economy, the number of private cars is rapidly increased, and most of people go out by the private cars for work, play and the like, so that the number of parking spaces in a parking lot in a city is more and more impressive. The imbalance of supply and demand leads to more and more car owners to cause great trouble because the parking stall is in short supply, often need to find out the parking stall for a long time around the destination, and the difficult phenomenon that becomes general of parkking.
In the parking spaces of the parking lot, due to the geographical position factor of the parking lot, some parking spaces close to the wall, parking spaces with bearing columns and the like are inevitably generated in the construction process, the parking spaces have higher requirements on driving of car owners, in patent cn201811208241.x personalized parking space recommendation method and system with application date 2018.10.17, the analysis of the parking difficulty is provided, however, the parking difficulty is only roughly substituted by setting a mode that parking difficulty indexes D of parking spaces with blocking side directions on two sides are 0.8, parking difficulty indexes D of parking spaces for backing and warehousing are 0.6, parking difficulty indexes D of parking spaces with blocking parking spaces on one side are 0.4, and parking difficulty indexes D of parking spaces with blocking parking spaces on two sides are not 0.2, the influence on the accuracy of the system is large, the current digital development cannot be met, the accurate parking space recommendation is greatly interfered, and the final recommendation result is not friendly to the vehicle owner.
Disclosure of Invention
The invention aims to provide a parking lot parking space recommendation system and method based on big data, and aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a parking lot parking space recommendation system based on big data comprises an instruction information acquisition module, a three-dimensional check module, a multi-source data analysis module, a parking space change prediction module and a recommendation module;
the instruction information acquisition module is used for acquiring parking instruction information issued by a vehicle owner, acquiring a planned position of the vehicle owner, establishing a parking recommendation area and calculating a time difference value when the vehicle owner reaches the planned position; the three-dimensional checking module is used for acquiring basic information of the vehicle, constructing a three-dimensional model of the vehicle, comparing idle parking spaces, deleting the parking spaces which do not meet the requirements and reducing data redundancy; the multi-source data analysis module is used for acquiring multi-source data, constructing a driving proficiency model of a vehicle owner and a parking difficulty model of a parking space, and analyzing the driving proficiency of the vehicle owner and the parking difficulty of the parking space; the parking space change prediction module is used for establishing a parking space change prediction model and predicting parking preference and parking duration of a parking space; the recommendation module is used for sequencing the parking spaces from large to small according to the final recommendation scores of the parking spaces and sequentially recommending the parking spaces to the car owners;
the output end of the instruction information acquisition module is connected with the input end of the three-dimensional calibration module; the output end of the three-dimensional checking module is connected with the input end of the multi-source data analysis module; the output end of the multi-source data analysis module is connected with the input end of the parking space change prediction module; the output end of the parking space change prediction module is connected with the input end of the recommendation module.
According to the technical scheme, the instruction information acquisition module comprises an owner positioning unit and an owner reservation unit;
the vehicle owner positioning unit is used for positioning and recording the current position of a vehicle owner; the vehicle owner reservation unit is used for acquiring reserved parking time and reserved parking place provided by a vehicle owner;
the output end of the vehicle owner positioning unit is connected with the input end of the three-dimensional checking module; the output end of the vehicle owner reservation unit is connected with the input end of the three-dimensional checking module.
According to the technical scheme, the multi-source data analysis module comprises a multi-source data acquisition unit, an owner driving proficiency analysis unit and a parking space parking difficulty analysis unit;
the multi-source data acquisition unit is used for acquiring multi-source data of a vehicle owner and a parking space; the vehicle owner driving proficiency analyzing unit is used for constructing a vehicle owner driving proficiency model and analyzing the driving proficiency of a vehicle owner; the parking space parking difficulty analysis unit is used for constructing a parking space parking difficulty model and analyzing the parking difficulty of the parking space;
the output end of the multi-source data acquisition unit is respectively connected with the input ends of the vehicle owner driving proficiency analysis unit and the parking space parking difficulty analysis unit; the output ends of the car owner driving proficiency analysis unit and the parking space parking difficulty analysis unit are connected with the input end of the parking space change prediction module.
According to the technical scheme, the parking space change prediction module comprises a parking preference analysis unit and a parking duration analysis unit;
the parking preference analysis unit is used for analyzing the parking preference condition of each parking space in the parking lot and acquiring the parking preference probability; the parking duration analysis unit is used for analyzing the parking duration of the car owner in the parking lot and taking a mode as the predicted parking duration;
the output end of the parking preference analysis unit is connected with the input end of the recommendation module; the output end of the parking time length analysis unit is connected with the input end of the recommendation module.
According to the technical scheme, the recommendation module comprises a parking space recommendation score sorting unit and a recommendation unit;
the parking space recommendation score sorting unit is used for constructing an optimal parking space recommendation model, calculating parking space recommendation scores according to the optimal parking space recommendation model and sorting the calculated parking space recommendation scores from large to small; the recommendation unit is used for recommending the corresponding parking spaces to the vehicle owners according to the sequence;
the output end of the parking place recommendation score sorting unit is connected with the input end of the recommendation unit; and the output end of the recommendation unit is connected to the vehicle owner port.
A parking lot parking space recommendation method based on big data comprises the following steps:
s1, acquiring vehicle owner parking instruction information data, acquiring a planned position of a vehicle owner, establishing a parking recommendation area A by taking the planned position of the vehicle owner as a circle center and taking R as a radius, and acquiring a time difference value of the vehicle owner to the planned position;
s2, obtaining basic information of the vehicle, constructing a three-dimensional model of the vehicle, comparing all idle parking spaces in the parking recommendation area A, and deleting a recommendation system for the parking spaces which do not meet the size or height of the vehicle;
s3, acquiring vehicle owner information data, extracting vehicle owner characteristics, establishing a vehicle owner driving proficiency model, and calculating to obtain vehicle owner driving proficiency;
s4, obtaining the information of the free parking spaces, extracting the characteristics of the parking spaces, establishing a parking difficulty model of the parking spaces, and calculating the parking difficulty of each parking space;
s5, obtaining parking place historical data in the parking recommendation area A, establishing a parking place change prediction model, and analyzing parking preference and parking time of the parking place;
and S6, establishing an optimal parking space recommendation model, and recommending the corresponding parking space list to the vehicle owner according to the sequence of the parking space recommendation scores from large to small.
According to the above technical solution, in step S3, the vehicle owner driving proficiency model includes:
acquiring owner information data including driving age, accident rate, average vehicle speed and violation rate;
the weight ratio of construction is g1、g2、g3、g4；
Constructing a vehicle owner driving proficiency model:
Figure 294222DEST_PATH_IMAGE001
+
Figure 824560DEST_PATH_IMAGE002
+
Figure 436807DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 193542DEST_PATH_IMAGE004
representing a driving age conversion value;
Figure 690382DEST_PATH_IMAGE005
representing the accident rate conversion value;
Figure 199861DEST_PATH_IMAGE006
represents the average vehicle speed conversion value;
Figure 858375DEST_PATH_IMAGE007
representing the violation rate conversion value.
Setting a functional relation in the system, and converting the driving age, the accident rate, the average vehicle speed and the violation rate to facilitate calculation; for example, driving age conversion can utilize a linear function because the conversion ratio is simple, and the proficiency is inevitably higher and higher as the driving age is increased; the average vehicle speed can be converted by utilizing a parabola, because the low vehicle speed represents that the tendency of low proficiency is large, and the influence degree of the normal vehicle speed on the proficiency is low, namely if the vehicle runs at a low vehicle speed all the time, the driving proficiency condition of the vehicle owner is more prone to be judged, and if the vehicle runs at the normal vehicle speed, the driving proficiency condition of the vehicle owner is more difficult to be judged.
According to the above technical scheme, in step S4, the parking difficulty model includes:
acquiring parking space information data;
constructing a model relation between parking space parking difficulty and parking space information data:
Figure 600941DEST_PATH_IMAGE008
b is a parking space parking difficulty matrix; h is a parking space information data matrix;
Figure 494948DEST_PATH_IMAGE009
is a coefficient vector matrix;
Figure 265458DEST_PATH_IMAGE010
is a matrix of interference terms;
Figure 970240DEST_PATH_IMAGE011
Figure 826200DEST_PATH_IMAGE012
wherein, p represents the characteristic quantity of the parking space information; n represents n normalized data values for each feature;
parking space parking difficulty value of any parking space i
Figure 258319DEST_PATH_IMAGE013
Comprises the following steps:
Figure 991657DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 991974DEST_PATH_IMAGE015
is the intercept;
Figure 194285DEST_PATH_IMAGE016
、
Figure 100002_DEST_PATH_IMAGE017
、
Figure 915248DEST_PATH_IMAGE018
、
Figure 253825DEST_PATH_IMAGE019
is a coefficient vector;
Figure 804804DEST_PATH_IMAGE020
、
Figure 369778DEST_PATH_IMAGE021
、
Figure 409278DEST_PATH_IMAGE022
the parking space information characteristic value is obtained;
Figure 353095DEST_PATH_IMAGE023
is an interference value; the ratio of i =1, 2,
Figure 960793DEST_PATH_IMAGE018
，n；
and estimating by using a least square method to obtain an optimal coefficient vector and an interference value.
The information data of the parking spaces can comprise characteristic factors such as parking spaces close to walls, columns on the parking spaces and the like.
For the parking difficulty of the parking space, the measurement standard set by the invention is mean square error, and the mean square error refers to the mean square error between the predicted value and the actual value. The smaller the mean variance, the smaller the difference between the test value and the actual value, i.e. the better the model performance. Therefore, B and H are fixed by using training data, and then the optimal beta and epsilon are found by using a least square method, so that an optimal model is obtained.
According to the above technical solution, in step S5, the parking space change prediction model includes:
obtaining historical parking data of a parking lot, and respectively constructing a parking space preference model and a parking duration prediction model;
setting the number of time series
Figure 606538DEST_PATH_IMAGE024
；
The parking space preference model is as follows:
Figure 433418DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 887533DEST_PATH_IMAGE026
the parking initial probability is the parking space parking initial probability;
Figure 525188DEST_PATH_IMAGE027
the number of time series contained in the historical data time interval is shown;
Figure 940120DEST_PATH_IMAGE028
is as follows
Figure 196789DEST_PATH_IMAGE024
Parking times of the parking places in a period of time;
Figure 100002_DEST_PATH_IMAGE029
is as follows
Figure 207208DEST_PATH_IMAGE024
Total number of parking times of the parking lot in a period of time;
obtaining a time difference value, and establishing a growth curve function:
Figure 891130DEST_PATH_IMAGE030
wherein, the first and the second end of the pipe are connected with each other,
Figure 245888DEST_PATH_IMAGE031
parking for the parking space and growing probability;
Figure 447193DEST_PATH_IMAGE032
、
Figure 469376DEST_PATH_IMAGE033
、
Figure 432521DEST_PATH_IMAGE034
are all the values of the parameters,
Figure 100002_DEST_PATH_IMAGE035
(ii) a In that
Figure 540155DEST_PATH_IMAGE036
When the temperature of the water is higher than the set temperature,
Figure 138626DEST_PATH_IMAGE031
=
Figure 31627DEST_PATH_IMAGE026
；
obtaining the inflection point of the growth curve function, and recording as M: (
Figure 447565DEST_PATH_IMAGE037
，
Figure 917861DEST_PATH_IMAGE038
) The inflection point is the growth speed saturation point of the growth curve function;
in the growth curve function, the initial stage follows
Figure 953644DEST_PATH_IMAGE024
In the growth of the plant, the growth rate of the plant,
Figure 684840DEST_PATH_IMAGE031
the increasing speed of the curve is gradually increased, and the curve shows a rapidly rising situation; after reaching an inflection point M, the saturation degree of the function reaches the end stage and begins to follow
Figure 147045DEST_PATH_IMAGE024
Increase in
Figure 245582DEST_PATH_IMAGE031
The growth is slow, the growth speed approaches to 0,
Figure 310490DEST_PATH_IMAGE031
approaching 100%, the curve develops horizontally; the growth speed change point is denoted as an inflection point. This is because the parking lot is entered with the increase of time at the beginningThe vehicles are continuously increased, the probability of parking in the parking space is increased at a higher speed, the time is gradually increased, the probability of parking in the parking space reaches a certain height, the increase is gradually slow, the increase speed is close to 0, the parking probability is close to 100%, but the parking probability cannot reach 100%.
Obtaining the parking time of all vehicles in the parking lot, selecting a mode as the predicted parking time and recording the predicted parking time as the predicted parking time
Figure 37138DEST_PATH_IMAGE039
；
Constructing a judgment function
Figure 309725DEST_PATH_IMAGE040
：
Figure 754613DEST_PATH_IMAGE041
Wherein the content of the first and second substances,
Figure 623212DEST_PATH_IMAGE042
；
Figure 610891DEST_PATH_IMAGE043
the time difference value of the set vehicle owner arriving at the planned position comprises the time sequence number
Figure 414899DEST_PATH_IMAGE044
Is detected.
According to the above technical solution, in step S6, the best parking space recommendation model further includes:
respectively acquiring data sets of driving proficiency data, parking space parking difficulty data and parking space change data of a vehicle owner;
constructing an optimal parking space recommendation model:
Figure 471716DEST_PATH_IMAGE045
wherein K is the parking space recommendation score；
Figure 19372DEST_PATH_IMAGE046
Scoring the normalized base parking space;
Figure 100002_DEST_PATH_IMAGE047
、
Figure 422409DEST_PATH_IMAGE048
、
Figure 397319DEST_PATH_IMAGE049
respectively obtaining data mean values of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner;
Figure 957744DEST_PATH_IMAGE050
、
Figure 168146DEST_PATH_IMAGE051
、
Figure 989471DEST_PATH_IMAGE052
respectively obtaining standard deviations of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner; b is a parking difficulty value of the parking space;
and sorting the parking spaces from large to small according to the parking space recommendation score, and sequentially pushing the parking spaces to the car owner.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of firstly obtaining basic information of a vehicle, constructing a three-dimensional model of the vehicle, comparing all idle parking spaces in a parking recommendation area, deleting a recommendation system for the parking spaces which do not meet the size or height of the vehicle, reducing data redundancy and operation complexity; calculating the driving proficiency of the vehicle owner by establishing a vehicle owner driving proficiency model; establishing a parking space parking difficulty model, and calculating the parking difficulty of each parking space; establishing a parking space change prediction model, and analyzing parking preference and parking time of a parking space; by combining the factors, an optimal parking place recommendation model is established, and corresponding parking place lists are recommended to car owners according to the sequence of the parking place recommendation scores from large to small.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart diagram of a big data-based parking lot space recommendation system and method according to the present invention;
FIG. 2 is a schematic diagram of a growth curve function of a big data-based parking lot stall recommendation method of the present invention;
FIG. 3 is a schematic diagram of a judgment function of the method for recommending parking spaces in a parking lot based on big data.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides a technical solution:
a parking lot parking space recommendation system based on big data comprises an instruction information acquisition module, a three-dimensional check module, a multi-source data analysis module, a parking space change prediction module and a recommendation module;
the instruction information acquisition module is used for acquiring parking instruction information issued by a vehicle owner, acquiring a planned position of the vehicle owner, establishing a parking recommendation area and calculating a time difference value when the vehicle owner reaches the planned position; the three-dimensional checking module is used for acquiring basic information of the vehicle, constructing a three-dimensional model of the vehicle, comparing idle parking spaces, deleting the parking spaces which do not meet the requirements and reducing the data redundancy; the multi-source data analysis module is used for acquiring multi-source data, constructing a driving proficiency model of a vehicle owner and a parking difficulty model of a parking space, and analyzing the driving proficiency of the vehicle owner and the parking difficulty of the parking space; the parking space change prediction module is used for establishing a parking space change prediction model and predicting parking preference and parking duration of a parking space; the recommendation module is used for sequencing the parking spaces from large to small according to the final recommendation scores of the parking spaces and sequentially recommending the parking spaces to the car owners;
the output end of the instruction information acquisition module is connected with the input end of the three-dimensional calibration module; the output end of the three-dimensional checking module is connected with the input end of the multi-source data analysis module; the output end of the multi-source data analysis module is connected with the input end of the parking space change prediction module; the output end of the parking space change prediction module is connected with the input end of the recommendation module.
The instruction information acquisition module comprises an owner positioning unit and an owner reservation unit;
the vehicle owner positioning unit is used for positioning and recording the current position of a vehicle owner; the vehicle owner reservation unit is used for acquiring reserved parking time and reserved parking place provided by a vehicle owner;
the output end of the vehicle owner positioning unit is connected with the input end of the three-dimensional checking module; the output end of the vehicle owner reservation unit is connected with the input end of the three-dimensional checking module.
The multi-source data analysis module comprises a multi-source data acquisition unit, an owner driving proficiency analysis unit and a parking space parking difficulty analysis unit;
the multi-source data acquisition unit is used for acquiring multi-source data of a vehicle owner and a parking space; the vehicle owner driving proficiency analyzing unit is used for constructing a vehicle owner driving proficiency model and analyzing the driving proficiency of a vehicle owner; the parking space parking difficulty analysis unit is used for constructing a parking space parking difficulty model and analyzing the parking difficulty of the parking space;
the output end of the multi-source data acquisition unit is respectively connected with the input ends of the vehicle owner driving proficiency analysis unit and the parking space parking difficulty analysis unit; the output ends of the car owner driving proficiency analysis unit and the parking space parking difficulty analysis unit are connected with the input end of the parking space change prediction module.
The parking space change prediction module comprises a parking preference analysis unit and a parking duration analysis unit;
the parking preference analysis unit is used for analyzing the parking preference condition of each parking space in the parking lot and acquiring the parking preference probability; the parking duration analysis unit is used for analyzing the parking duration of the car owner in the parking lot and taking a mode as the predicted parking duration;
the output end of the parking preference analysis unit is connected with the input end of the recommendation module; and the output end of the parking time length analysis unit is connected with the input end of the recommendation module.
The recommendation module comprises a parking space recommendation score sorting unit and a recommendation unit;
the parking space recommendation score sorting unit is used for constructing an optimal parking space recommendation model, calculating parking space recommendation scores according to the optimal parking space recommendation model and sorting the calculated parking space recommendation scores from large to small; the recommendation unit is used for recommending the corresponding parking spaces to the vehicle owners according to the sequence;
the output end of the parking place recommendation score sorting unit is connected with the input end of the recommendation unit; and the output end of the recommendation unit is connected to the vehicle owner port.
A parking lot parking space recommendation method based on big data comprises the following steps:
s1, acquiring vehicle owner parking instruction information data, acquiring a planned position of a vehicle owner, establishing a parking recommendation area A by taking the planned position of the vehicle owner as a circle center and taking R as a radius, and acquiring a time difference value of the vehicle owner to the planned position;
s2, obtaining basic information of the vehicle, constructing a three-dimensional model of the vehicle, comparing all idle parking spaces in the parking recommendation area A, and deleting a recommendation system for the parking spaces which do not meet the size or height of the vehicle;
s3, acquiring vehicle owner information data, extracting vehicle owner characteristics, establishing a vehicle owner driving proficiency model, and calculating to obtain vehicle owner driving proficiency;
s4, obtaining the information of the free parking spaces, extracting the characteristics of the parking spaces, establishing a parking difficulty model of the parking spaces, and calculating the parking difficulty of each parking space;
s5, obtaining parking place historical data in the parking recommendation area A, establishing a parking place change prediction model, and analyzing parking preference and parking time of the parking place;
and S6, establishing an optimal parking space recommendation model, and recommending the corresponding parking space list to the vehicle owner according to the sequence of the parking space recommendation scores from large to small.
In step S3, the vehicle owner driving proficiency model includes:
acquiring owner information data including driving age, accident rate, average vehicle speed and violation rate;
the weight ratio of construction is g1、g2、g3、g4；
Constructing a vehicle owner driving proficiency model:
Figure 774762DEST_PATH_IMAGE001
+
Figure 681538DEST_PATH_IMAGE002
+
Figure 430052DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 371463DEST_PATH_IMAGE004
representing a driving age conversion value;
Figure 563541DEST_PATH_IMAGE005
representing the accident rate conversion value;
Figure 957613DEST_PATH_IMAGE006
means for expressingAverage vehicle speed conversion value;
Figure 509817DEST_PATH_IMAGE007
representing the violation rate conversion value.
In step S4, the parking difficulty model includes:
acquiring parking space information data;
constructing a model relation between the parking space parking difficulty and the parking space information data:
Figure 685496DEST_PATH_IMAGE008
b is a parking space parking difficulty matrix; h is a parking space information data matrix;
Figure 438688DEST_PATH_IMAGE009
is a coefficient vector matrix;
Figure 444690DEST_PATH_IMAGE010
is a matrix of interference terms;
Figure 675951DEST_PATH_IMAGE053
Figure 201742DEST_PATH_IMAGE012
wherein, p represents the characteristic quantity of the parking space information; n represents n normalized data values for each feature;
parking space parking difficulty value of any parking space i
Figure 125835DEST_PATH_IMAGE013
Comprises the following steps:
Figure 353554DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 496829DEST_PATH_IMAGE015
is the intercept;
Figure 1759DEST_PATH_IMAGE016
、
Figure 955809DEST_PATH_IMAGE017
、
Figure 811769DEST_PATH_IMAGE018
、
Figure 994620DEST_PATH_IMAGE019
is a coefficient vector;
Figure 744270DEST_PATH_IMAGE020
、
Figure 744587DEST_PATH_IMAGE021
、
Figure 196166DEST_PATH_IMAGE022
the parking space information characteristic value is obtained;
Figure 838500DEST_PATH_IMAGE023
is an interference value; the ratio of i =1, 2,
Figure 177077DEST_PATH_IMAGE018
，n；
and estimating by using a least square method to obtain an optimal coefficient vector and an interference value.
In step S5, the space change prediction model includes:
obtaining historical parking data of a parking lot, and respectively constructing a parking space preference model and a parking duration prediction model;
setting the number of time series
Figure 223662DEST_PATH_IMAGE024
；
The parking space preference model is as follows:
Figure 788636DEST_PATH_IMAGE025
wherein, the first and the second end of the pipe are connected with each other,
Figure 562557DEST_PATH_IMAGE026
the parking initial probability is the parking space parking initial probability;
Figure 536066DEST_PATH_IMAGE027
the number of time series contained in the historical data time interval is shown;
Figure 143765DEST_PATH_IMAGE028
is as follows
Figure 320669DEST_PATH_IMAGE024
Parking times of the parking places in a period of time;
Figure 773647DEST_PATH_IMAGE029
is as follows
Figure 103128DEST_PATH_IMAGE024
Total number of parking times of the parking lot in a period of time;
obtaining a time difference value, and establishing a growth curve function:
Figure 881728DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 280349DEST_PATH_IMAGE031
parking for the parking space and growing probability;
Figure 6576DEST_PATH_IMAGE032
、
Figure 49619DEST_PATH_IMAGE033
、
Figure 123754DEST_PATH_IMAGE034
are all made ofThe value of the parameter(s) is,
Figure 885037DEST_PATH_IMAGE035
(ii) a In that
Figure 86342DEST_PATH_IMAGE036
When the temperature of the water is higher than the set temperature,
Figure 249470DEST_PATH_IMAGE031
=
Figure 228927DEST_PATH_IMAGE026
；
obtaining the inflection point of the growth curve function, and recording as M: (
Figure 116987DEST_PATH_IMAGE037
，
Figure 981037DEST_PATH_IMAGE038
) The inflection point is the growth curve function growth speed saturation point;
obtaining the parking time of all vehicles in the parking lot, selecting a mode as the predicted parking time and recording the predicted parking time as the predicted parking time
Figure 123306DEST_PATH_IMAGE039
；
Constructing a judgment function
Figure 680189DEST_PATH_IMAGE040
：
Figure 291430DEST_PATH_IMAGE054
Wherein the content of the first and second substances,
Figure 693593DEST_PATH_IMAGE042
；
Figure 690367DEST_PATH_IMAGE043
the time difference value of the set vehicle owner arriving at the planned position comprises the time sequence number
Figure 418152DEST_PATH_IMAGE044
Is detected.
In step S6, the best space recommendation model further includes:
respectively acquiring data sets of driving proficiency data, parking space parking difficulty data and parking space change data of a vehicle owner;
constructing an optimal parking space recommendation model:
Figure 749645DEST_PATH_IMAGE055
wherein K is the parking space recommendation score;
Figure 221078DEST_PATH_IMAGE046
scoring the normalized base parking space;
Figure 337938DEST_PATH_IMAGE047
、
Figure 971045DEST_PATH_IMAGE048
、
Figure 556878DEST_PATH_IMAGE049
respectively obtaining data mean values of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner;
Figure 566422DEST_PATH_IMAGE050
、
Figure 537790DEST_PATH_IMAGE051
、
Figure 607377DEST_PATH_IMAGE052
respectively obtaining standard deviations of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner; b is a parking difficulty value of the parking space;
and sorting the parking spaces from large to small according to the parking space recommendation score, and sequentially pushing the parking spaces to the car owner.
In this embodiment:
a vehicle owner issues instruction information data, and the vehicle is parked at a Y point, so that a parking recommendation area is established around the Y point;
acquiring vehicle information data of the vehicle owner, establishing a three-dimensional model, acquiring information of idle parking spaces in a recommended parking area, and deleting the unsatisfactory parking spaces;
acquiring owner information data, including the driving age of 3 years, the accident rate of 0%, the average vehicle speed of 65km/h and the violation rate of 25%;
the weight ratio of construction is g1=0.6、g2=0.1、g3=0.2、g4=0.1；
The conversion model is respectively set as a linear function, a parabola and a linear function;
constructing a vehicle owner driving proficiency model:
Figure 913462DEST_PATH_IMAGE001
+
Figure 726697DEST_PATH_IMAGE002
+
Figure 818150DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 793059DEST_PATH_IMAGE004
representing a driving age conversion value;
Figure 353485DEST_PATH_IMAGE005
representing the accident rate conversion value;
Figure 704832DEST_PATH_IMAGE006
represents the average vehicle speed conversion value;
Figure 385212DEST_PATH_IMAGE007
representing the violation rate conversion value.
Calculating to obtain the driving proficiency of the vehicle ownerDegree of a1；
Acquiring parking space information data;
parking space parking difficulty value of any parking space i
Figure 910783DEST_PATH_IMAGE013
Comprises the following steps:
Figure 817559DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 831651DEST_PATH_IMAGE015
is the intercept;
Figure 586112DEST_PATH_IMAGE016
、
Figure 27458DEST_PATH_IMAGE017
、
Figure 529852DEST_PATH_IMAGE018
、
Figure 550898DEST_PATH_IMAGE019
is a coefficient vector;
Figure 691023DEST_PATH_IMAGE020
、
Figure 37691DEST_PATH_IMAGE021
、
Figure 653480DEST_PATH_IMAGE022
the parking space information characteristic value is obtained;
Figure 258642DEST_PATH_IMAGE023
is an interference value; the ratio of i =1, 2,
Figure 33700DEST_PATH_IMAGE018
，n；
acquiring a parking space parking difficulty value of each parking space;
setting the number of time series
Figure 957794DEST_PATH_IMAGE024
；
Constructing a parking space preference model:
Figure 201825DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 236777DEST_PATH_IMAGE026
the parking initial probability is the parking space parking initial probability;
Figure 866341DEST_PATH_IMAGE027
the number of time series contained in the historical data time interval;
Figure 961336DEST_PATH_IMAGE028
is a first
Figure 925619DEST_PATH_IMAGE024
Parking times of the parking places in a period of time;
Figure 764262DEST_PATH_IMAGE029
is as follows
Figure 513912DEST_PATH_IMAGE024
Total number of parking times of the parking lot in a period of time;
obtaining a time difference value, and establishing a growth curve function:
Figure 779808DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 732852DEST_PATH_IMAGE031
parking for the parking space and growing probability;
Figure 109607DEST_PATH_IMAGE032
、
Figure 448184DEST_PATH_IMAGE033
、
Figure 884982DEST_PATH_IMAGE034
are all the values of the parameters,
Figure 817997DEST_PATH_IMAGE035
(ii) a In that
Figure 998443DEST_PATH_IMAGE036
When the temperature of the water is higher than the set temperature,
Figure 722685DEST_PATH_IMAGE031
=
Figure 330384DEST_PATH_IMAGE026
；
obtaining the inflection point of the growth curve function, and recording as M: (
Figure 992441DEST_PATH_IMAGE037
，
Figure 976577DEST_PATH_IMAGE038
) The inflection point is the growth curve function growth speed saturation point;
obtaining the parking time of all vehicles in the parking lot, selecting a mode as the predicted parking time and recording the predicted parking time as the predicted parking time
Figure 289747DEST_PATH_IMAGE039
；
Constructing a judgment function
Figure 442248DEST_PATH_IMAGE040
：
Figure 716235DEST_PATH_IMAGE057
Wherein the content of the first and second substances,
Figure 363117DEST_PATH_IMAGE042
；
Figure 671739DEST_PATH_IMAGE043
the time difference value of the set vehicle owner arriving at the planned position comprises the time sequence number
Figure 496606DEST_PATH_IMAGE044
Is detected.
Obtaining
Figure 523468DEST_PATH_IMAGE043
And calculate to obtain the parking space of each parking space
Figure 442883DEST_PATH_IMAGE031
；
Constructing an optimal parking space recommendation model:
Figure 871590DEST_PATH_IMAGE058
wherein K is the parking space recommendation score;
Figure 365894DEST_PATH_IMAGE046
scoring the normalized base parking space;
Figure 614473DEST_PATH_IMAGE047
、
Figure 603157DEST_PATH_IMAGE048
、
Figure 886371DEST_PATH_IMAGE049
respectively obtaining data mean values of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner;
Figure 53041DEST_PATH_IMAGE050
、
Figure 788916DEST_PATH_IMAGE051
、
Figure 315712DEST_PATH_IMAGE052
respectively obtaining standard deviations of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner; b is a parking difficulty value of the parking space;
obtaining the parking place recommendation scores of all the free parking places;
and sorting the parking spaces from large to small according to the parking space recommendation score, and sequentially pushing the parking spaces to the car owner.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. The utility model provides a parking area parking stall recommendation method based on big data which characterized in that: the method comprises the following steps:
s1, acquiring vehicle owner parking instruction information data, acquiring a planned position of a vehicle owner, establishing a parking recommendation area A by taking the planned position of the vehicle owner as a circle center and taking R as a radius, and acquiring a time difference value of the vehicle owner to the planned position;
s2, obtaining basic information of the vehicle, constructing a three-dimensional model of the vehicle, comparing all idle parking spaces in the parking recommendation area A, and deleting a recommendation system for the parking spaces which do not meet the size or height of the vehicle;
s3, acquiring vehicle owner information data, extracting vehicle owner characteristics, establishing a vehicle owner driving proficiency model, and calculating to obtain vehicle owner driving proficiency;
s4, obtaining the information of the free parking spaces, extracting the characteristics of the parking spaces, establishing a parking difficulty model of the parking spaces, and calculating the parking difficulty of each parking space;
s5, obtaining parking place historical data in the parking recommendation area A, establishing a parking place change prediction model, and analyzing parking preference and parking time of the parking place;
s6, establishing an optimal parking place recommendation model, and recommending the corresponding parking place list to the vehicle owner according to the sequence of the parking place recommendation scores from large to small;
in step S3, the vehicle owner driving proficiency model includes:
acquiring owner information data including driving age, accident rate, average vehicle speed and violation rate;
the weight ratio of construction is g1、g2、g3、g4；
Constructing a vehicle owner driving proficiency model:
Figure 933671DEST_PATH_IMAGE001
+
Figure 61027DEST_PATH_IMAGE002
+
Figure 429692DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 199065DEST_PATH_IMAGE004
representing a driving age conversion value;
Figure 969575DEST_PATH_IMAGE005
representing the accident rate conversion value;
Figure 798990DEST_PATH_IMAGE006
represents the average vehicle speed conversion value;
Figure 153486DEST_PATH_IMAGE007
a representative violation rate conversion value;
in step S4, the parking difficulty model includes:
acquiring parking space information data;
constructing a model relation between the parking space parking difficulty and the parking space information data:
Figure 992129DEST_PATH_IMAGE008
b is a parking space parking difficulty matrix; h is a parking space information data matrix;
Figure 351566DEST_PATH_IMAGE009
is a coefficient vector matrix;
Figure 617463DEST_PATH_IMAGE010
is a matrix of interference terms;
Figure 695140DEST_PATH_IMAGE011
Figure 337474DEST_PATH_IMAGE012
wherein, p represents the characteristic quantity of the parking space information; n represents n normalized data values for each feature;
parking space parking difficulty value of any parking space i
Figure 816997DEST_PATH_IMAGE013
Comprises the following steps:
Figure 253794DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 317303DEST_PATH_IMAGE015
is the intercept;
Figure 232169DEST_PATH_IMAGE016
、
Figure DEST_PATH_IMAGE017
、
Figure 238303DEST_PATH_IMAGE018
、
Figure 580422DEST_PATH_IMAGE019
is a coefficient vector;
Figure 131227DEST_PATH_IMAGE020
、
Figure 849784DEST_PATH_IMAGE021
、
Figure 303899DEST_PATH_IMAGE022
the parking space information characteristic value is obtained;
Figure 82500DEST_PATH_IMAGE023
is an interference value; the ratio of i =1, 2,
Figure 622065DEST_PATH_IMAGE018
，n；
estimating by using a least square method to obtain an optimal coefficient vector and an interference value;
in step S5, the space change prediction model includes:
obtaining historical parking data of a parking lot, and respectively constructing a parking space preference model and a parking duration prediction model;
setting the number of time series
Figure 144314DEST_PATH_IMAGE024
；
The parking space preference model is as follows:
Figure 452935DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 402437DEST_PATH_IMAGE026
the parking initial probability is the parking space parking initial probability;
Figure 662254DEST_PATH_IMAGE027
the number of time series contained in the historical data time interval is shown;
Figure 988194DEST_PATH_IMAGE028
is as follows
Figure 151322DEST_PATH_IMAGE024
Parking times of the parking places in a period of time;
Figure DEST_PATH_IMAGE029
is as follows
Figure 6145DEST_PATH_IMAGE024
Total number of parking times of the parking lot in a period of time;
obtaining a time difference value, and establishing a growth curve function:
Figure 520303DEST_PATH_IMAGE030
wherein, the first and the second end of the pipe are connected with each other,
Figure 384354DEST_PATH_IMAGE031
parking for the parking space and growing probability;
Figure 401988DEST_PATH_IMAGE032
、
Figure 958872DEST_PATH_IMAGE033
、
Figure 193282DEST_PATH_IMAGE034
are all the values of the parameters,
Figure DEST_PATH_IMAGE035
(ii) a s is greater than 1; in that
Figure 329865DEST_PATH_IMAGE036
When the temperature of the water is higher than the set temperature,
Figure 202006DEST_PATH_IMAGE031
=
Figure 929791DEST_PATH_IMAGE026
；
obtaining the inflection point of the growth curve function, and recording as M: (
Figure 152962DEST_PATH_IMAGE037
，
Figure 358815DEST_PATH_IMAGE038
) The inflection point is the growth curve function growth speed saturation point;
obtaining the parking time of all vehicles in the parking lot, selecting a mode as the predicted parking time and recording the predicted parking time as the predicted parking time
Figure 351042DEST_PATH_IMAGE039
；
Constructing a judgment function
Figure 748263DEST_PATH_IMAGE040
：
Figure 193151DEST_PATH_IMAGE041
Wherein the content of the first and second substances,
Figure 202695DEST_PATH_IMAGE042
；
Figure 315007DEST_PATH_IMAGE043
the time difference value of the set vehicle owner arriving at the planned position comprises the time sequence number
Figure 119015DEST_PATH_IMAGE044
A threshold number of;
in step S6, the best space recommendation model further includes:
respectively acquiring data sets of driving proficiency data, parking space parking difficulty data and parking space change data of an automobile owner;
constructing an optimal parking space recommendation model:
Figure 582358DEST_PATH_IMAGE045
wherein K is the parking space recommendation score;
Figure 864434DEST_PATH_IMAGE046
scoring the normalized base parking space;
Figure DEST_PATH_IMAGE047
、
Figure 64209DEST_PATH_IMAGE048
、
Figure 39119DEST_PATH_IMAGE049
respectively obtaining data mean values of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner;
Figure 724178DEST_PATH_IMAGE050
、
Figure 75525DEST_PATH_IMAGE051
、
Figure 896850DEST_PATH_IMAGE052
respectively obtaining standard deviations of driving proficiency data, parking space parking difficulty data and parking space change data of the car owner; b is a parking difficulty value of the parking space;
and sorting the parking spaces from large to small according to the parking space recommendation score, and sequentially pushing the parking spaces to the car owner.
2. The big data-based parking lot space recommendation system applied to the big data-based parking lot space recommendation method according to claim 1 is characterized in that: the system comprises an instruction information acquisition module, a three-dimensional calibration module, a multi-source data analysis module, a parking space change prediction module and a recommendation module;
the instruction information acquisition module is used for acquiring parking instruction information issued by a vehicle owner, acquiring a planned position of the vehicle owner, establishing a parking recommendation area and calculating a time difference value of the vehicle owner reaching the planned position; the three-dimensional checking module is used for acquiring basic information of the vehicle, constructing a three-dimensional model of the vehicle, comparing idle parking spaces, deleting the parking spaces which do not meet the requirements and reducing data redundancy; the multi-source data analysis module is used for acquiring multi-source data, constructing a driving proficiency model of a vehicle owner and a parking difficulty model of a parking space, and analyzing the driving proficiency of the vehicle owner and the parking difficulty of the parking space; the parking space change prediction module is used for establishing a parking space change prediction model and predicting parking preference and parking duration of a parking space; the recommendation module is used for sequencing the parking spaces from large to small according to the final recommendation scores of the parking spaces and sequentially recommending the parking spaces to the car owners;
the output end of the instruction information acquisition module is connected with the input end of the three-dimensional calibration module; the output end of the three-dimensional checking module is connected with the input end of the multi-source data analysis module; the output end of the multi-source data analysis module is connected with the input end of the parking space change prediction module; the output end of the parking space change prediction module is connected with the input end of the recommendation module.
3. The big data-based parking lot space recommendation system of the big data-based parking lot space recommendation method according to claim 2, characterized in that: the instruction information acquisition module comprises an owner positioning unit and an owner reservation unit;
the vehicle owner positioning unit is used for positioning and recording the current position of a vehicle owner; the vehicle owner reservation unit is used for acquiring reserved parking time and reserved parking place provided by a vehicle owner;
the output end of the vehicle owner positioning unit is connected with the input end of the three-dimensional checking module; the output end of the vehicle owner reservation unit is connected with the input end of the three-dimensional checking module.
4. The big data-based parking lot space recommendation system of the big data-based parking lot space recommendation method according to claim 2, characterized in that: the multi-source data analysis module comprises a multi-source data acquisition unit, an owner driving proficiency analysis unit and a parking space parking difficulty analysis unit;
the multi-source data acquisition unit is used for acquiring multi-source data of a vehicle owner and a parking space; the vehicle owner driving proficiency analyzing unit is used for constructing a vehicle owner driving proficiency model and analyzing the driving proficiency of a vehicle owner; the parking space parking difficulty analysis unit is used for constructing a parking space parking difficulty model and analyzing the parking difficulty of the parking space;
the output end of the multi-source data acquisition unit is respectively connected with the input ends of the vehicle owner driving proficiency analysis unit and the parking space parking difficulty analysis unit; the output ends of the car owner driving proficiency analysis unit and the parking space parking difficulty analysis unit are connected with the input end of the parking space change prediction module.
5. The big data-based parking lot space recommendation system of the big data-based parking lot space recommendation method according to claim 2, characterized in that: the parking space change prediction module comprises a parking preference analysis unit and a parking duration analysis unit;
the parking preference analysis unit is used for analyzing the parking preference condition of each parking space in the parking lot and acquiring the parking preference probability; the parking duration analysis unit is used for analyzing the parking duration of the car owner in the parking lot and taking a mode as the predicted parking duration;
the output end of the parking preference analysis unit is connected with the input end of the recommendation module; the output end of the parking time length analysis unit is connected with the input end of the recommendation module.
6. The big data-based parking lot space recommendation system of the big data-based parking lot space recommendation method according to claim 2, characterized in that: the recommendation module comprises a parking space recommendation score sorting unit and a recommendation unit;
the parking space recommendation score sorting unit is used for constructing an optimal parking space recommendation model, calculating parking space recommendation scores according to the optimal parking space recommendation model and sorting the calculated parking space recommendation scores from large to small; the recommendation unit is used for recommending the corresponding parking spaces to the vehicle owners according to the sequence;
the output end of the parking place recommendation score sorting unit is connected with the input end of the recommendation unit; and the output end of the recommendation unit is connected to the vehicle owner port.
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