CN115775467B - Parking lot service intelligent management system and method based on Internet of things - Google Patents
Parking lot service intelligent management system and method based on Internet of things Download PDFInfo
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- CN115775467B CN115775467B CN202310101604.4A CN202310101604A CN115775467B CN 115775467 B CN115775467 B CN 115775467B CN 202310101604 A CN202310101604 A CN 202310101604A CN 115775467 B CN115775467 B CN 115775467B
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Abstract
The invention discloses an intelligent management system and method for parking lot services based on the Internet of things, and relates to the technical field of parking lot service management. The method comprises the steps of collecting data information of a vehicle and marking the vehicle exceeding a length threshold value and a width threshold value of the vehicle; constructing a parking time prediction model, and calculating a predicted value of the consumed time for marking the arrival of the vehicle at each idle parking space; constructing a parked vehicle judging model, and judging the type of parked vehicles on adjacent parking spaces of each idle parking space; constructing an optimal parking space recommendation model, and taking an idle parking space with the highest parking space recommendation score as an initial optimal parking space; the method comprises the steps of monitoring the entry states of owners of all entrances of a parking lot in real time, and judging whether the total time for the owners to forecast the vehicle to exit a parking space exceeds the time for marking the vehicles to reach the parking space where the owners are located; if the vehicle is not exceeded, calculating the recommended score of the parking space where the vehicle owner and the adjacent parking spaces, and outputting the parking space with higher score to the user port as the optimal parking space.
Description
Technical Field
The invention relates to the technical field of parking lot service management, in particular to an intelligent parking lot service management system and method based on the Internet of things.
Background
Along with the high-speed development of the economy of China and the rising of income level of people in China, the number of private cars in China is rapidly increased, the dilemma of less cars in most cities in China is faced, on one hand, the number of parking spaces in a parking lot is limited, and when people go out to eat or play on holidays or weekends, people often need to find the parking spaces in the parking lot for a long time; on the other hand, the parking of vehicles in a parking lot is often unordered, and has parking places, and when seeking parking places for some large-sized vehicles with longer length or wider width, the vehicles on the left and right adjacent parking places are also large-sized vehicles, and the problems that the parking is difficult or the opening angle of a vehicle door is too small due to a narrow space even if the vehicles are parked in the parking lot, and the people get off the vehicle are difficult exist.
Disclosure of Invention
The invention aims to provide a parking lot service intelligent management system and method based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
an intelligent management method for parking lot services based on the Internet of things comprises the following steps:
step S1: acquiring instruction information of a vehicle entering a parking lot, acquiring data information of the vehicle, setting a length threshold value and a width threshold value of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, and generating a marked vehicle;
step S2: acquiring the number and position number information of all the idle parking spaces in the parking lot and the speed of the marked vehicle at the entrance of the parking lot, constructing a parking time prediction model, and calculating a predicted value of the consumed time of the marked vehicle reaching each idle parking space;
step S3: acquiring parking space characteristics of adjacent parking spaces of each idle parking space, constructing a parked vehicle judging model, and judging the types of parked vehicles on the adjacent parking spaces of each idle parking space;
step S4: constructing an optimal parking space recommendation model, calculating the recommendation score of each idle parking space, and taking the idle parking space with the highest parking space recommendation score as an initial optimal parking space;
step S5: the method comprises the steps of monitoring the entry states of owners at all entrances of a parking lot in real time, if the owners enter the parking lot to drive out the vehicles, obtaining the position information of the parking spaces where the owners are located, and judging whether the total time of the owners for predicting the vehicle to drive out the parking spaces exceeds the time for marking the vehicles to reach the parking spaces where the owners are located;
Step S6: if the vehicle is not beyond, the characteristics of the adjacent parking spaces of the vehicle owner and the vehicle are obtained, the recommended score of the parking space of the vehicle owner and the adjacent parking spaces is calculated, the recommended score of the vehicle owner and the adjacent parking spaces is compared with the recommended score of the initial optimal parking space, and the parking space with the higher score is used as the optimal parking space to be output to a user port.
Further, in steps S1-S2,
acquiring the length of a vehicle at the entrance of a parking lot, and marking the length as a; acquiring the width of the vehicle, and marking as b;
setting a length threshold of the vehicle, denoted as a 0 The method comprises the steps of carrying out a first treatment on the surface of the Setting a width threshold of the vehicle, denoted b 0 ；
When a+b > a 0 +b 0 Generating a marked vehicle;
the construction of the parking time prediction model comprises the following steps:
the number of all the idle parking spaces in the parking lot is obtained and recorded as m; acquiring position number information of all idle parking spaces in a parking lot, and storing the position number information into a set from near to far according to the sequence from the entrance of the parking lot, wherein the set is denoted as B= { B 1 、B 2 、……、B m -a }; wherein B is 1 、B 2 、……、B m Position numbers of the m free parking spaces are respectively represented by 1, 2; the corresponding shortest route of the generation marking vehicle from the parking lot entrance to each free parking space is marked as S= { S 1 、S 2 、……、S m -a }; wherein S is 1 、S 2 、……、S m Respectively representing shortest route values of the marked vehicles from the parking lot entrance to the 1 st, 2 nd..once..m. free parking spaces;
Using sensors to obtain the speed of the marked vehicle at the entrance of the parking lot, noted v 0 ；
According to the formula: t is t k =S k /v 0
Wherein t is k A predicted value indicating the time taken for the marked vehicle to reach the kth free space; s is S k The shortest route value from the entrance of the parking lot to the kth free parking space of the marked vehicle is indicated.
In the technical scheme, the main object of the application is large vehicles with longer length or wider width, and the optimal parking space is found for the large vehicles, so that the length and the width of the vehicles are detected at the entrance of a parking lot, the data information of the vehicles is collected, the large vehicles meeting the conditions are marked, and the object of the system is determined; in consideration of the fact that the distances of the idle vehicles at the entrance of the parking lot are different, searching and position information obtaining are carried out on all the idle vehicles in the parking lot, a recycling system generates the shortest route reaching each idle vehicle, the time consumed by the user for reaching the idle vehicle is predicted according to the speed of the user entering the entrance of the parking lot, the user can be helped to reduce the time for searching the idle vehicle, and intelligent management of parking lot service is achieved.
Further, in step S3, the constructing a parked vehicle judgment model includes:
Taking the direction of the headstock of a parked vehicle on a parking space as a positive direction, marking the parking space on the left side of the parking space as a left adjacent parking space, and marking the parking space on the right side of the parking space as a right adjacent parking space;
acquiring the length and width of the vehicle on the left adjacent parking space of the kth idle parking space and marking the length and width as { a } lk ，b lk }；
Acquiring the length and width of the vehicle on the right adjacent parking space of the kth idle parking space and marking the length and width as { a } rk ，b rk }；
According to the formula: h lk =a lk +b lk 、H rk =a rk +b rk
Wherein H is lk Representing the sum of the length and the width of the vehicle on the left adjacent parking space of the kth idle parking space; h rk Representing the sum of the length and the width of the vehicle on the right adjacent parking space of the kth idle parking space;
setting the threshold value of the sum of the length and the width of the vehicle on the left and right adjacent parking spaces of each idle parking space, and marking as H 0 ；
Setting the vehicle with the sum of the length and the width exceeding a threshold value as a large vehicle;
constructing a parking vehicle judging function: p (P) k =0 or 1; wherein P is k =0 indicates that at least one of the vehicles on the left and right adjacent parking spaces of the kth free parking space is a large vehicle; p (P) k =1 indicates that the vehicles on the k-th free parking space left and right adjacent parking spaces are not large vehicles.
In the technical scheme, considering that when an idle parking space exists, the length and the width of the parked vehicles on left and right adjacent parking spaces of the idle parking space can influence the parking of the idle parking space vehicles under a certain length, especially for the idle parking spaces of large vehicles in all the left and right adjacent parking spaces, even if a user drives a small private car, the adjustable space can be reduced in the process of parking the vehicles, therefore, for the large vehicles, the system needs to avoid parking the large vehicles together as much as possible when parking the vehicles, and on one hand, the user needs to be ensured to have a certain adjustable space for adjusting the parking position of the vehicles; on the other hand, the opening angle of the door can ensure that the user can get off smoothly and can not touch the parked vehicle on the adjacent parking space when the user gets off.
Further, in step S4, the constructing the optimal parking space recommendation model includes:
setting an influence coefficient of a predicted value for marking the time spent by the vehicle reaching each idle parking space, and marking the influence coefficient as a 1 ；
Setting the influence coefficient of the type of parked vehicles on the left and right adjacent parking spaces of each idle parking space as a 2 ；
Constructing an optimal parking space recommendation model: w (W) k =a 1 *t k +a 2 *P k
Wherein W is k A recommendation score representing the kth free parking space; a, a 1 The influence coefficient of the predicted value for indicating the time spent by the marked vehicle to reach each idle parking space; t is t k A predicted value indicating the time taken for the marked vehicle to reach the kth free space; a, a 2 The influence coefficient of the type of the parked vehicle on the adjacent parking spaces of each idle parking space is represented; p (P) k And indicating the types of parked vehicles on the left and right adjacent parking spaces of the kth idle parking space.
In the above technical scheme, when a plurality of parking spaces exist in the parking lot, in order to avoid the problem of difficulty in parking and getting off due to parking of a plurality of large vehicles together as much as possible, when an idle parking space with no large vehicle exists in a position with a far distance, the system can determine the optimal parking space by calculating the recommendation score of the idle parking space with a far distance, so that when the optimal parking space recommendation model is constructed, the time consumed for predicting the idle parking space and the types of parked vehicles on the left and right adjacent parking spaces of the idle parking space are taken as influencing factors, and the accuracy of the system is improved.
Further, in steps S5-S6,
monitoring the entry state of the vehicle owner at each entry of the parking lot in real time, and if the vehicle owner enters the parking lot to drive out the vehicle, acquiring the position of the parking space where the vehicle owner is locatedInformation, denoted as B 0 ；
The shortest distance value of the marked vehicle from the entrance of the parking lot to the parking space where the vehicle owner is located is obtained and is recorded as S 0 ；
According to the formula: t is t B0 =S 0 /v 0
Wherein t is B0 Indicating the predicted consumed time for the marked vehicle to reach the parking space of the vehicle owner from the entrance of the parking lot;
the distance between the car owner and the parking space where the car is located is obtained and is recorded as x 1 The method comprises the steps of carrying out a first treatment on the surface of the The moving speed of the car owner is obtained and recorded as v 1 ；
According to the formula: t (T) 0 =x 1 /v 1
Wherein T is 0 The predicted travelling time of the car owner reaching the parking space where the car is located is represented:
obtaining G parking lot vehicle owner history time-lifting sequences G= { T by using big data 1 、T 2 、……、T g -a }; the vehicle lifting time refers to the time when a vehicle owner opens a vehicle from a parking space;
according to the formula: t (T) y0 Σtj=1/g, sum range j=1 to g;
wherein T is y0 Indicating the predicted vehicle lifting time of the vehicle owner, T j Representing the historic vehicle lifting time of the vehicle owner of the jth parking lot;
according to the formula: t (T) z =T 0 +T y0
Wherein T is z The total time for the vehicle owner to forecast and consume the vehicle to leave the parking space is represented;
if T z ＞t B0 Discarding the parking space where the vehicle owner vehicle is located, and recommending an initial optimal parking space;
If T z ≤t B0 The method comprises the steps of obtaining the parking space characteristics of the adjacent parking spaces of the vehicle owners, calculating the recommended scores of the parking spaces of the vehicle owners and the adjacent parking spaces, comparing the recommended scores with the recommended scores of the initial optimal parking spaces, and outputting the parking spaces with higher scores as the optimal parking spaces.
According to the technical scheme, considering that when the marked vehicle just enters the parking lot entrance, a vehicle owner who leaves enters the parking lot from other entrances, the distance between the vehicle owner's parking space and the marked vehicle is possibly short, the time consumed by the vehicle to start the vehicle is less than the time consumed by the marked vehicle to reach the parking space where the vehicle owner's vehicle is located, the parking space where the vehicle owner's vehicle is located can be included in the optimal parking space recommendation range of the marked vehicle, meanwhile, the recommendation scores of the vehicle owner's vehicle location and the adjacent parking spaces are calculated, compared with the recommendation scores of the initial optimal parking space, the parking space with higher score is output to a user port as the optimal parking space, and the accuracy of the system can be ensured.
The intelligent management system for the parking lot service based on the Internet of things comprises a data acquisition module, a prediction model construction analysis module, a vehicle judgment module, a parking space recommendation module, a real-time monitoring prediction module and a recommendation output module;
The data acquisition module is used for acquiring data information of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, generating marked vehicles, acquiring the number and position number information of all the idle parking spaces in the parking lot, acquiring the speed of the marked vehicles at the entrance of the parking lot by using the speed sensor, and acquiring the parking space characteristics of the adjacent parking spaces of each idle parking space; the prediction model construction analysis module is used for constructing a parking time prediction model and calculating a prediction value of the consumption time of the marked vehicle reaching each idle parking space; the vehicle judging module is used for constructing a parked vehicle judging model and judging the types of parked vehicles on adjacent parking spaces of each idle parking space; the parking space recommending module is used for constructing an optimal parking space recommending model, calculating the recommending score of each idle parking space, and taking the idle parking space with the highest recommending score as an initial optimal parking space; the real-time monitoring prediction module is used for monitoring the entry state of the vehicle owner at each entry of the parking lot in real time, and judging whether the total time consumed by the vehicle owner for predicting the vehicle to enter the parking lot exceeds the time for marking the vehicle to reach the parking lot where the vehicle owner is located or not if the vehicle owner enters the parking lot to enter the parking lot to leave the vehicle; the recommendation output module is used for acquiring the parking space characteristics of the adjacent parking space of the vehicle owner, calculating the recommendation score of the parking space of the vehicle owner and the adjacent parking space, comparing the recommendation score with the recommendation score of the initial optimal parking space, and outputting the parking space with higher score as the optimal parking space to the user port when the total time of the vehicle owner for starting the parking space prediction to consume does not exceed the time for marking the vehicle to reach the parking space of the vehicle owner;
The output end of the data acquisition module is connected with the input end of the prediction model construction analysis module; the output end of the prediction model construction analysis module is connected with the input end of the vehicle judgment module; the output end of the vehicle judging module is connected with the input end of the parking space recommending module; the output end of the parking space recommending module is connected with the input end of the real-time monitoring and predicting module; and the output end of the real-time monitoring and predicting module is connected with the input end of the recommending output module.
Further, the data acquisition module comprises a vehicle information acquisition unit and a parking space information acquisition unit;
the vehicle information acquisition unit is used for acquiring data information of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, generating a marked vehicle, and acquiring the speed of the marked vehicle at the entrance of the parking lot by using the speed sensor;
the parking space information acquisition unit is used for acquiring the number and position number information of all the idle parking spaces in the parking lot and the parking space characteristics of adjacent parking spaces of each idle parking space;
the output end of the vehicle information acquisition unit is connected with the input end of the parking space information acquisition unit; the output end of the parking space information acquisition unit is connected with the input end of the prediction model construction analysis module;
The prediction model construction analysis module comprises a prediction model construction unit and a prediction model analysis unit;
the prediction model construction unit is used for constructing a parking time prediction model;
the prediction model analysis unit is used for calculating a predicted value of the time consumed by the marked vehicle to reach each idle parking space based on the position number information of all the idle parking spaces and the speed of the marked vehicle at the entrance of the parking lot;
the output end of the prediction model construction unit is connected with the input end of the prediction model analysis unit; the output end of the prediction model analysis unit is connected with the input end of the vehicle judgment module.
Further, the vehicle judging module comprises a judging model constructing unit and a judging model analyzing unit;
the judging model building unit is used for building a parked vehicle judging model;
the judging model analysis unit is used for judging the type of the parked vehicle on each idle parking space adjacent parking space based on the parking space characteristics of each idle parking space adjacent parking space;
the output end of the judgment model construction unit is connected with the input end of the judgment model analysis unit; the output end of the judgment model analysis unit is connected with the input end of the parking space recommendation module;
The parking space recommendation module comprises an optimal parking space recommendation model construction unit and an initial output unit;
the optimal parking space recommendation model construction unit is used for constructing an optimal parking space recommendation model;
the initial output unit is used for calculating the recommended score of each idle parking space, and taking the idle parking space with the highest recommended score of the parking space as an initial optimal parking space;
the output end of the optimal parking space recommendation model building unit is connected with the input end of the initial output unit; and the output end of the initial output unit is connected with the input end of the real-time monitoring and predicting module.
Further, the real-time monitoring prediction module comprises a real-time monitoring unit and a prediction judging unit;
the real-time monitoring unit is used for monitoring the entry state of the car owners at each entrance of the parking lot in real time;
the prediction judging unit is used for calculating the total time of the vehicle owner for predicting the vehicle to leave the parking space when the vehicle owner enters the parking lot to leave the vehicle, and judging whether the total time of the vehicle owner for predicting the vehicle to leave the parking space exceeds the time for marking the vehicle to reach the parking space where the vehicle owner is located;
the output end of the real-time monitoring unit is connected with the input end of the prediction judging unit; and the output end of the prediction judging unit is connected with the input end of the recommendation output module.
Further, the recommendation output module comprises a parking space analysis unit and a final output unit;
the parking space analysis unit is used for acquiring the parking space characteristics of the adjacent parking space of the vehicle owner, calculating the recommended score of the parking space of the vehicle owner and the adjacent parking space, and comparing the recommended score with the recommended score of the initial optimal parking space when the total time for the vehicle owner to drive the vehicle out of the parking space is not longer than the time for marking the vehicle to reach the parking space of the vehicle owner;
the final output unit is used for outputting the parking space with higher score to the user port as the optimal parking space;
the output end of the parking space analysis unit is connected with the input end of the final output unit.
Compared with the prior art, the invention has the following beneficial effects: the method can collect and detect the length and the width of the vehicle at the entrance of the parking lot, mark the vehicle exceeding the length threshold value and the width threshold value of the vehicle, and generate a marked vehicle; the method comprises the steps of constructing an optimal parking space recommendation model, and screening an initial optimal parking space based on a predicted value of the consumption time of marking vehicles to reach each idle parking space and the type of parked vehicles in adjacent parking spaces of each idle parking space; meanwhile, analyzing an owner entering a parking lot and needing to drive a vehicle out, calculating the recommended score of the parking space of the owner where the vehicle is located and the adjacent parking spaces, comparing the recommended score with the recommended score of the initial optimal parking space, and outputting the parking space with higher score as the optimal parking space to a user port; the invention can realize more accurate parking space recommendation for the large-sized vehicles, avoid the problems of difficult parking and difficult lifting of the users caused by parking a plurality of large-sized vehicles together, further save more time cost for the users and promote the intelligent management of the parking lot.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow diagram of a parking lot service intelligent management method based on the Internet of things;
fig. 2 is a schematic structural diagram of a parking lot service intelligent management system based on the internet of things.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
an intelligent management method for parking lot services based on the Internet of things comprises the following steps:
step S1: acquiring instruction information of a vehicle entering a parking lot, acquiring data information of the vehicle, setting a length threshold value and a width threshold value of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, and generating a marked vehicle;
Step S2: acquiring the number and position number information of all the idle parking spaces in the parking lot and the speed of the marked vehicle at the entrance of the parking lot, constructing a parking time prediction model, and calculating a predicted value of the consumed time of the marked vehicle reaching each idle parking space;
step S3: acquiring parking space characteristics of adjacent parking spaces of each idle parking space, constructing a parked vehicle judging model, and judging the types of parked vehicles on the adjacent parking spaces of each idle parking space;
step S4: constructing an optimal parking space recommendation model, calculating the recommendation score of each idle parking space, and taking the idle parking space with the highest parking space recommendation score as an initial optimal parking space;
step S5: the method comprises the steps of monitoring the entry states of owners at all entrances of a parking lot in real time, if the owners enter the parking lot to drive out the vehicles, obtaining the position information of the parking spaces where the owners are located, and judging whether the total time of the owners for predicting the vehicle to drive out the parking spaces exceeds the time for marking the vehicles to reach the parking spaces where the owners are located;
step S6: if the vehicle is not beyond, the characteristics of the adjacent parking spaces of the vehicle owner and the vehicle are obtained, the recommended score of the parking space of the vehicle owner and the adjacent parking spaces is calculated, the recommended score of the vehicle owner and the adjacent parking spaces is compared with the recommended score of the initial optimal parking space, and the parking space with the higher score is used as the optimal parking space to be output to a user port.
Further, in steps S1-S2,
acquiring the length of a vehicle at the entrance of a parking lot, and marking the length as a; acquiring the width of the vehicle, and marking as b;
setting a length threshold of the vehicle, denoted as a 0 The method comprises the steps of carrying out a first treatment on the surface of the Setting a width threshold of the vehicle, denoted b 0 ；
When a+b > a 0 +b 0 Generating a marked vehicle;
the construction of the parking time prediction model comprises the following steps:
the number of all the idle parking spaces in the parking lot is obtained and recorded as m; acquiring position number information of all idle parking spaces in a parking lot, and storing the position number information into a set from near to far according to the sequence from the entrance of the parking lot, wherein the set is denoted as B= { B 1 、B 2 、……、B m -a }; wherein B is 1 、B 2 、……、B m Position numbers of the m free parking spaces are respectively represented by 1, 2; the corresponding shortest route of the generation marking vehicle from the parking lot entrance to each free parking space is marked as S= { S 1 、S 2 、……、S m -a }; wherein S is 1 、S 2 、……、S m Respectively representing shortest route values of the marked vehicles from the parking lot entrance to the 1 st, 2 nd..once..m. free parking spaces;
acquiring a marked vehicle using a sensorThe speed at the entrance of the parking lot, denoted v 0 ；
According to the formula: t is t k =S k /v 0
Wherein t is k A predicted value indicating the time taken for the marked vehicle to reach the kth free space; s is S k The shortest route value from the entrance of the parking lot to the kth free parking space of the marked vehicle is indicated.
Further, the constructing a parked vehicle judgment model includes:
taking the direction of the headstock of a parked vehicle on a parking space as a positive direction, marking the parking space on the left side of the parking space as a left adjacent parking space, and marking the parking space on the right side of the parking space as a right adjacent parking space;
acquiring the length and width of the vehicle on the left adjacent parking space of the kth idle parking space and marking the length and width as { a } lk ，b lk }；
Acquiring the length and width of the vehicle on the right adjacent parking space of the kth idle parking space and marking the length and width as { a } rk ，b rk }；
According to the formula: h lk =a lk +b lk 、H rk =a rk +b rk
Wherein H is lk Representing the sum of the length and the width of the vehicle on the left adjacent parking space of the kth idle parking space; h rk Representing the sum of the length and the width of the vehicle on the right adjacent parking space of the kth idle parking space;
setting the threshold value of the sum of the length and the width of the vehicle on the left and right adjacent parking spaces of each idle parking space, and marking as H 0 ；
Setting the vehicle with the sum of the length and the width exceeding a threshold value as a large vehicle;
constructing a parking vehicle judging function: p (P) k =0 or 1; wherein P is k =0 indicates that at least one of the vehicles on the left and right adjacent parking spaces of the kth free parking space is a large vehicle; p (P) k =1 indicates that the vehicles on the k-th free parking space left and right adjacent parking spaces are not large vehicles.
Further, in step S4, the constructing the optimal parking space recommendation model includes:
Setting an influence coefficient of a predicted value for marking the time spent by the vehicle reaching each idle parking space, and marking the influence coefficient as a 1 ；
Setting the influence coefficient of the type of parked vehicles on the left and right adjacent parking spaces of each idle parking space as a 2 ；
Constructing an optimal parking space recommendation model: w (W) k =a 1 *t k +a 2 *P k
Wherein W is k A recommendation score representing the kth free parking space; a, a 1 The influence coefficient of the predicted value for indicating the time spent by the marked vehicle to reach each idle parking space; t is t k A predicted value indicating the time taken for the marked vehicle to reach the kth free space; a, a 2 The influence coefficient of the type of the parked vehicle on the adjacent parking spaces of each idle parking space is represented; p (P) k And indicating the types of parked vehicles on the left and right adjacent parking spaces of the kth idle parking space.
Further, in steps S5-S6,
monitoring the entry state of the vehicle owner at each entry of the parking lot in real time, if the vehicle owner enters the parking lot to drive out the vehicle, acquiring the position information of the parking space where the vehicle owner is located, and marking as B 0 ；
The shortest distance value of the marked vehicle from the entrance of the parking lot to the parking space where the vehicle owner is located is obtained and is recorded as S 0 ；
According to the formula: t is t B0 =S 0 /v 0
Wherein t is B0 Indicating the predicted consumed time for the marked vehicle to reach the parking space of the vehicle owner from the entrance of the parking lot;
the distance between the car owner and the parking space where the car is located is obtained and is recorded as x 1 The method comprises the steps of carrying out a first treatment on the surface of the The moving speed of the car owner is obtained and recorded as v 1 ；
According to the formula: t (T) 0 =x 1 /v 1
Wherein T is 0 The predicted travelling time of the car owner reaching the parking space where the car is located is represented:
obtaining G parking lot vehicle owner history time-lifting sequences G= { T by using big data 1 、T 2 、……、T g -a }; the vehicle lifting time refers to the time when a vehicle owner opens a vehicle from a parking space;
according to the formula: t (T) y0 Σtj=1/g, sum range j=1 to g;
wherein T is y0 Indicating the predicted vehicle lifting time of the vehicle owner, T j Representing the historic vehicle lifting time of the vehicle owner of the jth parking lot;
according to the formula: t (T) z =T 0 +T y0
Wherein T is z The total time for the vehicle owner to forecast and consume the vehicle to leave the parking space is represented;
if T z ＞t B0 Discarding the parking space where the vehicle owner vehicle is located, and recommending an initial optimal parking space;
if T z ≤t B0 The method comprises the steps of obtaining the parking space characteristics of the adjacent parking spaces of the vehicle owners, calculating the recommended scores of the parking spaces of the vehicle owners and the adjacent parking spaces, comparing the recommended scores with the recommended scores of the initial optimal parking spaces, and outputting the parking spaces with higher scores as the optimal parking spaces.
The intelligent management system for the parking lot service based on the Internet of things comprises a data acquisition module, a prediction model construction analysis module, a vehicle judgment module, a parking space recommendation module, a real-time monitoring prediction module and a recommendation output module;
The data acquisition module is used for acquiring data information of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, generating marked vehicles, acquiring the number and position number information of all the idle parking spaces in the parking lot, acquiring the speed of the marked vehicles at the entrance of the parking lot by using the speed sensor, and acquiring the parking space characteristics of the adjacent parking spaces of each idle parking space; the prediction model construction analysis module is used for constructing a parking time prediction model and calculating a prediction value of the consumption time of the marked vehicle reaching each idle parking space; the vehicle judging module is used for constructing a parked vehicle judging model and judging the types of parked vehicles on adjacent parking spaces of each idle parking space; the parking space recommending module is used for constructing an optimal parking space recommending model, calculating the recommending score of each idle parking space, and taking the idle parking space with the highest recommending score as an initial optimal parking space; the real-time monitoring prediction module is used for monitoring the entry state of the vehicle owner at each entry of the parking lot in real time, and judging whether the total time consumed by the vehicle owner for predicting the vehicle to enter the parking lot exceeds the time for marking the vehicle to reach the parking lot where the vehicle owner is located or not if the vehicle owner enters the parking lot to enter the parking lot to leave the vehicle; the recommendation output module is used for acquiring the parking space characteristics of the adjacent parking space of the vehicle owner, calculating the recommendation score of the parking space of the vehicle owner and the adjacent parking space, comparing the recommendation score with the recommendation score of the initial optimal parking space, and outputting the parking space with higher score as the optimal parking space to the user port when the total time of the vehicle owner for starting the parking space prediction to consume does not exceed the time for marking the vehicle to reach the parking space of the vehicle owner;
The output end of the data acquisition module is connected with the input end of the prediction model construction analysis module; the output end of the prediction model construction analysis module is connected with the input end of the vehicle judgment module; the output end of the vehicle judging module is connected with the input end of the parking space recommending module; the output end of the parking space recommending module is connected with the input end of the real-time monitoring and predicting module; and the output end of the real-time monitoring and predicting module is connected with the input end of the recommending output module.
Further, the data acquisition module comprises a vehicle information acquisition unit and a parking space information acquisition unit;
the vehicle information acquisition unit is used for acquiring data information of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, generating a marked vehicle, and acquiring the speed of the marked vehicle at the entrance of the parking lot by using the speed sensor;
the parking space information acquisition unit is used for acquiring the number and position number information of all the idle parking spaces in the parking lot and the parking space characteristics of adjacent parking spaces of each idle parking space;
the output end of the vehicle information acquisition unit is connected with the input end of the parking space information acquisition unit; the output end of the parking space information acquisition unit is connected with the input end of the prediction model construction analysis module;
The prediction model construction analysis module comprises a prediction model construction unit and a prediction model analysis unit;
the prediction model construction unit is used for constructing a parking time prediction model;
the prediction model analysis unit is used for calculating a predicted value of the time consumed by the marked vehicle to reach each idle parking space based on the position number information of all the idle parking spaces and the speed of the marked vehicle at the entrance of the parking lot;
the output end of the prediction model construction unit is connected with the input end of the prediction model analysis unit; the output end of the prediction model analysis unit is connected with the input end of the vehicle judgment module.
Further, the vehicle judging module comprises a judging model constructing unit and a judging model analyzing unit;
the judging model building unit is used for building a parked vehicle judging model;
the judging model analysis unit is used for judging the type of the parked vehicle on each idle parking space adjacent parking space based on the parking space characteristics of each idle parking space adjacent parking space;
the output end of the judgment model construction unit is connected with the input end of the judgment model analysis unit; the output end of the judgment model analysis unit is connected with the input end of the parking space recommendation module;
The parking space recommendation module comprises an optimal parking space recommendation model construction unit and an initial output unit;
the optimal parking space recommendation model construction unit is used for constructing an optimal parking space recommendation model;
the initial output unit calculates the recommended score of each idle parking space and is used for taking the idle parking space with the highest recommended score of the parking space as an initial optimal parking space;
the output end of the optimal parking space recommendation model building unit is connected with the input end of the initial output unit; and the output end of the initial output unit is connected with the input end of the real-time monitoring and predicting module.
Further, the real-time monitoring prediction module comprises a real-time monitoring unit and a prediction judging unit;
the real-time monitoring unit is used for monitoring the entry state of the car owners at each entrance of the parking lot in real time;
the prediction judging unit is used for calculating the total time of the vehicle owner for predicting the vehicle to leave the parking space when the vehicle owner enters the parking lot to leave the vehicle, and judging whether the total time of the vehicle owner for predicting the vehicle to leave the parking space exceeds the time for marking the vehicle to reach the parking space where the vehicle owner is located;
the output end of the real-time monitoring unit is connected with the input end of the prediction judging unit; and the output end of the prediction judging unit is connected with the input end of the recommendation output module.
Further, the recommendation output module comprises a parking space analysis unit and a final output unit;
the parking space analysis unit is used for acquiring the parking space characteristics of the adjacent parking space of the vehicle owner, calculating the recommended score of the parking space of the vehicle owner and the adjacent parking space, and comparing the recommended score with the recommended score of the initial optimal parking space when the total time for the vehicle owner to drive the vehicle out of the parking space is not longer than the time for marking the vehicle to reach the parking space of the vehicle owner;
the final output unit is used for outputting the parking space with higher score to the user port as the optimal parking space;
the output end of the parking space analysis unit is connected with the input end of the final output unit.
In this embodiment:
acquiring the length a=4.8 of the vehicle; acquiring a width b=2 of the vehicle;
setting a length threshold a of a vehicle 0 =4.3; setting a width threshold b of a vehicle 0 =1.8；
Because a+b > a 0 +b 0 Generating a marking vehicle;
obtaining the number m=6 of all the idle parking spaces in the parking lot; acquiring position number information of all idle parking spaces in a parking lot, and moving from near to far from an entrance of the parking lotThe sequential-store set is denoted as b= { B 1 、B 2 、……、B 6 -a }; wherein B is 1 、B 2 、……、B 6 Position numbers of the 1 st, 2 nd and 6 th free parking spaces are respectively represented; the corresponding shortest route of the generation marking vehicle from the parking lot entrance to each free parking space is marked as S= { S 1 、S 2 、……、S 6 -a }; wherein S is 1 、S 2 、……、S 6 The shortest route values from the parking lot entrance to the 1 st, 2 nd, and 6 th free parking spaces of the marked vehicle are respectively represented;
obtaining speed v of marked vehicle at parking lot entrance by using sensor 0 =250m/min；
According to the formula: t is t k =S k /v 0
Wherein t is k A predicted value indicating the time taken for the marked vehicle to reach the kth free space; s is S k The shortest route value from the entrance of the parking lot to the kth free parking space of the marked vehicle is indicated.
Obtaining t 1 =1.6min、t 2 =1.72min、t 3 =1.84min、t 4 =2.49min、t 5 =3.19min、t 6 =3.2min；
Taking the direction of the headstock of a parked vehicle on a parking space as a positive direction, marking the parking space on the left side of the parking space as a left adjacent parking space, and marking the parking space on the right side of the parking space as a right adjacent parking space;
acquiring length and width { a } of vehicles on left adjacent parking space of 1 st idle parking space L1 =4.5，b L1 =1.8}；
The sum H of the length and the width of the vehicle on the left adjacent parking space of the 1 st idle parking space L1 =6.3；
The length and width of the vehicle on the right adjacent parking space of the 1 st idle parking space are obtained and recorded as { a } r1 =4.4，b r1 =1.6}；
The sum H of the length and the width of the vehicle on the left adjacent parking space of the 1 st idle parking space r1 =6.0；
And so on can be obtained:
{a L2 =4.6，b L2 =1.8}；H L2 =6.4；{a r2 =4.4，b r2 =1.6}；H r2 =6.0；
{a L3 =4.7，b L3 =1.8}；H L3 =6.5；{a r3 =4.6，b r3 =1.6}；H r3 =6.2；
{a L4 =4.3，b L4 =1.6}；H L4 =5.9；{a r4 =4.3，b r4 =1.7}；H r4 =6.0；
{a L5 =4.8，b L5 =2}；H L5 =6.8；{a r5 =0，b r5 =0}；H r5 =0；
{a L6 =0，b L6 =0}；H L6 =0；{a r6 =4.3，b r6 =1.6}；H r6 =5.9；
setting a threshold H of sum of length and width of vehicles on left and right adjacent parking spaces of each idle parking space 0 =6.1；
Setting the vehicle with the sum of the length and the width exceeding a threshold value as a large vehicle;
Constructing a parking vehicle judging function:
P k =0 or 1; (H) lk 、H rk )≥H 0 Time P k =0；0≤(H lk 、H rk )＜H 0 Time P k =1；
Wherein P is k =0 indicates that at least one of the vehicles on the left and right adjacent parking spaces of the kth free parking space is a large vehicle; p (P) k =1 indicates that the vehicles on the k-th free parking space left and right adjacent parking spaces are not large vehicles. Thus, there are: p (P) 1 =0、P 2 =0、P 3 =0、P 4 =1、P 5 =0、P 6 =1；
Setting an influence coefficient a of a predicted value of the time spent by marking the arrival of the vehicle at each free parking space 1 =0.6；
Setting the influence coefficient of the type of parked vehicles on the adjacent parking spaces of each idle parking space, and marking the influence coefficient as a 2 =0.4；
According to the formula:
W k =a 1 *t k +a 2 *P k
w1=0.96; w2=1.032; w3=1.104; w4=1.494; w5= 1.914; w6=1.92;
because W6 > W5 > W4 > W3 > W2 > W1;
the sixth free parking space is the initial optimal parking space;
monitoring the entry state of the vehicle owner at each entry of the parking lot in real time, if the vehicle owner enters the parking lot to drive out the vehicle, acquiring the position information of the parking space where the vehicle owner is located, and marking as B 0 ；
Obtaining the shortest distance value S of the marked vehicle from the entrance of the parking lot to the parking space where the vehicle owner vehicle is located 0 =200m；
Marking the time t for the vehicle to reach the parking space of the owner from the entrance of the parking lot B0 =S 0 /v 0 =0.8min；
The distance between the car owner and the parking space where the car is located is obtained and is recorded as x 1 =50m; the moving speed of the car owner is obtained and recorded as v 1 =80m/min；
According to the formula: t (T) 0 =x 1 /v 1 =0.625min
The vehicle owner achieves the predicted travelling time of the parking space where the vehicle is located:
obtaining G parking lot vehicle owner history time-lifting sequences G= { T by using big data 1 、T 2 、……、T g -a }; the vehicle lifting time refers to the time when a vehicle owner opens a vehicle from a parking space;
according to the formula: t (T) y0 Σtj=0.5 min, sum range j=1 to g;
wherein T is y0 Indicating the predicted vehicle lifting time of the vehicle owner, T j Representing the historic vehicle lifting time of the vehicle owner of the jth parking lot;
according to the formula, the vehicle owner predicts the total time consumed by the vehicle in the parking space:
according to the formula: t (T) z =T 0 +T y0 =1.125min
Wherein T is z The total time for the vehicle owner to forecast and consume the vehicle to leave the parking space is represented;
because of T z ＞t B0 And discarding the parking space where the vehicle owner vehicle is located, and recommending the initial optimal parking space.
It is noted that relational terms such as first and second, and the like are 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. Moreover, 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: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The intelligent management method for the parking lot service based on the Internet of things is characterized by comprising the following steps of:
step S1: acquiring instruction information of a vehicle entering a parking lot, acquiring data information of the vehicle, setting a length threshold value and a width threshold value of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, and generating a marked vehicle;
step S2: acquiring the number and position number information of all the idle parking spaces in the parking lot and the speed of the marked vehicle at the entrance of the parking lot, constructing a parking time prediction model, and calculating a predicted value of the consumed time of the marked vehicle reaching each idle parking space;
Step S3: acquiring parking space characteristics of adjacent parking spaces of each idle parking space, constructing a parked vehicle judging model, and judging the types of parked vehicles on the adjacent parking spaces of each idle parking space;
step S4: constructing an optimal parking space recommendation model, calculating the recommendation score of each idle parking space, and taking the idle parking space with the highest parking space recommendation score as an initial optimal parking space;
step S5: the method comprises the steps of monitoring the entry states of owners at all entrances of a parking lot in real time, if the owners enter the parking lot to drive out the vehicles, obtaining the position information of the parking spaces where the owners are located, and judging whether the total time of the owners for predicting the vehicle to drive out the parking spaces exceeds the time for marking the vehicles to reach the parking spaces where the owners are located;
step S6: if the vehicle is not exceeded, obtaining the characteristics of the adjacent parking spaces of the vehicle owners, calculating the recommended scores of the parking spaces of the vehicle owners and the adjacent parking spaces, comparing the recommended scores with the recommended scores of the initial optimal parking spaces, and outputting the parking spaces with higher scores to a user port as the optimal parking spaces;
in the steps S1-S2,
acquiring the length of a vehicle at the entrance of a parking lot, and marking the length as a; acquiring the width of the vehicle, and marking as b;
setting a length threshold of the vehicle, denoted as a 0 The method comprises the steps of carrying out a first treatment on the surface of the Setting a width threshold of the vehicle, denoted b 0 ；
When a+b > a 0 +b 0 Generating a marked vehicle;
the construction of the parking time prediction model comprises the following steps:
the number of all the idle parking spaces in the parking lot is obtained and recorded as m; acquiring position number information of all idle parking spaces in a parking lot, and storing the position number information into a set from near to far according to the sequence from the entrance of the parking lot, wherein the set is denoted as B= { B 1 、B 2 、……、B m -a }; wherein B is 1 、B 2 、……、B m Position numbers of the m free parking spaces are respectively represented by 1, 2; the corresponding shortest route of the generation marking vehicle from the parking lot entrance to each free parking space is marked as S= { S 1 、S 2 、……、S m -a }; wherein S is 1 、S 2 、……、S m Respectively representing shortest route values of the marked vehicles from the parking lot entrance to the 1 st, 2 nd..once..m. free parking spaces;
using sensors to obtain the speed of the marked vehicle at the entrance of the parking lot, noted v 0 ；
According to the formula: t is t k =S k /v 0
Wherein t is k A predicted value indicating the time taken for the marked vehicle to reach the kth free space; s is S k Representing a shortest route value of the marked vehicle from the entrance of the parking lot to the kth free parking space;
in step S3, the constructing a parked vehicle judgment model includes:
taking the direction of the headstock of a parked vehicle on a parking space as a positive direction, marking the parking space on the left side of the parking space as a left adjacent parking space, and marking the parking space on the right side of the parking space as a right adjacent parking space;
Acquiring the length and width of the vehicle on the left adjacent parking space of the kth idle parking space and marking the length and width as { a } lk ，b lk }；
Acquiring the length and width of the vehicle on the right adjacent parking space of the kth idle parking space and marking the length and width as { a } rk ，b rk }；
According to the formula: h lk =a lk +b lk 、H rk =a rk +b rk
Wherein H is lk Representing the sum of the length and the width of the vehicle on the left adjacent parking space of the kth idle parking space; h rk Representing the sum of the length and the width of the vehicle on the right adjacent parking space of the kth idle parking space;
setting the threshold value of the sum of the length and the width of the vehicle on the left and right adjacent parking spaces of each idle parking space, and marking as H 0 ；
Setting the vehicle with the sum of the length and the width exceeding a threshold value as a large vehicle;
constructing a parking vehicle judging function: p (P) k =0 or 1; wherein P is k =0 indicates that at least one of the vehicles on the left and right adjacent parking spaces of the kth free parking space is a large vehicle; p (P) k =1 indicates the k free parking space left and right adjacent parking spacesNone of the vehicles above are large vehicles;
in step S4, the constructing an optimal parking space recommendation model includes:
setting an influence coefficient of a predicted value for marking the time spent by the vehicle reaching each idle parking space, and marking the influence coefficient as a 1 ；
Setting the influence coefficient of the type of parked vehicles on the left and right adjacent parking spaces of each idle parking space as a 2 ；
Constructing an optimal parking space recommendation model: w (W) k =a 1 *t k +a 2 *P k
Wherein W is k A recommendation score representing the kth free parking space; a, a 1 The influence coefficient of the predicted value for indicating the time spent by the marked vehicle to reach each idle parking space; t is t k A predicted value indicating the time taken for the marked vehicle to reach the kth free space; a, a 2 The influence coefficient of the type of the parked vehicle on the adjacent parking spaces of each idle parking space is represented; p (P) k And indicating the types of parked vehicles on the left and right adjacent parking spaces of the kth idle parking space.
2. The intelligent management method for parking lot services based on the internet of things according to claim 1, wherein the intelligent management method is characterized by comprising the following steps: in the steps S5-S6,
monitoring the entry state of the vehicle owner at each entry of the parking lot in real time, if the vehicle owner enters the parking lot to drive out the vehicle, acquiring the position information of the parking space where the vehicle owner is located, and marking as B 0 ；
The shortest distance value of the marked vehicle from the entrance of the parking lot to the parking space where the vehicle owner is located is obtained and is recorded as S 0 ；
According to the formula: t is t B0 =S 0 /v 0
Wherein t is B0 Indicating the predicted consumed time for the marked vehicle to reach the parking space of the vehicle owner from the entrance of the parking lot;
the distance between the car owner and the parking space where the car is located is obtained and is recorded as x 1 The method comprises the steps of carrying out a first treatment on the surface of the The moving speed of the car owner is obtained and recorded as v 1 ；
According to the formula: t (T) 0 =x 1 /v 1
Wherein T is 0 Indicating the predicted travelling time of the vehicle owner reaching the parking space where the vehicle is located;
Obtaining G parking lot vehicle owner history time-lifting sequences G= { T by using big data 1 、T 2 、……、T g -a }; the vehicle lifting time refers to the time when a vehicle owner opens a vehicle from a parking space;
according to the formula: t (T) y0 Σtj=1/g, sum range j=1 to g;
wherein T is y0 Indicating the predicted vehicle lifting time of the vehicle owner, T j Representing the historic vehicle lifting time of the vehicle owner of the jth parking lot;
according to the formula: t (T) z =T 0 +T y0
Wherein T is z The total time for the vehicle owner to forecast and consume the vehicle to leave the parking space is represented;
if T z ＞t B0 Discarding the parking space where the vehicle owner vehicle is located, and recommending an initial optimal parking space;
if T z ≤t B0 The method comprises the steps of obtaining the parking space characteristics of the adjacent parking spaces of the vehicle owners, calculating the recommended scores of the parking spaces of the vehicle owners and the adjacent parking spaces, comparing the recommended scores with the recommended scores of the initial optimal parking spaces, and outputting the parking spaces with higher scores as the optimal parking spaces.
3. The intelligent management system for parking lot services based on the internet of things, which applies the intelligent management method for parking lot services based on the internet of things as claimed in any one of claims 1-2, is characterized in that: the system comprises a data acquisition module, a prediction model construction analysis module, a vehicle judgment module, a parking stall recommendation module, a real-time monitoring prediction module and a recommendation output module;
The data acquisition module is used for acquiring data information of vehicles, marking the vehicles exceeding the length threshold value and the width threshold value of the vehicles, generating marked vehicles, acquiring the number and position number information of all idle parking spaces in the parking lot, acquiring the speed of the marked vehicles at the entrance of the parking lot by using the speed measuring sensor, and acquiring the parking space characteristics of the adjacent parking spaces of each idle parking space; the prediction model construction analysis module is used for constructing a parking time prediction model and calculating a prediction value of the consumption time of the marked vehicle reaching each idle parking space; the vehicle judging module is used for constructing a parked vehicle judging model and judging the types of parked vehicles on adjacent parking spaces of each idle parking space; the parking space recommending module is used for constructing an optimal parking space recommending model, calculating the recommending score of each idle parking space, and taking the idle parking space with the highest recommending score as an initial optimal parking space; the real-time monitoring prediction module is used for monitoring the entry state of the vehicle owner at each entry of the parking lot in real time, and judging whether the total time consumed by the vehicle owner for predicting the vehicle to enter the parking lot exceeds the time for marking the vehicle to reach the parking lot where the vehicle owner is located or not if the vehicle owner enters the parking lot to enter the parking lot to leave the vehicle; the recommendation output module is used for acquiring the parking space characteristics of the adjacent parking space of the vehicle owner, calculating the recommendation score of the parking space of the vehicle owner and the adjacent parking space, comparing the recommendation score with the recommendation score of the initial optimal parking space, and outputting the parking space with higher score as the optimal parking space to the user port when the total time of the vehicle owner for starting the parking space prediction to consume does not exceed the time for marking the vehicle to reach the parking space of the vehicle owner;
The output end of the data acquisition module is connected with the input end of the prediction model construction analysis module; the output end of the prediction model construction analysis module is connected with the input end of the vehicle judgment module; the output end of the vehicle judging module is connected with the input end of the parking space recommending module; the output end of the parking space recommending module is connected with the input end of the real-time monitoring and predicting module; and the output end of the real-time monitoring and predicting module is connected with the input end of the recommending output module.
4. The intelligent parking lot service management system based on the internet of things according to claim 3, wherein: the data acquisition module comprises a vehicle information acquisition unit and a parking space information acquisition unit;
the vehicle information acquisition unit is used for acquiring data information of the vehicle, marking the vehicle exceeding the length threshold value and the width threshold value of the vehicle, generating a marked vehicle, and acquiring the speed of the marked vehicle at the entrance of the parking lot by using the speed sensor;
the parking space information acquisition unit is used for acquiring the number and position number information of all the idle parking spaces in the parking lot and the parking space characteristics of adjacent parking spaces of each idle parking space;
The output end of the vehicle information acquisition unit is connected with the input end of the parking space information acquisition unit; the output end of the parking space information acquisition unit is connected with the input end of the prediction model construction analysis module;
the prediction model construction analysis module comprises a prediction model construction unit and a prediction model analysis unit;
the prediction model construction unit is used for constructing a parking time prediction model;
the prediction model analysis unit is used for calculating a predicted value of the time consumed by the marked vehicle to reach each idle parking space based on the position number information of all the idle parking spaces and the speed of the marked vehicle at the entrance of the parking lot;
the output end of the prediction model construction unit is connected with the input end of the prediction model analysis unit; the output end of the prediction model analysis unit is connected with the input end of the vehicle judgment module.
5. The intelligent parking lot service management system based on the internet of things according to claim 3, wherein: the vehicle judging module comprises a judging model constructing unit and a judging model analyzing unit;
the judging model building unit is used for building a parked vehicle judging model;
The judging model analysis unit is used for judging the type of the parked vehicle on each idle parking space adjacent parking space based on the parking space characteristics of each idle parking space adjacent parking space;
the output end of the judgment model construction unit is connected with the input end of the judgment model analysis unit; the output end of the judgment model analysis unit is connected with the input end of the parking space recommendation module;
the parking space recommendation module comprises an optimal parking space recommendation model construction unit and an initial output unit;
the optimal parking space recommendation model construction unit is used for constructing an optimal parking space recommendation model;
the initial output unit is used for calculating the recommended score of each idle parking space, and taking the idle parking space with the highest recommended score of the parking space as an initial optimal parking space;
the output end of the optimal parking space recommendation model building unit is connected with the input end of the initial output unit; and the output end of the initial output unit is connected with the input end of the real-time monitoring and predicting module.
6. The intelligent parking lot service management system based on the internet of things according to claim 3, wherein: the real-time monitoring prediction module comprises a real-time monitoring unit and a prediction judging unit;
The real-time monitoring unit is used for monitoring the entry state of the car owners at each entrance of the parking lot in real time;
the prediction judging unit is used for calculating the total time of the vehicle owner for predicting the vehicle to leave the parking space when the vehicle owner enters the parking lot to leave the vehicle, and judging whether the total time of the vehicle owner for predicting the vehicle to leave the parking space exceeds the time for marking the vehicle to reach the parking space where the vehicle owner is located;
the output end of the real-time monitoring unit is connected with the input end of the prediction judging unit; and the output end of the prediction judging unit is connected with the input end of the recommendation output module.
7. The intelligent parking lot service management system based on the internet of things according to claim 3, wherein: the recommendation output module comprises a parking space analysis unit and a final output unit;
the parking space analysis unit is used for acquiring the parking space characteristics of the adjacent parking space of the vehicle owner, calculating the recommended score of the parking space of the vehicle owner and the adjacent parking space, and comparing the recommended score with the recommended score of the initial optimal parking space when the total time for the vehicle owner to drive the vehicle out of the parking space is not longer than the time for marking the vehicle to reach the parking space of the vehicle owner;
The final output unit is used for outputting the parking space with higher score to the user port as the optimal parking space;
the output end of the parking space analysis unit is connected with the input end of the final output unit.
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| CN105427660A (en) * | 2015-11-24 | 2016-03-23 | 上海汽车集团股份有限公司 | Intelligent parking information providing method and apparatus |
| JP2018159996A (en) * | 2017-03-22 | 2018-10-11 | 日本電気株式会社 | Information provision system, information provision method and program |
| CN107248315A (en) * | 2017-06-21 | 2017-10-13 | 深圳市盛路物联通讯技术有限公司 | The commending system and method in a kind of parking navigation path |
| CN107945568A (en) * | 2017-12-01 | 2018-04-20 | 深圳市赛亿科技开发有限公司 | Parking lot management method and system |
| CN110136471A (en) * | 2019-01-25 | 2019-08-16 | 北京车和家信息技术有限公司 | A kind of parking lot management method and system |
| CN109949604A (en) * | 2019-04-01 | 2019-06-28 | 南京邮电大学 | A kind of large parking lot scheduling air navigation aid, system and application method |
| CN110969892A (en) * | 2019-11-06 | 2020-04-07 | 重庆市城投金卡信息产业(集团)股份有限公司 | Parking lot management method and system |
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