CN111797323A - Parking lot intelligent recommendation system based on big data - Google Patents
Parking lot intelligent recommendation system based on big data Download PDFInfo
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Abstract
The invention relates to the technical field of parking lots, in particular to a parking lot intelligent recommendation system based on big data, which comprises a parking lot end, a user end and a cloud server, wherein the parking lot end comprises a communication module, a user management module and a parking lot management module, the user end comprises a parking service module, a position estimation module, a parameter setting module and a GPS module, and the cloud server comprises a communication module, an operation instruction module, an appointment module, an interest estimation module, a cloud computing module and a database module. The intelligent parking and vehicle taking method is high in practicability, the DIN model is used for analyzing the preference of the user, the big data analysis is used for reasonably recommending the parking lot for the user, and the Dijkstra algorithm is used for planning the optimal path, so that intelligent parking and vehicle taking are achieved, the use efficiency of the parking lot is effectively improved, and the waiting time of the user is reduced.
Description
Technical Field
The invention relates to the technical field of parking lots, in particular to a parking lot intelligent recommendation system based on big data.
Background
With the continuous development of social economy and the continuous improvement of the living standard of people, automobiles are owned by more and more families. The problem that brings along is that each shopping mall and district's demand for large-scale parking area is increasing, but current parking area intelligence recommendation system is based on either the distance between parking area and the destination is far and near, or based on the parking price recommends suitable parking area to the user. In the parking lot, a central management system is mainly used for controlling, monitoring and managing the use condition of parking spaces, the problem of access control can be simply solved, and meanwhile, an ultrasonic parking space sensor and an induction information board are used for providing certain information to guide a user to select an idle parking space.
Therefore, the parking lot recommended based on the single standard is easy to cause the situation that a user finds that no proper parking space still exists when the user parks in the parking lot after arriving at the parking lot, the queuing time is long when the user gets in and out of the parking lot, and the parking experience of the user is not ideal; secondly, as the structural design of the parking lot is more complex, the parking lot cannot provide an intuitive navigation route, and the problems of position loss, difficulty in reversely finding a car and the like of a user often occur in the parking lot; thirdly, the parking stall is not utilized to the maximum extent, makes the parking stall use and distributes unevenly in time and space, increases the parking and car taking time on foot, can increase the difficulty of parking area management, has still reduced the operational benefits in parking area.
Based on the above, the invention designs the intelligent parking lot recommendation system based on the big data, which obtains the use conditions of parking spaces and lanes in the parking lot in real time through various sensors, contacts the cloud server to process data, recommends proper parking lots and parking spaces for users, and simultaneously quickly searches an optimal path from a starting point to a target point for the users, thereby realizing intelligent parking and vehicle taking. The system effectively improves the use efficiency of the parking lot.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a parking lot intelligent recommendation system based on big data.
In order to achieve the purpose, the invention adopts the following technical scheme: the parking lot intelligent recommendation system based on the big data comprises a parking lot end, a user end and a cloud server, wherein the parking lot end comprises a communication module, a user management module and a parking lot management module, the user end comprises a parking service module, a position estimation module, a parameter setting module and a GPS module, the cloud server comprises a communication module, an operation instruction module, an appointment making module, an interest estimation module, a cloud computing module and a database module, the communication module of the parking lot end is used for receiving and sending information, the user management module of the parking lot end is used for managing information of users and comprises personal basic information management, user parking record management and user feedback information management, the parking lot management module of the parking lot end is used for managing parking lots and comprises parking lot basic information management, equipment management and parking record management, the parking service module of the client is used for parking services of a user and comprises parking lot recommendation service, parking lot reservation service, historical reservation inquiry service and automatic checkout service, the position estimation module of the client is used for helping the user to quickly find the position of a vehicle and the position of the vehicle in the parking lot, the parameter setting module of the client can record interest preference and common places of the user and quickly match the parking lot and parking space of the most suitable user by contacting a DIN model, the GPS module of the client is used for guiding the user to find the reserved parking space to realize path navigation, the communication module of the cloud server can receive and send information, the operation instruction module of the cloud server is used for generating an instruction for controlling the opening of the parking lot, and the reservation module of the cloud server is used for reserving the parking lot and parking space which are most suitable for the user, the interest estimation module of the cloud server estimates parking lots and parking spaces which are most suitable for users by using a DIN algorithm of a deep network and recommends the parking lots and the parking spaces for the users, the cloud computing module of the cloud server is used for computing and controlling other modules, and the database module of the cloud server is used for storing data.
Further, the above-mentioned parking lot intelligence recommendation system based on data, be provided with front end page, controller, business logic layer, data persistence layer and the data layer that connects gradually in the system, controller, business logic layer, data persistence layer and data layer are connected with DIN (deep Interest network) model respectively.
Further, the communication module of the parking lot end can transmit the data request to the corresponding controller according to the data request sent by the front-end page, then the controller forwards the data request to the corresponding service logic layer, and loads the DIN model, after the service logic layer receives the entity class of the DIN model, the data request is analyzed according to the entity class, the operation to be performed on the data is sent to the data persistence layer, the data persistence layer performs data interaction with the database, the obtained data is encapsulated in the entity class, the data is returned to the service logic layer and returned to the controller by the service logic layer, and finally the data comes to the front-end page and is rendered and displayed by the front-end page.
Further, the parking service module of the user side can inquire parking lots around a target point according to the parking requirements of the user, and after submitting a parking lot reservation service request to the system, the system generates a parking lot reservation order, inquires the state information of the target parking lot according to the details of the reservation order, including whether business is available and whether parking space information exists, and after an empty parking space exists in the target parking lot, the system confirms the order and returns the order to the user interface of the user to complete parking space reservation.
Further, the position estimation module of the user side can dynamically position the position of the user in the parking lot, acquire the position data of the user every 1 second, return the position data to the system to perform positioning service once, and finally return the data to the user.
Furthermore, the interest estimation module of the cloud server uses a DIN algorithm of a deep interest network to estimate the optimal parking lot and parking space development of a user, and the matching degree and the satisfaction degree of the recommendation system are greatly improved by using a self-adaptive regularization technology, a Dice activation function and an attention mechanism in the DIN algorithm.
Further, a plurality of databases and position databases are arranged in a database module of the cloud server and used for storing various entities generated by the user in the process of using the system on the cloud, and the entities comprise the user, position information, reservation information, a parking lot, feedback information and equipment.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, through the big data intelligent parking lot and the recommendation management of the user on the access and the proper parking space of the parking lot, the whole intelligent parking lot recommendation system can reduce the queuing waiting time of the user on the premise of meeting the interest requirements of different users, improve the operation efficiency and the user satisfaction of the intelligent parking lot to a certain extent, and greatly improve the user experience of the intelligent parking lot recommendation system.
Drawings
Fig. 1 is a schematic structural diagram of the intelligent parking lot recommendation system according to the present invention.
Fig. 2 is a schematic view of a parking lot end of the intelligent parking lot recommendation system according to the present invention.
Fig. 3 is a schematic diagram of a user side of the intelligent parking lot recommendation system according to the present invention.
Fig. 4 is a schematic diagram of a cloud server of the intelligent parking lot recommendation system according to the present invention.
Fig. 5 is a system framework diagram of the parking lot intelligent recommendation system according to the present invention.
Fig. 6 is a flowchart of the parking lot intelligent recommendation system according to the present invention.
Description of reference numerals: 1. a parking lot end; 2. a user side; 3. a cloud server; 4. a communication module; 5. a user management module; 6. a parking lot management module; 7. a parking service module; 8. a location estimation module; 9. a parameter setting module; 10. a GPS module; 11. a communication module; 12. an instruction running module; 13. a reservation module; 14. an interest estimation module; 15. a cloud computing module; 16. and a database module.
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.
Fig. 1 is a schematic structural diagram of the intelligent parking lot recommendation system according to the present invention.
As shown in fig. 1, the parking lot intelligent recommendation system based on big data of the present invention includes a parking lot terminal 1, a user terminal 2 and a cloud server 3.
Fig. 2 is a schematic view of a parking lot end of the intelligent parking lot recommendation system according to the present invention.
As shown in fig. 2, the parking lot end diagram of the present invention includes a communication module 4, a customer management module 5, and a parking lot management module 6.
The communication module 4 is used for receiving and sending information, the user management module 5 is used for managing user information, including personal basic information management, user parking record management and user feedback information management, and the parking lot management module 6 is used for managing parking lots, including parking lot basic information management, equipment management and parking record management.
Fig. 3 is a schematic diagram of a user side of the intelligent parking lot recommendation system according to the present invention.
As shown in fig. 3, the user-side schematic diagram of the present invention includes a parking service module 7, a location estimation module 8, a parameter setting module 9, and a GPS module 10.
The parking service module 7 is used for parking services of a user, and comprises a parking lot query service, a parking lot reservation service, a historical reservation query service and an automatic checkout service, the position estimation module 8 is used for helping the user to quickly find the position of a vehicle and the position of the vehicle in the parking lot, the parameter setting module 9 is used for recording interest preference of the user, big data can be conveniently matched with the most comfortable parking lot and parking stall for the user, and the GPS module 10 guides the user to find a reserved parking stall, wherein the parking service comprises a path recommendation service and a path navigation service.
Fig. 4 is a schematic diagram of a cloud server of the intelligent parking lot recommendation system according to the present invention.
As shown in fig. 4, the cloud server schematic diagram of the present invention includes a communication module 11, an operation instruction module 12, a reservation module 13, an interest estimation module 14, a cloud computing module 15, and a database module 16.
Fig. 5 is a system framework diagram of the parking lot intelligent recommendation system according to the present invention.
As shown in fig. 5, a front-end page, a controller, a service logic layer, a data persistence layer and a data layer which are connected in sequence are arranged in the parking lot intelligent recommendation system based on big data, the controller, the service logic layer, the data persistence layer and the data layer are respectively connected with a DIN model, and a frame used by the data persistence layer is Hibernate.
When a user prepares to reserve a parking space, the parking service module 7 sends a data request to a corresponding controller through a front-end page, then the controller forwards the data request to a corresponding business logic layer, and loads a DIN model, after the business logic layer receives an entity class of the DIN model, the business logic layer analyzes the entity class according to the entity class, and sends an operation to be performed on the data to the data persistence layer, the data persistence layer performs data interaction with a database, the obtained data is packaged in the entity class, returns to the business logic layer, returns to the controller through the business logic layer, and finally, the data comes to the front-end page and is rendered and displayed by the front-end page.
Fig. 6 is a flowchart of the parking lot intelligent recommendation system according to the present invention.
Step S1: after the user logs in the user terminal 2 through the account, the parking lot reservation is made using the parking service module 7, and the position estimation module 8 acquires the user position, and then the process proceeds to step S2.
Step S2: the user selects the recommendation preference of the travel parking lot and the parking space through the parameter setting module 9, and then the process goes to step S3.
Step S3: the user terminal 2 transmits the information to the cloud server 3 through the communication module 11, the cloud server 3 sets the reservation module 13, stores the data in the own database module 16, and then proceeds to step S4.
Step S4: the cloud server 3 firstly performs area division on the destination, divides parking lots close to each other into one area, uses a KNN algorithm, matches the parking lot area close to each other and on a highway for the user by using big data according to the target position and the recommendation preference of the user, loads a DIN model through the cloud computing module 15, allocates the most appropriate parking lot for the user, and then enters step S5.
Step S5: the cloud server 3 feeds back the reservation appeal of the user to the parking lot terminal 1, inquires whether the parking lot has an empty parking space through the parking lot management module 6, if so, the step S7 is executed, otherwise, the step S6 is executed.
Step S6: when the recommended parking lot has no vacant parking space, the cloud server 3 recommends a vacant parking lot with the optimal distance and price in the same area to the user, and then the process goes to step S7.
Step S7: the parking lot terminal 1 sends the free parking space information to the cloud server 3 through the communication module 4 and the parking lot management module 6, the cloud server 3 recommends and displays the available parking space information to the user through the communication module 11, and then the process goes to step S8.
Step S8: after the user side 2 receives the available parking space information sent back by the cloud server 3, the user selects a parking space according to the recommended parking space, and then the process goes to step S9.
Step S9: the user performs the shortest parking route planning by using the dijkstra algorithm loaded on the cloud computing module 15 according to the selected parking space, and then the process proceeds to step S10.
Step S10: the cloud server 3 transmits the shortest parking path plan to the user terminal 2 through the communication module 11, the user terminal 2 generates a parking plan map, and the user parks according to the planned path.
Compared with the prior art, the method can estimate the parking interest points of the user by utilizing technologies such as big data analysis, deep learning and the like, match the parking lots satisfied by the user and reserve the parking places, realize reasonable utilization of parking resources, quickly search an optimal path from a starting point to a target point for the user by acquiring the use conditions of the parking places and the traffic lanes in the parking lots in real time, realize functions of parking information query, parking place reservation or allocation, parking guidance and the like by real-time communication between mobile phone end software and a server end or a city traffic management cloud end, plan the optimal path on a cloud server by adopting a Dijkstra algorithm, form dynamic display of a navigation map on the mobile phone end or an unmanned vehicle interface for parking guidance, greatly improve the use efficiency of the parking lots and improve the user experience.
In summary, the preferred embodiments of the present invention are described above, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the scope of the present invention, and equivalents and modifications of the technical solutions and concepts of the present invention should be included in the scope of the present invention.
Claims (8)
1. The utility model provides a parking area intelligence recommendation system based on big data which characterized in that: the system comprises a parking lot end, a user end and a cloud server, wherein the parking lot end comprises a communication module, a user management module and a parking lot management module, the user end comprises a parking service module, a position estimation module, a parameter setting module and a GPS module, the cloud server comprises a communication module, an operation instruction module, an appointment module, an interest estimation module, a cloud computing module and a database module, the user management module of the parking lot end is used for managing information of users and comprises personal basic information management, user parking record management and user feedback information management, the parking lot management module of the parking lot end is used for managing a parking lot and comprises parking lot basic information management, equipment management and parking record management, and the parking service module of the user end is used for parking service of the users and comprises parking lot recommendation service, parking lot appointment service, user information, The system comprises a historical reservation inquiry service and an automatic checkout service, wherein a position estimation module of a user side is used for helping a user to quickly find the position of a vehicle and the position of the vehicle in a parking lot, a parameter setting module of the user side can record interest preference of the user and quickly match the parking lot and the parking space of the most suitable user in connection with a DIN model, a GPS module of the user side is used for guiding the user to find the reserved parking space to finally realize path navigation, a communication module of a cloud server can receive and send information, an operation instruction module of the cloud server is used for generating an instruction for controlling the opening of the parking lot, a reservation module of the cloud server is used for reserving the most suitable parking lot and parking space for the user, and an interest estimation module of the cloud server predicts the parking lot and the parking space which are most suitable for the user by using a DIN algorithm of a deep network and recommends the, the cloud computing module of the cloud server is used for computing and controlling other modules, and the database module of the cloud server is used for storing data.
2. The intelligent big-data-based parking lot recommendation system according to claim 1, characterized in that: the system is internally provided with a front-end page, a controller, a service logic layer, a data persistence layer and a data layer which are sequentially connected, wherein the controller, the service logic layer, the data persistence layer and the data layer are respectively connected with the DIN model.
3. The intelligent big-data-based parking lot recommendation system according to claim 2, characterized in that: the communication module of the parking lot end can transmit the data request to a corresponding controller according to the data request sent by the front-end page, then the controller forwards the data request to a corresponding service logic layer, and simultaneously loads a DIN model, after the service logic layer receives the entity class of the DIN model, the entity class is analyzed according to the entity class, the operation required to be performed on the data is sent to the data persistence layer, the data persistence layer and the database perform data interaction, the obtained data is packaged in the entity class, the data is returned to the service logic layer and returned to the controller by the service logic layer, and finally the data comes to the front-end page and is rendered and displayed by the front-end page.
4. The intelligent big-data-based parking lot recommendation system according to claim 1, characterized in that: the parking service module of the user side uses a KNN algorithm to inquire a parking lot near a target point according to parking requirements such as parking selection preference of a user, recommends a proper parking lot to the user, and generates a parking space reservation order after submitting a parking lot reservation service request to a system, the system inquires whether a vacant parking space exists in the target point parking lot according to order details, and the system confirms the order after the vacant parking space exists in the target point parking lot, returns order information to a user interface of the user and completes parking space reservation.
5. The intelligent big-data-based parking lot recommendation system according to claim 1, characterized in that: the position estimation module of the user side can dynamically position the position of the user in the parking lot, acquire the position data of the user every 1 second, return the position data to the system to perform positioning service once, and finally return the data to the user.
6. The intelligent big-data-based parking lot recommendation system according to claim 1, characterized in that: the user-side parameter setting module can record interest preference of the user and common places of the user, and parking lots near the common places are considered when the parking lots are recommended.
7. The intelligent big-data-based parking lot recommendation system according to claim 1, characterized in that: the interest estimation module of the cloud server can acquire vehicle conditions such as vehicle types, sizes, oil consumption, current vehicle conditions and energy forms of the user, and the parking spaces which accord with selection preference are recommended for the user by combining environmental conditions such as the height limit of the parking lot, the distance between the parking spaces and an elevator entrance of a shopping mall and the like.
8. The intelligent big-data-based parking lot recommendation system according to claim 1, characterized in that: the system comprises a cloud server and is characterized in that a plurality of databases and position databases are arranged in a database module of the cloud server and used for storing various entities generated by a user in the process of using the system on the cloud, the entities comprise the user, position information, reservation information, a parking lot, feedback information and equipment, the user obtains the position information through positioning, a reservation flow is achieved for a matched parking lot reservation or the feedback information is formed for the parking lot, and the parking lot manages the equipment in the parking lot after receiving the reservation information or the feedback information.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113284359A (en) * | 2021-07-22 | 2021-08-20 | 腾讯科技（深圳）有限公司 | Parking space recommendation method, device, equipment and computer readable storage medium |
| CN114220292A (en) * | 2021-12-17 | 2022-03-22 | 谭苏梦源 | Method and system for realizing intelligent parking |
| WO2022193470A1 (en) * | 2021-03-17 | 2022-09-22 | 博泰车联网科技(上海)股份有限公司 | User preference query method and device for cloud platform, and storage medium and terminal |
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| CN103871270A (en) * | 2014-02-28 | 2014-06-18 | 张剑锋 | Cloud computing and big data-based parking method and system |
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103871270A (en) * | 2014-02-28 | 2014-06-18 | 张剑锋 | Cloud computing and big data-based parking method and system |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022193470A1 (en) * | 2021-03-17 | 2022-09-22 | 博泰车联网科技(上海)股份有限公司 | User preference query method and device for cloud platform, and storage medium and terminal |
| CN113284359A (en) * | 2021-07-22 | 2021-08-20 | 腾讯科技（深圳）有限公司 | Parking space recommendation method, device, equipment and computer readable storage medium |
| CN114220292A (en) * | 2021-12-17 | 2022-03-22 | 谭苏梦源 | Method and system for realizing intelligent parking |
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