CN113345265A - Intelligent parking navigation and commercial guidance system and method based on user data - Google Patents
Intelligent parking navigation and commercial guidance system and method based on user data Download PDFInfo
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- CN113345265A CN113345265A CN202110687450.2A CN202110687450A CN113345265A CN 113345265 A CN113345265 A CN 113345265A CN 202110687450 A CN202110687450 A CN 202110687450A CN 113345265 A CN113345265 A CN 113345265A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/148—Management of a network of parking areas
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
Abstract
The invention provides an intelligent parking navigation and commercial guidance system and method based on user data, wherein the method comprises the steps of determining a business circle corresponding to each elevator; constructing an elevator portrait corresponding to the elevator according to store information of stores contained in each business district; constructing a user portrait according to the shopping mall behaviors and activity data of the user; constructing an electronic fence corresponding to the mall parking lot; after a user side corresponding to a user is detected to enter the electronic fence, acquiring a user portrait of the user; matching the user portrait of the user with each elevator portrait, and determining the elevator portrait with the highest matching degree with the user; acquiring an idle parking space closest to the elevator corresponding to the determined elevator image; and generating navigation information according to the free parking space and the current position of the user. The invention is convenient for the user to shop and optimizes the shopping experience of the user; and the parking management of the shopping mall can be optimized, the performance of the shopping mall is improved, and the bidirectional optimization effect is achieved.
Description
Technical Field
The invention relates to the technical field of parking platforms, in particular to an intelligent parking navigation and commercial guidance system and method based on user data.
Background
Self-driving travel has become the first choice today, especially when going to shopping malls for purchases. The current large-scale shopping malls also aim at one-stop purchasing, and ensure shopping categories in the shopping malls to have in large-scale shopping malls. Therefore, most parking lots in large shopping malls have the characteristics of short parking spaces, large scale, complex structure and high regional similarity. Especially, the underground parking lot of the market is only provided with an underground indicating line and an indicating sign hung above a lane, and the underground parking lot is not enough in light and is similar to the underground parking lot in height on the regional structure. After entering the parking lot, the user cannot basically correspond the underground parking lot to the upstairs shopping mall in the regional position, and is difficult to park nearby near the elevator entrance close to the purchase target. Moreover, most users often prefer to find an idle parking space nearby after entering the parking lot, which easily causes a high shortage of parking spaces near the entrance of the parking lot, and even causes congestion, while parking spaces far away from the parking lot, especially parking spaces actually closer to the purchase target, are in an idle state for a long time.
It can be seen that the user enters a large shopping mall and parks in nearby free parking spaces substantially randomly. For a user, the parking is difficult and the parking efficiency is low, and the user has the problem that the position of the user is unknown or uncertain after entering a shopping mall, and if the user has a definite shopping habit or a shopping target, the user is worried about how to go to a destination or increase the travel and time. For a large-scale market, the position of the user entering the market is random, so that the purchasing desire of the user is not favorably improved, the expected order of the client is efficiently promoted, and the performance of the market is improved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the intelligent parking navigation and commercial guidance system and method based on the user data are provided, so that the parking efficiency of the user can be improved, and the user can conveniently shop.
In order to solve the technical problems, the invention adopts the technical scheme that:
an intelligent parking navigation and commercial guidance system based on user data, comprising: a cloud server and a user side; the cloud server comprises a MySQL database and a parking management platform, and the MySQL database is connected with the parking management platform;
the parking management platform comprises: the system comprises an image service subsystem, a monitoring subsystem and a parking service subsystem; the monitoring subsystem is respectively connected with the parking service subsystem and the user side, and the parking service subsystem is connected with the portrait service subsystem;
the portrait service subsystem comprises:
the business circle determining module is used for determining the business circles corresponding to the elevators by taking the positions of the elevators in the shopping mall as the centers and taking the preset distance as the radiation radius;
the figure constructing module is used for constructing an elevator figure corresponding to the elevator according to store information of stores contained in each business district; the system comprises a user terminal and a user portrait constructing module, wherein the user portrait is constructed according to the mall behavior and activity data of the user collected from the user terminal;
the monitoring subsystem includes:
the fence construction module is used for constructing electronic fences corresponding to the mall parking lots;
the detection module is used for triggering the parking subsystem to acquire a user portrait of a user from the portrait service subsystem after detecting that a user side corresponding to the user enters the electronic fence;
the parking service subsystem comprises:
the matching module is used for matching the user portrait with each elevator portrait after receiving the user portrait and determining the elevator portrait with the highest matching degree with the user;
the parking space acquisition module is used for acquiring an idle parking space which is closest to the elevator image determined by the matching module and corresponds to the elevator image;
and the navigation module is used for generating navigation information according to the idle parking space acquired by the parking space acquisition module and the current position of the user, and sending the navigation information to the user side corresponding to the user.
Optionally, the sketch constructing module includes:
the shop portrait constructing unit is used for generating a shop portrait of the shop according to the shop information of the shop;
and the elevator portrait constructing unit is used for acquiring elevator portraits of elevators corresponding to the business circles according to the shop portraits of shops contained in the business circles.
Optionally, the shop representation building unit includes:
the system comprises a first data processing layer, a second data processing layer and a third data processing layer, wherein the first data processing layer is used for classifying and sorting data in store information of a store against a preset characteristic data field to obtain original store characteristic information, and the characteristic data field comprises a store name, a sold commodity, transaction data and public equipment environment parameters;
the system comprises a first label system building layer, a second label system building layer and a third label system building layer, wherein the first label system building layer is used for building a shop label system according to original shop characteristic information, and the characteristic label categories in the shop label system comprise audience groups, commodity structures, customer orders and service functions;
and the shop portrait construction layer is used for clustering the shop label system by using a K-means clustering algorithm, and generating the shop portrait of the shop by taking a clustering result as a label of the shop portrait.
Optionally, the elevator figure constructing unit includes:
the image acquisition layer is used for acquiring shop images of shops contained in a trade area;
and the elevator portrait construction layer is used for clustering the acquired shop portraits of all shops by using Fast Unfolding clustering algorithm, and generating the elevator portrait of the elevator corresponding to the business circle by using a clustering result as a label of the elevator portrait.
Optionally, the sketch constructing module includes:
the user portrait construction unit is used for constructing a user portrait according to the shopping mall behaviors and activity data of the user;
the user portrait construction unit comprises:
the second data processing layer is used for classifying and sorting the market behavior and activity data of a user against preset characteristic data fields to acquire original user characteristic information, wherein the characteristic data fields comprise user names, purchased commodities, transaction data and the residence conditions of all shops;
the second label system construction layer is used for constructing a user label system according to original user characteristic information, wherein the characteristic label categories in the user label system comprise group positioning, interested commodities, consumption capacity and shopping habits;
and the user portrait construction layer is used for clustering the user label system by using a K-means clustering algorithm, and generating the user portrait of the user by taking a clustering result as a label of the user portrait.
The invention provides another technical scheme as follows:
the intelligent parking navigation and commercial guidance method based on user data comprises the following steps:
determining a business circle corresponding to each elevator by taking the position of each elevator in a market as a center and a preset distance as a radiation radius;
constructing an elevator portrait corresponding to the elevator according to store information of stores contained in each business district;
constructing a user portrait according to the shopping mall behaviors and activity data of the user;
constructing an electronic fence corresponding to the mall parking lot;
after a user side corresponding to a user is detected to enter the electronic fence, acquiring a user portrait of the user;
matching the user portrait of the user with each elevator portrait, and determining the elevator portrait with the highest matching degree with the user; acquiring an idle parking space closest to the elevator corresponding to the determined elevator image;
and generating navigation information according to the free parking space and the current position of the user.
Optionally, the building of the elevator portrait corresponding to the elevator according to the store information of the stores contained in each business district includes:
generating a shop image of the shop according to the shop information of the shop;
and obtaining the elevator images of the corresponding elevators of each business district according to the shop images of the shops in each business district.
Optionally, the generating a shop image of a shop according to shop information of the shop includes:
classifying and sorting data in store information of a store against a preset characteristic data field to obtain original store characteristic information, wherein the characteristic data field comprises a store name, a sold commodity, transaction data and public equipment environment parameters;
building a shop label system according to original shop characteristic information, wherein characteristic label categories in the shop label system comprise audience groups, commodity structures, customer orders and service functions;
and clustering the shop label system by using a K-means clustering algorithm, and generating the shop image of the shop by taking the clustering result as the label of the shop image.
Optionally, the building of the elevator portrait corresponding to the elevator according to the store information of the stores contained in each business district includes:
obtaining shop images of shops contained in a business circle;
and clustering the obtained shop images of all shops by using a Fast Unfolding clustering algorithm, and generating the elevator image of the elevator corresponding to the business circle by using a clustering result as an elevator image label.
Optionally, the constructing a user portrait according to the mall behavior and activity data of the user includes:
classifying and sorting market behavior and activity data of a user against preset characteristic data fields to acquire original user characteristic information, wherein the characteristic data fields comprise user names, purchased commodities, transaction data and the residence conditions of all shops;
constructing a user label system according to original user characteristic information, wherein the characteristic label categories in the user label system comprise group positioning, interested commodities, consumption capacity and shopping habits;
and clustering the user label system by using a K-means clustering algorithm, and generating the user portrait of the user by taking a clustering result as a label of the user portrait.
The invention has the beneficial effects that: according to the invention, firstly, each elevator is respectively taken as a center to radiate a certain area range outwards, and a large-scale shopping mall is divided into a plurality of business circles corresponding to the elevators; then, elevator images obtained by taking the store information as an analysis object are respectively constructed corresponding to each business district. Therefore, multidimensional consumption attribute analysis is carried out on the superstore by taking the business circle radiated by the elevator as a target, and the consumption attribute corresponding to each elevator is obtained. Meanwhile, the invention also constructs the user portrait of the vehicle user based on the market behavior and activity data of the vehicle user so as to obtain the consumption attribute of the user. And finally, immediately calling a user image of the user after the user enters the parking lot through the electronic fence, matching the user image with the elevator images of the elevators, determining a business district closest to the consumption attribute of the user, and guiding the user to park to an idle parking lot near the elevator corresponding to the business district. Therefore, the invention realizes that the parking guidance corresponding to the clear idle parking space can be received after the user enters the parking lot. To the user, can remove time and the energy that the user wore for the idle parking stall from, improve the convenience that the user parkd, to market management, can optimize the parking management, optimize user experience. Meanwhile, the relation between the parking space and the elevator position and the relation between the elevator position and the corresponding business circle can be associated through the user consumption attribute and the business circle consumption attribute, so that the user can be accurately guided to the interest area of a market based on the consumption tendency of the user, the shopping by the user is facilitated, and the shopping experience of the user is optimized; but also can improve the purchasing desire of the user and the performance of the market, thereby obtaining the effect of bidirectional optimization.
Drawings
Fig. 1 is a schematic structural diagram of an intelligent parking guidance and commercial guidance system based on user data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a business district obtained by centering on the position of an elevator in a parking lot in the embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for intelligent parking guidance and commercial guidance based on user data according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of user/elevator portrait construction according to the second embodiment of the present invention.
Description of reference numerals:
1. a user side; 2. a cloud server;
21. a parking management platform; 22. a MySQL database;
211. a monitoring subsystem; 212. a parking service subsystem; 213. a representation service subsystem;
2111. a fence construction module; 2112. a detection module;
2121. a matching module; 2122. a parking space acquisition module; 2123. a navigation module;
2131. a business circle determining module; 2132. and an image construction module.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Example one
The embodiment provides an intelligent parking navigation and commercial guidance method based on user data, as shown in fig. 3, the method includes the following steps:
s1: and determining the business circles corresponding to the elevators by taking the positions of the elevators in the shopping mall as centers and the preset distance as the radiation radius.
Wherein, the elevator refers to an elevator which is arranged in a parking lot and is used as an entrance of a shopping mall. The position where the elevator is located refers to the center point of the elevator shaft. Referring to fig. 2, in a multi-floor mall, the position of the elevator m refers to the position of the center line of the elevator shaft penetrating through the floors, and the quotient circle m' determined by the elevator m is actually a cylinder with the center line of the elevator as the center axis. It can be understood that the business circle corresponding to the elevator is formed by a space range which is formed by taking the position of the elevator in the parking lot as the center of a circle and taking the preset distance as the radius as the bottom surface and translating the bottom surface upwards to the top layer.
In addition, the preset distance is preferably determined according to the total area of the shopping mall and the position of each elevator, so that the total space range formed by all shopping malls can cover all shops in the shopping mall.
In this case, the present exemplary embodiment makes it possible to radiate a business circle for each parking lot elevator in the business park.
S2: and constructing an elevator portrait corresponding to the elevator according to the store information of the stores contained in each business district.
The store information includes information such as a store name, an operation range (including commodity category, variety and service item), order information (including unit price, bargaining price and commodity quantity of each order and commodity therein), scale and site planning. The acquisition of the site plan is used for confirming service functions which can be provided by the store, such as providing tea break service, and can be determined according to supporting facilities such as leisure seats and the like in the site plan. Preferably, the store information further includes member information.
The elevator image can use precise labels to convert and link the business characteristic attributes of all stores in the corresponding business circle of the elevator. Through the elevator portrait, the commercial characteristics of the corresponding business circle of the elevator can be intuitively known.
In one specific example, the tag categories of the elevator representation include audience segment, product structure, passenger unit price, and service function.
It should be noted that the specific content of the store information defined in the present embodiment has a direct relationship with the shopping mall behavior and activities of the user. Based on the direct relationship, the elevator portrait constructed based on the store information and the user portrait constructed based on the user store behavior and activity data can be associated or corresponded in the label attribute. Thus, the "user" can be associated with the "business turn" in a subsequent step by matching the elevator image with the user image.
In a specific example, the accuracy of the elevator image can be further improved by defining the timeliness of the store information of the store. That is, the store information used for analyzing the elevator image is limited to be corresponding to a specific time period in the near future, so that the store information serving as the basic data for analysis is ensured to have higher timeliness, and the elevator image obtained according to the timeliness is more accurate. The specific time period in the recent period is preferably the time period of the past half month, the past three months and the like.
S3: and constructing a user portrait according to the shopping mall behaviors and activity data of the user.
Wherein the user is specifically a driver who has a need to park the vehicle in a mall.
The shopping mall behavior and activity data can be acquired from a user side (mobile phone) (the acquired data can include positioning information and consumption records in the shopping mall), can be acquired through monitoring equipment at each position of the shopping mall, and can even be combined with order information (payment account information, commodity unit price, bargain price, commodity quantity and the like of each order) of each store. Of course, the shopping mall behavior and activity data of the user can also be obtained based on the data collected by the three channels.
In a specific example, the user representation will also be constructed from app behavior data of the user at the same time. The app behavior data is acquired from the user side and refers to data generated by daily operation of the user in the app on the user side, and the operation comprises praise, subscription, playing and the like. The app behavior data directly reflects the daily attention of the user. For example, a user can know that the user has great daily interest in household products through app behavior data, and meanwhile, the user has intensive research on skin care and makeup.
In a specific example, a specific app may be implemented based on the method of this embodiment, and the specific app is registered and authorized to perform data collection, so that the specific app becomes a "user" in the method of this embodiment.
In a particular example, the tag categories of the user representation include group location, items of interest, consumer capabilities, and shopping habits. For example, user A's user representation information includes group positioning: a fine modern girl between 20-30 years old; interest goods: beauty, apparel, entertainment and their corresponding most interesting brand names; consumption capacity: the unit price of the customer is 50-200, and the average monthly expenditure is 1000-; shopping habits: zaa, then consume at star gram and stay for more than half an hour.
Through the user portrait, the behavior and the activity characteristics of the user in the market can be intuitively known.
In one embodiment, the accuracy of the constructed user image may also be further improved by limiting the timeliness of the marketplace and activity data and app behavior data (if any) used. Preferably, the mall behavior and activity data and app behavior data (if any) used may be defined to correspond to a particular time period in the near future. Optionally, the specific time period is a time period of one week, a half month, a month, three months, etc.
S4: and constructing an electronic fence corresponding to the mall parking lot.
Specifically, a monitoring subsystem is configured in a parking lot of a shopping mall in advance, and a closed electronic fence corresponding to the parking lot of the shopping mall is constructed based on the monitoring subsystem, so that whether the real-time position of any user side is located in the electronic fence can be judged in real time.
S5: and when detecting that a user side corresponding to a user enters the electronic fence, acquiring a user portrait of the user.
In one embodiment, whether the ue enters the electronic fence can be determined according to the positioning information of the ue. For the cloud server, acquiring and judging the positioning information of the user side in real time after the authorization is obtained, and determining whether the user side enters the electronic fence or not; if the user terminal enters the cloud server, the cloud server can call the user portrait of the user corresponding to the user terminal.
S6: matching the user portrait of the user with each elevator portrait, and determining the elevator portrait with the highest matching degree with the user; and acquiring an idle parking space which is closest to the elevator corresponding to the determined elevator image.
Corresponding to the specific example above, the tag categories of the user representation include group location, interest, consumption capabilities, and shopping habits; the tag categories of the elevator representation include audience groups, product structure, passenger order, and service functions. Wherein the group position corresponds to the audience group; the interest commodity corresponds to the commodity structure; the consumption capacity corresponds to the guest unit price; the shopping habits correspond to the service functions. Therefore, the business district closest to the behavior and activity characteristics in the business district of the user can be obtained through matching, and if the user is guided to enter the business district from the business district elevator in the parking lot, the shopping preference of the user can be met at the maximum probability, and the shopping desire of the user is stimulated, so that the accurate delivery of the user interest is realized, the shopping of the user is facilitated, and the shopping experience is improved; but also can effectively improve the performance of the market.
S7: and generating navigation information according to the free parking space and the current position of the user.
The delivery of navigation information can facilitate a user to quickly reach a target parking space, the trouble of finding the parking space and the time consumption are avoided, and efficient parking is realized.
Example two
Referring to fig. 4, the present embodiment further defines the process of constructing an image based on the first embodiment.
Specifically, step S2 in the first embodiment: the elevator portrait corresponding to the elevator is constructed according to the store information of the stores contained in each business district, and the method can be realized by the following substeps:
s21: generating a shop image of the shop according to the shop information of the shop;
the following describes in detail a process in which one store generates a corresponding store representation.
Firstly, the store information of the store is sorted, and the data in the store information is divided and integrated against a preset characteristic data field to obtain the original store characteristic information. Wherein the characteristic data field comprises at least a store name, a merchandise item, transaction data, and a public device environment parameter.
And then, building a shop label system according to the original shop characteristic information. The characteristic label categories in the shop label system at least comprise audience groups, commodity structures, customer orders and service functions. That is, the feature tag contents in the finally generated shop image are classified into the above categories.
And finally, clustering the shop label system by using a K-means clustering algorithm, and generating the shop image of the shop by taking the clustering result as the label of the shop image. The K-means clustering algorithm has the characteristics of easiness in implementation, high convergence rate, excellent clustering effect, small calculation force and the like, and the efficiency and the accuracy of portrait construction can be improved by applying the K-means clustering algorithm in the embodiment.
Here, the process of clustering the store tag system by using the K-means clustering algorithm and obtaining a clustering result may be embodied as follows:
taking all original data corresponding to one feature tag category in the original store feature information as sample data, and setting the sample data set as: a ═ b1,b2,…,bnThe number of the clustering categories is k;
setting initial coefficient of cluster class k as [ mu ]1,μ2,…,μnSetting a clustering center as C;
sample data biTo the average coefficient mujThe distance d between is:
d＝(bi-μj)2formula (1);
wherein i is 1,2, …, n; 1,2, …, n;
determining b according to equation (1)iCluster mark of (2):
biargmin d formula (2);
substituting the sample data into the formula (2) to divide the sample data, and calculating new mean data:
and taking the calculated data as updating data, performing iterative calculation on the sample data to finish information clustering, and taking a clustering result as the label content corresponding to the characteristic label category, namely one of the labels of the user portrait. And obtaining the labels corresponding to other characteristic label categories, wherein the label contents under all the characteristic label column categories form a shop image of the shop.
And S22, obtaining the elevator image of the elevator corresponding to each business district according to the shop image of the shop contained in each business district.
The following description will be given by taking an example of a process of constructing an elevator figure corresponding to one of the elevators:
firstly, acquiring shop images of all shops contained in a business circle corresponding to the elevator; and then clustering the obtained shop images of all shops by using a Fast Unfolding clustering algorithm, and generating the elevator image of the elevator corresponding to the business circle by using a clustering result as an elevator image label. The Fast Unfolding clustering algorithm is very suitable for clustering analysis of known community structures (equivalent to known shop figures in the embodiment), has the multi-level characteristic, and can accurately and efficiently obtain the characteristics of a plurality of complex shop label systems.
Similarly, in one embodiment, the user profile is constructed according to the user' S market behavior and activity data in step S3, and is also obtained based on the K-means clustering algorithm. The following describes the process of constructing a user profile of a user by way of example:
s31: and classifying and sorting the market behavior and activity data of a user by comparing with preset characteristic data fields to obtain the original user characteristic information. Wherein the characteristic data field comprises user name, purchased commodity, transaction data and residence condition of each store. It can be seen that the characteristic data field does not exactly correspond to the characteristic data field of the store representation construction process in content, but actually has a direct correspondence and belongs to the same characteristics on different objects.
S32: and constructing a user label system according to the original user characteristic information, wherein the characteristic label categories in the user label system comprise group positioning, interested commodities, consumption capacity and shopping habits. Similarly, the feature tag category of the user tag system has a correspondence relationship with the feature tag category of the elevator tag system.
S33: and clustering the user label system by using a K-means clustering algorithm, and generating the user portrait of the user by taking a clustering result as a label of the user portrait. The formula calculation for clustering using the K-means clustering algorithm in this step is substantially the same as the formula calculation for clustering the store tag system using the K-means clustering algorithm, and reference is made to the above description, and therefore, the description thereof will not be repeated here.
In this embodiment, user portrait has been established to every vehicle user's market action and activity characteristic promptly, also carries out characteristic attribute analysis and summary respectively based on each business district that the embodiment was planned simultaneously, and accurate elevator portrait is obtained to the high efficiency to the realization is with market subregion labeling, and through parking navigation with further realization, and puts in its interest business district with vehicle user's precision, acquires high-efficient parking from this simultaneously, optimizes parking management, promotes multiple effects such as user's shopping experience and improvement market achievement.
EXAMPLE III
In a first embodiment, an intelligent parking guidance and commercial guidance system based on user data is provided, referring to fig. 1, including: a cloud server 2 and a user side 1; the cloud server 2 comprises a MySQL database 22 and a parking management platform 21, wherein the MySQL database 22 is connected with the parking management platform 21;
the parking management platform 21 includes: a representation service subsystem 213, a monitoring subsystem 211 and a parking service subsystem 212; the monitoring subsystem 211 is respectively connected to the parking service subsystem 212 and the user terminal 1, and the parking service subsystem 212 is connected to the representation service subsystem 213;
the representation service subsystem 213 comprises:
a business circle determining module 2131, configured to determine a business circle corresponding to each elevator by taking a position of each elevator in a mall as a center and taking a preset distance as a radiation radius; the preset distance is determined according to the total area of the shopping mall and the position of each elevator, so that all shopping malls can cover all shops of the shopping mall;
the figure constructing module 2132 is used for constructing an elevator figure corresponding to the elevator according to store information of stores contained in each business district; the system comprises a user terminal and a user portrait constructing module, wherein the user portrait is constructed according to the mall behavior and activity data of the user collected from the user terminal;
the monitoring subsystem 211, including:
the fence constructing module 2111 is used for constructing an electronic fence corresponding to the mall parking lot;
the detection module 2112 is configured to trigger the parking subsystem to obtain a user portrait of a user from the portrait service subsystem when it is detected that a user side corresponding to the user enters the electronic fence;
the parking service subsystem 212, comprising:
the matching module 2121 is used for matching the user portrait with each elevator portrait after receiving the user portrait and determining the elevator portrait with the highest matching degree with the user;
the parking space acquisition module 2122 is used for acquiring an idle parking space which is closest to the elevator corresponding to the elevator portrait determined by the matching module;
and the navigation module 2123 is configured to generate navigation information according to the idle parking space acquired by the parking space acquisition module and the current position of the user, and send the navigation information to a user side corresponding to the user.
In a specific example, the representation construction module includes:
the shop portrait constructing unit is used for generating a shop portrait of the shop according to the shop information of the shop;
and the elevator portrait constructing unit is used for acquiring elevator portraits of elevators corresponding to the business circles according to the shop portraits of shops contained in the business circles.
In another specific example, the shop representation construction unit includes:
the system comprises a first data processing layer, a second data processing layer and a third data processing layer, wherein the first data processing layer is used for classifying and sorting data in store information of a store against a preset characteristic data field to obtain original store characteristic information, and the characteristic data field comprises a store name, a sold commodity, transaction data and public equipment environment parameters;
the system comprises a first label system building layer, a second label system building layer and a third label system building layer, wherein the first label system building layer is used for building a shop label system according to original shop characteristic information, and the characteristic label categories in the shop label system comprise audience groups, commodity structures, customer orders and service functions;
and the shop portrait construction layer is used for clustering the shop label system by using a K-means clustering algorithm, and generating the shop portrait of the shop by taking a clustering result as a label of the shop portrait.
Specifically, the elevator figure constructing unit includes:
the image acquisition layer is used for acquiring shop images of shops contained in a trade area;
and the elevator portrait construction layer is used for clustering the acquired shop portraits of all shops by using Fast Unfolding clustering algorithm, and generating the elevator portrait of the elevator corresponding to the business circle by using a clustering result as a label of the elevator portrait.
Correspondingly, the image construction module comprises:
the user portrait construction unit is used for constructing a user portrait according to the shopping mall behaviors and activity data of the user;
the user portrait construction unit comprises:
the second data processing layer is used for classifying and sorting the market behavior and activity data of a user against preset characteristic data fields to acquire original user characteristic information, wherein the characteristic data fields comprise user names, purchased commodities, transaction data and the residence conditions of all shops;
the second label system construction layer is used for constructing a user label system according to original user characteristic information, wherein the characteristic label categories in the user label system comprise group positioning, interested commodities, consumption capacity and shopping habits;
and the user portrait construction layer is used for clustering the user label system by using a K-means clustering algorithm, and generating the user portrait of the user by taking a clustering result as a label of the user portrait.
Therefore, the intelligent parking navigation and commercial guidance system based on the user data provided by the embodiment can divide a plurality of business circles of a shopping mall according to the parking entrance elevator based on the image service subsystem, construct an elevator image according to the information of all shops in the business circles, and then construct a user image of a vehicle user; when the monitoring subsystem monitors that the vehicle user enters the mall parking lot, the user image of the user can be obtained, the parking service subsystem matches the user image with each elevator image, so that a business district closest to the user interest is determined, and the business district is guided to an idle parking space nearest to the business district to park through navigation information. Therefore, the system provided by the embodiment can provide efficient parking service for users, and is more beneficial to parking management of shopping malls; the shopping experience can be improved, the good shopping experience can be provided for the user, and the shopping requirement can be met efficiently; furthermore, the performance of the market can be improved, and multiple purposes can be achieved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (10)
1. Intelligent parking navigation and commercial guidance system based on user data, characterized by comprising: a cloud server and a user side; the cloud server comprises a MySQL database and a parking management platform, and the MySQL database is connected with the parking management platform;
the parking management platform comprises: the system comprises an image service subsystem, a monitoring subsystem and a parking service subsystem; the monitoring subsystem is respectively connected with the parking service subsystem and the user side, and the parking service subsystem is connected with the portrait service subsystem;
the portrait service subsystem comprises:
the business circle determining module is used for determining the business circles corresponding to the elevators by taking the positions of the elevators in the shopping mall as the centers and taking the preset distance as the radiation radius;
the figure constructing module is used for constructing an elevator figure corresponding to the elevator according to store information of stores contained in each business district; the system comprises a user terminal and a user portrait constructing module, wherein the user portrait is constructed according to the mall behavior and activity data of the user collected from the user terminal;
the monitoring subsystem includes:
the fence construction module is used for constructing electronic fences corresponding to the mall parking lots;
the detection module is used for triggering the parking subsystem to acquire a user portrait of a user from the portrait service subsystem after detecting that a user side corresponding to the user enters the electronic fence;
the parking service subsystem comprises:
the matching module is used for matching the user portrait with each elevator portrait after receiving the user portrait and determining the elevator portrait with the highest matching degree with the user;
the parking space acquisition module is used for acquiring an idle parking space which is closest to the elevator image determined by the matching module and corresponds to the elevator image;
and the navigation module is used for generating navigation information according to the idle parking space acquired by the parking space acquisition module and the current position of the user, and sending the navigation information to the user side corresponding to the user.
2. The intelligent parking guidance and commercial guidance system based on user data as claimed in claim 1, wherein the representation construction module comprises:
the shop portrait constructing unit is used for generating a shop portrait of the shop according to the shop information of the shop;
and the elevator portrait constructing unit is used for acquiring elevator portraits of elevators corresponding to the business circles according to the shop portraits of shops contained in the business circles.
3. The intelligent parking guidance and commercial guidance system based on user data as claimed in claim 2, wherein the shop representation construction unit comprises:
the system comprises a first data processing layer, a second data processing layer and a third data processing layer, wherein the first data processing layer is used for classifying and sorting data in store information of a store against a preset characteristic data field to obtain original store characteristic information, and the characteristic data field comprises a store name, a sold commodity, transaction data and public equipment environment parameters;
the system comprises a first label system building layer, a second label system building layer and a third label system building layer, wherein the first label system building layer is used for building a shop label system according to original shop characteristic information, and the characteristic label categories in the shop label system comprise audience groups, commodity structures, customer orders and service functions;
and the shop portrait construction layer is used for clustering the shop label system by using a K-means clustering algorithm, and generating the shop portrait of the shop by taking a clustering result as a label of the shop portrait.
4. The intelligent parking guidance and commercial guidance system based on user data of claim 3, wherein the elevator representation construction unit comprises:
the image acquisition layer is used for acquiring shop images of shops contained in a trade area;
and the elevator portrait construction layer is used for clustering the acquired shop portraits of all shops by using Fast Unfolding clustering algorithm, and generating the elevator portrait of the elevator corresponding to the business circle by using a clustering result as a label of the elevator portrait.
5. The intelligent parking guidance and commercial guidance system based on user data as claimed in claim 1, wherein the representation construction module comprises:
the user portrait construction unit is used for constructing a user portrait according to the shopping mall behaviors and activity data of the user;
the user portrait construction unit comprises:
the second data processing layer is used for classifying and sorting the market behavior and activity data of a user against preset characteristic data fields to acquire original user characteristic information, wherein the characteristic data fields comprise user names, purchased commodities, transaction data and the residence conditions of all shops;
the second label system construction layer is used for constructing a user label system according to original user characteristic information, wherein the characteristic label categories in the user label system comprise group positioning, interested commodities, consumption capacity and shopping habits;
and the user portrait construction layer is used for clustering the user label system by using a K-means clustering algorithm, and generating the user portrait of the user by taking a clustering result as a label of the user portrait.
6. The intelligent parking navigation and commercial guidance method based on user data is characterized by comprising the following steps:
determining a business circle corresponding to each elevator by taking the position of each elevator in a market as a center and a preset distance as a radiation radius;
constructing an elevator portrait corresponding to the elevator according to store information of stores contained in each business district;
constructing a user portrait according to the shopping mall behaviors and activity data of the user;
constructing an electronic fence corresponding to the mall parking lot;
after a user side corresponding to a user is detected to enter the electronic fence, acquiring a user portrait of the user;
matching the user portrait of the user with each elevator portrait, and determining the elevator portrait with the highest matching degree with the user; acquiring an idle parking space closest to the elevator corresponding to the determined elevator image;
and generating navigation information according to the free parking space and the current position of the user.
7. The intelligent parking guidance and commercial guidance method based on user data as claimed in claim 6, wherein the building of the elevator figure corresponding to the elevator according to the store information of the stores included in each business district comprises:
generating a shop image of the shop according to the shop information of the shop;
and obtaining the elevator images of the corresponding elevators of each business district according to the shop images of the shops in each business district.
8. The intelligent parking guidance and commercial guidance method based on user data as claimed in claim 7, wherein the generating of the shop image of the shop according to the shop information of the shop comprises:
classifying and sorting data in store information of a store against a preset characteristic data field to obtain original store characteristic information, wherein the characteristic data field comprises a store name, a sold commodity, transaction data and public equipment environment parameters;
building a shop label system according to original shop characteristic information, wherein characteristic label categories in the shop label system comprise audience groups, commodity structures, customer orders and service functions;
and clustering the shop label system by using a K-means clustering algorithm, and generating the shop image of the shop by taking the clustering result as the label of the shop image.
9. The intelligent parking guidance and commercial guidance method based on user data as claimed in claim 8, wherein the building of the elevator representation corresponding to the elevator based on the store information of the stores included in each business district comprises:
obtaining shop images of shops contained in a business circle;
and clustering the obtained shop images of all shops by using a Fast Unfolding clustering algorithm, and generating the elevator image of the elevator corresponding to the business circle by using a clustering result as an elevator image label.
10. The intelligent parking guidance and commercial guidance method based on user data as claimed in claim 1, wherein the constructing a user representation according to the user's shopping behavior and activity data comprises:
classifying and sorting market behavior and activity data of a user against preset characteristic data fields to acquire original user characteristic information, wherein the characteristic data fields comprise user names, purchased commodities, transaction data and the residence conditions of all shops;
constructing a user label system according to original user characteristic information, wherein the characteristic label categories in the user label system comprise group positioning, interested commodities, consumption capacity and shopping habits;
and clustering the user label system by using a K-means clustering algorithm, and generating the user portrait of the user by taking a clustering result as a label of the user portrait.
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