CN113139118A - Parking lot recommendation method and device, electronic equipment and medium - Google Patents
Parking lot recommendation method and device, electronic equipment and medium Download PDFInfo
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Abstract
The application discloses a parking lot recommendation method and device, electronic equipment and a medium, and relates to the field of big data. The specific implementation scheme is as follows: acquiring attribute information of each candidate parking lot, and respectively determining parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot; acquiring historical parking information of a current user, and determining user characteristic information of the current user according to the historical parking information; and selecting a target recommended parking lot from the candidate parking lots according to the characteristic information of each parking lot and the characteristic information of the user, and recommending the target recommended parking lot to the current user. According to the method and the device, the parking lot is recommended to the user according to the parking lot characteristic information of each candidate parking lot and the user characteristic information of the current user, and due to the fact that the parking lot characteristic information and the user characteristic information are considered during recommendation, the recommendation result can meet the personalized requirements of the user.
Description
Technical Field
The embodiment of the application relates to a data processing technology, in particular to a big data technology, and specifically relates to a parking lot recommendation method and device, electronic equipment and a medium.
Background
At present, most of car owner users carry out navigation operation based on map application software when going out, no matter go out official business, take a family to go on a journey or go out across the city, car owner users all need arrive a not familiar place with the help of map application software route navigation, because it is not very known to the destination and the periphery of destination, after arriving the destination, can recommend ideal peripheral parking area for the user and become a problem that waits to solve urgently.
The existing parking lot recommendation method carries out recommendation based on the dynamic or static attributes of the parking lot, so that the recommendation result is low in fineness and cannot meet the personalized requirements of users.
Disclosure of Invention
The embodiment of the application discloses a parking lot recommendation method, a device, electronic equipment and a medium, and can solve the problem that the existing parking lot recommendation method cannot meet the personalized requirements of users.
In a first aspect, an embodiment of the present application discloses a parking lot recommendation method, including:
acquiring attribute information of each candidate parking lot, and respectively determining parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot;
obtaining historical parking information of a current user, and determining user characteristic information of the current user according to the historical parking information;
and selecting a target recommended parking lot from the candidate parking lots according to the parking lot characteristic information and the user characteristic information, and recommending the target recommended parking lot to the current user.
One embodiment in the above application has the following advantages or benefits: the method comprises the steps of respectively determining parking lot characteristic information corresponding to candidate parking lots according to the attribute information of the candidate parking lots, determining user characteristic information of a current user according to historical parking information, and finally determining a target recommended parking lot and recommending the target recommended parking lot to the user according to the parking lot characteristic information and the user characteristic information.
In addition, according to the parking lot recommendation method of the above embodiment of the present application, the following additional technical features may also be provided:
optionally, determining the user characteristic information of the current user according to the historical parking information includes:
determining the correlation degree between the current user and each candidate parking lot according to the historical parking information;
and determining the user characteristic information of the current user according to the correlation between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot.
One embodiment in the above application has the following advantages or benefits: the relevance between the current user and each candidate parking lot is determined according to historical parking information, and the user characteristic information is determined according to the relevance and the parking characteristic information of each candidate parking lot, so that a data base is laid for determining a target recommended parking lot according to the user characteristic information.
Optionally, determining the user feature information of the current user according to the relevance between the current user and each of the candidate parking lots and the parking lot feature information of each of the candidate parking lots, where the determining includes:
and taking the sum of the products of the correlation of each candidate parking lot and the parking lot characteristic information corresponding to the candidate parking lot as the user characteristic information of the current user.
One embodiment in the above application has the following advantages or benefits: the sum of the products of the correlation of each candidate parking lot and the parking lot feature information of each candidate parking lot is used as the user feature information of the current user, so that the technical effect of determining the user feature information is realized, and a data base is laid for subsequently determining the target recommended parking lot according to the user feature information.
Optionally, determining, according to the historical parking information, a degree of correlation between a current user and each of the candidate parking lots respectively includes:
if the historical parking information is associated with any candidate parking lot, the correlation degree between the current user and the candidate parking lot is a first numerical value, and if not, the correlation degree is a second numerical value; wherein the first value is different from the second value.
One embodiment in the above application has the following advantages or benefits: the correlation degree of the candidate parking lot is determined according to the correlation condition between the historical parking information and the candidate parking lot, so that a data base is laid for determining the user characteristic information according to the correlation degree of the candidate parking lot in the follow-up process.
Optionally, the historical parking information includes: at least one of an entrance and exit record, a reservation record, a parking record, a payment record and a parking duration record in the parking lot.
One embodiment in the above application has the following advantages or benefits: at least one of the entrance and exit records, the reservation records, the parking records, the payment records and the parking duration records of the parking lot is used as historical parking information, so that the information contained in the historical parking information is enriched, and the relevance of the candidate parking lot determined according to the historical parking information is more accurate.
Optionally, selecting a target recommended parking lot from the candidate parking lots according to the parking lot feature information and the user feature information, including:
calculating the similarity between the user characteristic information and each parking lot characteristic information according to a preset similarity calculation algorithm;
and selecting the candidate parking lot corresponding to the calculated highest similarity as a target recommended parking lot.
One embodiment in the above application has the following advantages or benefits: by calculating the similarity between the user characteristic information and each parking lot characteristic information and taking the candidate parking lot corresponding to the highest similarity as the target recommended parking lot, the target recommended parking lot not only considers the parking lot characteristic information, but also considers the user characteristic information, so that the recommendation result can meet the personalized requirements of the user.
Optionally, selecting a target recommended parking lot from the candidate parking lots according to the parking lot feature information and the user feature information, including:
calculating the similarity between the user characteristic information of the current user and the user characteristic information of other users according to a preset similarity calculation algorithm;
determining other users with similarity of the user characteristic information of the current user larger than a set threshold value, and recommending parking lots for the targets determined by the other users;
and determining the target recommended parking lot determined for other users as the target recommended parking lot corresponding to the current user.
One embodiment in the above application has the following advantages or benefits: by calculating the similarity between the user characteristic information of the current user and the user characteristic information of other users, and using the target recommended parking lot of other users with the similarity larger than a set threshold as the target parking lot corresponding to the current user, the technical effect of recommending the target recommended parking lot based on the similarity between the users is achieved, and the requirement of user individuation is further met.
In a second aspect, an embodiment of the present application further discloses a parking lot recommendation device, including:
the parking lot characteristic information determining module is used for acquiring the attribute information of each candidate parking lot and respectively determining the parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot;
the system comprises a user characteristic information determining module, a parking information determining module and a parking information determining module, wherein the user characteristic information determining module is used for acquiring historical parking information of a current user and determining user characteristic information of the current user according to the historical parking information;
and the parking lot recommending module is used for selecting a target recommended parking lot from the candidate parking lots according to the parking lot characteristic information and the user characteristic information and recommending the target recommended parking lot to the current user.
In a third aspect, an embodiment of the present application further discloses an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the parking lot recommendation method of any embodiment of the present application.
In a fourth aspect, embodiments of the present application further disclose a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the parking lot recommendation method according to any of the embodiments of the present application.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a schematic flow chart of a parking lot recommendation method according to a first embodiment of the present application;
fig. 2 is a schematic flow chart of a parking lot recommendation method according to a second embodiment of the present application;
fig. 3 is a schematic flow chart of a parking lot recommendation method according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a parking lot recommendation device according to a fourth embodiment of the present application;
fig. 5 is a block diagram of an electronic device for implementing the parking lot recommendation method according to the embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example one
Fig. 1 is a schematic flowchart of a parking lot recommendation method according to an embodiment of the present application. The embodiment is suitable for recommending the parking lot to the user, and can be executed by the parking lot recommending device provided by the embodiment of the application, and the device can be realized in a software and/or hardware mode. As shown in fig. 1, the method may include:
s101, acquiring attribute information of each candidate parking lot, and respectively determining parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot.
The candidate parking lots represent all available parking lots within a preset distance threshold from the target position of the user, and the preset distance threshold can be set at will. The attribute information of the candidate parking lot comprises at least one of static attribute information, dynamic attribute information and attribute information of an area to which the attribute information belongs, wherein the static attribute information represents inherent attribute information of the parking lot and comprises at least one of parking lot charging information, total parking space size, parking lot type, driving distance between the user navigation terminal and the parking lot, driving time between the user navigation terminal and the parking lot, walking distance between the user navigation terminal and the parking lot and walking time between the user navigation terminal and the parking lot; the dynamic attribute information comprises at least one of the current remaining parking space information, the parking time consumption and the parking difficulty information of the parking lot; the belonging area attribute information represents information on an area to which the parking lot belongs, and includes: information of a parent POI (Point of Interest) belonging to the site, for example, level information of a hospital belonging to the site, level information of a tourist attraction belonging to the site, loop information of a local area, information of a prosperous degree of a market where the site is located, and the like.
Specifically, when a user uses navigation application software, if the user wants to query candidate parking lots near a target position, wherein the target position can be the current position or any other position, the user clicks a query button on a navigation application software interface, a server responds to a query request, determines a target search area by taking the target position as a center and a preset distance threshold as a radius, and determines the candidate parking lots in the target search area. After the candidate parking lots are determined, the server determines static attribute information and attribute information of the region to which the parking lots belong in the attribute information of the candidate parking lots according to the stored historical acquisition information, and determines dynamic attribute information in the attribute information of the candidate parking lots according to the real-time acquisition information. And finally, according to the acquired attribute information of each candidate parking lot, converting the acquired attribute information of each candidate parking lot into the parking lot feature information of the corresponding candidate parking lot by methods including a convolutional neural network, Word2Vec, Doc2Vec and the like.
By acquiring the attribute information of each candidate parking lot and respectively determining the parking lot characteristic information corresponding to the candidate parking lot according to the attribute information of each candidate parking lot, a foundation is laid for subsequently determining a target recommended parking lot according to the parking lot characteristic information.
S102, obtaining historical parking information of a current user, and determining user characteristic information of the current user according to the historical parking information.
With the continuous improvement of network functions of unmanned parking lots and electronic parking lots, online reservation, payment, parking records and the like, historical parking information of users can be conveniently acquired, and the historical parking information of the users represents related record information generated by the users in the parking lots in the past.
Specifically, after each time the user performs various operations related to each candidate parking lot, the information is recorded through navigation application software and is transmitted to the server to be stored as the historical parking information of the current user, and when the user needs to inquire the parking lot, the server acquires the historical parking information related to the user according to the identity information of the user. And inquiring whether each associated candidate parking lot is associated from the acquired historical parking information, and determining the user characteristic information of the current user according to the inquiry result and the parking lot characteristic information of each candidate parking lot.
Optionally, the historical parking information includes: at least one of an entrance and exit record, a reservation record, a parking record, a payment record and a parking duration record in the parking lot.
Optionally, the step 102 of "determining the user characteristic information of the current user according to the historical parking information" includes: determining the correlation degree between the current user and each candidate parking lot according to the historical parking information; and determining the user characteristic information of the current user according to the correlation between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot.
The historical parking information of the current user is obtained, and the user characteristic information of the current user is determined according to the historical parking information, so that a foundation is laid for determining the target recommended parking lot according to the user characteristic information in the follow-up process.
S103, selecting a target recommended parking lot from the candidate parking lots according to the parking lot characteristic information and the user characteristic information, and recommending the target recommended parking lot to a current user.
Specifically, after the characteristic information of each parking lot and the characteristic information of the current user are obtained, the association degree between the characteristic information of the user and the characteristic information of each parking lot is determined, and a target recommended parking lot is selected from candidate parking lots according to the association degree, wherein the number of the target recommended parking lots may be one or more. And finally recommending the obtained target recommended parking lot to a user, wherein the recommending mode comprises that the target recommended parking lot is visually displayed in a navigation application software interface, when the user clicks a confirmation button, a navigation route is generated according to the target position of the user and the position of the target recommended parking lot, and auxiliary information including distance information, required time information, the number of traffic lights, road condition information, weather information and the like is generated on the navigation application software interface.
Optionally, S103 includes: calculating the similarity between the user characteristic information and each parking lot characteristic information according to a preset similarity calculation algorithm; and selecting the candidate parking lot corresponding to the calculated highest similarity as a target recommended parking lot.
The corresponding parking lot can be provided for the user by simply considering the attribute information of the parking lot, but with the improvement of the living standard of people, the requirement on self personalization is higher and higher, the recommendation of information flow needs personalization, the recommendation of goods needs personalization, similarly, for the owner user, if the personalized parking lot can be recommended according to the historical parking information, the user can be provided with more refined parking lots meeting the parking requirement of the user, for example, some users tend to stop at underground parking lots of large shopping malls, but do not mind that the distance is slightly far away, some users tend to stop at parking lots with closer distance around the destination or more remaining parking lots, and the users need to dig deeper, therefore, the target recommended parking lot is selected from candidate parking lots according to the characteristic information and the user characteristic information of each parking lot, the demand of user individuation can be satisfied.
According to the technical scheme provided by the embodiment of the application, the parking lot characteristic information corresponding to the candidate parking lots is respectively determined according to the attribute information of the candidate parking lots, the user characteristic information of the current user is determined according to the historical parking information, and finally the target recommended parking lots are determined and recommended to the user according to the parking lot characteristic information and the user characteristic information.
Example two
Fig. 2 is a schematic flowchart of a parking lot recommendation method according to a second embodiment of the present application. The embodiment provides a specific implementation manner for the above embodiment, and as shown in fig. 2, the method may include:
s201, acquiring attribute information of each candidate parking lot, and respectively determining parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot.
S202, obtaining historical parking information of the current user, and determining the relevance of the current user and each candidate parking lot according to the historical parking information.
Specifically, the historical parking information is inquired in each record information included in the historical parking information, the association relation between the historical parking information and each candidate parking lot is determined, and the relevance between the current user and each candidate parking lot is determined according to the inquiry result.
Optionally, S202 includes: if the historical parking information is associated with any candidate parking lot, the correlation degree between the current user and the candidate parking lot is a first numerical value, and if not, the correlation degree is a second numerical value; wherein the first value is different from the second value.
For example, if there is a record of the candidate parking lot a in the "entry and exit records of parking lots" in the historical parking information, it indicates that the historical parking information is associated with the candidate parking lot a, and the correlation between the corresponding current user and the candidate parking lot a is a first numerical value; if the record of the candidate parking lot B exists in the reservation record in the historical parking information, the historical parking information is associated with the candidate parking lot B, and the correlation degree between the corresponding current user and the candidate parking lot B is a first numerical value; if there is no record of the candidate parking lot C in the "entry and exit record, the" reservation record ", the" parking record ", the" payment record "and the" parking duration record "of the parking lot in the historical parking information, it indicates that the historical parking information is not associated with the candidate parking lot C, and the degree of correlation between the corresponding current user and the candidate parking lot C is the second numerical value. In this embodiment, the first value is optionally "1" and the second value is optionally "0".
And S203, determining the user characteristic information of the current user according to the correlation between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot.
Optionally, S203 includes: and taking the sum of the products of the correlation of each candidate parking lot and the parking lot characteristic information of each candidate parking lot as the user characteristic information of the current user.
The calculation process of the user characteristic information can be represented by the following formula:
wherein xuUser characteristic information, x, representing the current useriParking lot characteristic information r of candidate parking lot iuiIs the correlation degree between the current user u and the candidate parking lot i, I (u) is a candidateA collection of parking lots.
Exemplarily, it is assumed that the set of candidate parking lots is composed of candidate parking lot a, candidate parking lot B, candidate parking lot C, and candidate parking lot D; the parking lot characteristic information of the candidate parking lot A is A1, the parking lot characteristic information of the candidate parking lot B is B1, the parking lot characteristic information of the candidate parking lot C is C1, and the parking lot characteristic information of the candidate parking lot D is D1; the relevance of the current user X to the candidate parking lot a is 1, the relevance to the candidate parking lot B is 1, the relevance to the candidate parking lot C is 0, and the relevance to the candidate parking lot D is 1, so that the user characteristic information X of the current user X is X1 × a1+1 × B1+0 × C1+1 × D1 — a1+ B1+ D1.
And S204, calculating the similarity between the user characteristic information and each parking lot characteristic information according to a preset similarity calculation algorithm.
The preset similarity calculation algorithm includes, but is not limited to, a cosine similarity calculation method, a pearson correlation coefficient algorithm, and the like.
Specifically, the user characteristic information and the parking lot characteristic information are used as input parameters and substituted into a preset similarity calculation algorithm to calculate the similarity. In this embodiment, a cosine similarity algorithm is taken as an example of a preset similarity calculation algorithm, and a specific calculation process can be represented by the following formula:
wherein x isiParking lot characteristic information, x, of candidate parking lot iuUser characteristic information, S, representing the current useruiParking lot characteristic information and user characteristic information x of candidate parking lot iuThe similarity between them.
S205, selecting the candidate parking lot corresponding to the calculated highest similarity as a target recommended parking lot, and recommending the target recommended parking lot to the current user.
Specifically, the similarity obtained by the calculation in the step S204 is ranked according to a principle from high to low, the highest similarity is determined, the candidate parking lot corresponding to the highest similarity is used as a target recommended parking lot, and finally the target recommended parking lot is recommended to the current user.
According to the technical scheme provided by the embodiment of the application, historical parking information of a current user is obtained, the relevance between the current user and each candidate parking lot is determined according to the historical parking information, the user characteristic information of the current user is determined according to the relevance between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot, the similarity between the user characteristic information and each parking lot characteristic information is calculated finally according to a preset similarity calculation algorithm, the candidate parking lot corresponding to the maximum similarity is recommended to the current user, and due to the fact that the parking lot characteristic information and the user characteristic information are considered during recommendation, the recommendation result can meet the personalized requirements of the user.
EXAMPLE III
Fig. 3 is a schematic flowchart of a parking lot recommendation method according to a third embodiment of the present application. The embodiment provides a specific implementation manner for the above embodiment, and as shown in fig. 3, the method may include:
s301, obtaining attribute information of each candidate parking lot, and respectively determining parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot.
S302, historical parking information of the current user is obtained, and the correlation degree between the current user and each candidate parking lot is determined according to the historical parking information.
And S303, determining the user characteristic information of the current user according to the correlation between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot.
S304, calculating the similarity between the user characteristic information of the current user and the user characteristic information of other users according to a preset similarity calculation algorithm.
The preset similarity calculation algorithm includes, but is not limited to, a cosine similarity calculation method, a pearson correlation coefficient algorithm, and the like.
Specifically, the user characteristic information of the current user and the user characteristic information of other users are used as input parameters and substituted into a preset similarity calculation algorithm to calculate the similarity.
S305, determining other users with similarity of the user characteristic information of the current user larger than a set threshold, and recommending parking lots for the targets determined by the other users.
Specifically, the similarity degrees obtained in S304 are screened according to a set threshold, and other users whose similarity degrees with the user feature information of the current user are greater than the set threshold are determined. And determining the target recommended parking lot determined for the other users.
For example, if the similarity between the user characteristic information of the current user a and the user characteristic information of the other user B, the other user C, and the other user D is 80%, 85%, and 90%, respectively, and the threshold is set to 83%, the other user C and the other user D are determined, and the target recommended parking lot is determined for the other user C and the other user D: parking lot C and parking lot D.
S306, determining the target recommended parking lot determined for other users as the target recommended parking lot corresponding to the current user, and recommending the target recommended parking lot to the current user.
According to the technical scheme provided by the embodiment of the application, the similarity between the user characteristic information of the current user and the user characteristic information of other users is calculated, and the target recommended parking lot of other users with the similarity larger than the set threshold value is used as the target parking lot corresponding to the current user, so that the technical effect of recommending the target recommended parking lot based on the similarity between the users is achieved, and the requirement of user individuation is further met.
Example four
Fig. 4 is a schematic structural diagram of a parking lot recommendation device 40 according to a fourth embodiment of the present application, which is capable of executing a parking lot recommendation method according to any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 4, the apparatus may include:
the parking lot characteristic information determining module 41 is configured to obtain attribute information of each candidate parking lot, and determine parking lot characteristic information corresponding to each candidate parking lot according to the attribute information of each candidate parking lot;
the user characteristic information determining module 42 is configured to obtain historical parking information of a current user, and determine user characteristic information of the current user according to the historical parking information;
and the parking lot recommending module 43 is configured to select a target recommended parking lot from the candidate parking lots according to the parking lot feature information and the user feature information, and recommend the target recommended parking lot to the current user.
On the basis of the foregoing embodiment, the user characteristic information determining module 42 is specifically configured to:
determining the correlation degree between the current user and each candidate parking lot according to the historical parking information;
and determining the user characteristic information of the current user according to the correlation between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot.
On the basis of the foregoing embodiment, the user characteristic information determining module 42 is further specifically configured to:
and taking the sum of the products of the correlation of each candidate parking lot and the parking lot characteristic information of each candidate parking lot as the user characteristic information of the current user.
On the basis of the foregoing embodiment, the user characteristic information determining module 42 is further specifically configured to:
if the historical parking information is associated with any candidate parking lot, the correlation degree between the current user and the candidate parking lot is a first numerical value, and if not, the correlation degree is a second numerical value; wherein the first value is different from the second value.
On the basis of the above embodiment, the historical parking information includes: at least one of an entrance and exit record, a reservation record, a parking record, a payment record and a parking duration record in the parking lot.
On the basis of the above embodiment, the parking lot recommending module 43 is specifically configured to:
calculating the similarity between the user characteristic information and each parking lot characteristic information according to a preset similarity calculation algorithm;
and selecting the candidate parking lot corresponding to the calculated highest similarity as a target recommended parking lot.
On the basis of the above embodiment, the parking lot recommendation module 43 is further specifically configured to:
calculating the similarity between the user characteristic information of the current user and the user characteristic information of other users according to a preset similarity calculation algorithm;
determining other users with similarity of the user characteristic information of the current user larger than a set threshold value, and recommending parking lots for the targets determined by the other users;
and determining the target recommended parking lot determined for other users as the target recommended parking lot corresponding to the current user.
The parking lot recommendation device 40 provided in the embodiment of the present application can execute a parking lot recommendation method provided in any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method. For details of the technology that are not described in detail in this embodiment, reference may be made to a parking lot recommendation method provided in any embodiment of the present application.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 5, the disclosure is a block diagram of an electronic device according to an embodiment of the disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 5, one processor 501 is taken as an example.
The memory 502, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the parking lot recommendation method in the embodiment of the present application (for example, the parking lot characteristic information determination module 41, the user characteristic information determination module 42, and the parking lot recommendation module 43 shown in fig. 4). The processor 501 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 502, that is, implements the parking lot recommendation method in the above-described method embodiment.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device recommended by the parking lot, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 502 may optionally include memory remotely located from the processor 501, which may be connected to the parking recommendation electronics over a network. Examples of such networks include, but are not limited to, the internet, intranets, blockchain networks, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the parking lot recommendation method may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic equipment recommended for the parking lot, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the parking lot is recommended to the user according to the parking lot characteristic information of each candidate parking lot and the user characteristic information of the current user, and the parking lot is recommended to the user according to the parking lot characteristic information and the user characteristic information.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (16)
1. A parking lot recommendation method, the method comprising:
acquiring attribute information of each candidate parking lot, and respectively determining parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot;
obtaining historical parking information of a current user, and determining user characteristic information of the current user according to the historical parking information;
and selecting a target recommended parking lot from the candidate parking lots according to the parking lot characteristic information and the user characteristic information, and recommending the target recommended parking lot to the current user.
2. The method of claim 1, wherein determining user characteristic information of a current user from the historical parking information comprises:
determining the correlation degree between the current user and each candidate parking lot according to the historical parking information;
and determining the user characteristic information of the current user according to the correlation between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot.
3. The method of claim 2, wherein determining the user characteristic information of the current user according to the correlation degree of the current user with each candidate parking lot and the parking lot characteristic information of each candidate parking lot comprises:
and taking the sum of the products of the correlation of each candidate parking lot and the parking lot characteristic information corresponding to the candidate parking lot as the user characteristic information of the current user.
4. The method of claim 2, wherein determining the relevance of the current user to each of the candidate parking lots according to the historical parking information comprises:
if the historical parking information is associated with any candidate parking lot, the correlation degree between the current user and the candidate parking lot is a first numerical value, and if not, the correlation degree is a second numerical value; wherein the first value is different from the second value.
5. The method of claim 2, wherein the historical parking information comprises: at least one of an entrance and exit record, a reservation record, a parking record, a payment record and a parking duration record in the parking lot.
6. The method according to any one of claims 1 to 5, wherein selecting a target recommended parking lot from the candidate parking lots according to the parking lot feature information and the user feature information includes:
calculating the similarity between the user characteristic information and each parking lot characteristic information according to a preset similarity calculation algorithm;
and selecting the candidate parking lot corresponding to the calculated highest similarity as a target recommended parking lot.
7. The method according to any one of claims 1 to 5, wherein selecting a target recommended parking lot from the candidate parking lots according to the parking lot feature information and the user feature information includes:
calculating the similarity between the user characteristic information of the current user and the user characteristic information of other users according to a preset similarity calculation algorithm;
determining other users with similarity of the user characteristic information of the current user larger than a set threshold value, and recommending parking lots for the targets determined by the other users;
and determining the target recommended parking lot determined for other users as the target recommended parking lot corresponding to the current user.
8. A parking lot recommendation device, the device comprising:
the parking lot characteristic information determining module is used for acquiring the attribute information of each candidate parking lot and respectively determining the parking lot characteristic information corresponding to the candidate parking lots according to the attribute information of each candidate parking lot;
the system comprises a user characteristic information determining module, a parking information determining module and a parking information determining module, wherein the user characteristic information determining module is used for acquiring historical parking information of a current user and determining user characteristic information of the current user according to the historical parking information;
and the parking lot recommending module is used for selecting a target recommended parking lot from the candidate parking lots according to the parking lot characteristic information and the user characteristic information and recommending the target recommended parking lot to the current user.
9. The apparatus of claim 8, wherein the user characteristic information determining module is specifically configured to:
determining the correlation degree between the current user and each candidate parking lot according to the historical parking information;
and determining the user characteristic information of the current user according to the correlation between the current user and each candidate parking lot and the parking lot characteristic information of each candidate parking lot.
10. The apparatus according to claim 9, wherein the user characteristic information determining module is further configured to:
and taking the sum of the products of the correlation of each candidate parking lot and the parking lot characteristic information corresponding to the candidate parking lot as the user characteristic information of the current user.
11. The apparatus according to claim 9, wherein the user characteristic information determining module is further configured to:
if the historical parking information is associated with any candidate parking lot, the correlation degree between the current user and the candidate parking lot is a first numerical value, and if not, the correlation degree is a second numerical value; wherein the first value is different from the second value.
12. The apparatus of claim 9, wherein the historical parking information comprises: at least one of an entrance and exit record, a reservation record, a parking record, a payment record and a parking duration record in the parking lot.
13. The apparatus according to any one of claims 8-12, wherein the parking lot recommendation module is specifically configured to:
calculating the similarity between the user characteristic information and each parking lot characteristic information according to a preset similarity calculation algorithm;
and selecting the candidate parking lot corresponding to the calculated highest similarity as a target recommended parking lot.
14. The apparatus according to any one of claims 8 to 12, wherein the parking lot recommendation module is further configured to:
calculating the similarity between the user characteristic information of the current user and the user characteristic information of other users according to a preset similarity calculation algorithm;
determining other users with similarity of the user characteristic information of the current user larger than a set threshold value, and recommending parking lots for the targets determined by the other users;
and determining the target recommended parking lot determined for other users as the target recommended parking lot corresponding to the current user.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the parking lot recommendation method of any of claims 1-7.
16. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the parking lot recommendation method according to any one of claims 1 to 7.
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