CN116839620A

CN116839620A - Parking lot recommendation method and device, computer equipment and storage medium

Info

Publication number: CN116839620A
Application number: CN202310783693.5A
Authority: CN
Inventors: 陈润泉
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2023-06-29
Filing date: 2023-06-29
Publication date: 2023-10-03

Abstract

The embodiment of the application belongs to the field of artificial intelligence, and relates to a parking lot recommendation method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring parking demand voice of a user, and generating a parking demand text according to the parking demand voice; generating parking demand information according to the parking demand text, wherein the parking demand information comprises a plurality of keywords; acquiring tag information of each candidate parking lot, wherein the tag information comprises a plurality of characteristic tags of the candidate parking lots; screening each candidate parking lot according to the parking demand information and the label information of each candidate parking lot to obtain at least one target parking lot; and generating recommendation information according to the at least one target parking lot, and sending the recommendation information to a terminal held by the user. In addition, the application also relates to a blockchain technology, and the label information of the parking lot can be stored in the blockchain _。 The method and the device improve the accuracy of parking lot recommendation.

Description

Parking lot recommendation method and device, computer equipment and storage medium

Technical Field

The application relates to the field of artificial intelligence, in particular to a parking lot recommendation method, a device, computer equipment and a storage medium.

Background

Drivers often use map applications to navigate while driving vehicles and find parking lots through the map applications. Map applications are typically recommended based on the distance from the current location of the vehicle to the parking lot when recommending the parking lot. However, the parking lot recommendation method is simple, and the accuracy of parking lot recommendation is low due to single dimension.

Disclosure of Invention

The embodiment of the application aims to provide a parking lot recommendation method, a device, computer equipment and a storage medium, so as to improve the accuracy of parking lot recommendation.

In order to solve the technical problems, the embodiment of the application provides a parking lot recommending method, which adopts the following technical scheme:

acquiring parking demand voice of a user, and generating a parking demand text according to the parking demand voice;

generating parking demand information according to the parking demand text, wherein the parking demand information comprises a plurality of keywords;

acquiring tag information of each candidate parking lot, wherein the tag information comprises a plurality of characteristic tags of the candidate parking lots;

screening each candidate parking lot according to the parking demand information and the label information of each candidate parking lot to obtain at least one target parking lot;

And generating recommendation information according to the at least one target parking lot, and sending the recommendation information to a terminal held by the user.

In order to solve the technical problems, the embodiment of the application also provides a parking lot recommendation device, which adopts the following technical scheme:

the text generation module is used for acquiring parking demand voice of a user and generating a parking demand text according to the parking demand voice;

the demand generation module is used for generating parking demand information according to the parking demand text, wherein the parking demand information comprises a plurality of keywords;

the tag acquisition module is used for acquiring tag information of each candidate parking lot, wherein the tag information comprises a plurality of characteristic tags of the candidate parking lots;

the parking lot screening module is used for screening each candidate parking lot according to the parking demand information and the label information of each candidate parking lot to obtain at least one target parking lot;

and the recommendation generation module is used for generating recommendation information according to the at least one target parking lot and sending the recommendation information to a terminal held by the user.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

Compared with the prior art, the embodiment of the application has the following main beneficial effects: generating a parking demand text according to the parking demand voice of the user, and further generating parking demand information, wherein the parking demand information comprises a plurality of keywords and can mainly represent the parking demand of the user; acquiring tag information of each candidate parking lot, wherein the tag information comprises a plurality of characteristic tags of the candidate parking lots, and different characteristic tags reflect the conditions of the parking lots from different dimensions, so that more dimensions of information of the parking lots participate in parking lot screening; and matching the parking requirement information with the label information of each candidate parking lot so as to screen each candidate parking lot to obtain at least one target parking lot, wherein the target parking lot integrates the user requirement and the multi-dimensional information of the parking lot, thereby more meeting the user requirement and improving the accuracy of the recommendation of the parking lot.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a parking lot recommendation method according to the present application;

FIG. 3 is a schematic view of a structure of an embodiment of a parking lot recommendation device according to the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the parking lot recommending method provided by the embodiment of the application is generally executed by a server, and accordingly, the parking lot recommending device is generally arranged in the server.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow chart of one embodiment of a parking lot recommendation method according to the present application is shown. The parking lot recommending method comprises the following steps:

Step S201, obtaining parking demand voice of a user, and generating a parking demand text according to the parking demand voice.

In this embodiment, the electronic device (for example, the server shown in fig. 1) on which the parking lot recommendation method operates may communicate with the terminal device through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection.

Specifically, the user can express the parking requirement through voice, the terminal collects the parking requirement voice of the user and sends the parking requirement voice to the server, and the server carries out text recognition on the parking requirement voice to obtain a parking requirement text.

Step S202, generating parking requirement information according to the parking requirement text, wherein the parking requirement information comprises a plurality of keywords.

Specifically, keywords in the parking demand text are identified, and a sequence of the identified keywords can be used as parking demand information, which can highlight the parking demand of the user.

In step S203, tag information of each candidate parking lot is acquired, the tag information including a plurality of feature tags of the candidate parking lot.

Specifically, tag information of each candidate parking lot is acquired, the tag information contains a plurality of feature tags of the candidate parking lot, and different feature tags reflect the conditions of the parking lot from different dimensions, such as the parking lot position, the total number of vehicles in the parking lot, the number of real-time vehicles, the parking charging rule, whether to open to the outside, business hours, limit information and the like.

Step S204, screening each candidate parking lot according to the parking demand information and the label information of each candidate parking lot to obtain at least one target parking lot.

Specifically, the parking demand information and the label information of each candidate parking lot are compared, and the parking lot with the label information conforming to the parking demand information is screened to obtain at least one target parking lot.

Step S205, generating recommendation information according to at least one target parking lot, and sending the recommendation information to a terminal held by a user.

Specifically, the server generates recommendation information according to at least one target parking lot, and sends the recommendation information to a terminal held by a user. The recommendation information can be a list of target parking lots, and a user can select the target parking lots in the list; or the server performs descending order sorting on each target parking lot according to the matching degree of the parking lot and the parking demand information, selects the target parking lot with the highest matching degree, generates navigation information according to the target parking lot with the highest matching degree, and then sends the target parking lot and the navigation information corresponding to the target parking lot as recommended information to a terminal held by a user.

In this embodiment, a parking demand text is generated according to a parking demand voice of a user, and parking demand information is further generated, where the parking demand information includes a plurality of keywords, and may mainly represent a parking demand of the user; acquiring tag information of each candidate parking lot, wherein the tag information comprises a plurality of characteristic tags of the candidate parking lots, and different characteristic tags reflect the conditions of the parking lots from different dimensions, so that more dimensions of information of the parking lots participate in parking lot screening; and matching the parking requirement information with the label information of each candidate parking lot so as to screen each candidate parking lot to obtain at least one target parking lot, wherein the target parking lot integrates the user requirement and the multi-dimensional information of the parking lot, thereby more meeting the user requirement and improving the accuracy of the recommendation of the parking lot.

Further, the step of generating the parking requirement information according to the parking requirement text may include: word segmentation processing is carried out on the parking requirement text to obtain a plurality of word segments; determining keywords in the plurality of segmented words; acquiring keyword historical data; and adjusting each keyword according to the keyword history data, and generating parking demand information according to the adjusted keywords.

Specifically, word segmentation processing is carried out on the parking requirement text to obtain a plurality of word segments; keywords in the plurality of segmentations are determined, and the number of the keywords is at least one. The user can search the parking lot through voice in the past time, the historical voice can also generate keywords, and the data related to the historical keywords is the keyword historical data.

The server acquires the keyword historical data of the user, adjusts each keyword according to the keyword historical data, and generates parking requirement information according to the adjusted keywords. The adjustment includes various processes, for example, when a certain keyword is incomplete, the keyword may be complemented according to the keyword history data, such as an existing keyword: the free parking is searched through the keyword historical data, and the keywords which are generated in the past by the user and are related or similar to the free parking are searched to be free parking lots and free parking spaces, so that the free parking can be completed according to the free parking lots and the free parking spaces; in addition, similar historical keywords can be added to keywords generated based on the parking requirement text, for example, similar free parking in a free parking lot, free parking and free parking space are added, and comprehensiveness and accuracy of subsequent matching are ensured.

In the embodiment, keywords in a parking demand text are acquired, and keyword historical data are acquired; and adjusting each keyword according to the keyword historical data, so as to ensure the accuracy of parking lot recommendation according to the adjusted parking demand information.

Further, in an embodiment, the step of adjusting each keyword according to the keyword history data and generating the parking requirement information according to the adjusted keyword may include: acquiring historical hit data of each keyword according to the historical data of the keywords; and sorting the keywords according to the historical hit data of the keywords, generating parking demand information according to the sorted keywords, and using the sorting of the keywords as a weight and screening the candidate parking lots.

Specifically, the keyword history data records history keywords and their corresponding history hit data, and the history hit data may be the number of occurrences of the history keywords, or the number of user selections/uses. Acquiring historical hit data of each keyword based on the keyword historical data, wherein the historical hit data reflects the preference degree of a user on the keywords, the use frequency of the keywords and the popularity degree; sorting the keywords according to the historical hit data, and generating parking demand information according to the sorted keywords; for example, the keywords are ranked in order of the history hit data from large to small, and the higher the ranking keywords are, the more historically hit times, the more important the keywords can be considered to be to the user. The ranking of each keyword can be attached with a weight, the weight is larger when the ranking is closer to the front, and the weight can participate in screening of candidate parking lots; it will be appreciated that the greater the weight, the greater the impact of the keywords on the degree of matching.

In this embodiment, according to the history data of the keywords, history hit data of each keyword is obtained; the historical hit data reflects the preference of the user to the keywords, the keywords are ranked according to the historical hit data of the keywords, and parking demand information is generated according to the ranked keywords, so that the importance of the keywords in the parking demand information is distinguished; the sorting of the keywords is used as the weight, and the larger the weight is, the larger the influence on the screening process is, so that the screening of the candidate parking lot can be ensured to accord with the user preference.

Further, in another embodiment, the step of adjusting each keyword according to the keyword history data and generating the parking requirement information according to the adjusted keyword may include: for each keyword, querying similar historical keywords of the keywords in the keyword historical data; setting the keywords and each similar historical keyword as candidate words, and acquiring historical hit data of each candidate word through the historical data of the keywords; and determining the candidate word with the largest history hit data in the candidate words as a target word, and enabling the target word to replace the key word.

Specifically, each keyword has a similar word, and if a word similar to the keyword has been used by the user, the word is recorded into the keyword history data and is a similar history keyword of the keyword.

In the keyword history data, similar history keywords of keywords are queried; and uniformly setting the keywords and the similar historical keywords as candidate words, and acquiring historical hit data of each candidate word through the historical data of the keywords. It will be appreciated that the number of hits made by the user on the candidate word may be different. The candidate word with the largest historical hit data in each candidate word can be determined as the target word, and the target word is made to replace the keyword, for example, the hit number of the candidate word for "free" by the user is 100, the hit number of the candidate word for "no money" is 40, the keyword in the text of the parking requirement at this time is "no money", and then the "no money" can be replaced by "free".

In this embodiment, through keyword association, keywords that the user may want to input may be presumed, so as to provide accurate suggestions and selections, reduce input errors and unnecessary attempts of the user, and improve accuracy and efficiency of parking lot recommendation. The keyword association can correct the input error or fuzzy expression of the user to a certain extent, so that the accuracy of the parking requirement information is improved. Meanwhile, through keyword association, keywords possibly interested by the user can be presumed according to personal preference and keyword historical data of the user, personalized parking lot recommendation and screening can be provided for the user, and user experience is enhanced.

In this embodiment, similar history keywords of each keyword are queried; setting the keywords and each similar historical keyword as candidate words, and acquiring historical hit data of each candidate word; and determining the candidate word with the largest historical hit data in the candidate words as a target word, and enabling the target word to replace the key word, so that the screening of the candidate parking lot is more in line with the habit and preference of the user.

Further, the step of obtaining tag information of each candidate parking lot may include: acquiring destination information of a user; determining the geographical range of the destination information according to the preset distance; searching each candidate parking lot within the geographic range; and acquiring label information of each candidate parking lot from a preset information source, wherein the label information comprises a distance characteristic label, a parking space characteristic label and an operation characteristic label of each parking lot.

Specifically, destination information of the user is obtained, and the destination information can be from parking demand voice or can be determined according to current navigation information of map application in the terminal.

Determining a geographic range of destination information according to a preset distance, and searching each candidate parking lot in the geographic range to ensure that the candidate parking lots are not far away from the destination of the user; the preset distance may be a preset distance for map application, or may be from a parking demand voice.

The method comprises the steps of obtaining tag information of each candidate parking lot from a preset information source, wherein the information source is a source of the tag information of the candidate parking lot, and the tag information can be a third party platform or a server which obtains the parking lot information through a crawler and then stores the information in a database or caches the information. The label information comprises a distance characteristic label, a parking space characteristic label and an operation characteristic label of each parking lot; the distance characteristic tag reflects distance information and traffic condition information from the candidate parking lot to the user destination; the parking space feature tag comprises the total number of vehicles in the candidate parking lot, the real-time number of vehicles and the estimated number of vehicles when the vehicles reach the candidate parking lot; the operation feature tag includes parking charging rules of the candidate parking lot, whether to open the outside, operation time, limitation information (for example, some parking lots prohibit the entrance of a car as a house, and the information can be used as limitation information), etc.

In the embodiment, destination information of a user is acquired, a geographic range of the destination information is determined according to a preset distance, and each candidate parking lot in the geographic range is searched to ensure that the candidate parking lots and the destination are kept in a relatively close range; the label information of each candidate parking lot is obtained from a preset information source, the label information comprises a distance characteristic label, a parking space characteristic label and an operation characteristic label of each parking lot, and the abundant characteristic dimension ensures that the parking lots meeting the user requirements can be screened.

Further, the step S204 may include: for each candidate parking lot, calculating the feature matching degree and dimension matching degree of the parking demand information and the tag information of the candidate parking lot, and taking the ranking of the keywords as the weight to participate in the calculation of the feature matching degree when the keywords in the parking demand information are ranked; screening each candidate parking lot according to the matching degree corresponding to each candidate parking place to obtain at least one target parking lot, wherein the matching degree comprises characteristic matching degree and dimension matching degree.

Specifically, for each candidate parking lot, the feature matching degree and the dimension matching degree of the parking demand information and the tag information of the candidate parking lot are calculated. The dimension matching degree is calculated mainly based on the dimension of the keywords in the parking requirement information, and the ratio of the number of the keywords which can be matched to the total number of the keywords in the parking requirement information can be used as the dimension matching degree; or, the number of keywords which can be matched is directly used as the dimension matching degree. The feature matching degree between the keyword and the feature tag needs to be calculated, for example, the feature matching degree is judged based on a character string matching mode, a text similarity calculating mode or a word vector matching mode.

The keywords in the parking requirement information may have a ranking, and the ranking of the keywords may be used as a weight to participate in the calculation of the feature matching degree. For example, if the weight of the keyword "free parking lot" is 3 and the weight of the keyword "500 m" is 1, the feature matching degree of the "free parking lot" is multiplied by the weight 3 to be used as the feature matching degree of the keyword "free parking lot"; the feature matching degree of the '500 m distance' is multiplied by the weight 1 to be used as the feature matching degree brought by the '500 m distance' of the key word.

And calculating the total matching degree of the candidate parking lot according to the feature matching degree and the dimension matching degree corresponding to the candidate parking place, for example, weighting and calculating the feature matching degree and the dimension matching degree. Screening each candidate parking lot according to the total matching degree corresponding to each candidate parking place to obtain at least one target parking lot; for example, the total matching degree is sorted in a descending order, and the parking lot with the top N-bit total matching degree is selected as the target parking lot.

In one embodiment, the parking requirement information and the label information of the candidate parking lot may be converted into vectors, and cosine similarity between the vectors is calculated as the matching degree between the parking requirement information and the candidate parking lot. For example, assume that the user's parking demand label is: parking for 3 hours, and parking for free. The label information of a certain candidate parking lot is as follows: the total parking space number is 100, the charging rule is 10 yuan per hour, the vehicle is open to the outside, and the business hours are 8:00-20:00. Firstly, converting label information of a parking lot into a vector form: [100,10,1,8,20] each represents the total number of seats, charging rules, opening to the outside, opening time, and closing time. The parking demand information is then converted into the same vector form: [3,0,0,0,0], wherein 3 means that the parking time is 3 hours, and 0 means that no other requirements are involved. The cosine similarity can be used for similarity measurement, and the similarity between the label information and the parking requirement information is calculated. And calculating cosine similarity of the two vectors and taking the cosine similarity as matching degree of the parking requirement information and the candidate parking lot. Assuming that the calculation result is 0.8, the label information representing the parking lot has higher similarity with the user demand information, namely, the characteristics of the parking lot are matched with the user demand. And screening and sorting the candidate parking lots according to the calculated matching degree.

In the embodiment, the feature matching degree and dimension matching degree of the parking requirement information and the label information of each candidate parking lot are calculated, the ranking of the keywords in the parking requirement information is used as weight to participate in the calculation of the feature matching degree, and the accuracy of the feature matching degree calculation is ensured; according to the matching degree corresponding to each candidate parking place, at least one target parking lot is screened out, the target parking lot is more in line with the requirements of users, and the accuracy of parking lot recommendation is improved.

Further, after the step of sending the recommendation information to the terminal held by the user, the method may further include: receiving an adjustment instruction triggered by a user; according to the adjustment instruction, sequencing and adjusting at least one target parking lot in the recommendation information; or, the parking lot recommendation is carried out again according to the adjustment instruction.

Specifically, the user can view the recommendation information through the terminal, and trigger the adjustment instruction through voice control or touch operation. The adjustment instruction may include a feature tag selected by the user, and the ranking adjustment needs to be performed on at least one target parking lot in the recommendation information according to the feature. For example, if the user selects to sort according to the distance, the distance is used as a sorting factor to reorder each target parking lot in the recommendation information; and (3) sorting according to the prices by the user, and re-sorting each target parking lot according to the order of low parking cost.

In the application, the map application supports voice control and voice broadcasting, and can broadcast recommended information in voice; also support the continuous dialogue mode, the user can continuously give the adjustment instruction through voice.

The adjustment instructions may also instruct the server to make a parking lot recommendation again. At this time, the server needs to regenerate the recommendation information.

In this embodiment, an adjustment instruction triggered by a user is received; according to the adjustment instruction, the target parking lot is subjected to sorting adjustment, or the parking lot is recommended again according to the adjustment instruction, so that the flexibility and convenience of the parking lot recommendation are improved.

It should be emphasized that, to further ensure the privacy and security of the tag information of the parking lot, the tag information of the parking lot may also be stored in a node of a blockchain.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

The application can be applied to the fields of smart cities, smart traffic and the like, thereby promoting the construction of the smart cities.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a parking lot recommendation device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device is specifically applicable to various electronic devices.

As shown in fig. 3, the parking lot recommendation device 300 according to the present embodiment includes: a text generation module 301, a demand generation module 302, a tag acquisition module 303, a parking lot screening module 304, and a recommendation generation module 305, wherein:

The text generation module 301 is configured to obtain a parking request voice of a user, and generate a parking request text according to the parking request voice.

The demand generation module 302 is configured to generate parking demand information according to the parking demand text, where the parking demand information includes a plurality of keywords.

The tag obtaining module 303 is configured to obtain tag information of each candidate parking lot, where the tag information includes a plurality of feature tags of the candidate parking lot.

The parking lot screening module 304 is configured to screen each candidate parking lot according to the parking requirement information and the tag information of each candidate parking lot, so as to obtain at least one target parking lot.

The recommendation generation module 305 is configured to generate recommendation information according to at least one target parking lot, and send the recommendation information to a terminal held by a user.

In some alternative implementations of the present embodiment, the demand generation module 302 may include: the system comprises a word segmentation processing sub-module, a keyword determination sub-module, a history acquisition sub-module and a keyword adjustment sub-module, wherein:

and the word segmentation processing sub-module is used for carrying out word segmentation processing on the parking requirement text to obtain a plurality of segmented words.

And the keyword determination submodule is used for determining keywords in the plurality of segmented words.

And the history acquisition sub-module is used for acquiring keyword history data.

And the keyword adjustment sub-module is used for adjusting each keyword according to the keyword historical data and generating parking requirement information according to the adjusted keywords.

In some optional implementations of this embodiment, the keyword adjustment sub-module may include: hit acquisition unit and keyword sequencing unit, wherein:

and the hit acquisition unit is used for acquiring the history hit data of each keyword according to the keyword history data.

The keyword sorting unit is used for sorting the keywords according to the historical hit data of the keywords, generating parking demand information according to the sorted keywords, taking the sorting of the keywords as a weight, and screening the candidate parking lots.

In other optional implementations of this embodiment, the keyword adjustment sub-module may include: the system comprises a similarity query unit, a keyword setting unit and a target word determining unit, wherein:

and a similar query unit for querying similar historical keywords of the keywords in the keyword historical data for each keyword.

And the keyword setting unit is used for setting the keywords and the similar historical keywords as candidate words and acquiring the historical hit data of the candidate words through the keyword historical data.

And the target word determining unit is used for determining the candidate word with the largest history hit data in the candidate words as a target word and enabling the target word to replace the key word.

In some alternative implementations of the present embodiment, the tag acquisition module 303 may include: destination acquisition submodule, range determination submodule, parking area search submodule and label acquisition submodule, wherein:

and the destination acquisition sub-module is used for acquiring destination information of the user.

And the range determination submodule is used for determining the geographical range of the destination information according to the preset distance.

And the parking lot searching sub-module is used for searching each candidate parking lot in the geographic range.

The label acquisition sub-module is used for acquiring label information of each candidate parking lot from a preset information source, wherein the label information comprises a distance characteristic label, a parking space characteristic label and an operation characteristic label of each parking lot.

In some alternative implementations of the present embodiment, parking lot screening module 304 may include: matching degree calculation submodule and screening submodule, wherein:

and the matching degree calculating sub-module is used for calculating the feature matching degree and the dimension matching degree of the parking requirement information and the label information of the candidate parking lots for each candidate parking lot, and when each keyword in the parking requirement information has a ranking, the ranking of each keyword is used as a weight to participate in the calculation of the feature matching degree.

And the screening sub-module is used for screening each candidate parking lot according to the matching degree corresponding to each candidate parking place to obtain at least one target parking lot, wherein the matching degree comprises characteristic matching degree and dimension matching degree.

In some optional implementations of the present embodiment, the parking lot recommendation device 300 may further include: the device comprises an instruction receiving module, a sequencing adjustment module and a re-recommendation module, wherein:

the instruction receiving module is used for receiving the adjustment instruction triggered by the user.

And the ordering adjustment module is used for ordering adjustment of at least one target parking lot in the recommended information according to the adjustment instruction.

And the recommendation module is used for recommencing the parking lot according to the adjustment instruction.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a parking lot recommendation method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the parking lot recommendation method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

The computer device provided in the present embodiment may execute the above-described parking lot recommendation method. The parking lot recommendation method here may be the parking lot recommendation method of each of the above embodiments.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the parking lot recommendation method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. A method for recommending a parking lot, comprising the steps of:

2. The parking lot recommendation method according to claim 1, wherein the step of generating parking demand information from the parking demand text includes:

word segmentation processing is carried out on the parking requirement text to obtain a plurality of word segments;

determining keywords in the plurality of segmented words;

acquiring keyword historical data;

and adjusting each keyword according to the keyword historical data, and generating parking demand information according to the adjusted keywords.

3. The parking lot recommendation method according to claim 2, wherein the step of adjusting each keyword based on the keyword history data and generating parking demand information based on the adjusted keyword comprises:

acquiring historical hit data of each keyword according to the keyword historical data;

And sorting the keywords according to the historical hit data of the keywords, generating parking demand information according to the sorted keywords, wherein the sorting of the keywords is used as a weight and used for screening candidate parking lots.

4. The parking lot recommendation method according to claim 2, wherein the step of adjusting each keyword based on the keyword history data and generating parking demand information based on the adjusted keyword comprises:

for each keyword, querying similar historical keywords of the keyword in the keyword historical data;

setting the keywords and the similar historical keywords as candidate words, and acquiring historical hit data of the candidate words through the keyword historical data;

and determining the candidate word with the largest historical hit data in the candidate words as a target word, and enabling the target word to replace the key word.

5. The parking lot recommendation method according to claim 1, wherein the step of acquiring tag information of each candidate parking lot includes:

acquiring destination information of the user;

determining the geographical range of the destination information according to a preset distance;

Searching each candidate parking lot within the geographic range;

and acquiring label information of each candidate parking lot from a preset information source, wherein the label information comprises a distance characteristic label, a parking space characteristic label and an operation characteristic label of each parking lot.

6. The method according to claim 1, wherein the step of screening each candidate parking lot based on the parking demand information and the tag information of each candidate parking lot to obtain at least one target parking lot comprises:

for each candidate parking lot, calculating the feature matching degree and dimension matching degree of the parking demand information and the tag information of the candidate parking lot, and taking the ranking of each keyword as weight to participate in the calculation of the feature matching degree when each keyword in the parking demand information has ranking;

screening each candidate parking lot according to the matching degree corresponding to each candidate parking place to obtain at least one target parking lot, wherein the matching degree comprises the characteristic matching degree and the dimension matching degree.

7. The parking lot recommendation method according to claim 1, characterized by further comprising, after the step of transmitting the recommendation information to the terminal held by the user:

Receiving an adjustment instruction triggered by a user;

according to the adjustment instruction, sequencing and adjusting at least one target parking lot in the recommendation information; or,

and recommending the parking lot again according to the adjustment instruction.

8. A parking lot recommendation device, characterized by comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the parking lot recommendation method of any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the parking lot recommendation method of any one of claims 1 to 7.