CN117194780A - Travel route customization method, device and equipment for generating artificial intelligence - Google Patents
Travel route customization method, device and equipment for generating artificial intelligence Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology and discloses a travel route customizing method, device and equipment for generating artificial intelligence and a storage medium. The method comprises the following steps: carrying out structural processing on the travel comment information to obtain a scenic spot-comment-send-out data table; obtaining the corresponding good grade of each comment text by utilizing semantic analysis; the personal comprehensive information corresponding to each criticizing person is crawled, and a user travel well-rated portrait is constructed with the scenic spot-comment-criticizing person data table and each well-rated degree; obtaining a game consumption behavior keyword label of the target user to obtain a user label set; clustering scenic spots of the user travel good score portrait by using the user tag set to obtain target scenic spots; and identifying surrounding scenic spots of the target scenic spots, and automatically generating a playing route to obtain a travel journey recommendation result. The invention can automatically generate tourist attractions and travel routes meeting the self requirements of customers through the personal labels of the users.
Description
Technical Field
The present invention relates to the field of artificial intelligence technology, and in particular, to a method, apparatus, device and computer readable storage medium for customizing travel itinerary of generated artificial intelligence.
Background
The current tourism industry is that a user inputs a desired destination on a tourism platform, then the tourism platform displays terms such as scenic spots, activities and the like of the destination for the user to select, and then the platform automatically generates a tourism route such as a traffic route, a sightseeing route, a living food recommendation and the like according to the scenic spots, activities and budgets selected by the user.
However, some people have no explicit travel goals, do not have too much interest preference, neither know where to travel, nor have budgets, resulting in the trending recommendations provided by the travel platform not meeting the psychological needs of the customer.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for customizing travel routes of generated artificial intelligence, which mainly aim at automatically generating tourist attractions and travel routes meeting the self requirements of customers through personal tags of users.
In order to achieve the above object, the present invention provides a travel route customizing method of generating artificial intelligence, comprising:
Obtaining travel comment information of a pre-constructed travel platform, and carrying out structural processing on the travel comment information to obtain a scenic spot-comment-critique data list;
carrying out semantic analysis on comment texts in the scenic spot-comment data table to obtain the corresponding good comments of each comment text;
basic identity information of each critique in the scenic spot-comment-critique data table is obtained from a user database of the travel platform, and personal comprehensive information corresponding to each critique is crawled according to the basic identity information by utilizing a preset interface of the travel platform;
constructing a user travel well-rated portrait according to the scenic spot-comment-commentary data table, the well-rated and the personal comprehensive information;
acquiring playing consumption behavior information of a target user within a preset time period, and screening keyword tags of the target user according to personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set;
clustering the user travel good score portraits according to the user tag set to obtain a target type tourist travel portraits cluster;
Performing weighted average calculation of the good scores of the scenic spots in the target type tourist travel portrait cluster, and screening the scenic spots with the highest numerical value in the weighted average calculation result of the good scores to obtain target scenic spots;
and acquiring a peripheral scenic spot set of the target scenic spot, recommending the peripheral scenic spot set according to the user tag set by utilizing a pre-trained travel recommendation model to obtain the target peripheral scenic spot set, and automatically generating a travel route for the target scenic spot and the target peripheral scenic spot set to obtain a travel recommendation result.
Optionally, the performing semantic analysis on the comment text in the scenic spot-comment data table to obtain a comment degree corresponding to each comment text includes:
performing word segmentation operation on comment texts in the scenic spot-comment sending data list to obtain a word segmentation result set;
performing stop word and special symbol deletion operation on the word segmentation result set to obtain an effective word segmentation set;
carrying out quantization change on the effective word segmentation to obtain a word segmentation vector set;
performing self-attention weight calculation on the word segmentation vector set to obtain a context semantic enhancement vector set;
And carrying out semantic recognition operation on the context semantic enhancement vector set, and carrying out normalization processing on semantic recognition results to obtain the corresponding commentary texts.
Optionally, the clustering the user travel good score portrait according to the user tag set to obtain a target type tourist travel portrait cluster includes:
carrying out cluster classification on the user travel good score portrait by using a K-means clustering algorithm to obtain a cluster set, and identifying and obtaining a central coordinate point mapped by each cluster in the cluster set;
mapping the user tag set into a dimension space of the user travel well-rated portrait, and calculating a cluster space of the user tag set;
and identifying each cluster in the cluster space in each central coordinate point as an associated cluster, and collecting each associated cluster to obtain the target type tourist travel portrait cluster.
Optionally, the step of crawling personal comprehensive information corresponding to each criticizing person according to the basic identity information by using a preset interface of the travel platform includes:
performing webpage crawling on a preset social platform to obtain a webpage cluster, performing webpage structure analysis on the webpage cluster, and identifying and obtaining an effective information area according to webpage structure analysis results and preset data source keywords;
Automatically crawling the effective information area according to the basic identity information to obtain a personal public information set;
and carrying out data cleaning on the personal primary information set according to a preset data storage rule to obtain personal comprehensive information.
Optionally, the constructing a user travel well-rated portrait according to the scenic spot-comment-critique data table, each well-rated degree and each personal comprehensive information includes:
acquiring a scenic spot-comment-critique data table, each good score and each personal comprehensive information to obtain a user travel comment data set;
performing abnormal and repeated data cleaning operation on the user travel comment data set to obtain an effective travel comment data set, and performing cluster classification on the effective travel comment data set to obtain a travel comment label set;
and constructing and obtaining the user travel good score portrait according to the travel comment label set by using a pre-constructed user portrait tool.
Optionally, the screening the keyword tags of the target user according to the personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set includes:
Performing word segmentation quantization operation on the personal comprehensive information and the game consumption behavior information of the target user to obtain a user information word vector set;
and carrying out a preset type entity identification operation on the user information word vector set by using a conditional random field to obtain a user tag set.
Optionally, after the travel route recommendation result is obtained, the method further includes:
acquiring detailed information of each scenic spot in the travel route recommendation result, and visualizing the detailed information to a pre-constructed user interface;
and acquiring a food sink site set on the playing route in the travel route recommendation result, and sending detailed information of each food sink site in the food sink site set to the user interface.
In order to solve the above problems, the present invention also provides a travel route customizing apparatus of a generated artificial intelligence, the apparatus comprising:
the travel comment data structuring module is used for obtaining travel comment information of a pre-built travel platform, carrying out structuring processing on the travel comment information to obtain a scenic spot-comment sending data table, carrying out semantic analysis on comment texts in the scenic spot-comment sending data table to obtain a corresponding commentary degree of each comment text, obtaining basic identity information of each comment sender in the scenic spot-comment sending data table from a user database of the travel platform, and crawling personal comprehensive information corresponding to each comment sender according to the basic identity information by utilizing a preset interface of the travel platform;
The travel comment portrait construction module is used for constructing a user travel comment portrait according to the scenic spot-comment sender data table, each comment degree and each personal comprehensive information;
the user data acquisition module is used for acquiring the playing consumption behavior information of the target user in a preset time period, and screening keyword tags of the target user according to the personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set;
the target scenic spot recommendation module is used for clustering the user travel good score portrait according to the user tag set to obtain a target type tourist travel portrait cluster, carrying out good score weighted average calculation on scenic spots in the target type tourist travel portrait cluster, and screening scenic spots with highest numerical values in the good score weighted average calculation result to obtain target scenic spots;
and the travel setting module is used for acquiring a peripheral scenic spot set of the target scenic spot, recommending the peripheral scenic spot set according to the user tag set by utilizing a pre-trained travel recommendation model to obtain the target peripheral scenic spot set, and automatically generating a travel route for the target scenic spot and the target peripheral scenic spot set to obtain a travel recommendation result.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the generated artificial intelligence travel itinerary customization method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned travel route customizing method of generating artificial intelligence.
According to the embodiment of the invention, through the travel comment information of the travel platform, the scenic spot-comment-report data table is obtained, the comment text is subjected to comment degree identification, the personal comprehensive information of each comment sender is identified, then through the use of the scenic spot-comment-report data table, each comment degree and each personal comprehensive information, the user travel comment image of the travel platform is obtained, through the user travel comment image, people with different types such as each consumption habit, travel habit, regional address and the like can be divided, so that after the user tag set of a target user is identified, the type of the crowd where the target user is located can be quickly found through clustering operation, the scenic spot which meets the expectations of the target user is obtained, then the appropriate scenic spot of the accessory is intelligently selected through the pre-trained travel recommendation model, and the final travel route recommendation result is obtained. Therefore, the method, the device, the equipment and the storage medium for customizing the travel itinerary of the generated artificial intelligence can automatically generate tourist attractions and itinerary routes meeting the demands of customers through personal tags of the users.
Drawings
FIG. 1 is a flow chart of a method for customizing travel itinerary with generated artificial intelligence according to an embodiment of the present application;
FIG. 2 is a detailed flow chart of one step in a method for customizing travel itinerary for generating artificial intelligence according to one embodiment of the present application;
FIG. 3 is a detailed flow chart of one step in a method for customizing travel itinerary for generating artificial intelligence according to one embodiment of the present application;
FIG. 4 is a functional block diagram of a travel itinerary customizing device with artificial intelligence generated according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device for implementing the travel route customizing method of the generated artificial intelligence according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a travel route customizing method of a generated artificial intelligence. In the embodiment of the application, the execution subject of the generated artificial intelligence travel route customization method includes, but is not limited to, at least one of a server, a terminal and the like, which can be configured to execute the electronic device of the method provided by the embodiment of the application. In other words, the travel itinerary customization method of the generated artificial intelligence may be performed by software or hardware installed on a terminal device or a server device, the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for customizing travel itinerary with generated artificial intelligence according to an embodiment of the present invention is shown. In this embodiment, the travel route customizing method of the generated artificial intelligence includes:
s1, obtaining travel comment information of a pre-constructed travel platform, and carrying out structural processing on the travel comment information to obtain a scenic spot-comment data list.
The travel platform in the embodiment of the invention can comprise a sharing communication platform, a social platform, a travel management platform and other platforms. According to the embodiment of the invention, the travel comment information in the travel platform can be acquired through the crawler tool, but the formats of the crawled travel comment information are different, and unified structuring processing is needed.
According to the embodiment of the invention, through text structure identification, scenic spot information, comment information and comment sender information in the travel comment information are extracted through factors such as text position, text length and font format, and are stored in a pre-constructed list according to the corresponding relation, so that a scenic spot-comment sender data table is obtained.
S2, carrying out semantic analysis on comment texts in the scenic spot-comment data list to obtain the corresponding comment degree of each comment text.
According to the invention, comment texts are extracted from comment fields in the scenic spot-comment data table, and then semantic recognition is carried out on the comment texts through a natural language neural network, so that the corresponding good evaluation degree of each comment text is obtained.
In detail, referring to fig. 2, in the embodiment of the present invention, performing semantic analysis on comment texts in the scenic spot-comment data table to obtain a corresponding comment degree of each comment text includes:
s21, performing word segmentation operation on comment texts in the scenic spot-comment data list to obtain a word segmentation result set;
s22, performing stop word and special symbol deletion operation on the word segmentation result set to obtain an effective word segmentation set;
s23, carrying out quantization change on the effective word segmentation to obtain a word segmentation vector set;
s24, performing self-attention weight calculation on the word segmentation vector set to obtain a context semantic enhancement vector set;
s25, carrying out semantic recognition operation on the context semantic enhancement vector set, and carrying out normalization processing on semantic recognition results to obtain the corresponding praise degree of each comment text.
According to the embodiment of the invention, the Chinese word segmentation system is used for carrying out word segmentation operation on each evaluation paper, and word segmentation results are ordered according to the original text sequence of the comment text, so that a word segmentation result set is obtained. And deleting the stop words in the word segmentation result set by inquiring the stop word dictionary, and clearing special characters such as pigment characters and expression packages in the word segmentation result set by inquiring special characters to obtain an effective word segmentation set. The stop words are common words which are filtered or deleted in the text processing process, are common functional words or words without practical meaning, such as prepositions, conjunctions, pronouns and the like, and generally do not provide useful information in text analysis, so that the stop words can be removed from the word segmentation result set, noise in the natural language processing process is reduced, and processing efficiency is improved.
The embodiment of the invention carries out quantization coding on each effective word in the effective word segmentation set through a single-hot coding algorithm to obtain a word segmentation vector set, then carries out self-attention weight calculation based on context semantic enhancement on the word segmentation vector set by utilizing a self-attention mechanism in a BERT network to obtain a context semantic enhancement vector set, carries out classification judgment on the context semantic enhancement vector set through a full-connection layer in a natural language neural network, and carries out normalization processing on a judgment result through a sigmoid function to obtain the commentary degree corresponding to each comment text. Wherein, the one-hot coding algorithm is a method for representing discrete features as binary vectors, and in one-hot coding, the value of each feature is represented as a unique binary bit.
S3, acquiring basic identity information of each critique in the scenic spot-comment-critique data table from a user database of the travel platform, and crawling personal comprehensive information corresponding to each critique according to the basic identity information by utilizing a preset interface of the travel platform.
In the embodiment of the invention, the basic identity information, such as the name, the age, the sex, the travel record and the like, of each comment person can be obtained through the user database of the travel platform, and then the publicable data of other third-party platforms can be obtained through the preset interface, so that the personal comprehensive information corresponding to the comment person, such as the location, the consumption behavior, the work unit, the travel article browsing behavior and the like, can be obtained.
In detail, in the embodiment of the present invention, the crawling, by using the preset interface of the travel platform, the personal comprehensive information corresponding to each critique according to the basic identity information includes:
performing webpage crawling on a preset social platform to obtain a webpage cluster, performing webpage structure analysis on the webpage cluster, and identifying and obtaining an effective information area according to webpage structure analysis results and preset data source keywords;
automatically crawling the effective information area according to the basic identity information to obtain a personal public information set;
and carrying out data cleaning on the personal primary information set according to a preset data storage rule to obtain personal comprehensive information.
The preset social platform can be a third party platform such as a business, a book, a search and the like, and the embodiment of the invention can configure a scenic spot detail interface, a my personal information interface of each platform as a data source by analyzing a webpage structure, and then crawl data in the data source through keywords such as comments, scenic spots, travel records, work units and the like to obtain personal comprehensive information.
S4, constructing a user travel well-rated portrait according to the scenic spot-comment-criticism sender data table, the well-rated and the personal comprehensive information.
In detail, in the embodiment of the present invention, the constructing a user travel well-rated portrait according to the scenic spot-comment-critique data table, each well-rated and each personal comprehensive information includes:
acquiring a scenic spot-comment-critique data table, each good score and each personal comprehensive information to obtain a user travel comment data set;
performing abnormal and repeated data cleaning operation on the user travel comment data set to obtain an effective travel comment data set, and performing cluster classification on the effective travel comment data set to obtain a travel comment label set;
and constructing and obtaining the user travel good score portrait according to the travel comment label set by using a pre-constructed user portrait tool.
According to the embodiment of the invention, the user travel comment data set such as gender, age, business state and marital state is cleaned by the method, the scenic spot article times are watched and the content categories are cleaned, the effective travel comment data set is obtained, then the travel comment data set is divided by the common clustering methods such as k-means, BIRCH or DBSCAN, and the like, so that a travel comment label set is obtained, and further common open source user portrait tools such as ER diagrams and Freedgo are searched for carrying out portrait automatic construction, and a user travel comment portrait is obtained.
S5, obtaining playing consumption behavior information of the target user in a preset time period, and screening keyword tags of the target user according to personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set.
In the embodiment of the invention, the preset time period can be one year or more, and the playing consumption behavior can include scenic spot ticket cost, non-business hotel cost, holiday travel traffic cost, and remote consumption cost. According to the invention, the consumption behavior and habit of the user can be obtained by identifying the game consumption behavior information, so that the consumption style of the target user is evaluated, and the approval degree of the target user corresponding to the travel destination recommendation is improved.
In detail, in the embodiment of the present invention, the screening the keyword tag of the target user according to the personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set includes:
performing word segmentation quantization operation on the personal comprehensive information and the game consumption behavior information of the target user to obtain a user information word vector set;
and carrying out a preset type entity identification operation on the user information word vector set by using a conditional random field to obtain a user tag set.
According to the embodiment of the invention, the personal comprehensive information and the game consumption behavior information can be subjected to cleaning word segmentation quantization operation through the Chinese word segmentation system and the single-hot encoding algorithm, so that a user information word vector set is obtained. Then, the embodiment of the invention carries out entity identification on the user information word vector set through a pre-constructed conditional random field (Conditional Random Field, CRF) to obtain a user tag set, wherein the pre-set type entity can comprise age, gender, address, consumption level, travel record and the like.
The conditional random field is a statistical modeling method and is mainly used for sequence labeling problems such as entity identification, named entity identification, part-of-speech labeling and the like.
And S6, clustering the user travel good score portraits according to the user tag set to obtain a target type tourist travel portrait cluster.
In detail, referring to fig. 3, in the embodiment of the present invention, the clustering the user travel well-rated portraits according to the user tag set to obtain a target type tourist travel portrait cluster includes:
s61, carrying out cluster classification on the user travel well-rated portrait by using a K-means clustering algorithm to obtain a cluster set, and identifying and obtaining a central coordinate point mapped by each cluster in the cluster set;
S62, mapping the user tag set into a dimension space of the user travel well-rated portrait, and calculating a cluster space of the user tag set;
and S63, identifying each cluster in the cluster space in each central coordinate point as an associated cluster, and collecting each associated cluster to obtain the target type tourist travel portrait cluster.
Wherein the K-means clustering is a commonly used distance-based clustering method that divides the data set into K different clusters such that each data point belongs to the cluster closest to it.
According to the embodiment of the invention, the K-means clustering algorithm is utilized to divide the user travel well-rated portrait into clusters to obtain a cluster set, and the central coordinate points mapped by each cluster in the cluster set are obtained through recognition, wherein the central coordinate points can be in more than ten dimensions, and the five-six dimensions can be obtained through dimension reduction by principal component analysis.
Then, the embodiment of the invention maps the user tag set, calculates the mathematical region of the user tag as a cluster space, judges whether each cluster in the user travel good score portrait belongs to the associated cluster of the target user by identifying whether each central coordinate point is in the cluster space, and can perform the collection extraction operation after all the associated clusters are inquired to obtain the target type tourist portrait cluster.
And S7, performing weighted average calculation of the good scores of the scenic spots in the target type tourist travel portrait cluster, and screening the scenic spots with the highest numerical values in the weighted average calculation result of the good scores to obtain the target scenic spots.
And each commentary in the target type tourist travel portrait cluster is similar to the target user in the aspects of geographic position, economic level and travel style, and comments of the commentary play an important role in scenic spot recommendation of the target user. Therefore, the embodiment of the invention extracts all comments and scenic spots in the target type tourist travel portrait cluster, and calculates the good score of each scenic spot through weighted average to obtain the ranking of the good scores of the scenic spots, so that the recommendation is carried out according to the high score and the low score of the good score to obtain the target scenic spot.
S8, acquiring a peripheral scenic spot set of the target scenic spot, recommending the peripheral scenic spot set according to the user tag set by utilizing a pre-trained travel recommendation model to obtain the target peripheral scenic spot set, and automatically generating a travel route for the target scenic spot and the target peripheral scenic spot set to obtain a travel recommendation result.
In the embodiment of the invention, the surrounding scenic spots of the target scenic spot can be inquired through a browser tool, a map tool and other tools. Wherein the perimeter may include food streets, vacation villages, other attractions, holiday venues, and the like.
Furthermore, the travel recommendation model is a regression prediction model based on a neural network, and can be used for constructing a mapping relation between a user portrait and scenic spot styles for model construction.
When the target scenic spots and the target surrounding scenic spot sets are obtained, the target surrounding scenic spot sets and the target scenic spots can be connected in a topological graph or shortest route mode according to the information such as time abundance, so that a playing route of the target scenic spot at the place is obtained, and finally the playing route is output as a travel route recommendation result.
In addition, in another embodiment of the present invention, after the travel itinerary recommendation result is obtained, the method further includes:
acquiring detailed information of each scenic spot in the travel route recommendation result, and visualizing the detailed information to a pre-constructed user interface;
and acquiring a food sink site set on the playing route in the travel route recommendation result, and sending detailed information of each food sink site in the food sink site set to the user interface.
In the embodiment of the invention, the detailed information of each scenic spot and the detailed information of each food destination site are sent to the target user in a visual mode, can be freely adjusted, and can be formulated to be more in line with the travel route planning of the target user.
According to the embodiment of the invention, through the travel comment information of the travel platform, the scenic spot-comment-report data table is obtained, the comment text is subjected to comment degree identification, the personal comprehensive information of each comment sender is identified, then through the use of the scenic spot-comment-report data table, each comment degree and each personal comprehensive information, the user travel comment image of the travel platform is obtained, through the user travel comment image, people with different types such as each consumption habit, travel habit, regional address and the like can be divided, so that after the user tag set of a target user is identified, the type of the crowd where the target user is located can be quickly found through clustering operation, the scenic spot which meets the expectations of the target user is obtained, then the appropriate scenic spot of the accessory is intelligently selected through the pre-trained travel recommendation model, and the final travel route recommendation result is obtained. Therefore, the travel route customizing method of the generated artificial intelligence provided by the embodiment of the invention can automatically generate tourist attractions and travel routes meeting the self requirements of customers through the personal tags of the users.
FIG. 4 is a functional block diagram of an artificial intelligence generated travel itinerary customizing device according to an embodiment of the present invention.
The travel itinerary customizing apparatus 100 of the generated artificial intelligence according to the present invention may be installed in an electronic device. Depending on the functions implemented, the generated artificial intelligence travel itinerary customization device 100 may include a travel comment data structuring module 101, a travel comment portrait construction module 102, a user data acquisition module 103, a target attraction recommendation module 104, and an itinerary formulation module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the travel comment data structuring module 101 is configured to obtain travel comment information of a pre-built travel platform, perform structuring processing on the travel comment information to obtain a scenic spot-comment sending data table, perform semantic analysis on comment texts in the scenic spot-comment sending data table to obtain a comment degree corresponding to each comment text, obtain basic identity information of each comment sender in the scenic spot-comment sending data table from a user database of the travel platform, and climb personal comprehensive information corresponding to each comment sender according to the basic identity information by using a preset interface of the travel platform;
The travel comment portrait construction module 102 is configured to construct a user travel comment portrait according to the sight spot-comment-critique data table, each comment and each personal comprehensive information;
the user data obtaining module 103 is configured to obtain playing consumption behavior information of a target user within a preset period of time, and screen keyword tags of the target user according to personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set;
the target scenic spot recommendation module 104 is configured to cluster the user travel good score portrait according to the user tag set to obtain a target type tourist travel portrait cluster, perform weighted average calculation on the scenic spots in the target type tourist travel portrait cluster, and screen the scenic spot with the highest value in the weighted average calculation result of the good score to obtain a target scenic spot;
the trip formulation module 105 is configured to obtain a set of surrounding points of the target scenic spot, recommend the surrounding points of the set of surrounding points to obtain the set of surrounding points of the target scenic spot according to the set of user labels by using a pre-trained travel recommendation model, and automatically generate a travel route for the set of surrounding points of the target scenic spot and the set of surrounding points of the target scenic spot to obtain a travel trip recommendation result.
In detail, each module in the travel route customizing device 100 with the generated artificial intelligence according to the embodiment of the present application adopts the same technical means as the travel route customizing method with the generated artificial intelligence described in fig. 1 to 3, and can generate the same technical effects, which is not repeated here.
Fig. 5 is a schematic structural diagram of an electronic device 1 implementing a travel route customization method with generated artificial intelligence according to an embodiment of the present application.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a travel itinerary customization program for generating artificial intelligence.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the entire electronic device using various interfaces and lines, executes programs or modules stored in the memory 11 (for example, executing a travel itinerary customizing program of a generated artificial intelligence, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in an electronic device and various types of data, such as codes of travel itinerary customizing programs of generated artificial intelligence, but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The travel itinerary customization program of the generated artificial intelligence stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
obtaining travel comment information of a pre-constructed travel platform, and carrying out structural processing on the travel comment information to obtain a scenic spot-comment-critique data list;
carrying out semantic analysis on comment texts in the scenic spot-comment data table to obtain the corresponding good comments of each comment text;
Basic identity information of each critique in the scenic spot-comment-critique data table is obtained from a user database of the travel platform, and personal comprehensive information corresponding to each critique is crawled according to the basic identity information by utilizing a preset interface of the travel platform;
constructing a user travel well-rated portrait according to the scenic spot-comment-commentary data table, the well-rated and the personal comprehensive information;
acquiring playing consumption behavior information of a target user within a preset time period, and screening keyword tags of the target user according to personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set;
clustering the user travel good score portraits according to the user tag set to obtain a target type tourist travel portraits cluster;
performing weighted average calculation of the good scores of the scenic spots in the target type tourist travel portrait cluster, and screening the scenic spots with the highest numerical value in the weighted average calculation result of the good scores to obtain target scenic spots;
and acquiring a peripheral scenic spot set of the target scenic spot, recommending the peripheral scenic spot set according to the user tag set by utilizing a pre-trained travel recommendation model to obtain the target peripheral scenic spot set, and automatically generating a travel route for the target scenic spot and the target peripheral scenic spot set to obtain a travel recommendation result.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
obtaining travel comment information of a pre-constructed travel platform, and carrying out structural processing on the travel comment information to obtain a scenic spot-comment-critique data list;
carrying out semantic analysis on comment texts in the scenic spot-comment data table to obtain the corresponding good comments of each comment text;
Basic identity information of each critique in the scenic spot-comment-critique data table is obtained from a user database of the travel platform, and personal comprehensive information corresponding to each critique is crawled according to the basic identity information by utilizing a preset interface of the travel platform;
constructing a user travel well-rated portrait according to the scenic spot-comment-commentary data table, the well-rated and the personal comprehensive information;
acquiring playing consumption behavior information of a target user within a preset time period, and screening keyword tags of the target user according to personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set;
clustering the user travel good score portraits according to the user tag set to obtain a target type tourist travel portraits cluster;
performing weighted average calculation of the good scores of the scenic spots in the target type tourist travel portrait cluster, and screening the scenic spots with the highest numerical value in the weighted average calculation result of the good scores to obtain target scenic spots;
and acquiring a peripheral scenic spot set of the target scenic spot, recommending the peripheral scenic spot set according to the user tag set by utilizing a pre-trained travel recommendation model to obtain the target peripheral scenic spot set, and automatically generating a travel route for the target scenic spot and the target peripheral scenic spot set to obtain a travel recommendation result.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
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.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for customizing travel itineraries with generated artificial intelligence, the method comprising:
obtaining travel comment information of a pre-constructed travel platform, and carrying out structural processing on the travel comment information to obtain a scenic spot-comment-critique data list;
carrying out semantic analysis on comment texts in the scenic spot-comment data table to obtain the corresponding good comments of each comment text;
basic identity information of each critique in the scenic spot-comment-critique data table is obtained from a user database of the travel platform, and personal comprehensive information corresponding to each critique is crawled according to the basic identity information by utilizing a preset interface of the travel platform;
Constructing a user travel well-rated portrait according to the scenic spot-comment-commentary data table, the well-rated and the personal comprehensive information;
acquiring playing consumption behavior information of a target user within a preset time period, and screening keyword tags of the target user according to personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set;
clustering the user travel good score portraits according to the user tag set to obtain a target type tourist travel portraits cluster;
performing weighted average calculation of the good scores of the scenic spots in the target type tourist travel portrait cluster, and screening the scenic spots with the highest numerical value in the weighted average calculation result of the good scores to obtain target scenic spots;
and acquiring a peripheral scenic spot set of the target scenic spot, recommending the peripheral scenic spot set according to the user tag set by utilizing a pre-trained travel recommendation model to obtain the target peripheral scenic spot set, and automatically generating a travel route for the target scenic spot and the target peripheral scenic spot set to obtain a travel recommendation result.
2. The method for customizing travel itinerary for generating artificial intelligence according to claim 1, wherein said performing semantic analysis on comment texts in said scenic spot-comment data table to obtain a corresponding goodness degree of each comment text comprises:
Performing word segmentation operation on comment texts in the scenic spot-comment sending data list to obtain a word segmentation result set;
performing stop word and special symbol deletion operation on the word segmentation result set to obtain an effective word segmentation set;
carrying out quantization change on the effective word segmentation to obtain a word segmentation vector set;
performing self-attention weight calculation on the word segmentation vector set to obtain a context semantic enhancement vector set;
and carrying out semantic recognition operation on the context semantic enhancement vector set, and carrying out normalization processing on semantic recognition results to obtain the corresponding commentary texts.
3. The method for customizing a travel itinerary for generating artificial intelligence according to claim 1, wherein said clustering said user travel well-rated portraits according to said set of user tags to obtain a target type tourist travel portrait cluster comprises:
carrying out cluster classification on the user travel good score portrait by using a K-means clustering algorithm to obtain a cluster set, and identifying and obtaining a central coordinate point mapped by each cluster in the cluster set;
mapping the user tag set into a dimension space of the user travel well-rated portrait, and calculating a cluster space of the user tag set;
And identifying each cluster in the cluster space in each central coordinate point as an associated cluster, and collecting each associated cluster to obtain the target type tourist travel portrait cluster.
4. The method for customizing travel itinerary with artificial intelligence according to claim 1, wherein said crawling individual comprehensive information corresponding to each of said criticizing persons according to said basic identity information using a preset interface of said travel platform comprises:
performing webpage crawling on a preset social platform to obtain a webpage cluster, performing webpage structure analysis on the webpage cluster, and identifying and obtaining an effective information area according to webpage structure analysis results and preset data source keywords;
automatically crawling the effective information area according to the basic identity information to obtain a personal public information set;
and carrying out data cleaning on the personal primary information set according to a preset data storage rule to obtain personal comprehensive information.
5. The method of claim 1, wherein said constructing a user travel well-rated portrait from said sight-comment-critique data table, each of said well-rated and each of said personal integrated information, comprises:
Acquiring a scenic spot-comment-critique data table, each good score and each personal comprehensive information to obtain a user travel comment data set;
performing abnormal and repeated data cleaning operation on the user travel comment data set to obtain an effective travel comment data set, and performing cluster classification on the effective travel comment data set to obtain a travel comment label set;
and constructing and obtaining the user travel good score portrait according to the travel comment label set by using a pre-constructed user portrait tool.
6. The method for customizing travel itinerary with artificial intelligence according to claim 1, wherein said screening keyword tags of said target user according to personal comprehensive information and game consumption behavior information of said target user to obtain a user tag set comprises:
performing word segmentation quantization operation on the personal comprehensive information and the game consumption behavior information of the target user to obtain a user information word vector set;
and carrying out a preset type entity identification operation on the user information word vector set by using a conditional random field to obtain a user tag set.
7. The method for customizing a travel itinerary for generating an artificial intelligence according to claim 1, wherein after obtaining the recommended travel itinerary, the method further comprises:
Acquiring detailed information of each scenic spot in the travel route recommendation result, and visualizing the detailed information to a pre-constructed user interface;
and acquiring a food sink site set on the playing route in the travel route recommendation result, and sending detailed information of each food sink site in the food sink site set to the user interface.
8. A travel itinerary customizing device that generates artificial intelligence, the device comprising:
the travel comment data structuring module is used for obtaining travel comment information of a pre-built travel platform, carrying out structuring processing on the travel comment information to obtain a scenic spot-comment sending data table, carrying out semantic analysis on comment texts in the scenic spot-comment sending data table to obtain a corresponding commentary degree of each comment text, obtaining basic identity information of each comment sender in the scenic spot-comment sending data table from a user database of the travel platform, and crawling personal comprehensive information corresponding to each comment sender according to the basic identity information by utilizing a preset interface of the travel platform;
the travel comment portrait construction module is used for constructing a user travel comment portrait according to the scenic spot-comment sender data table, each comment degree and each personal comprehensive information;
The user data acquisition module is used for acquiring the playing consumption behavior information of the target user in a preset time period, and screening keyword tags of the target user according to the personal comprehensive information and the playing consumption behavior information of the target user to obtain a user tag set;
the target scenic spot recommendation module is used for clustering the user travel good score portrait according to the user tag set to obtain a target type tourist travel portrait cluster, carrying out good score weighted average calculation on scenic spots in the target type tourist travel portrait cluster, and screening scenic spots with highest numerical values in the good score weighted average calculation result to obtain target scenic spots;
and the travel setting module is used for acquiring a peripheral scenic spot set of the target scenic spot, recommending the peripheral scenic spot set according to the user tag set by utilizing a pre-trained travel recommendation model to obtain the target peripheral scenic spot set, and automatically generating a travel route for the target scenic spot and the target peripheral scenic spot set to obtain a travel recommendation result.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
A memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the generated artificial intelligence travel itinerary customization method of any one of claims 1-7.
10. A computer readable storage medium storing a computer program which when executed by a processor implements the generated artificial intelligence travel itinerary customizing method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117456588A (en) * | 2023-12-20 | 2024-01-26 | 福建先行网络服务有限公司 | Wisdom museum user management system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117456588A (en) * | 2023-12-20 | 2024-01-26 | 福建先行网络服务有限公司 | Wisdom museum user management system |
| CN117456588B (en) * | 2023-12-20 | 2024-04-09 | 福建先行网络服务有限公司 | Wisdom museum user management system |
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