Data processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a data processing method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. A specific embodiment of the method comprises the steps of obtaining corresponding user data in response to a recommendation instruction of a user, determining a target personality tag of the user, and determining a recommended scenic spot corresponding to the user in combination with a preset mapping relationship; determining a target related personality tag corresponding to the target personality tag from the tourism personality system, and matching the target related personality tag with the recommended scenic spot based on the mapping relationship; and in response to the matching result meeting a preset condition, determining a target personality type corresponding to the target related personality tag based on the tourism personality system, and sending the recommended scenic spot and the target personality type to the user. According to the embodiment, the problem that the recommendation accuracy is reduced due to the fact that personalized requirements of users are difficult to meet when hot or active scenic spot information is selected to be recommended to the users can be solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] Tourism has become a way for users to enjoy services and experience culture. However, with the rapid development of information in recent years, users often need to sift through massive amounts of tourism information to find content that meets their personalized needs. Related technologies typically collect information from various scenic spots and select popular or event-related attractions to recommend to users. However, this method struggles to meet users' personalized needs, reducing the accuracy of the recommendations. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a data processing method, apparatus, electronic device, and storage medium that can solve the problem that recommending popular or active scenic spot information to users is difficult to meet users' personalized needs and reduces the accuracy of the recommendations.

[0004] To achieve the above objectives, according to one aspect of the present invention, a data processing method is provided.

[0005] An embodiment of the present invention provides a data processing method comprising: responding to a user's recommendation instruction, acquiring corresponding user data, determining the user's target personality tag, and determining a recommended scenic spot corresponding to the user by combining a preset mapping relationship; wherein, the mapping relationship includes the mapping relationship between each scenic spot and each personality tag in a preset tourism personality system, the tourism personality system includes multiple personality tags and related personality tags associated with each personality tag, and each personality tag corresponds to a different personality type; From the tourism personality system, determine the target-related personality tags corresponding to the target personality tags, and based on the mapping relationship, match the target-related personality tags with the recommended scenic spots; In response to the matching result meeting the preset conditions, the target personality type corresponding to the target-related personality tag is determined based on the tourism personality system, and the recommended scenic spot and the target personality type are sent to the user.

[0006] In one embodiment, before determining the recommended scenic spot corresponding to the user based on a preset mapping relationship, the method further includes: Acquire a set of tourism data, call the first major model, and generate a corresponding tourism personality system; Acquire scenic area data for each scenic area, generate corresponding scenic area prompts, input the scenic area prompts and the tourism personality system into the second major model, and generate the mapping relationship between each scenic area and the personality tag.

[0007] In yet another embodiment, after generating the corresponding tourism personality system, the method further includes: The detection rules for each personality type are obtained, and the third major model is invoked to detect the tourism personality system based on the detection rules, thereby obtaining the detection results. In response to the detection result being "failed", a new tourism personality system is generated based on the tourism data.

[0008] In yet another embodiment, obtaining the corresponding user data and determining the user's personality tag includes: The system acquires feedback data for each test information item corresponding to the user, identifies the weight of each feedback data item corresponding to each personality type, and determines the user's target personality label based on the weight. The test information is generated based on the travel personality system.

[0009] In yet another embodiment, the target-related personality tags include multiple tags; Based on the mapping relationship, matching the target-related personality tags with the recommended scenic spots includes: Based on the mapping relationship, each target-related personality tag is matched with the personality tag corresponding to the recommended scenic spot; In response to a matching failure, the personality types in the personality tags corresponding to the recommended scenic spots are obtained and matched with the personality types corresponding to the target-related personality tags respectively to obtain the matching personality types, which are then determined as the matching results.

[0010] To achieve the above objectives, according to another aspect of the present invention, a data processing apparatus is provided.

[0011] An embodiment of the present invention provides a data processing apparatus comprising: a determining unit, configured to, in response to a user's recommendation instruction, acquire corresponding user data, determine the user's target personality tag, and determine a recommended scenic spot corresponding to the user in conjunction with a preset mapping relationship; wherein, the mapping relationship includes a mapping relationship between each scenic spot and each personality tag in a preset tourism personality system, the tourism personality system including multiple personality tags and related personality tags associated with each personality tag, and each personality tag corresponding to a different personality type; A matching unit is used to determine the relevant personality tags corresponding to the user's personality tags from the tourism personality system, and match the relevant personality tags with the recommended scenic spots based on the mapping relationship; The sending unit is used to send the personality type corresponding to the relevant personality tag to the user in response to the matching result meeting the preset conditions.

[0012] In one embodiment, the apparatus further includes: The acquisition unit is used to acquire a tourism data set, call the first major model, and generate a corresponding tourism personality system. The tourism personality system includes multiple personality tags, and each personality tag corresponds to a different personality type. The generation unit is used to acquire scenic area data for each scenic area, generate corresponding scenic area prompts, input the scenic area prompts and the tourism personality system into the second major model, and generate the mapping relationship between each scenic area and the personality tag.

[0013] In yet another embodiment, the apparatus further includes: The detection unit is used to obtain the detection rules between each personality type, call the third major model, and detect the tourism personality system based on the detection rules to obtain the detection results; The generation unit is also configured to generate a new tourism personality system based on the tourism data in response to the detection result being "not passed".

[0014] In yet another embodiment, the acquisition unit is specifically used for: The system acquires feedback data for each test information item corresponding to the user, identifies the weight of each feedback data item corresponding to each personality type, and determines the user's target personality label based on the weight. The test information is generated based on the travel personality system.

[0015] In yet another embodiment, the relevant personality tags include multiple tags; The matching unit is specifically used for: Based on the mapping relationship, each of the relevant personality tags is matched with the personality tags corresponding to the recommended scenic spots; In response to a matching failure, the personality types in the personality tags corresponding to the recommended scenic spots are obtained and matched with the personality types corresponding to each of the relevant personality tags to obtain the matching personality types, which are then determined as the matching results.

[0016] To achieve the above objectives, according to another aspect of the present invention, an electronic device is provided.

[0017] An electronic device according to an embodiment of the present invention includes: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method provided in the embodiment of the present invention.

[0018] To achieve the above objectives, according to another aspect of the present invention, a computer-readable medium is provided.

[0019] An embodiment of the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method provided in the embodiment of the present invention.

[0020] To achieve the above objectives, according to another aspect of the present invention, a computer program product is provided.

[0021] A computer program product according to an embodiment of the present invention includes a computer program that, when executed by a processor, implements the data processing method provided in the embodiment of the present invention.

[0022] One embodiment of the above invention has the following advantages or beneficial effects: In this embodiment of the invention, a tourism personality system is pre-constructed, comprising multiple personality tags and related personality tags associated with each tag. Each personality tag corresponds to a different personality type, and a mapping relationship is established between each scenic spot and each personality tag in the tourism personality system. For user recommendation commands, the user's personality tag can be determined based on user data, and then, combined with the mapping relationship, the corresponding recommended scenic spot can be determined and sent to the user. In this embodiment of the invention, through the pre-constructed travel personality system and its association with various scenic spots, scenic spots can be recommended to users more accurately, along with the related personality types that match the recommended scenic spots, meeting users' personalized needs and improving the accuracy of scenic spot recommendations even in cold start situations.

[0023] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0024] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein: Figure 1 This is a schematic diagram of a main flow of a data processing method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of another main flow of a data processing method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the main units of a data processing apparatus according to an embodiment of the present invention; Figure 4 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied; Figure 5 This is a schematic diagram of the structure of a computer system suitable for implementing embodiments of the present invention. Detailed Implementation

[0025] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other. The acquisition, transmission, storage, use, and processing of data in this application comply with relevant national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0027] This invention provides a data processing system that can be used for scenic area data processing, specifically for recommending scenic areas to users.

[0028] This invention provides a data processing method, which can be executed by a data processing system, such as... Figure 1 As shown, the method includes the following steps.

[0029] S101: In response to the user's recommendation instruction, obtain the corresponding user data, determine the user's target personality tag, and determine the recommended scenic spot corresponding to the user by combining the preset mapping relationship.

[0030] The mapping relationship includes the mapping relationship between each scenic spot and each personality tag in the preset tourism personality system. The tourism personality system includes multiple personality tags and related personality tags associated with each personality tag, and each personality tag corresponds to a different personality type. The user's recommendation instruction can be automatically triggered or sent by other devices, and it is used to instruct the user to recommend scenic spots. The recommendation instruction can include user information, such as user identifiers, etc., so based on this user information, corresponding user data can be obtained, and then the user's target personality tag can be determined. Based on the established mapping relationship, the user's target personality tag can be matched with the personality tags mapped to the scenic spots, thereby determining the matching scenic spots, which are then recommended to the user.

[0031] In this embodiment of the invention, the resulting recommended scenic spots may include one or more. After obtaining multiple recommendable scenic spots, a random selection or all of them can be recommended. When recommending scenic spots to users, data on the user's corresponding target personality tag can also be pushed to allow users to better understand the characteristics of the recommended attractions.

[0032] Specifically, in this embodiment of the invention, the step of determining the user's target personality label can be performed as follows: obtaining various feedback data of the user's corresponding test information, identifying the weight of each feedback data corresponding to each personality type, and determining the user's target personality label based on the weight, wherein the test information is generated based on a preset travel personality system.

[0033] Test information can be specifically test questions, which may include multiple test questions. Therefore, the weight of the personality type corresponding to each test question can be determined from the feedback data. Then, the weights of each personality type corresponding to each feedback data are added together to determine the personality type with the highest value. This can be determined as the user's target personality type, that is, the corresponding target personality label.

[0034] It should be noted that the method for determining the target personality label in the embodiments of the present invention is not limited. For example, a large model for determining personality labels can be pre-trained to determine the user's target personality label through the large model.

[0035] In one implementation, the present invention can pre-construct a tourism personality system. The construction method can be: acquiring a tourism data set, calling a first major model, and generating the corresponding tourism personality system. The tourism personality system can include multiple personality tags, each corresponding to a different personality type. The tourism data set can include various data related to tourism, such as user reviews of scenic spots, relevant information about scenic spots, news reports, and other travel-related data. The tourism personality system represents a system of various personality tags and corresponding types. Personality tags are determined based on personality types, and personality types can be multiple, such as different personality types set from different dimensions.

[0036] Specifically, in this embodiment of the invention, the tourism personality system may include personality tag names, personality descriptions, personality types, and classic figure representatives. Personality tag names can be set according to needs, such as "Azure Phoenix" or "Black Tortoise"; personality descriptions describe the personality tags and can be implemented in various ways, such as describing "Azure Phoenix" as "fire"; personality types can include multiple types, implemented from multiple dimensions, such as from the Five Elements perspective (metal, wood, water, fire, earth), or from the MBIT dimension, etc.; classic figure representatives represent the tasks corresponding to the personality tags, usually representative historical figures or public figures, to facilitate understanding of the personality tags.

[0037] Furthermore, the travel personality system can also include related personality tags, indicating other personality tags associated with the corresponding personality tag. Users corresponding to these two personality tags are considered compatible and can travel together. For example, the personality tag "Azure Phoenix" could be associated with the personality tag "Black Tortoise," meaning that users corresponding to both "Azure Phoenix" and "Black Tortoise" are suitable for traveling together.

[0038] In this embodiment of the invention, the first large model is pre-trained. Considering the semantic understanding and generation capabilities of the large model, it can be used to generate an intelligent agent to generate a tourism personality system with high accuracy. In this step, the various tourism data can be pre-processed to form a travel text set, which is then input into the summary intelligent agent module corresponding to the first large model to summarize and generate relevant travel theme descriptions, which serve as prompt words for inputting into the first model, thereby generating a tourism personality system through the first large model.

[0039] In another implementation, after generating the tourism personality system, it can be evaluated and tested to improve its accuracy. Specifically, this can be done by: obtaining the detection rules between each personality type, calling the third major model to test the tourism personality system based on the detection rules, and obtaining the detection results; in response to a failure result, generating a new tourism personality system based on tourism data.

[0040] In this embodiment of the invention, a pre-trained third model can be used to detect the tourism personality system. The detection rules can be set according to the complementary or conflicting characteristics between personality tags. Specifically, they can include logical contradiction detection, repetition detection, cultural compatibility detection, etc. For example, in logical contradiction detection, if the personality type corresponding to the same personality tag is "Fire" in the Five Elements theory, the corresponding MBIT personality type cannot be "Introverted" or "Extroverted," meaning that the personality types corresponding to the same personality tag cannot conflict with each other. As another example, in repetition detection, the system can detect whether the personality descriptions corresponding to different personality tags are repeated, meaning that the personality descriptions corresponding to different personality tags are not repeated, in order to distinguish different personality tags. As yet another example, in cultural compatibility detection, the system can detect whether there are conflicts between the personality descriptions, personality types, and classic figures corresponding to different personality tags. For example, if the personality type corresponding to a personality tag includes "Fire," its corresponding classic figure cannot be "xxx," meaning that there cannot be conflicts between the various data corresponding to a personality tag. Based on this, detection rules between various personality types can be generated in this step, and then the tourism personality system can be tested through the third major model to obtain the detection results. If the detection result is passed, the generated tourism personality system can be stored for later use; if the detection result is failed, a new tourism personality system can be generated based on tourism data through the first major model in this step.

[0041] In this embodiment of the invention, the third major model can specifically be a major model that includes self-reflective iteration. That is, the generated tourism personality system is detected and evaluated by combining the self-reflective iteration function in the major model. Therefore, in this embodiment of the invention, the number of detection iterations can also be set. Before the number of detection iterations is reached, the steps of generating the tourism personality system can be repeated and detected again until the number of iterations is reached to obtain the final tourism personality system.

[0042] It should be noted that after generating the tourism personality system, to ensure its accuracy, this step can also incorporate multi-expert verification to obtain the final personality system. The expert verification method is not limited; for example, a pre-set transfer interface can be used to transmit the tourism personality system to experts, who can then verify and confirm the system online.

[0043] In another implementation, after deriving the travel personality system, test information can be generated for users to take tests. Specifically, this can be test questions, allowing users to provide feedback on the test information. Based on the feedback data, personality tags can be determined, thus identifying the user's preferences. In this embodiment, a pre-trained large model can be used to generate the test information. After generation, the test information can be detected and evaluated to ensure its quality and the accuracy of the feedback. Specifically, generating the test information can use a large model that includes self-reflective iteration, and detection and evaluation can be achieved through expert verification. In this embodiment, after obtaining the user's feedback data for the corresponding test information, it can be identified as the user's travel data for updating the travel personality system and test information.

[0044] In another implementation, the mapping relationship between scenic spots and personality tags can also be generated in this embodiment of the invention. Specifically, it can be performed as follows: obtain scenic spot data for each scenic spot, generate corresponding scenic spot prompt words, input the scenic spot prompt words and the tourism personality system into the second major model, and generate the mapping relationship between scenic spots and personality tags.

[0045] The scenic area data can include publicly available information such as basic introductions, photos, and reviews. The second major model is pre-trained. In this step, corresponding prompts are generated based on the scenic area data and then input into the second major model to label the scenic areas with personality tags, i.e., to generate a mapping relationship between each scenic area and a personality tag.

[0046] In this embodiment of the invention, for subsequent scenic spot recommendations, considering that scenic spots may have multiple attributes, multi-personality tags can be used to label scenic spots, meaning each scenic spot can correspond to one or more personality tags. In this step, considering that information about scenic spots and tourism personality systems typically includes rich textual information, a large model combined with prompt words can be used for multi-personality tag labeling.

[0047] It should be noted that in this embodiment of the invention, the acquired scenic area data can be cleaned first. This cleaning process may include filtering out meaningless data with insufficient content, standardizing the format, and multi-source fusion (e.g., deduplicating and merging content related to the same scenic area from multiple data sources). After processing the scenic area data, standardized data can be obtained, such as data corresponding to fields like scenic area name, description, and photos. This data can then be used to generate a scenic area knowledge base. After deriving the mapping relationships, this knowledge base can be stored for subsequent scenic area recommendations.

[0048] S102: Determine the target-related personality tags corresponding to the target personality tags from the tourism personality system, and match the target-related personality tags with recommended scenic spots based on the mapping relationship.

[0049] S103: In response to the matching result meeting the preset conditions, determine the target personality type corresponding to the target-related personality tag based on the tourism personality system, and send the recommended scenic spots and target personality type to the user.

[0050] The tourism personality system can obtain a user's target-related personality tag and determine whether it matches the recommended scenic spots, i.e., whether the user corresponding to the target-related personality tag is interested in the recommended scenic spots. The matching process can be as follows: determine whether the personality tag mapped to the recommended scenic spot includes the user's target-related personality tag. If so, the matching result meets the preset conditions, and the personality type corresponding to the target-related personality tag can be sent to the user. If not, the matching result does not meet the preset conditions, i.e., the recommended scenic spot does not match the user's target-related personality tag, and in this case, the target-related personality tag can not be sent to the user, i.e., no recommendation is made to the relevant user.

[0051] Specifically, when there are multiple target-related personality tags, each relevant personality tag can be matched with the personality tag corresponding to the recommended scenic spot; if the matching result is successful, the matching result is determined to meet the preset conditions; if the matching result is unsuccessful, each personality type in the personality tag corresponding to the recommended scenic spot is obtained and matched with the target personality type corresponding to each target-related personality tag to obtain the matching personality type, which is then determined as the matching result.

[0052] Each target-related personality tag is directly matched with the personality tags corresponding to the recommended scenic spots. If a match is successful, the matching target-related personality tag can be directly identified and recommended to the user. If the match is unsuccessful, it means that the target-related personality tags and the recommended scenic spots are inconsistent. In this case, matching can be performed again according to the target personality type. That is, each personality type in the personality tags corresponding to the recommended scenic spots is obtained and matched with the target personality types corresponding to the target-related personality tags to obtain matching personality types. Then, these matching personality types are combined, and the personality tags corresponding to the combined personality types are selected from the tourism personality system. If the tourism personality system has a personality tag corresponding to the combined personality type, it can be identified as the target personality tag to be recommended to the user, that is, identified as a matching result; if the tourism personality system does not have a personality tag corresponding to the combined personality type, the matching result is determined to be none, that is, the relevant personality tag is not recommended to the user.

[0053] In this embodiment of the invention, a first large model is used to generate a corresponding travel personality system based on each user's travel data. This system includes multiple personality tags, each corresponding to a different personality type. A second large model is then used to generate a mapping relationship between scenic spots and personality tags based on the scenic spot data and the travel personality system, thus establishing an association between the travel personality system and the scenic spots. Therefore, when recommendations are needed to a user, the user's personality tag can be determined based on the user data, and then the corresponding recommended scenic spot can be determined by combining the mapping relationship, and then the recommended scenic spot can be sent to the user. In this embodiment of the invention, by using a large model combined with a large amount of relevant data, a more accurate travel personality system is constructed and an association relationship is established with various scenic spots. This allows for more accurate recommendations of scenic spots to users, meeting their personalized needs, and improving the accuracy of scenic spot recommendations even in a cold start environment.

[0054] The following is combined Figure 1 The illustrated embodiments provide a detailed description of the data processing methods in the embodiments of the present invention, such as... Figure 2 As shown, the method includes the following steps.

[0055] S201: Obtain various tourism data, call the first major model, and generate the corresponding tourism personality system.

[0056] S202: Obtain the detection rules between each personality type, call the third major model to detect the tourism personality system based on the detection rules, and obtain the detection results; in response to the detection result being unsuccessful, generate a new tourism personality system based on tourism data.

[0057] S203: Obtain scenic area data for each scenic area, generate corresponding scenic area prompts, input the scenic area prompts and the tourism personality system into the second major model, and generate the mapping relationship between each scenic area and personality label.

[0058] S204: In response to the user's recommendation instruction, obtain the corresponding user data, determine the user's target personality tag, and determine the recommended scenic spot corresponding to the user based on the mapping relationship.

[0059] S205: Determine the target-related personality tags corresponding to the target personality tags from the tourism personality system, and match the target-related personality tags with recommended scenic spots based on the mapping relationship.

[0060] S206: In response to the matching result meeting the preset conditions, the recommended scenic spots and the target personality type corresponding to the target-related personality tags are sent to the user.

[0061] It should be noted that the data processing principle in the embodiments of the present invention is the same as... Figure 1 The data processing principles in the illustrated embodiments are the same and will not be repeated here.

[0062] To address the problems existing in the prior art, embodiments of the present invention provide a data processing apparatus 300, such as... Figure 3 As shown, the device 300 includes: a determining unit 301, configured to respond to a user's recommendation instruction, acquire corresponding user data, determine the user's target personality tag, and determine the recommended scenic spot corresponding to the user in combination with a preset mapping relationship; wherein, the mapping relationship includes the mapping relationship between each scenic spot and each personality tag in a preset tourism personality system, the tourism personality system includes multiple personality tags and related personality tags associated with each personality tag, and each personality tag corresponds to a different personality type; The matching unit 302 is used to determine the relevant personality tags corresponding to the user's personality tags from the tourism personality system, and match the relevant personality tags with the recommended scenic spots based on the mapping relationship; The sending unit 303 is used to send the personality type corresponding to the relevant personality tag to the user in response to the matching result meeting the preset conditions.

[0063] It should be understood that the manner in which embodiments of the present invention are implemented is different from the implementation method. Figure 1 The embodiments shown are the same and will not be described again here.

[0064] In one embodiment, the device 300 further includes: The acquisition unit is used to acquire a tourism data set, call the first major model, and generate a corresponding tourism personality system. The tourism personality system includes multiple personality tags, and each personality tag corresponds to a different personality type. The generation unit is used to acquire scenic area data for each scenic area, generate corresponding scenic area prompts, input the scenic area prompts and the tourism personality system into the second major model, and generate the mapping relationship between each scenic area and the personality tag.

[0065] In yet another embodiment, the device 300 further includes: The detection unit is used to obtain the detection rules between each personality type, call the third major model, and detect the tourism personality system based on the detection rules to obtain the detection results; The generation unit is also configured to generate a new tourism personality system based on the tourism data in response to the detection result being "not passed".

[0066] In yet another embodiment, the acquisition unit is specifically used for: The system acquires feedback data for each test information item corresponding to the user, identifies the weight of each feedback data item corresponding to each personality type, and determines the user's target personality label based on the weight. The test information is generated based on the travel personality system.

[0067] In yet another embodiment, the relevant personality tags include multiple tags; The matching unit 302 is specifically used for: Based on the mapping relationship, each of the relevant personality tags is matched with the personality tags corresponding to the recommended scenic spots; In response to a matching failure, the personality types in the personality tags corresponding to the recommended scenic spots are obtained and matched with the personality types corresponding to each of the relevant personality tags to obtain the matching personality types, which are then determined as the matching results.

[0068] It should be understood that the manner in which embodiments of the present invention are implemented is different from the implementation method. Figure 1 , 2 The embodiments shown are the same and will not be described again here.

[0069] In this embodiment of the invention, a tourism personality system is pre-constructed, comprising multiple personality tags and related personality tags associated with each tag. Each personality tag corresponds to a different personality type, and a mapping relationship is established between each scenic spot and each personality tag in the tourism personality system. For user recommendation commands, the user's personality tag can be determined based on user data, and then, combined with the mapping relationship, the corresponding recommended scenic spot can be determined and sent to the user. In this embodiment of the invention, through the pre-constructed travel personality system and its association with various scenic spots, scenic spots can be recommended to users more accurately, along with the related personality types that match the recommended scenic spots, meeting users' personalized needs and improving the accuracy of scenic spot recommendations even in cold start situations.

[0070] According to embodiments of the present invention, an electronic device and a readable storage medium are also provided.

[0071] An electronic device according to an embodiment of the present invention includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the data processing method provided in the embodiment of the present invention.

[0072] Figure 4 An exemplary system architecture 400 is shown, which can be applied to a data processing method or a data processing apparatus according to embodiments of the present invention.

[0073] like Figure 4As shown, system architecture 400 may include terminal devices 401, 402, and 403, a network 404, and a server 405. Network 404 serves as the medium for providing communication links between terminal devices 401, 402, and 403 and server 405. Network 404 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0074] Users can use terminal devices 401, 402, and 403 to interact with server 405 via network 404 to receive or send messages, etc. Various client applications can be installed on terminal devices 401, 402, and 403.

[0075] Terminal devices 401, 402, and 403 can be various electronic devices with displays that support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0076] Server 405 can be a server that provides various services. The server can analyze and process data such as received product information query requests, and feed back the processing results (such as product information - just an example) to the terminal device.

[0077] It should be noted that the data processing method provided in the embodiments of the present invention is generally executed by server 405, and correspondingly, the data processing device is generally disposed in server 405.

[0078] It should be understood that Figure 4 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0079] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing embodiments of the present invention. Figure 5 The computer system shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0080] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0081] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0082] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined above in the system of this invention.

[0083] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a unit, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0085] The units described in the embodiments of the present invention can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including a determining unit, a matching unit, and a generating unit. The names of these units do not necessarily limit the specific unit; for example, the determining unit can also be described as a "unit that determines functions."

[0086] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to perform the data processing method provided by the present invention.

[0087] In another aspect, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the data processing method provided in the embodiments of the present invention.

[0088] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data processing method, characterized in that, include: In response to a user's recommendation, the system acquires the corresponding user data, determines the user's target personality tag, and determines the recommended scenic spot corresponding to the user based on a preset mapping relationship. The mapping relationship includes the mapping relationship between each scenic spot and each personality tag in a preset tourism personality system. The tourism personality system includes multiple personality tags and related personality tags associated with each personality tag, and each personality tag corresponds to a different personality type. From the tourism personality system, determine the target-related personality tags corresponding to the target personality tags, and based on the mapping relationship, match the target-related personality tags with the recommended scenic spots; In response to the matching result meeting the preset conditions, the target personality type corresponding to the target-related personality tag is determined based on the tourism personality system, and the recommended scenic spot and the target personality type are sent to the user.

2. The method according to claim 1, characterized in that, Before determining the recommended scenic spot corresponding to the user based on the preset mapping relationship, the method further includes: Acquire a set of tourism data, call the first major model, and generate a corresponding tourism personality system; Acquire scenic area data for each scenic area, generate corresponding scenic area prompts, input the scenic area prompts and the tourism personality system into the second major model, and generate the mapping relationship between each scenic area and the personality tag.

3. The method according to claim 2, characterized in that, After generating the corresponding tourism personality system, the following is also included: The detection rules for each personality type are obtained, and the third major model is invoked to detect the tourism personality system based on the detection rules, thereby obtaining the detection results. In response to the detection result being "failed", a new tourism personality system is generated based on the tourism data.

4. The method according to claim 1, characterized in that, The step of obtaining the corresponding user data and determining the user's personality tag includes: The system acquires feedback data for each test information item corresponding to the user, identifies the weight of each feedback data item corresponding to each personality type, and determines the user's target personality label based on the weight. The test information is generated based on the travel personality system.

5. The method according to claim 1, characterized in that, The target-related personality tags include multiple categories; Based on the mapping relationship, matching the target-related personality tags with the recommended scenic spots includes: Based on the mapping relationship, each target-related personality tag is matched with the personality tag corresponding to the recommended scenic spot; In response to a matching failure, the personality types in the personality tags corresponding to the recommended scenic spots are obtained and matched with the personality types corresponding to the target-related personality tags respectively to obtain the matching personality types, which are then determined as the matching results.

6. A data processing apparatus, characterized in that, include: The determining unit is used to respond to the user's recommendation instruction, obtain the corresponding user data, determine the user's target personality tag, and determine the recommended scenic spot corresponding to the user in combination with the preset mapping relationship; wherein, the mapping relationship includes the mapping relationship between each scenic spot and each personality tag in the preset tourism personality system, the tourism personality system includes multiple personality tags and related personality tags associated with each personality tag, and each personality tag corresponds to a different personality type; A matching unit is used to determine the relevant personality tags corresponding to the user's personality tags from the tourism personality system, and match the relevant personality tags with the recommended scenic spots based on the mapping relationship; The sending unit is used to send the personality type corresponding to the relevant personality tag to the user in response to the matching result meeting the preset conditions.

7. The apparatus according to claim 6, characterized in that, The device further includes: The acquisition unit is used to acquire a tourism data set, call the first major model, and generate a corresponding tourism personality system. The tourism personality system includes multiple personality tags, and each personality tag corresponds to a different personality type. The generation unit is used to acquire scenic area data for each scenic area, generate corresponding scenic area prompts, input the scenic area prompts and the tourism personality system into the second major model, and generate the mapping relationship between each scenic area and the personality tag.

8. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-5.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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