Video generation method, system and equipment based on user data and storage medium

Abstract

The invention provides a video generation method, system and device based on user data, and a storage medium. The method comprises the following steps: acquiring a tourism keyword set from historical tourism data of a user himself / herself based on AI text recognition; carrying out tourism product classification on the self-tourism keyword set, obtaining a first percentage of a keyword subset corresponding to each type of tourism product classification in the total number of keywords, and obtaining a first recommended keyword set; based on each keyword subset, matching a second percentage of the corresponding tourism route set in the total number of tourism route products in the tourism product library; taking the keyword subset with the maximum absolute value of the difference value between the first percentage and the second percentage as a second recommended keyword set; and generating a tourism product video based on a set of the first recommendation keyword set and the second recommendation keyword set, and pushing the tourism product video to the user. According to the invention, the personalized demand of each user can be automatically identified to generate a personalized tourism product introduction video, so that the user experience and the conversion rate are improved.

Description

Background Art

[0002] With the popularization of the Internet and mobile devices, users' demand for video content has grown rapidly. AIGC technology can quickly generate a large amount of high-quality video content to meet market demand. In addition, users are increasingly inclined to personalized and customized content. AIGC technology can generate personalized video content according to user preferences, improving user experience and satisfaction. The traditional video production process is time-consuming and costly, and AIGC technology can significantly reduce the time and cost of video production, improving efficiency, especially in fields such as advertising, education, and entertainment, with significant economic benefits. AIGC technology also provides new creative tools for content marketing creators, enabling the generation of visual effects and storylines that were previously impossible to achieve, promoting the development of the creative industry.

[0003] AIGC technology can achieve the automated production and intelligent editing of video content, reducing manual intervention and improving production efficiency. This is particularly important for industries such as content marketing platforms that require a large amount of content updates.

[0004] The technical background of AIGC for generating video content is based on the development of deep learning, computing power, big data, and related technologies, while the real needs come from the growth of content demand, personalized needs, cost efficiency, creative innovation, and the need for automation and intelligence. With the continuous progress of technology and the expansion of application scenarios, AIGC for generating video content will play an increasingly important role in the future.

[0005] For a travel platform (OTA platform) with a large number of travel products and tourism resources, due to the rapid development of travel products, it is difficult to accurately grasp user needs. The user needs in the travel market are diverse, and users of different ages and interests have different focuses on travel products. Video materials are difficult to accurately meet the needs of all users, resulting in poor advertising effects. For example: Currently, AIGC for generating video content is basically based on the most prominent labels of travel products or tourist attractions themselves for video editing and text generation. Coupled with the homogenization phenomenon of travel products themselves, the content of video materials is also prone to being stereotyped. For example, many travel advertisements focus on showing the scenery of popular attractions, lacking uniqueness, and it is difficult to attract users' attention and trigger users' emotional resonance.

[0006] Therefore, the present invention provides a video generation method, system, device, and storage medium based on user data. Summary of the Invention

[0007] Aiming at the problems in the prior art, the purpose of the present invention is to provide a video generation method, system, device and storage medium based on user data, which overcomes the difficulties of the prior art and can automatically identify the personalized needs of each user to generate personalized travel product introduction videos, improving user experience and conversion rate.

[0008] An embodiment of the present invention provides a video generation method based on user data, including the following steps:

[0009] S110. Obtain a set of travel keywords from the user's past historical travel data through AI-based text recognition.

[0010] S120. Classify travel products from the set of travel keywords, obtain the first percentage of the keyword subset corresponding to each category of travel product classification in the total number of keywords in the travel keyword set, and obtain a first recommended keyword set from several of the keyword subsets with the largest number of keywords.

[0011] S130. Based on each keyword subset, match the corresponding set of travel routes in the travel product library to obtain the second percentage of the total number of travel route products in the travel product library.

[0012] S140. Use several of the keyword subsets with the largest absolute value of the difference between the first percentage corresponding to each keyword subset and the second percentage as the second recommended keyword set.

[0013] S150. Generate a travel product video based on the set of the first recommended keyword set and the second recommended keyword set, match the corresponding travel route product in the travel product library, establish a mapping relationship between the travel route product and the travel product video, and push the travel product video to the user.

[0014] Preferably, in step S110, it includes:

[0015] S111. Collect the user's past historical travel data, and collect relevant introduction information of travel route products and tourist attractions.

[0016] S112. Obtain a set of travel keywords from the introduction information through AI-based text recognition.

[0017] Preferably, in step S120, it includes:

[0018] S121. Classify travel products from the set of travel keywords to obtain the keyword subset corresponding to each category of travel product classification.

[0019] S122. Obtain the first percentage of the number of keywords in each keyword subset in the total number of keywords in the travel keyword set.

[0020] S123. Obtain a first recommended keyword set from the union of several keyword subsets with the largest number of keywords based at least on the number of keywords.

[0021] Preferably, in the step S130, it includes:

[0022] S131. Match a corresponding set of travel routes in the travel product library of the travel platform based on each keyword subset.

[0023] S132. Obtain a second percentage of the number of travel route products in each set of travel routes to the total number of travel route products in the travel product library.

[0024] Preferably, in the step S140, it includes:

[0025] S141. Obtain the difference between the first percentage corresponding to each keyword subset and the second percentage.

[0026] S142. Use several keyword subsets with the largest absolute value of the difference as the second recommended keyword set.

[0027] Preferably, in the step S150, it includes:

[0028] S151. Generate a travel product video based on the set of the first recommended keyword set and the second recommended keyword set.

[0029] S152. Match corresponding recommended travel route products in the travel product library based on the set of the first recommended keyword set and the second recommended keyword set, establish a mapping relationship between the recommended travel route products and the travel product video, and push the travel product video to the user.

[0030] S153. When the user clicks to interact with the travel product video, push the recommended travel route products to the user.

[0031] Preferably, in the step S152, it includes:

[0032] S1521. Match several corresponding recommended travel route products in the travel product library based on the set of the first recommended keyword set and the second recommended keyword set.

[0033] S1522. Obtain the corresponding photo-taking tourist attractions according to the location information corresponding to the photos taken by the user's history, and classify and summarize according to the preset keywords corresponding to the photo-taking tourist attractions to obtain a photo-taking keyword set.

[0034] S1523. Obtain the matching value between the recommended travel route product and the set of photo-taking keywords.

[0035] And

[0036] S1524. Map the recommended travel route product with the highest matching value to the travel product video, and push the travel product video to the user.

[0037] An embodiment of the present invention further provides a video generation system based on user data for implementing the above-mentioned video generation method based on user data. The video generation system based on user data includes:

[0038] A keyword collection module that obtains a set of travel keywords from the user's past historical travel data based on AI-based text recognition.

[0039] A product classification module that classifies travel products from the set of travel keywords, obtains the first percentage of the keyword subset corresponding to each travel product classification in the total number of keywords in the travel keyword set, and obtains a first recommended keyword set from several keyword subsets with the largest number of keywords.

[0040] A product matching module that matches the corresponding travel route set for each keyword subset in the travel product library, accounting for the second percentage of the total number of travel route products in the travel product library.

[0041] A personality difference module that selects several keyword subsets with the largest absolute value of the difference between the first percentage and the second percentage corresponding to each keyword subset as the second recommended keyword set.

[0042] A product recommendation module that generates a travel product video based on the set of the first recommended keyword set and the second recommended keyword set, matches the corresponding travel route product in the travel product library, and establishes a mapping relationship between the travel route product and the travel product video, and pushes the travel product video to the user.

[0043] An embodiment of the present invention further provides a video generation device based on user data, including:

[0044] A processor;

[0045] A memory that stores executable instructions of the processor;

[0046] Wherein, the processor is configured to execute the steps of the above-mentioned video generation method based on user data by executing the executable instructions.

[0047] An embodiment of the present invention also provides a computer-readable storage medium for storing a program, which when executed implements the steps of the above video generation method based on user data.

[0048] The object of the present invention is to provide a video generation method, system, device and storage medium based on user data, which can automatically identify the personalized needs of each user to generate personalized travel product introduction videos, improving user experience and conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objects and advantages of the present invention will become more apparent.

[0050] Figure 1 is a flowchart of the video generation method based on user data of the present invention.

[0051] Figure 2 is a schematic structural diagram of the video generation system based on user data of the present invention.

[0052] Figure 3 is a schematic structural diagram of the video generation device based on user data of the present invention.

[0053] Figure 4 is a schematic structural diagram of the computer-readable storage medium of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The following illustrates the embodiments of the present application through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the present application. The present application can also be implemented or applied through other different specific embodiments. Various details in the present application can also be modified or changed according to different viewpoints and application systems without departing from the spirit of the present application. It should be noted that, without conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0055] The following takes the drawings as a reference and details the embodiments of the present application so that those skilled in the technical field to which the present application belongs can easily implement it. The present application can be embodied in many different forms and is not limited to the embodiments described herein.

[0056] In the descriptions of the present application, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials or characteristics represented in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics represented can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples represented in the present application and the features of different embodiments or examples.

[0057] In addition, the terms "first" and "second" are used only for the purpose of indication, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one such feature. In the descriptions of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0058] In order to clearly illustrate the present application, devices irrelevant to the description are omitted, and the same or similar components throughout the specification are given the same reference numerals.

[0059] Throughout the specification, when it is said that a device is "connected" to another device, this includes not only the case of "direct connection", but also the case of "indirect connection" with other elements placed therebetween. In addition, when it is said that a certain device "includes" a certain component, unless there is a particularly contrary record, it does not exclude other components, but means that other components can also be included.

[0060] When it is said that a device is "above" another device, this can be directly above the other device, but there can also be other devices therebetween. When it is said contrastively that a device is "directly" "above" another device, there are no other devices therebetween.

[0061] Although in some instances the terms first, second, etc. are used herein to denote various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first interface and a second interface, etc. are indicated. Furthermore, as used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising", "including" indicate the presence of the features, steps, operations, elements, components, items, kinds, and / or groups, but do not preclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" used herein are to be construed as inclusive, or meaning any one or any combination. Thus, "A, B, or C" or "A, B, and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B, and C". An exception to this definition only occurs when the combination of elements, functions, steps, or operations are mutually exclusive in some way.

[0062] The technical terms used herein are only for referring to specific embodiments and are not intended to limit the present application. The singular forms used herein also include the plural forms as long as the statement does not clearly indicate the contrary meaning. The meaning of "including" used in the specification is to embody specific characteristics, regions, integers, steps, operations, elements, and / or components, and does not exclude the existence or addition of other characteristics, regions, integers, steps, operations, elements, and / or components.

[0063] Although not defined differently, all terms, including the technical terms and scientific terms used herein, have the same meaning as generally understood by those skilled in the technical field to which the present application pertains. Terms defined in commonly used dictionaries are additionally interpreted to have a meaning consistent with the relevant technical literature and the content currently presented, and should not be over-interpreted as ideal or overly formulaic meanings as long as they are not defined.

[0064] Currently, there are a very large number of travel routes that OTA websites can offer. However, for the promotional videos of travel routes or travel products, they are usually customized based on a common template, lacking differentiation. It is very difficult for users to be attracted by such videos (usually the completion rate of such videos is very low), and users will just swipe away. This also results in the videos with high production costs having a very low conversion rate due to the lack of personalization.

[0065] Figure 1 is a flowchart of the video generation method based on user data of the present invention. As Figure 1 shown, the video generation method based on user data of the present invention includes:

[0066] S110. Obtain a set of travel keywords from the user's past historical travel data based on AI-based text recognition.

[0067] S120. Classify travel products from the set of travel keywords, obtain the first percentage of the number of keywords in each subset corresponding to a travel product classification in the total number of keywords in the set of travel keywords, and obtain a first recommended keyword set from several keyword subsets with the largest number of keywords.

[0068] S130. Based on each keyword subset, match the corresponding set of travel routes in the travel product library, which accounts for the second percentage of the total number of travel route products in the travel product library.

[0069] S140. Use several keyword subsets with the largest absolute value of the difference between the first percentage and the second percentage corresponding to each keyword subset as the second recommended keyword set.

[0070] S150. Generate travel product videos based on the set of the first recommended keyword set and the second recommended keyword set, match the corresponding travel route products in the travel product library, establish a mapping relationship between the travel route products and the travel product videos, and push the travel product videos to the user.

[0071] In a preferred embodiment, in step S110, it includes:

[0072] S111. Collect the user's past historical travel data, and collect relevant introduction information about travel route products and tourist attractions.

[0073] S112. Obtain a set of travel keywords from the introduction information based on AI-based text recognition.

[0074] In a preferred embodiment, in step S120, it includes:

[0075] S121. Classify travel products from the set of travel keywords to obtain keyword subsets corresponding to each travel product classification.

[0076] S122. Obtain the first percentage of the number of keywords in each keyword subset in the total number of keywords in the set of travel keywords, which is equivalent to obtaining the proportion representing the user's preference for each keyword subset. For example, if the user likes surfing, the first percentage of the keyword subset "surfing" in the total number of keywords in the set of travel keywords may exceed 35%.

[0077] S123. Obtain a first recommended keyword set from at least the union of several keyword subsets with the largest number of keywords.

[0078] In a preferred embodiment, in step S130, it includes:

[0079] S131. Match the corresponding set of travel routes in the travel product library of the travel platform based on each keyword subset.

[0080] S132. Obtain the second percentage of the travel route products in each set of travel routes accounting for the total number of travel route products in the travel product library, which is equivalent to obtaining the ratio between the set of travel routes and the total number, so as to reflect the popularity of this set of travel routes.

[0081] In a preferred embodiment, in step S140, it includes:

[0082] S141. Obtain the difference between the first percentage and the second percentage corresponding to each keyword subset.

[0083] S142. Use several keyword subsets with the largest absolute value of the difference as the second recommended keyword set. By obtaining the difference between the first percentage and the second percentage, the unique travel preferences of the user can be obtained, thus reflecting the user's preference for personalized and niche travel products. For example, surfing is a relatively niche travel product category, and the second percentage of surfing is only 5%. Then the absolute value of the difference between the first percentage and the second percentage is 30%, which will significantly exceed other sets of travel routes, so as to more accurately obtain this niche travel hobby of the user.

[0084] In a preferred embodiment, in step S150, it includes:

[0085] S151. Generate travel product videos based on the set of the first recommended keyword set and the second recommended keyword set. In the present invention, an existing production model of the prior art for generating videos based on prompts is used to generate travel product videos. For example, generative artificial intelligence AIGC (Artificial Intelligence Generated Content), which refers to technical methods of artificial intelligence such as generative adversarial networks and large pre-trained models. Through the learning and recognition of existing data, it generates relevant content with appropriate generalization ability. The core idea of AIGC technology is to use artificial intelligence algorithms to generate content with certain creativity and quality. Through training the model and learning a large amount of data, AIGC can generate relevant content according to the input conditions or guidance. For example, by inputting keywords, descriptions or samples, AIGC can generate matching articles, images, audio, etc., which will not be elaborated here.

[0086] S152. Match the corresponding recommended travel route products in the travel product library based on the set of the first recommended keyword set and the second recommended keyword set, establish a mapping relationship between the recommended travel route products and the travel product videos, and push the travel product videos to the user.

[0087] S153. When the user clicks to interact with the travel product video, recommend travel route products to the user.

[0088] In a preferred embodiment, in step S152, it includes:

[0089] S1521. Match a number of recommended travel route products corresponding to the set of the first recommended keyword set and the second recommended keyword set in the travel product library.

[0090] S1522. Obtain the corresponding photo-taking tourist attractions according to the location information corresponding to the photos taken by the user in the past, and classify and summarize according to the preset keywords corresponding to the photo-taking tourist attractions to obtain a photo-taking keyword set (through the frequency of the user's photo-taking, it also reflects the user's preference degree for playing in tourist attractions with such preset keywords, thereby further optimizing the user experience, and enabling the subsequent generated video to further stimulate the user's happy memories, thereby improving the conversion rate).

[0091] S1523. Obtain the matching value between the recommended travel route product and the photo-taking keyword set. And

[0092] S1524. Map the recommended travel route product with the highest matching value to the travel product video, and push the travel product video to the user.

[0093] The present invention automatically identifies the personalized needs and niche travel hobbies of each user to generate personalized travel product introduction videos, improving the user experience and conversion rate.

[0094] The specific implementation manners of the present invention include:

[0095] First, collect the user's own past historical travel data, and collect the relevant introduction information of travel route products and tourist attractions. Obtain a travel keyword set based on the AI-based text recognition self-introduction information.

[0096] Then, classify the travel products from the travel keyword set to obtain a keyword subset corresponding to each category of travel product classification. Obtain the first percentage of the number of keywords in each keyword subset accounting for the total number of keywords in the travel keyword set, which is equivalent to obtaining the proportion representing the user's preference degree for each keyword subset. For example, if the user likes surfing, the first percentage of the keyword subset "surfing" in the total number of keywords in the travel keyword set may exceed 35%. Obtain the first recommended keyword set from at least the union of several keyword subsets with the largest number of keywords.

[0097] Next, a corresponding set of tourist routes is matched in the tourist product library of the tourist platform based on each keyword subset. A second percentage of the tourist route products in each tourist route set to the total number of tourist route products in the tourist product library is obtained, which is equivalent to obtaining the ratio between the tourist route set and the total number, thereby reflecting the popularity of the tourist route set.

[0098] The difference between the first percentage and the second percentage corresponding to each keyword subset is obtained. The keyword subsets with the largest absolute value of the difference are used as the second recommended keyword set. By obtaining the difference between the first percentage and the second percentage, the user's unique travel preferences can be obtained, thereby reflecting the user's personal preference for personalized, niche travel products. For example, surfing is a relatively niche travel product category, and the second percentage of surfing is only 5%. If the absolute value of the difference between the first percentage and the second percentage is 30%, it will obviously exceed other travel route sets, thereby more accurately obtaining the user's niche travel hobby.

[0099] Finally, a tourism product video is generated based on the set of the first recommended keyword set and the second recommended keyword set. In the present invention, the existing production model of the prior art of generating videos based on prompt words is used to generate tourism product videos, which will not be repeated here. Based on the set of the first recommended keyword set and the second recommended keyword set, a number of corresponding recommended tourism route products are matched in the tourism product library. According to the positioning information corresponding to the photos taken by the user in history, the corresponding photo tourist attractions are obtained, and the photo keyword set is obtained by classifying and summarizing according to the preset keywords corresponding to the photo tourist attractions (the frequency of the user's photos also reflects the user's preference for playing in tourist attractions with such preset keywords, thereby further optimizing the user experience, and enabling the subsequently generated videos to further stimulate the user's happy memories, thereby improving the conversion rate). The matching value of the recommended tourism route product and the photo keyword set is obtained. The mapping relationship between the recommended tourism route product with the highest matching value and the tourism product video is pushed to the user. When the user clicks to interact with the tourism product video, the recommended tourism route product is pushed to the user.

[0100] The present invention can also involve the full-link operation of video creation, including the entire process from early target selection to script writing to video generation, which can be completed automatically. It is a system with strong artificial intelligence operation and weak human intervention, and its advantages are as follows:

[0101] First: You can customize the video based on the target selection, video clips and image-based videos for marketing and publicity purposes.

[0102] Second: An efficient creative environment that can produce videos quickly based on a huge material library and advanced algorithm system, greatly improving the production efficiency of video content without the need for strong human intervention throughout the process.

[0103] Third: With a more mature functional module design, this platform allows users to choose whether to generate voice-over and subtitles, giving users a certain amount of video creation space and control over the entire video.

[0104] Fourth: Customized templates facilitate efficient content creation, making video content richer and more standardized.

[0105] Based on the material library, relevant videos can be mass-produced according to the user's target selection. The present invention provides a new video content creation platform that can customize and efficiently complete the creation of video content according to the needs of users or enterprises for marketing or publicity purposes, thereby improving overall operational efficiency.

[0106] The effect of the progress of this invention is that: there are common problems in the market now, such as inefficient content creation and long production cycle. The present invention, based on the intervention of weak manual review assisted by AIGC technology, has built a bridge between market recognition and efficient content creation, so that the content has both efficiency and market value, and can be used for market activities such as marketing and business promotion. At the same time, it also provides a series of creation links such as creation scripts, image-to-video conversion, AI music, template selection, etc. to ensure the lower limit of video quality, break through the upper limit of quality, and launch content quality updates and upgrades.

[0107] Figure 2 Schematic diagram of the structure of the video generation system based on user data of the present invention. Figure 2 As shown, an embodiment of the present invention further provides a video generation system based on user data, which is used to implement the above-mentioned video generation method based on user data. The video generation system based on user data 5 includes:

[0108] The keyword collection module 51 obtains a set of travel keywords from the user's previous historical travel data based on AI text recognition.

[0109] The product classification module 52 classifies tourism products according to the travel keyword set, obtains the first percentage of the keyword subset corresponding to each type of tourism product classification in the total number of keywords in the tourism keyword set, and obtains the first recommended keyword set based on several keyword subsets with the largest number of keywords.

[0110] The product matching module 53 matches the corresponding travel route set in the travel product library based on each keyword subset, and the second percentage of the total number of travel route products in the travel product library.

[0111] The personality difference module 54 uses, as the second recommended keyword set, a number of keyword subsets with the largest absolute value of the difference between the first percentage and the second percentage corresponding to each keyword subset.

[0112] The product recommendation module 55 generates a travel product video based on the set of the first recommended keyword set and the second recommended keyword set, matches the corresponding travel route product in the travel product library, establishes a mapping relationship between the travel route product and the travel product video, and pushes the travel product video to the user.

[0113] In a preferred embodiment, the keyword collection module 51 is configured to collect the user's own past historical travel data, and collect relevant introduction information about travel route products and tourist attractions. Obtain a travel keyword set from the introduction information through AI-based text recognition, but not limited thereto.

[0114] In a preferred embodiment, the product classification module 52 is configured to classify travel products from the travel keyword set to obtain a keyword subset corresponding to each travel product classification. Obtain the first percentage of the number of keywords in each keyword subset accounting for the total number of keywords in the travel keyword set. Obtain the first recommended keyword set at least from the union of a number of keyword subsets with the largest number of keywords, but not limited thereto.

[0115] In a preferred embodiment, the product matching module 53 is configured to match a corresponding travel route set in the travel product library of the travel platform based on each keyword subset. Obtain the second percentage of the travel route products in each travel route set accounting for the total number of travel route products in the travel product library, but not limited thereto.

[0116] In a preferred embodiment, the personality difference module 54 is configured to obtain the difference between the first percentage and the second percentage corresponding to each keyword subset. It is configured to use a number of keyword subsets with the largest absolute value of the difference as the second recommended keyword set, but not limited thereto.

[0117] In a preferred embodiment, the product recommendation module 55 is configured to generate a travel product video based on the set of the first recommended keyword set and the second recommended keyword set. Match the corresponding recommended travel route product in the travel product library based on the set of the first recommended keyword set and the second recommended keyword set, establish a mapping relationship between the recommended travel route product and the travel product video, and push the travel product video to the user. When the user clicks to interact with the travel product video, push the recommended travel route product to the user, but not limited thereto.

[0118] In a preferred embodiment, the product recommendation module 55 is further configured to match a corresponding number of recommended travel route products in the travel product library based on the set of the first recommended keyword set and the second recommended keyword set. Obtain the corresponding photographed tourist attractions according to the positioning information corresponding to the photos taken by the user, and classify and summarize the preset keywords corresponding to the photographed tourist attractions to obtain a photographed keyword set. Obtain the matching value between the recommended travel route products and the photographed keyword set. And map the recommended travel route product with the highest matching value to the travel product video, and push the travel product video to the user, but not limited thereto.

[0119] The video generation system based on user data of the present invention can automatically identify the personalized needs of each user to generate personalized travel product introduction videos, improving user experience and conversion rate.

[0120] The embodiment of the present invention also provides a video generation device based on user data, including a processor. A memory storing executable instructions of the processor. Wherein, the processor is configured to execute the steps of the video generation method based on user data via executing the executable instructions.

[0121] As shown above, the video generation device based on user data of the present invention in this embodiment can automatically identify the personalized needs of each user to generate personalized travel product introduction videos, improving user experience and conversion rate.

[0122] Those skilled in the art of the present technology can understand that various aspects of the present invention can be implemented as a system, method, or program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0123] Figure 3 is a schematic structural diagram of the video generation device based on user data of the present invention. Refer to the following Figure 3 to describe the electronic device 600 according to this embodiment of the present invention. Figure 3 The electronic device 600 shown is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present invention.

[0124] As Figure 3 shown, the electronic device 600 is presented in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0125] Among them, the storage unit stores program code that can be executed by the processing unit 610, enabling the processing unit 610 to execute the steps according to various exemplary embodiments of the present invention described in the method section of this specification above. For example, the processing unit 610 can execute steps as shown in Figure 1 as shown.

[0126] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0127] The storage unit 620 may also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0128] The bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0129] The electronic device 600 may also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through an input / output (I / O) interface 650. Moreover, the electronic device 600 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0130] An embodiment of the present invention further provides a computer-readable storage medium for storing a program, and the steps of a video generation method based on user data are implemented when the program is executed. In some possible implementation manners, various aspects of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above method part of this specification.

[0131] As shown above, the video generation system based on user data of this embodiment of the present invention can automatically identify the personalized needs of each user to generate a personalized travel product introduction video, improving the user experience and conversion rate.

[0132] Figure 4 It is a schematic structural diagram of the computer-readable storage medium of the present invention. Refer to Figure 4 As shown, a program product 800 for implementing the above method according to an embodiment of the present invention is described. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0133] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The 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 of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0134] The computer-readable storage medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program used by or in combination with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0135] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0136] In summary, the purpose of the present invention is to provide a method, system, device and storage medium for video generation based on user data, which can automatically identify the personalized needs of each user to generate a personalized video introduction of travel products, improving the user experience and conversion rate.

[0137] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A video generation method based on user data, characterized in that: The following steps are involved: S110, obtaining a set of travel keywords from the user's previous travel data based on AI text recognition; S120, classifying tourism products from the tourism keyword set, obtaining a first percentage of a keyword subset corresponding to each tourism product classification to the total number of keywords in the tourism keyword set, and obtaining a first recommended keyword set based on a number of keyword subsets with the largest number of keywords; S130, matching a corresponding tourist route set in the tourist product library based on each of the keyword subsets to a second percentage of the total number of tourist route products in the tourist product library; S140, taking a number of the keyword subsets having the largest absolute value of the difference between the first percentage and the second percentage corresponding to each of the keyword subsets as the second recommended keyword set; S150. Generate a tourism product video based on the set of the first recommended keyword set and the second recommended keyword set, match the corresponding tourism route products in the tourism product library, establish a mapping relationship between the tourism route products and the tourism product video, and push the tourism product video to the user.

2. The video generation method based on user data according to claim 1, characterized in that: The step S110 includes: S111, collecting the user's previous travel data, and collecting relevant information about travel routes, products and tourist attractions; S112. Obtain a set of tourism keywords from the introduction information based on AI text recognition.

3. The video generation method based on user data according to claim 1, characterized in that: The step S120 includes: S121, classifying tourism products from the tourism keyword set to obtain a keyword subset corresponding to each tourism product classification; S122, obtaining a first percentage of the number of keywords in each keyword subset to the total number of keywords in the tourism keyword set; S123: Obtain a first recommended keyword set based at least on a collection of several keyword subsets having the largest number of keywords.

4. The video generation method based on user data according to claim 1, characterized in that: The step S130 includes: S131, matching a corresponding set of travel routes in a travel product library of a travel platform based on each of the keyword subsets; S132: Obtain a second percentage of the tourist route products in each tourist route set to the total number of tourist route products in the tourist product database.

5. The video generation method based on user data according to claim 1, characterized in that: The step S140 includes: S141, obtaining a difference between the first percentage and the second percentage corresponding to each of the keyword subsets; S142: taking the keyword subsets with the largest absolute values ​​of the differences as the second recommended keyword set.

6. The method for generating a video based on user data according to claim 1, characterized in that: The step S150 includes: S151, generating a travel product video based on the first recommended keyword set and the second recommended keyword set; S152: matching the corresponding recommended travel route products in the travel product library based on the first recommended keyword set and the second recommended keyword set, establishing a mapping relationship between the recommended travel route products and the travel product videos, and pushing the travel product videos to the user; S153: When the user clicks to interact with the travel product video, the recommended travel route product is pushed to the user.

7. The video generation method based on user data according to claim 6, characterized in that: The step S152 includes: S1521, matching a number of corresponding recommended travel route products in a travel product database based on the first recommended keyword set and the second recommended keyword set; S1522, obtaining corresponding tourist attractions for photographing according to the positioning information corresponding to the photos taken by the user in the past, and classifying and aggregating the preset keywords corresponding to the tourist attractions for photographing to obtain a set of photographing keywords; S1523, obtaining a matching value between the recommended travel route product and the photographing keyword set; and S1524: Map the recommended travel route product with the highest matching value to the travel product video, and push the travel product video to the user.

8. A video generation system based on user data, used to implement the video generation method based on user data according to claim 1, characterized in that: include: Keyword collection module, which uses AI-based text recognition to obtain a set of travel keywords from the user's previous travel data; A product classification module, classifying tourism products from the tourism keyword set, obtaining a first percentage of a keyword subset corresponding to each tourism product classification to the total number of keywords in the tourism keyword set, and obtaining a first recommended keyword set based on a number of keyword subsets with the largest number of keywords; A product matching module, based on each of the keyword subsets, matches a corresponding set of travel routes in the travel product library to a second percentage of the total number of travel route products in the travel product library; The individual difference module selects a plurality of the keyword subsets having the largest absolute value of the difference between the first percentage and the second percentage corresponding to each of the keyword subsets as the second recommended keyword set; The product recommendation module generates a tourism product video based on the set of the first recommended keyword set and the second recommended keyword set, matches the corresponding tourism route products in the tourism product library, establishes a mapping relationship between the tourism route products and the tourism product videos, and pushes the tourism product videos to the user.

9. A video generation device based on user data, characterized in that: include: processor; a memory storing executable instructions of the processor; The processor is configured to execute the steps of the method for generating video based on user data according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the steps of the method for generating a video based on user data according to any one of claims 1 to 7 are implemented.

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