Tourism fat reduction information recommendation method and device, electronic equipment and medium

Abstract

The invention provides a tourism fat reduction information recommendation method and device, electronic equipment and a medium. The method comprises the following steps: pre-configuring a target database comprising reference tourism fat reduction data of a plurality of reference users; based on a target user portrait of the target user on the target social platform, putting multiple pieces of target advertisement information to the target social platform of the target user, starting a tourism fat reduction recommendation module in response to the target advertisement information put on the target social platform clicked by the target user, obtaining user data of the target user under multiple different target dimensions, and sending the user data to the target social platform; the method comprises the steps of obtaining a plurality of reference tourism fat-reducing data, generating target tourism fat-reducing features, screening out a plurality of target reference tourism fat-reducing data of which the second similarity meets a second preset similarity condition based on the target tourism fat-reducing features, and generating target tourism route recommendation information for a target user based on reference tourism route information in the plurality of target reference tourism fat-reducing data. Therefore, a proper fat-reducing travel route can be actively recommended to the user.

Description

Technical Field

[0001] The present application relates to the technical field of information processing. Specifically, it relates to a method, device, electronic device and medium for recommending tourism fat loss information. Background Art

[0002] More and more people have the need for fat loss, and weight loss training camps have emerged as the times require. However, the exercise methods in weight loss training camps are boring, users often lack motivation, and they will face the problem of plateau. Moreover, after the boring closed training ends, it is easy to have a retaliatory mentality and lead to rebound.

[0003] Many people have the idea of losing weight during travel. On social media, people often ask whether travel can help lose weight. During travel, people often carry out large-scale hiking, mountain climbing, cycling and other activities. These activities themselves are very good exercise methods and full of fun. People often complete the amount of exercise that is usually difficult to complete without realizing it. However, for individual users with fat loss needs, they often have difficulties in choosing routes, and for tourism-related companies, it is also a new opportunity to increase the conversion rate. Summary of the Invention

[0004] In view of this, the purpose of the present application is to provide a method, device, electronic device and medium for recommending tourism fat loss information, which can recommend suitable fat loss tourism routes for users based on the personalized data of users.

[0005] A method for recommending tourism fat loss information provided by an embodiment of the present application includes the following steps:

[0006] Pre-configure a target database including reference tourism fat loss data of multiple reference users; each reference tourism fat loss data includes reference tourism route information and reference tourism fat loss characteristics; the reference tourism fat loss characteristics include reference tourism route characteristics and reference user attribute characteristics; the reference user attribute characteristics include fat loss feedback characteristics;

[0007] Based on the target user portrait of the target user on the target social platform, launch multiple target advertising information to the target social platform of the target user; the target user portrait at least includes target tourism attribute data, target individual attribute data and target fat loss attribute data;

[0008] In response to the target user clicking on the target advertising information launched on the target social platform, start the tourism fat loss recommendation module, and obtain the user data of the target user under multiple different target dimensions. The user data includes basic physiological data, sports equipment data, target fat loss data, the target user portrait, and the advertising feature data of the target advertising information clicked by the user;

[0009] Processing the user data of the target user under multiple different target dimensions to generate target travel fat loss features;

[0010] Respectively calculating the second similarities between the target travel fat loss feature of the target user and the reference travel fat loss features of a plurality of reference travel fat loss data, and screening out a plurality of target reference travel fat loss data whose second similarities satisfy a second preset similarity condition;

[0011] Based on the reference travel route information in the plurality of target reference travel fat loss data, target travel route recommendation information for the target user is generated.

[0012] In some embodiments, in the travel fat loss information recommendation method, the step of delivering multiple target advertising information to the target social platform of the target user based on the target user portrait on the target social platform includes:

[0013] Obtaining a target user portrait of a target user on a target social platform, calculating first similarities between the target user portrait and a plurality of reference portraits, and determining a similarity calculation result of the target user; the reference portrait is constructed based on a composite user group having both fat loss needs and travel needs;

[0014] When the similarity result calculation of the target user meets the first preset similarity requirement, based on the advertisement template corresponding to the target reference portrait with the highest similarity to the target user among the multiple reference portraits, multiple travel material images and multiple fat loss material images are automatically selected from the advertisement material library;

[0015] Combining the different travel material images and fat loss material images to generate a plurality of target advertising materials;

[0016] The multiple target advertising materials and advertising templates are processed to generate multiple target advertising information for target users; the target advertising information is used to be delivered to the target social platform of the target user.

[0017] In some embodiments, in the method for recommending tourism fat loss information, the combining of the different tourism material images and fat loss material images to generate a plurality of target advertising materials includes:

[0018] Combining the different travel material images and fat-reducing material images to generate a plurality of initial advertising material images;

[0019] Performing style migration on the initial advertising material image based on different style migration models to generate advertising material images of multiple styles corresponding to each initial advertising material;

[0020] Generate copy information for each style of advertising material image to obtain multiple target advertising materials; the style of the target advertising materials is related to the delivery strategy.

[0021] In some embodiments, in the tourism weight loss information recommendation method, calculating a second similarity between the target tourism weight loss features of the target user and the reference tourism weight loss features of multiple reference tourism weight loss data, and screening out multiple target reference tourism weight loss data whose second similarity meets the second preset similarity condition includes:

[0022] Performing clustering processing on the target tourism weight loss features of the target user and the reference tourism weight loss features of multiple reference tourism weight loss data to determine the clustering result corresponding to the target user;

[0023] Based on the clustering result corresponding to the target user, determining multiple target reference tourism weight loss data whose second similarity meets the second preset similarity condition.

[0024] In some embodiments, in the tourism weight loss information recommendation method, generating target tourism route recommendation information for the target user based on the reference tourism route information in the multiple target reference tourism weight loss data includes:

[0025] Obtaining the tourism route map corresponding to the reference tourism route information in each target reference tourism weight loss data, and multiple tourism scene images in the tourism route map;

[0026] Obtaining at least part of the weight loss result feedback information of the reference users corresponding to the multiple target reference tourism weight loss data;

[0027] Generating target tourism route recommendation information for the target user based on the tourism route map, multiple tourism scene images, and weight loss result feedback information.

[0028] In some embodiments, in the tourism weight loss information recommendation method, generating target tourism route recommendation information for the target user based on the tourism route map, multiple tourism scene images, and weight loss result feedback information includes:

[0029] Screening out target tourism scene images that match the target advertising information clicked by the user from multiple tourism scene images, and generating a first target description text for the target tourism scene image based on the target advertising information;

[0030] Generating a second target description text based on the weight loss result feedback information;

[0031] Combining the tourism route map, the target tourism scene image, the first target description text, and the second target description text to generate target tourism route recommendation information for the target user.

[0032] In some embodiments, in the tourism weight loss information recommendation method, the method further includes: combining a tourism route map, the target tourism scene image, a first target description text, and a second target description text to generate tourism route recommendation information for a target user, including:

[0033] Obtain the weight loss feedback text corresponding to each scenic spot in the second target description text and in the tourism route map;

[0034] Combine the tourism route map, the target tourism scene image, the first target description text, and the second target description text, and add the weight loss feedback text to the corresponding scenic spots in the tourism route map to generate tourism route recommendation information for the target user.

[0035] In some embodiments, a tourism weight loss recommendation device is further provided, and the device includes:

[0036] A configuration module, configured to pre-configure a target database including reference tourism weight loss data of multiple reference users; each reference tourism weight loss data includes reference tourism route information and reference tourism weight loss characteristics; the reference tourism weight loss characteristics include reference tourism route characteristics and reference user attribute characteristics;

[0037] A delivery module, configured to deliver multiple target advertisement messages to the target social platform of the target user based on the target user portrait of the target user on the target social platform; the target user portrait at least includes target tourism attribute data, target individual attribute data, and target weight loss attribute data;

[0038] An acquisition module, configured to start a tourism weight loss recommendation module in response to the target user clicking on the target advertisement message delivered on the target social platform, and acquire user data of the target user under multiple different target dimensions, where the user data includes basic physiological data, sports device data, target weight loss data, the target user portrait, and advertisement feature data of the target advertisement message clicked by the user;

[0039] A first generation module, configured to process the user data of the target user under multiple different target dimensions to generate target tourism weight loss characteristics;

[0040] A screening module, configured to calculate the second similarity between the target tourism weight loss characteristics of the target user and the reference tourism weight loss characteristics of multiple reference tourism weight loss data respectively, and screen out multiple target reference tourism weight loss data whose second similarity meets the second preset similarity condition;

[0041] A second generation module, configured to generate target tourism route recommendation information for the target user based on the reference tourism route information in the multiple target reference tourism weight loss data.

[0042] In some embodiments, an electronic device is further provided, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the travel weight loss information recommendation method are executed.

[0043] In some embodiments, a computer-readable storage medium is further provided. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the travel weight loss information recommendation method are executed.

[0044] In the embodiments of the present application, a travel weight loss information recommendation method, device, electronic device, and medium are provided. The method pre-configures a target database including reference travel weight loss data of multiple reference users; each reference travel weight loss data includes reference travel route information and reference travel weight loss characteristics; the reference travel weight loss characteristics include reference travel route characteristics and reference user attribute characteristics; based on the target user portrait of the target user on the target social platform, multiple target advertisement information is delivered to the target social platform of the target user; the target user portrait at least includes target travel attribute data, target individual attribute data, and target weight loss attribute data; in response to the target user clicking on the target advertisement information delivered on the target social platform, a travel weight loss recommendation module is started, and user data of the target user in multiple different target dimensions is obtained. The user data includes basic physiological data, sports device data, target weight loss data, the target user portrait, and advertisement feature data of the target advertisement information clicked by the user; the user data of the target user in multiple different target dimensions is processed to generate target travel weight loss characteristics; the second similarity between the target travel weight loss characteristics of the target user and the reference travel weight loss characteristics of multiple reference travel weight loss data is calculated respectively, and multiple target reference travel weight loss data whose second similarity meets the second preset similarity condition are screened out; based on the reference travel route information in the multiple target reference travel weight loss data, target travel route recommendation information for the target user is generated. In this way, relevant groups are attracted to travel and lose weight through precise advertisement delivery, and personalized travel weight loss recommendations are provided for different users, so that the travel routes recommended to users can highly fit their personal actual situations and target weight loss data, greatly improving the accuracy of the recommendation, so that there is no need to spend a lot of time and energy screening and comparing various travel routes, but a recommended solution suitable for oneself can be directly obtained, saving the user's selection cost and enhancing the user experience. Description of the Drawings

[0045] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0046] Figure 1 Shows the flowchart of the tourism weight loss information recommendation method described in the embodiments of the present application;

[0047] Figure 2 Shows the flowchart of the method for delivering multiple target advertisement messages to the target social platform of the target user described in the embodiments of the present application;

[0048] Figure 3 Shows the flowchart of the method for screening out multiple target reference tourism weight loss data whose second similarity meets the second preset similarity condition described in the embodiments of the present application;

[0049] Figure 4 Shows the flowchart of the method for generating target tourism route recommendation information for the target user described in the embodiments of the present application;

[0050] Figure 5 Shows the structural schematic diagram of the tourism weight loss recommendation device described in the embodiments of the present application;

[0051] Figure 6 Shows the structural schematic diagram of the electronic device described in the embodiments of the present application. Detailed implementation manners

[0052] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. It should be understood that the accompanying drawings in the present application only serve the purposes of illustration and description and are not used to limit the protection scope of the present application. Additionally, it should be understood that the schematic drawings are not drawn to the actual scale. The flowcharts used in the present application show the operations implemented according to some embodiments of the present application. It should be understood that the operations in the flowchart may not be implemented in sequence, and steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0053] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. Generally, the components of the embodiments of the present application described and illustrated in the accompanying drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0054] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.

[0055] More and more people have the need to reduce body fat, and weight loss training camps have emerged as the times require. However, the exercise methods in weight loss training camps are boring, users often lack motivation, and they will face the problem of plateau. Moreover, after the boring closed training, it is easy to have a retaliatory psychology, resulting in weight rebound.

[0056] Many people have the idea of losing weight through traveling, and often someone asks on social media whether traveling can help lose weight. When traveling, people often carry out large-scale hiking, mountain climbing, cycling and other activities. These activities themselves are very good exercise methods and full of fun. People often complete the exercise amount that is usually difficult to complete unconsciously. However, for individual users with the need to reduce body fat, there are often difficulties in choosing routes, and for tourism-related companies, it is also a new opportunity to increase the conversion rate.

[0057] Based on this, in the embodiments of the present application, a method, device, electronic device, and medium for recommending tourism weight loss information are provided. The method pre-configures a target database including reference tourism weight loss data of multiple reference users; each reference tourism weight loss data includes reference tourism route information and reference tourism weight loss characteristics; the reference tourism weight loss characteristics include reference tourism route characteristics and reference user attribute characteristics; based on the target user profile of the target user on the target social platform, a plurality of target advertisement information is delivered to the target social platform of the target user; the target user profile at least includes target tourism attribute data, target individual attribute data, and target weight loss attribute data; in response to the target user clicking on the target advertisement information delivered on the target social platform, a tourism weight loss recommendation module is started, and user data of the target user in multiple different target dimensions is obtained. The user data includes basic physiological data, sports device data, target weight loss data, the target user profile, and advertisement feature data of the target advertisement information clicked by the user; the user data of the target user in multiple different target dimensions is processed to generate target tourism weight loss characteristics; the second similarity between the target tourism weight loss characteristics of the target user and the reference tourism weight loss characteristics of multiple reference tourism weight loss data is calculated respectively, and multiple target reference tourism weight loss data whose second similarity meets the second preset similarity condition are screened out; based on the reference tourism route information in the multiple target reference tourism weight loss data, target tourism route recommendation information for the target user is generated. In this way, relevant groups are attracted to lose weight through tourism by precise advertisement delivery, and personalized tourism weight loss recommendations are provided for different users, so that the tourism routes recommended to users can highly fit their personal actual situations and target weight loss data, greatly improving the accuracy of the recommendation, making it no longer necessary to spend a lot of time and energy screening and comparing various tourism routes, but directly obtaining a recommended solution suitable for oneself, saving the user's selection cost and enhancing the user experience.

[0058] Please refer to Figure 1 , Figure 1 which shows the flowchart of the tourism weight loss information recommendation method described in the embodiments of the present application; as Figure 1 shown, the method includes the following steps S101 - S106:

[0059] S101. Pre-configure a target database including reference tourism weight loss data of multiple reference users; each reference tourism weight loss data includes reference tourism route information and reference tourism weight loss characteristics; the reference tourism weight loss characteristics include reference tourism route characteristics and reference user attribute characteristics;

[0060] S102. Based on the target user profile of the target user on the target social platform, deliver a plurality of target advertisement information to the target social platform of the target user; the target user profile at least includes target tourism attribute data, target individual attribute data, and target weight loss attribute data;

[0061] S103. In response to the target user clicking on the target advertisement information placed on the target social platform, start the travel fat loss recommendation module, and obtain the user data of the target user under multiple different target dimensions. The user data includes basic physiological data, sports equipment data, target fat loss data, the target user profile, and the advertisement feature data of the target advertisement information clicked by the user.

[0062] S104. Process the user data of the target user under multiple different target dimensions to generate target travel fat loss characteristics.

[0063] S105. Calculate the second similarity between the target travel fat loss characteristics of the target user and the reference travel fat loss characteristics of multiple reference travel fat loss data respectively, and screen out multiple target reference travel fat loss data whose second similarity meets the second preset similarity condition.

[0064] S106. Based on the reference travel route information in the multiple target reference travel fat loss data, generate target travel route recommendation information for the target user.

[0065] In step S101, a target database including reference travel fat loss data of multiple reference users is pre-configured; each reference travel fat loss data includes reference travel route information and reference travel fat loss characteristics; the reference travel fat loss characteristics include reference travel route characteristics and reference user attribute characteristics.

[0066] The reference travel route information includes the geographical location, number of days, route map, etc. of the travel route.

[0067] The reference travel fat loss characteristics include reference travel route characteristics and reference user attribute characteristics; the reference travel route characteristics are the characteristics after characterizing the reference travel route information and related information. For example, it includes the characteristic labels of the travel route, such as: the reference travel route characteristic labels of travel route A are: leisure, mountain climbing, farmhouse entertainment, etc.

[0068] User attribute characteristics include age, gender, exercise habits, height, weight, BMI, fat loss feedback characteristics; the fat loss feedback characteristics are determined based on the user's fat loss feedback information.

[0069] Exemplarily, after the user completes a 3-day hike on route A and uploads the body fat scale data, showing that the body fat percentage has decreased by 0.5% and the weight has decreased by 1.5 kg, the fat loss feedback characteristics are that the body fat percentage has decreased by 0.5% and the weight has decreased by 1.5 kg.

[0070] In the step S102, based on the target user profile of the target user on the target social platform, a plurality of target advertisement messages are delivered to the target social platform of the target user; the target user profile at least includes target travel attribute data, target individual attribute data, and target fat loss attribute data.

[0071] The following gives an example of a target user profile:

[0072] Travel attributes: prefer mountain landscapes, travel 3 times a year on average, often book homestays; individual attributes: male, 28 years old, BMI 26.5; fat loss attributes: monthly fat loss target of 2 kg, like running.

[0073] The target user profile is determined based on the behavior of the user in the target social dynamics, the content shared, etc. For example: Travel: liked diving content; Fat loss: purchased a shaping course.

[0074] Specifically, please refer to Figure 2 Based on the target user profile of the target user on the target social platform, delivering a plurality of target advertisement messages to the target social platform of the target user includes the following steps S201 - S205:

[0075] S201. Obtain the target user profile of the target user on the target social platform, calculate the first similarity between the target user profile and multiple reference profiles respectively, and determine the similarity calculation result of the target user; the reference profiles are constructed based on a composite user group with both fat loss needs and travel needs;

[0076] S202. When the similarity result calculation of the target user meets the first preset similarity requirement, based on the advertisement template corresponding to the target reference profile with the highest similarity to the target user among multiple reference profiles, and automatically select multiple travel material images and multiple fat loss material images from the advertisement material library;

[0077] S203. Combine the different travel material images and fat loss material images to generate a plurality of target advertisement materials;

[0078] S204. Process the plurality of target advertisement materials and the advertisement template to generate a plurality of target advertisement messages for the target user; the target advertisement messages are used for delivery to the target social platform of the target user.

[0079] S205. The similarity result calculation of the target user meets the first preset similarity requirement, that is, as long as the first similarity between the target user profile of the target user and any reference profile is greater than the first preset similarity threshold.

[0080] The reference profile is a typical user model pre - stored in the system, such as a sample person with "travel + fat loss" needs.

[0081] The multiple reference images, namely multiple sample characters, such as male, female, old, young, etc.

[0082] As long as the target user matches any one of the preset reference images, it indicates that the target user is a potential customer, and advertisements are delivered to them.

[0083] The tourism material images are some tourism-related images, such as grasslands, coral reefs, mountain peaks, and so on.

[0084] The weight loss material images are weight loss-related images, such as exercise equipment, exercise movements, and so on.

[0085] In particular, it should be noted that in some embodiments, the weight loss material images are images of the reference user before and after weight loss.

[0086] Combine the different tourism material images and weight loss material images to generate multiple target advertisement materials, that is, synthesize the tourism material images and weight loss material images.

[0087] Exemplarily, the synthesized target advertisement materials include images of the reference user before and after weight loss.

[0088] In some embodiments, the combining the different tourism material images and weight loss material images to generate multiple target advertisement materials includes:

[0089] Combine the different tourism material images and weight loss material images to generate multiple initial advertisement material images;

[0090] Perform style transfer on the initial advertisement material images based on different style transfer models to generate multiple style advertisement material images corresponding to each initial advertisement material;

[0091] Generate the copywriting information for each style advertisement material image to obtain multiple target advertisement materials; the style of the target advertisement materials is related to the delivery strategy.

[0092] Exemplarily, the styles of the images include cartoon, watercolor, cyberpunk, minimalist, etc.

[0093] Performing style transfer on the initial advertisement material images based on different style transfer models can increase the attractiveness and interestingness of the advertisements, reduce the repetition degree of the generated advertisements, and moreover, can reduce the sense of incongruity in the synthesized images.

[0094] Generate the copywriting information for each style advertisement material image. For example, for a minimalist image, the copywriting is: "Conquer an altitude of 5000 meters? You'll burn more fat per kilometer!"

[0095] For a watercolor hand-painted image, the copywriting is "Flowers bloom on the path, enjoy weight loss slowly".

[0096] For a cyberpunk-style advertisement, the copywriting is "Neon Night Run Burns the Future | Your body fat percentage in 2049 has arrived in advance".

[0097] After generating multiple target advertisement messages for the target users, the target advertisement messages are used for being delivered to the target social platforms of the target users. Specifically, based on the matching relationship between the style of the target advertisement materials and the target user portraits, a delivery strategy is determined.

[0098] Exemplarily, for the matching degrees between the target users and the target advertisement materials of different styles, those with higher matching degrees are delivered first, or those with higher matching degrees are delivered more times.

[0099] Exemplarily, for users over 50 years old, watercolor hand-painted style advertisements are preferentially delivered.

[0100] In the step S103, in response to the target user clicking on the target advertisement message delivered on the target social platform, a travel fat loss recommendation module is started, and user data of the target user under multiple different target dimensions is obtained. The user data includes basic physiological data, sports device data, target fat loss data, the target user portrait, and advertisement feature data of the target advertisement message clicked by the user.

[0101] The target user clicks on the target advertisement message delivered on the target social platform. Since the target advertisement message itself includes different types, and each type also includes different styles, the advertisement feature data of the target advertisement message clicked by the user can well represent the interests of the user.

[0102] The advertisement feature data of the target advertisement message represents the characteristics of the target advertisement message, such as cartoon, grassland.

[0103] The basic physiological data includes data such as age, gender, height, weight, body fat percentage, blood pressure, and heart rate.

[0104] The basic physiological data can be directly input by the user.

[0105] Among them, some basic physiological data can be directly obtained from health detection devices. For example, health detection devices such as body fat scales, sphygmomanometers, and heart rate monitors are connected to the mobile phone APP through Bluetooth or Wi-Fi, and the basic physiological data is uploaded to the cloud server.

[0106] The sports device data can be synchronized through sports devices (such as smart bracelets, smart watches, sports headphones, sports cameras, etc.). The user only needs to authorize the connection of the device in the APP to automatically obtain data such as the number of steps, exercise distance, exercise time, calories consumed, and heart rate of the exercise trajectory.

[0107] The target fat loss data is set by the user according to his or her actual situation and needs.

[0108] The basic physiological data and sports equipment data can reflect the user's sports ability, metabolic capacity and some sports limitations.

[0109] Based on the user's basic physiological data, sports equipment data and target fat loss data, travel routes suitable for the user's fat loss needs can be recommended. For example, for users with strong athletic ability and high target fat loss data, challenging mountaineering and hiking routes with higher calorie consumption can be recommended; for users with weak athletic ability or more moderate target fat loss data, relatively easy cycling and walking routes can be recommended.

[0110] In step S104, the user data of the target user under multiple different target dimensions is processed to generate target travel fat loss features.

[0111] Exemplarily, the user data of the target user under multiple different target dimensions is processed to generate the target travel fat loss features as shown below.

[0112] The calculation rules for BMI dimension are as follows: BMI = height (m) 2 weight (kg).

[0113] Classification standards for BMI dimension: normal range: 18.5≤BMI<24; overweight: 24≤BMI<28; obese: BMI≥28.

[0114] The calculation rules for the metabolic rate dimension are as follows: Women: BMR = 655 + 9.6 × weight (kg) + 1.8 × height (cm) - 4.7 × age; Men: BMR = 66 + 13.7 × weight (kg) + 5 × height (cm) - 6.8 × age.

[0115] The calculation rules of the heart rate dimension are as follows:

[0116] Maximum heart rate (HRmax): HRmax1 = 220-age, or, HRmax2 = 207-0.7×age.

[0117] The best heart rate range for fat loss: The maximum value of the target heart rate range is: HRmax×60%+resting heart rate, and the minimum value of the target heart rate range is HRmax×50%+resting heart rate.

[0118] For example, HRmax=130, resting heart rate RHR=60:

[0119] (130×0.5)+60=125 beats / minute, (130×0.6)+60=138 beats / minute, so the optimal heart rate range for fat loss is 125-138 beats / minute.

[0120] Knee joint pressure dimension: Convert body weight into knee joint pressure index. Among them, for every 1 kg increase in body weight, the knee joint pressure during walking increases by 3 - 4 kg; then correct it according to the exercise type correction coefficient:

[0121] Walking: Pressure = body weight × 3 times; Running: Pressure = body weight × 10 times; Climbing stairs / mountains: Pressure = body weight × 4.25 times.

[0122] Based on the calculated indicators, the target user portrait, and the advertising feature data of the target advertisement information clicked by the user, obtain the target travel fat loss characteristics of the target user.

[0123] In the step S105, calculate the second similarity between the target travel fat loss characteristics of the target user and the reference travel fat loss characteristics of multiple reference travel fat loss data respectively, and screen out multiple target reference travel fat loss data whose second similarity meets the second preset similarity condition.

[0124] Please refer to Figure 3 , calculating the second similarity between the target travel fat loss characteristics of the target user and the reference travel fat loss characteristics of multiple reference travel fat loss data respectively, and screening out multiple target reference travel fat loss data whose second similarity meets the second preset similarity condition, including the following steps S301 - S302:

[0125] S301. Perform clustering processing on the target travel fat loss characteristics of the target user and the reference travel fat loss characteristics of multiple reference travel fat loss data to determine the clustering result corresponding to the target user;

[0126] S302. Based on the clustering result corresponding to the target user, determine multiple target reference travel fat loss data whose second similarity meets the second preset similarity condition.

[0127] Perform clustering analysis on the target travel fat loss characteristics of the target user and the reference travel fat loss characteristics of multiple reference users. Group the users through clustering algorithms (such as K-Means, DBSCAN, etc.), determine the clustering result to which the target user belongs, and select a preset number of reference travel fat loss data from this clustering result. In this way, the accuracy of the travel route recommended to the user is guaranteed from multiple dimensions such as travel route, user attributes, and fat loss effect.

[0128] In the step S106, generate target travel route recommendation information for the target user based on the reference travel route information in the multiple target reference travel fat loss data.

[0129] Please refer to Figure 4, generate target travel route recommendation information for the target user based on the reference travel route information in the multiple target reference travel weight loss data, including the following steps S401 - S403:

[0130] S401. Obtain the travel route map corresponding to the reference travel route information in each target reference travel weight loss data, and multiple travel scene images in the travel route map;

[0131] S402. Obtain the weight loss result feedback information of at least some reference users corresponding to the multiple target reference travel weight loss data;

[0132] S403. Generate target travel route recommendation information for the target user based on the travel route map, multiple travel scene images, and weight loss result feedback information.

[0133] The travel route map can clearly show the travel route for the user, and the travel scene images can directly show the travel scenes on the route to attract the user; the weight loss feedback information provides effective information about weight loss for the user and improves the attractiveness of the travel route.

[0134] Generate target travel route recommendation information for the target user based on the travel route map, multiple travel scene images, and weight loss result feedback information. The multiple travel scene images can be used as background images of the travel route map or images linked by the travel route map, and the weight loss result feedback information can be directly displayed on the travel route map or displayed by means of page turning, jumping, etc.

[0135] In some embodiments, generating target travel route recommendation information for the target user based on the travel route map, multiple travel scene images, and weight loss result feedback information includes:

[0136] Screen out target travel scene images that match the target advertising information clicked by the user from multiple travel scene images, and generate the first target description text for the target travel scene images based on the target advertising information;

[0137] Generate the second target description text based on the weight loss result feedback information;

[0138] Combine the travel route map, the target travel scene images, the first target description text, and the second target description text to generate target travel route recommendation information for the target user.

[0139] Extract the explicit features (such as the seaside) and implicit intentions (such as pursuing low - intensity exercise) in the advertisement through the target advertising information clicked by the user, and screen out the advantages of the target travel scene images that match. On the one hand, it increases the credibility of the advertising information and avoids the user's feedback that the goods do not match the description. On the other hand, users are more easily attracted by familiar images, thus enhancing the recommendation effect.

[0140] The travel route recommendation information also includes descriptive text for the target travel scene image, such as: Cloud Sea Trail; and second target descriptive text generated based on the weight loss result feedback information, such as "User A hiked for 3 days and successfully broke through the plateau", so as to increase the accuracy and sense of experience of the travel route recommendation information recommended to users based on the triple association of target advertising information click behavior → scene image → weight loss data, and facilitate improving the conversion rate.

[0141] In some embodiments, the method further includes: combining a travel route map, the target travel scene image, the first target descriptive text, and the second target descriptive text to generate travel route recommendation information for the target user, including:

[0142] Obtain the weight loss feedback text corresponding to each scenic spot in the travel route map from the second target descriptive text;

[0143] Combine the travel route map, the target travel scene image, the first target descriptive text, and the second target descriptive text, and add the weight loss feedback text to the corresponding scenic spots in the travel route map to generate travel route recommendation information for the target user, so as to provide in detail the help of each scenic spot for weight loss during the travel.

[0144] For example, for the grassland of scenic spot A, its weight loss feedback text is: Walking on the grassland, painless weight loss; for the mountain peak of scenic spot B, its weight loss feedback text is Climbing the peak bravely, fat burning crazily.

[0145] Based on the same inventive concept, an embodiment of the present application also provides a travel weight loss recommendation device corresponding to the travel weight loss information recommendation method. Since the principle of solving problems by the device in the embodiment of the present application is similar to the above travel weight loss information recommendation method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be elaborated.

[0146] Please refer to Figure 5 , Figure 5 shows a schematic structural diagram of the travel weight loss recommendation device described in the embodiment of the present application; the travel weight loss recommendation device includes:

[0147] A configuration module 501, configured to pre-configure a target database including reference travel weight loss data of multiple reference users; each reference travel weight loss data includes reference travel route information and reference travel weight loss characteristics; the reference travel weight loss characteristics include reference travel route characteristics and reference user attribute characteristics;

[0148] A delivery module 502, configured to deliver multiple target advertisement messages to the target social platform of the target user based on the target user profile of the target user on the target social platform; the target user profile at least includes target travel attribute data, target individual attribute data, and target fat loss attribute data;

[0149] An acquisition module 503, configured to start a travel fat loss recommendation module in response to the target user clicking on the target advertisement message delivered on the target social platform, and acquire user data of the target user under multiple different target dimensions, where the user data includes basic physiological data, sports device data, target fat loss data, the target user profile, and advertisement feature data of the target advertisement message clicked by the user;

[0150] A first generation module 504, configured to process the user data of the target user under multiple different target dimensions to generate target travel fat loss features;

[0151] A screening module 505, configured to calculate a second similarity between the target travel fat loss features of the target user and the reference travel fat loss features of multiple reference travel fat loss data respectively, and screen out multiple target reference travel fat loss data whose second similarity meets a second preset similarity condition;

[0152] A second generation module 506, configured to generate target travel route recommendation information for the target user based on the reference travel route information in the multiple target reference travel fat loss data.

[0153] In some actual embodiments, when the delivery module delivers multiple target advertisement messages to the target social platform of the target user based on the target user profile of the target user on the target social platform, it is specifically configured to:

[0154] Acquire the target user profile of the target user on the target social platform, calculate the first similarity between the target user profile and multiple reference profiles respectively, and determine the similarity calculation result of the target user; the reference profiles are constructed based on a composite user group with both fat loss needs and travel needs;

[0155] When the similarity result calculation of the target user meets the first preset similarity requirement, based on the advertisement template corresponding to the target reference profile with the highest similarity to the target user among multiple reference profiles, and automatically select multiple travel material images and multiple fat loss material images from the advertisement material library;

[0156] Combine the different travel material images and fat loss material images to generate multiple target advertisement materials;

[0157] Process the multiple target advertisement materials and the advertisement template to generate multiple target advertisement messages for the target user; the target advertisement messages are used to be delivered to the target social platform of the target user.

[0158] In some actual embodiments, when combining the different tourism material images and fat loss material images to generate a plurality of target advertisement materials, the placement module is specifically configured to:

[0159] Combine the different tourism material images and fat loss material images to generate a plurality of initial advertisement material images;

[0160] Perform style transfer on the initial advertisement material images based on different style transfer models to generate a variety of style advertisement material images corresponding to each initial advertisement material;

[0161] Generate copywriting information for each style advertisement material image to obtain a plurality of target advertisement materials; the style of the target advertisement materials is related to the placement strategy.

[0162] In some actual embodiments, when calculating the second similarity between the target tourism fat loss characteristics of the target user and the reference tourism fat loss characteristics of a plurality of reference tourism fat loss data and screening out a plurality of target reference tourism fat loss data whose second similarity meets the second preset similarity condition, the screening module is specifically configured to:

[0163] Perform clustering processing on the target tourism fat loss characteristics of the target user and the reference tourism fat loss characteristics of a plurality of reference tourism fat loss data to determine the clustering result corresponding to the target user;

[0164] Based on the clustering result corresponding to the target user, determine a plurality of target reference tourism fat loss data whose second similarity meets the second preset similarity condition.

[0165] In some actual embodiments, when generating target tourism route recommendation information for the target user based on the reference tourism route information in the plurality of target reference tourism fat loss data, the second generation module is specifically configured to:

[0166] Obtain the tourism route map corresponding to the reference tourism route information in each target reference tourism fat loss data, and a plurality of tourism scene images in the tourism route map;

[0167] Obtain at least part of the fat loss result feedback information of the reference users corresponding to the plurality of target reference tourism fat loss data;

[0168] Generate target tourism route recommendation information for the target user based on the tourism route map, the plurality of tourism scene images, and the fat loss result feedback information.

[0169] In some actual embodiments, when generating target tourism route recommendation information for the target user based on the tourism route map, the plurality of tourism scene images, and the fat loss result feedback information, the second generation module is specifically configured to:

[0170] Filter target travel scene images that match the target advertisement information clicked by the user from multiple travel scene images, and generate a first target description text for the target travel scene images based on the target advertisement information;

[0171] Generate a second target description text based on the fat loss result feedback information;

[0172] Combine the travel route map, the target travel scene images, the first target description text, and the second target description text to generate travel route recommendation information for the target user.

[0173] In some actual embodiments, when the second generation module combines the travel route map, the target travel scene images, the first target description text, and the second target description text to generate travel route recommendation information for the target user, it is specifically used for:

[0174] Obtain the fat loss feedback text corresponding to each scenic spot in the second target description text and in the travel route map;

[0175] Combine the travel route map, the target travel scene images, the first target description text, and the second target description text, and add the fat loss feedback text to the corresponding scenic spots in the travel route map to generate travel route recommendation information for the target user.

[0176] Based on the same inventive concept, an electronic device corresponding to the travel fat loss information recommendation method is also provided in the embodiments of the present application. Since the principle of solving problems by the electronic device in the embodiments of the present application is similar to the above-mentioned travel fat loss information recommendation method in the embodiments of the present application, the implementation of the electronic device can refer to the implementation of the method, and the repeated parts will not be described again.

[0177] Please refer to Figure 6 , Figure 6 shows a schematic structural diagram of the electronic device described in the embodiments of the present application; the electronic device 600 includes: a processor 602, a memory 601, and a bus. The memory 601 stores machine-readable instructions executable by the processor 602. When the electronic device 600 runs, the processor 602 communicates with the memory 601 through the bus, and when the machine-readable instructions are executed by the processor 602, the steps of the travel fat loss information recommendation method are executed.

[0178] Based on the same inventive concept, a computer-readable storage medium corresponding to the travel fat loss information recommendation method is also provided in the embodiments of the present application. Since the principle of solving problems by the computer-readable storage medium in the embodiments of the present application is similar to the above-mentioned travel fat loss information recommendation method in the embodiments of the present application, the implementation of the computer-readable storage medium can refer to the implementation of the method, and the repeated parts will not be described again.

[0179] A computer-readable storage medium stores a computer program thereon, and when the computer program is run by a processor, it executes the steps of the travel fat loss information recommendation method described above.

[0180] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the method embodiments, and will not be elaborated herein in the present application. In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces, and the indirect coupling or communication connection of the devices or modules may be in an electrical, mechanical or other form.

[0181] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0182] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0183] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks or optical discs that can store program codes.

[0184] The above are only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for recommending tourism fat loss information, characterized in that The method comprises the following steps: A target database including reference travel fat loss data of multiple reference users is preconfigured; each reference travel fat loss data includes reference travel route information and reference travel fat loss features; the reference travel fat loss features include reference travel route features and reference user attribute features; the reference user attribute features include fat loss feedback features; Based on the target user portrait of the target user on the target social platform, multiple target advertising information is delivered to the target social platform of the target user; the target user portrait includes at least target travel attribute data, target individual attribute data and target fat loss attribute data; In response to a target user clicking on a target advertisement information placed on a target social platform, a travel fat loss recommendation module is started to obtain user data of the target user under multiple different target dimensions, wherein the user data includes basic physiological data, sports equipment data, target fat loss data, a portrait of the target user, and advertisement feature data of the target advertisement information clicked by the user; Processing the user data of the target user under multiple different target dimensions to generate target travel fat loss features; Respectively calculating the second similarities between the target travel fat loss feature of the target user and the reference travel fat loss features of a plurality of reference travel fat loss data, and screening out a plurality of target reference travel fat loss data whose second similarities satisfy a second preset similarity condition; Based on the reference travel route information in the plurality of target reference travel fat loss data, target travel route recommendation information for the target user is generated.

2. The method according to claim 1, wherein The method of placing multiple target advertisements on the target social platform of the target user based on the target user portrait on the target social platform includes: Obtaining a target user portrait of a target user on a target social platform, calculating first similarities between the target user portrait and a plurality of reference portraits, and determining a similarity calculation result of the target user; the reference portrait is constructed based on a composite user group having both fat loss needs and travel needs; When the similarity result calculation of the target user meets the first preset similarity requirement, based on the advertisement template corresponding to the target reference portrait with the highest similarity to the target user among the multiple reference portraits, multiple travel material images and multiple fat loss material images are automatically selected from the advertisement material library; Combining the different travel material images and fat loss material images to generate a plurality of target advertising materials; The multiple target advertising materials and advertising templates are processed to generate multiple target advertising information for target users; the target advertising information is used to be delivered to the target social platform of the target user.

3. The method according to claim 2, wherein The combining of the different travel material images and fat loss material images to generate a plurality of target advertising materials includes: Combining the different travel material images and fat-reducing material images to generate a plurality of initial advertising material images; Performing style migration on the initial advertising material image based on different style migration models to generate advertising material images of multiple styles corresponding to each initial advertising material; Generate copy information for each style of advertising material image to obtain multiple target advertising materials; the style of the target advertising materials is related to the delivery strategy.

4. The method according to claim 1, wherein Calculate the second similarity between the target travel fat loss characteristics of the target user and the reference travel fat loss characteristics of multiple reference travel fat loss data, and screen out multiple target reference travel fat loss data whose second similarity meets the second preset similarity condition, including: Perform clustering processing on the target travel fat loss characteristics of the target user and the reference travel fat loss characteristics of multiple reference travel fat loss data to determine the clustering result corresponding to the target user; Based on the clustering result corresponding to the target user, determine multiple target reference travel fat loss data whose second similarity meets the second preset similarity condition.

5. The method according to claim 1, wherein Generate target travel route recommendation information for the target user based on the reference travel route information in the multiple target reference travel fat loss data, including: Obtain the travel route map corresponding to the reference travel route information in each target reference travel fat loss data, and multiple travel scene images in the travel route map; Obtain the fat loss result feedback information of at least some reference users corresponding to the multiple target reference travel fat loss data; Generate travel route recommendation information for the target user based on the travel route map, multiple travel scene images, and fat loss result feedback information.

6. The method according to claim 5, wherein Generate travel route recommendation information for the target user based on the travel route map, multiple travel scene images, and fat loss result feedback information, including: Screen out target travel scene images that match the target advertisement information clicked by the user from multiple travel scene images, and generate the first target description text for the target travel scene image based on the target advertisement information; Generate the second target description text based on the fat loss result feedback information; Combine the travel route map, the target travel scene image, the first target description text, and the second target description text to generate travel route recommendation information for the target user.

7. The method according to claim 6, wherein The method further includes: combining the travel route map, the target travel scene image, the first target description text, and the second target description text to generate travel route recommendation information for the target user, including: Obtain the fat loss feedback text corresponding to each scenic spot in the travel route map in the second target description text; Combine the travel route map, the target travel scene image, the first target description text, and the second target description text, and add the fat loss feedback text to the corresponding scenic spot in the travel route map to generate travel route recommendation information for the target user.

8. A travel fat loss recommendation device, characterized in that, The device includes: A configuration module for pre-configuring a target database including reference travel fat loss data of multiple reference users; each reference travel fat loss data includes reference travel route information and reference travel fat loss characteristics; the reference travel fat loss characteristics include reference travel route characteristics and reference user attribute characteristics; A delivery module for delivering multiple target advertisement information to the target social platform of the target user based on the target user portrait of the target user on the target social platform; the target user portrait includes at least target travel attribute data, target individual attribute data, and target fat loss attribute data; An acquisition module, configured to start a travel fat loss recommendation module in response to a target user clicking on target advertisement information placed on a target social platform, and acquire user data of the target user under multiple different target dimensions, where the user data includes basic physiological data, sports equipment data, target fat loss data, the target user portrait, and advertisement feature data of the target advertisement information clicked by the user; A first generation module, configured to process the user data of the target user under multiple different target dimensions to generate target travel fat loss features; A screening module, configured to calculate a second similarity between the target travel fat loss features of the target user and the reference travel fat loss features of multiple reference travel fat loss data respectively, and screen out multiple target reference travel fat loss data whose second similarity meets a second preset similarity condition; A second generation module, configured to generate target travel route recommendation information for the target user based on the reference travel route information in the multiple target reference travel fat loss data.

9. An electronic device, characterized in that, Comprising: A processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the travel fat loss information recommendation method according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the travel fat loss information recommendation method according to any one of claims 1 to 7 are executed.

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