A vehicle-end control method and device for travel health diet recommendation and vehicle

Abstract

The application relates to a vehicle-side control method and device for travel health diet recommendation and a vehicle. The method comprises the following steps: generating a diet constraint condition according to health record information and real-time physiological monitoring data of a user; selecting a candidate restaurant and candidate dishes from candidate restaurants according to the diet constraint condition and navigation route information; generating a recommendation list according to the matching degree of the nutrition label of the candidate dishes and the diet constraint condition, the degree of deviation of the candidate restaurant from the navigation route and the expected arrival time; in response to a confirmation operation of the user on the recommendation list, sending a reservation order to a target restaurant; and outputting a meal pickup reminder information when the vehicle arrives within a preset distance range of the target restaurant. In the travel scene, the application combines the health record of the user and the real-time physiological monitoring data to select restaurants and dishes along the way that meet the diet constraints, and performs multi-dimensional sorting and recommendation by comprehensively considering the health matching degree, the path matching degree and the time matching degree, so that the health demand and the travel efficiency are considered.

Description

Technical Field

[0001] This application relates to the field of vehicle control, specifically to a vehicle-side control method, device, and vehicle for recommending healthy diets for travel. Background Technology

[0002] With increasing health awareness, personalized health management, especially dietary management, is receiving more and more attention. For users with a history of chronic diseases, allergies, or medication use, their daily diet needs to strictly follow dietary restrictions to avoid consuming prohibited foods or excessive nutrients.

[0003] However, existing ordering solutions have the following main shortcomings:

[0004] On the one hand, existing online food ordering platforms (such as food delivery apps) typically recommend dishes based solely on users' historical orders or taste preferences, failing to incorporate users' health records (such as chronic disease history, allergy history, and medication history) to match and screen dishes according to dietary restrictions. Users still need to determine for themselves whether the dishes they order meet their own health management needs, which can easily lead to accidentally consuming prohibited ingredients or exceeding nutrient intake limits, posing significant health and safety risks.

[0005] On the other hand, as vehicles become increasingly intelligent, users' demand for ordering food while driving is growing. However, existing in-vehicle ordering solutions only determine food information based on users' preset preferences and recommend restaurants based on navigation routes. They still lack the integration and processing of users' health data, and cannot provide safe and compliant travel meal recommendations for users with special health needs. In addition, after filtering out available restaurants, existing solutions only use proximity or shortest waiting time as the basis for recommendations. The recommendation dimensions are too simplistic and do not comprehensively consider the degree of matching between the dishes and the user's health constraints, the degree of deviation of the restaurant from the navigation route, and the matching relationship between the user's expected arrival time and the restaurant's food preparation time. Summary of the Invention

[0006] This application provides a vehicle-side control method, device, and vehicle for recommending healthy meals during travel. In a travel scenario, by combining the user's health record information and real-time physiological monitoring data, the system selects restaurants and their candidate dishes that meet the dietary constraints from the candidate restaurants along the route. It also comprehensively considers health matching degree, route matching degree, and time matching degree to perform multi-dimensional ranking and recommendation, so as to generate a restaurant and dish recommendation list that takes into account both health needs and travel efficiency.

[0007] The technical solution of this invention is as follows:

[0008] Firstly, this application provides a vehicle-side control method for recommending healthy diets during travel, the vehicle-side control method comprising:

[0009] Obtain user health record information, real-time physiological monitoring data, and vehicle navigation trip information;

[0010] Dietary restrictions are generated based on the health record information and the real-time physiological monitoring data.

[0011] Based on the dietary constraints and the navigation route information, select restaurants that meet the dietary constraints and their candidate dishes from the candidate restaurants in the vicinity of the path corresponding to the navigation route information.

[0012] Based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time, a recommendation list is generated in descending order of recommendation priority. Each set of recommendation information in the recommendation list includes a recommended restaurant and the corresponding recommended dishes.

[0013] In response to the user's confirmation of a set of target recommendation information in the recommendation list, a reservation order is sent to the target restaurant in the target recommendation information, the reservation order including the expected arrival time and the target dishes in the target recommendation information;

[0014] When the vehicle arrives within a preset distance of the target restaurant, a food pickup reminder message is output.

[0015] By integrating users' health records and real-time physiological monitoring data in travel scenarios, dynamic dietary constraints are generated. This ensures that subsequent food recommendations simultaneously meet both the user's long-term health restrictions and their current physical needs, effectively avoiding health and safety risks caused by accidentally consuming prohibited ingredients or excessive nutrients. This addresses the safety issues arising from existing food ordering platforms that rely solely on historical orders or taste preferences for recommendations. By combining navigation trip information to filter available restaurants in the vicinity of the route, the system ensures that recommendations do not deviate from the user's intended route. Furthermore, the recommendation ranking comprehensively considers three dimensions: health compatibility, route deviation, and estimated arrival time. This eliminates the need for users to repeatedly weigh multiple conflicting factors, reducing decision-making burden and attention distraction risks in driving scenarios. This solves the problem of existing in-vehicle food ordering solutions having a single recommendation dimension and failing to balance health needs with travel efficiency.

[0016] After the user confirms the recommendation, a reservation order containing the estimated arrival time is sent to the target restaurant, enabling the restaurant to prepare the food in advance and avoid long waits for the user upon arrival. The system also proactively sends a pick-up reminder when the vehicle reaches the preset distance range, so the user does not need to pay attention to the vehicle's progress. This further optimizes the smoothness of the food pickup process and solves the problems of long waiting times and poor user experience in the existing solutions.

[0017] In summary, the vehicle-side control method of this application deeply integrates health data with travel scenarios, forming a complete closed loop from dietary constraint generation, multi-dimensional recommendation ranking, meal preparation reservation to arrival reminder. It ensures users' food safety while taking into account travel efficiency and meal pickup convenience, effectively solving many technical problems existing in the background technology.

[0018] In some possible embodiments, the step of generating dietary restrictions based on the health record information and the real-time physiological monitoring data includes:

[0019] Based on the chronic disease history, allergy history, and medication history in the health record information, a list of prohibited food ingredients and restricted nutrient indicators are determined.

[0020] Based on the real-time physiological monitoring data, the user's real-time health tag is determined;

[0021] The dietary restrictions are generated based on the list of prohibited ingredients, the restricted nutrient indicators, and the real-time health label.

[0022] The process of generating dietary restrictions involves determining a list of prohibited ingredients and restricted nutrient indicators based on the user's chronic disease history, allergy history, and medication history from their health record information. This enables the dietary restrictions to qualitatively identify contraindications and quantitatively control nutrients. Simultaneously, real-time physiological monitoring data is used to determine the user's real-time health label, allowing the dietary restrictions to dynamically adapt to the user's current physical condition. Finally, the list of prohibited ingredients, restricted nutrient indicators, and real-time health label are integrated to generate complete dietary restrictions. This provides a unified and dynamically adjustable evaluation standard for subsequent selection of restaurants and candidate dishes, effectively avoiding the technical shortcomings of existing ordering platforms that cannot proactively exclude prohibited ingredients from users and cannot respond to real-time changes in vital signs.

[0023] In some possible embodiments, the step of generating a recommendation list in descending order of recommendation priority based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time, wherein a set of recommendation information in the recommendation list includes a recommended restaurant and the corresponding recommended dishes, includes:

[0024] Based on the degree of matching between the nutrition labels of the candidate dishes and the dietary constraints, calculate the health matching score of each candidate dish;

[0025] Calculate the path matching score for each restaurant based on the degree to which it deviates from the navigation route.

[0026] Based on the estimated arrival time and the preparation time of each restaurant, calculate the time matching score for each restaurant.

[0027] Based on the health matching score, the path matching score, and the time matching score, calculate the comprehensive recommendation score for each restaurant that can be ordered;

[0028] Based on the comprehensive recommendation scores from highest to lowest, the restaurants offering à la carte dishes and their corresponding candidate dishes are arranged into the recommendation list.

[0029] By separately calculating the health matching score of candidate dishes, the route matching score of available restaurants, and the time matching score of available restaurants, the three dimensions of health constraint compliance, route convenience, and food preparation time matching are quantified into comparable scores. Then, the comprehensive recommendation score of each available restaurant is calculated and sorted from high to low. This integrates multiple considerations into a single ranking result, allowing users to directly obtain the best comprehensive recommendation option without having to weigh multiple conflicting factors. This effectively reduces the decision-making burden and attention distraction risk in driving scenarios. At the same time, the comprehensive recommendation score itself integrates information from the three dimensions, so that the top-ranked recommendations have comprehensive advantages in terms of health and safety, travel efficiency, and food picking experience, avoiding the problem of serious mismatch in other dimensions caused by single-dimensional ranking.

[0030] In some possible embodiments, the step of outputting a food pickup reminder message when the vehicle arrives within a preset distance range of the target restaurant includes:

[0031] The current location of the vehicle can be obtained in real time;

[0032] Calculate the real-time distance between the vehicle and the target restaurant based on the current location and the location of the target restaurant;

[0033] When the real-time distance is less than or equal to the preset distance range, the meal pickup reminder information is output via voice and / or display on the central control screen.

[0034] By acquiring the vehicle's current location in real time and calculating the real-time distance to the target restaurant, the system proactively triggers a food pickup reminder when the real-time distance is less than or equal to a preset distance range. This allows users to know they are about to arrive and can prepare to pick up their food without having to monitor the driving progress or repeatedly check the remaining distance on the navigation system. The system also outputs reminder information through voice and central control screen display, balancing driving safety and information accessibility. This creates a seamless connection between the food pickup process and the navigation journey at the destination, further optimizing the smoothness of the overall food pickup process and the user experience.

[0035] In some possible embodiments, the vehicle-side control method further includes: in response to the activation of the vehicle's navigation function, arrival at a preset meal time, or an anomaly in the real-time physiological monitoring data, triggering the step of acquiring the health record information, the real-time physiological monitoring data, and the navigation trip information.

[0036] By automatically triggering subsequent data acquisition steps when the navigation function is activated, when the preset meal time is reached, or when real-time physiological monitoring data shows abnormalities, the entire healthy diet recommendation process can be proactively initiated at the appropriate time without requiring manual intervention from the user. At the same time, the abnormality of real-time physiological monitoring data serves as one of the trigger conditions, ensuring that when the user's physical condition fluctuates, timely intervention and dietary recommendation services can be provided, effectively improving the timeliness and intelligence of health management.

[0037] In some possible embodiments, the step of calculating the comprehensive recommendation score for each available restaurant based on the health matching score, the path matching score, and the time matching score includes:

[0038] Obtain the first weight coefficient corresponding to the health matching score, the second weight coefficient corresponding to the path matching score, and the third weight coefficient corresponding to the time matching score;

[0039] Calculate the first product of the first weight coefficient and the health matching score, the second product of the second weight coefficient and the path matching score, and the third product of the third weight coefficient and the time matching score;

[0040] The sum of the first product, the second product, and the third product is calculated as the comprehensive recommendation score.

[0041] By assigning first, second, and third weighting coefficients to the health matching score, path matching score, and time matching score respectively, the scores of the three dimensions are integrated into a comprehensive recommendation score through weighted summation. This allows the influence of different dimensions on the final ranking result to be balanced in an adjustable manner through the value of the weighting coefficients, avoiding the problem of severe mismatch in a certain dimension caused by simple averaging or a single dimension dominating. At the same time, the weighted summation calculation method ensures the interpretability and computational efficiency of the comprehensive recommendation score, making it easy to complete the ranking calculation in real time during driving.

[0042] In some possible embodiments, when the blood glucose, blood pressure or heart rate values ​​in the real-time physiological monitoring data exceed a preset threshold, the first weight coefficient corresponding to the health matching score is increased and the second weight coefficient corresponding to the path matching score and the third weight coefficient corresponding to the time matching score are decreased.

[0043] When the estimated arrival time is less than the minimum preparation time of the restaurant that offers the meal, the third weight coefficient corresponding to the time matching score is increased and the first weight coefficient corresponding to the health matching score and the second weight coefficient corresponding to the path matching score are decreased.

[0044] When the real-time traffic information in the navigation trip information indicates that the congestion level exceeds a preset congestion threshold, the second weight coefficient corresponding to the path matching score is increased, and the first weight coefficient corresponding to the health matching score and the third weight coefficient corresponding to the time matching score are decreased.

[0045] By dynamically adjusting the priority of each dimension based on real-time changes during the trip, the problem of recommendation mismatch in different scenarios with fixed weights is avoided, and the degree of adaptation of recommendation results to actual travel needs is further improved.

[0046] In some possible embodiments, the vehicle-side control method further includes:

[0047] After the user finishes their meal, obtain their meal feedback information and post-meal physiological monitoring data;

[0048] The dining feedback information and the post-meal physiological monitoring data are uploaded to the cloud server so that the cloud server can update the user's health record information based on the dining feedback information and the post-meal physiological monitoring data.

[0049] After a user finishes a meal, feedback and post-meal physiological monitoring data are collected and uploaded to a cloud server. This allows the cloud server to update the user's health record based on actual post-meal bodily responses. By using real post-meal physiological responses as a data source, health records no longer rely solely on static historical diagnostic records from medical visits. Instead, they continuously incorporate actual post-meal bodily feedback, making subsequent dietary restrictions more closely aligned with the user's actual bodily responses. This avoids the gradual deviation between a fixed health record and the user's actual physical condition, enabling adaptive iterative optimization of dietary recommendations as the user's physical condition changes.

[0050] Secondly, this application also provides a vehicle-side control method for recommending healthy diets during travel, the vehicle-side control device comprising:

[0051] The acquisition module is used to acquire the user's health record information, real-time physiological monitoring data, and vehicle navigation trip information;

[0052] The dietary constraint generation module is used to generate dietary constraints based on the health record information and the real-time physiological monitoring data.

[0053] The candidate information filtering module is used to filter out restaurants that meet the dietary constraints and candidate dishes of the restaurants from the candidate restaurants in the vicinity of the path corresponding to the navigation trip information, based on the dietary constraints and the navigation trip information.

[0054] The recommendation list generation module is used to generate a recommendation list in descending order of recommendation priority based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time. Each set of recommendation information in the recommendation list includes a recommended restaurant and the corresponding recommended dishes.

[0055] The recommendation module is used to send a reservation order to the target restaurant in the target recommendation information in response to the user's confirmation operation on a set of target recommendation information in the recommendation list. The reservation order includes the expected arrival time and the target dishes in the target recommendation information.

[0056] The reminder module is used to output a food pickup reminder message when the vehicle arrives within a preset distance range of the target restaurant.

[0057] Thirdly, this application also provides a vehicle including the aforementioned vehicle-side control device for recommending healthy diets for travel. Attached Figure Description

[0058] Figure 1 This is a flowchart of the vehicle-side control method in the embodiments of this application;

[0059] Figure 2 This is a schematic diagram of the interface for providing recommendation information in an embodiment of this application;

[0060] Figure 3 A schematic diagram of the interface for users to select target recommendation information;

[0061] Figure 4 A schematic diagram of the interface for displaying recommended information for the target;

[0062] Figure 5 A screenshot of the interface after the user confirms the target recommendation information;

[0063] Figure 6 This is an example of an interface used to remind users to pick up their meals.

[0064] Figure 7 This is a structural block diagram of the vehicle-side control device in the embodiments of this application. Detailed Implementation

[0065] Reference Figure 1This application provides a vehicle-side control method for recommending healthy diets during travel, the vehicle-side control method including:

[0066] S101, obtains the user's health record information, real-time physiological monitoring data and vehicle navigation trip information;

[0067] S102, Generate dietary restrictions based on the health record information and the real-time physiological monitoring data;

[0068] S103, Based on the dietary constraints and the navigation itinerary information, select restaurants that meet the dietary constraints and their candidate dishes from the candidate restaurants in the adjacent area of ​​the path corresponding to the navigation itinerary information.

[0069] S104. Based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time, a recommendation list is generated in descending order of recommendation priority. Each set of recommendation information in the recommendation list includes a recommended restaurant and the corresponding recommended dishes.

[0070] S105, in response to the user's confirmation operation on a set of target recommendation information in the recommendation list, a reservation order is sent to the target restaurant in the target recommendation information, the reservation order including the estimated arrival time and the target dishes in the target recommendation information;

[0071] S106, When the vehicle arrives within a preset distance range of the target restaurant, output a food pickup reminder message.

[0072] In step S101, the health record information includes at least a history of chronic diseases, allergies, and medication history. It may also include basic information such as age, gender, height, and weight, as well as historical dietary records and historical physiological data. The chronic disease history is used to determine whether the user suffers from chronic diseases requiring dietary control, such as diabetes, hypertension, and hyperlipidemia. The allergy history is used to identify allergens in specific foods, such as peanuts, seafood, and lactose. The medication history is used to determine whether the user's current medications interact with specific foods. The health record information can be obtained from a cloud server, which stores health records pre-entered by the user or shared with authorized medical institutions. After the vehicle establishes a communication connection with the cloud server through its onboard communication unit, it sends a request carrying the user's identification identifier to the cloud server. The cloud server then queries and returns the corresponding health record information based on the user's identification identifier.

[0073] Real-time physiological monitoring data includes one or more of the following: heart rate, blood pressure, blood glucose, and blood oxygen saturation. It may also include body temperature and respiratory rate. Real-time physiological monitoring data is acquired through vehicle-mounted sensors, which include at least heart rate and blood pressure sensors installed on the steering wheel, heart rate and respiratory sensors installed in the seat back or cushion, optical sensors for non-contact heart rate and blood oxygen monitoring installed in the rearview mirror or A-pillar, and wearable devices worn by the user, such as smartwatches and smart bracelets, that connect to the vehicle via an onboard communication unit. These wearable devices can continuously monitor and synchronize the user's blood glucose, heart rate, blood pressure, and blood oxygen data.

[0074] Navigation trip information includes the origin, destination, waypoints, estimated travel time, and real-time traffic information. This information is obtained through the vehicle's navigation system, which generates a route from the origin to the destination based on the user-defined destination and calculates the estimated arrival time, estimated travel time, and real-time traffic conditions for the current road segment in real time.

[0075] It should be noted that the acquisition, storage, and use of the health record information and real-time physiological monitoring data involved in this application have all been explicitly authorized by the user in advance and comply with the requirements of relevant laws and regulations and personal information protection standards. The user may withdraw authorization at any time, and after the authorization is withdrawn, this application will cease to acquire the aforementioned data.

[0076] When a user uses the travel health and diet recommendation function for the first time, an authorization interface pops up on the vehicle's central control screen. This interface includes at least the following: the data type to be acquired (health record information, real-time physiological monitoring data, navigation trip information), the specific scope of each data type (e.g., health record information includes chronic disease history, allergy history, medication history, etc.; real-time physiological monitoring data includes heart rate, blood pressure, blood sugar, blood oxygen, etc.), a description of the data's purpose (only used to generate dietary constraints and recommend restaurants and dishes that meet health needs), the data storage method (encrypted storage on a cloud server), and the scope of data sharing (health data that can identify a user will not be shared with any third party). The authorization interface also includes two options: agree and refuse. After the user selects agree via touch, the authorization is completed.

[0077] In step S102, firstly, chronic disease history, allergy history, and medication history are extracted from the health record information. The chronic disease history records whether the user suffers from chronic diseases requiring dietary control, such as diabetes, hypertension, hyperlipidemia, and kidney disease; the allergy history records whether the user has specific food allergies, such as peanut allergy, seafood allergy, or lactose intolerance; the medication history records the names and duration of medications the user is currently taking. Different chronic diseases correspond to different prohibited or restricted foods and nutrients. For example, diabetes corresponds to the prohibition of high-sugar and high-glycemic index foods, and restriction of carbohydrate intake; hypertension corresponds to the prohibition of high-sodium foods, and restriction of sodium intake; hyperlipidemia corresponds to the prohibition of high-fat foods, and restriction of fat and cholesterol intake. Allergy history directly determines the list of prohibited foods; for example, peanut allergy prohibits all foods containing peanuts. Medication history shows that some drugs interact with specific foods, for example, while taking warfarin, foods rich in vitamin K should be restricted, and while taking MAO inhibitors, foods containing tyramine should be avoided. Based on the chronic disease history, allergy history, and medication history, the list of prohibited foods and restricted nutrient indicators can be determined. Nutrient restriction indicators include the upper limit for calories per day or per meal, the upper limit for carbohydrates, the upper limit for fat, the lower limit for protein, the upper limit for sodium, and the upper limit for sugar. The upper and lower limits are determined according to the medical nutrition therapy guidelines for different chronic diseases.

[0078] Secondly, real-time physiological parameter values ​​of the user are extracted from real-time physiological monitoring data, including current heart rate, blood pressure, blood glucose, and blood oxygen levels. Each real-time physiological parameter value is compared with its corresponding health threshold to determine whether it is within the normal range, too high, or too low. Based on the comparison results, the user's real-time health tag is determined. Real-time health tags include abnormal blood glucose tags, abnormal blood pressure tags, and abnormal heart rate tags. For example, when blood glucose exceeds the first blood glucose threshold, it is labeled as high blood glucose; when blood glucose is below the second blood glucose threshold, it is labeled as low blood glucose; when blood pressure exceeds the first blood pressure threshold, it is labeled as high blood pressure; when blood pressure is below the second blood pressure threshold, it is labeled as low blood pressure; and when heart rate exceeds the heart rate threshold, it is labeled as tachycardia. Real-time health tags also carry information on the degree of deviation of each parameter; the greater the deviation, the higher the urgency of that health dimension.

[0079] Finally, the list of prohibited ingredients, restricted nutrient indicators, and real-time health labels are integrated to generate dietary constraints. The specific integration method is as follows: the list of prohibited ingredients serves as the first level of hard constraint, directly excluding dishes containing any ingredient from the list; the restricted nutrient indicators serve as the second level of numerical constraint, setting acceptable ranges for each nutrient, requiring the content of each nutrient in candidate dishes to fall within the acceptable range defined by the restricted nutrient indicators; and the real-time health labels serve as the third level of dynamic constraint. When abnormal blood sugar, abnormal blood pressure, or abnormal heart rate labels exist, additional constraints corresponding to these labels are added to the restricted nutrient indicators. For example, when a high blood sugar label exists, the upper limits for carbohydrates and sugar are further tightened; when a high blood pressure label exists, the upper limit for sodium is further tightened. This allows the dietary constraints to be dynamically tightened based on the user's current real-time physical condition, ensuring that recommended dishes meet long-term health restrictions while also being suitable for the user's current physical state.

[0080] In step S103, a preset width is extended outward from the navigation route to define the adjacent area of ​​the path, and restaurants whose geographical locations fall within this area are identified as candidate restaurants. The value of the preset width is determined based on the user-defined detour tolerance or the system default value.

[0081] For each candidate restaurant, obtain its complete menu, with each dish pre-labeled with nutrition information and ingredient composition details.

[0082] Nutrition labels and ingredient composition information can be obtained through at least one of the following methods:

[0083] Method 1: For restaurants that have connected to a third-party nutrition database, the pre-stored nutrition labels and ingredient composition information of each dish in the restaurant can be obtained directly from the cloud server. The nutrition labels include at least one or more of the following: calories, protein, fat, carbohydrates, dietary fiber, sodium content, and sugar content.

[0084] Method Two: For restaurants not connected to third-party nutrition databases, images of their electronic or paper menus are obtained. The names and descriptions of the dishes are then analyzed using text parsing or image recognition to extract these details. Based on these descriptions, and combined with a food composition database storing nutrient content data for various ingredients, the ingredient composition of each dish is identified. Furthermore, using a recipe knowledge base containing common recipes and ingredient ratios for different cuisines and dishes, the nutrient content range is estimated, generating a nutrition label and ingredient composition information for the dish.

[0085] For each candidate dish, its ingredient composition is first compared with the list of prohibited ingredients in the dietary restrictions. If any ingredient from the prohibited ingredients list is included, the candidate dish is eliminated. For candidate dishes that pass the prohibited ingredients check, the nutrient content on their nutrition labels is compared with the restricted nutrient levels in the dietary restrictions. If the content of any nutrient exceeds the upper limit corresponding to the restricted nutrient level, the candidate dish is eliminated. Candidate dishes that pass both the prohibited ingredients check and the restricted nutrient level check are considered to meet the dietary restrictions.

[0086] Candidate restaurants with at least one dish that meets the dietary restrictions are designated as restaurants where dishes can be ordered. All dishes from these restaurants that meet the dietary restrictions are then considered as candidate dishes for that restaurant. Candidate restaurants without any dishes that meet the dietary restrictions are excluded and will not be included in the subsequent recommendation ranking.

[0087] In step S104, firstly, the health matching score of each candidate dish is calculated.

[0088] Each candidate dish carries a nutrition label, which includes information on nutrients such as calories, protein, fat, carbohydrates, dietary fiber, sodium, and sugar content. Dietary restrictions include a list of prohibited ingredients, restricted nutrient standards, and a real-time health label. Each candidate dish's nutrition label is compared item by item with the dietary restrictions: if a candidate dish contains any ingredient from the prohibited ingredients list, it is immediately eliminated and not included in subsequent calculations. For candidate dishes that do not contain prohibited ingredients, the deviation of each nutrient content from the corresponding upper and lower limits of the restricted nutrient standards is calculated. Taking diabetic dietary restrictions as an example, if the restricted nutrient standards limit carbohydrates to 60g, sugars to 15g, and fats to 20g, then the closer the carbohydrate content is to zero and not exceeding 60g, the higher the score; the closer the sugar content is to zero and not exceeding 15g, the higher the score; and the closer the fat content is to zero and not exceeding 20g, the higher the score. The weighted sum of the deviations of each nutrient is the health matching score of the candidate dish. The higher the health matching score, the better the candidate dish meets the user's dietary constraints.

[0089] Among them, the health matching score H of the candidate dishes is determined by the formula:

[0090]

[0091] Where n is the number of nutrient types involved in the calculation. Let be the weight coefficient of the i-th nutrient and =1, Let be the single-match score for the i-th nutrient; (The last part is a series of seemingly unrelated characters and symbols, which cannot be translated accurately.) satisfy:

[0092]

[0093] Let be the content value of the i-th nutrient in the candidate dish. This represents the upper limit value corresponding to the i-th nutrient in the dietary constraints.

[0094] Next, the route matching score for each available restaurant is calculated. The navigation trip information includes the vehicle's navigation route. For each available restaurant, the distance between the restaurant's location and the navigation route is calculated; this distance can be the shortest perpendicular distance from the restaurant's location to the navigation route. The smaller the deviation, the higher the route matching score. A higher route matching score indicates that the available restaurant is more conveniently located, and the user does not need to deviate significantly from their planned route to reach it.

[0095] Next, the time matching score for each available restaurant is calculated. The estimated arrival time is calculated based on the vehicle's current location, navigation route, and real-time traffic conditions. For each available restaurant, the preparation time is obtained; this preparation time is determined by the target restaurant based on the food's cooking time and pre-stored on a cloud server. The estimated arrival time is compared to the preparation time. When the estimated arrival time is greater than or equal to the preparation time, the food will be ready or almost ready when the user arrives at the restaurant, requiring no further waiting, and the time matching score is high. When the estimated arrival time is less than the preparation time, the user will still need to wait for the food to be prepared upon arrival; the longer the waiting time, the lower the matching score. A higher time matching score indicates a better match between the estimated arrival time and the preparation time.

[0096] Next, the overall recommendation score for each available restaurant is calculated. The first weighting coefficient corresponding to the health matching score, the second weighting coefficient corresponding to the path matching score, and the third weighting coefficient corresponding to the time matching score are obtained. The values ​​of these weighting coefficients determine the influence of each dimension on the final ranking result. The overall recommendation score is calculated as follows: the first product of the first weighting coefficient and the health matching score, the second product of the second weighting coefficient and the path matching score, and the third product of the third weighting coefficient and the time matching score are calculated. The sum of these three products is then used as the overall recommendation score.

[0097] Finally, the restaurants and their corresponding candidate dishes are sorted according to their overall recommendation scores from highest to lowest. The restaurant with the highest overall recommendation score and its corresponding candidate dishes are placed at the top of the recommendation list, with the rest arranged in descending order. Each set of recommendations in the list includes a recommended restaurant and its corresponding recommended dishes. The same recommended restaurant can correspond to one or more recommended dishes, and multiple recommended dishes are arranged from highest to lowest according to their health matching score.

[0098] In this embodiment, the first to third weighting coefficients are dynamically adjustable. Specifically, when the blood glucose, blood pressure, or heart rate values ​​in the real-time physiological monitoring data exceed a preset threshold, the first weighting coefficient corresponding to the health matching score is increased, while the second weighting coefficient corresponding to the path matching score and the third weighting coefficient corresponding to the time matching score are decreased.

[0099] When the estimated arrival time is less than the minimum preparation time of the restaurant that offers the meal, the third weight coefficient corresponding to the time matching score is increased and the first weight coefficient corresponding to the health matching score and the second weight coefficient corresponding to the path matching score are decreased.

[0100] When the real-time traffic information in the navigation trip information indicates that the congestion level exceeds a preset congestion threshold, the second weight coefficient corresponding to the path matching score is increased, and the first weight coefficient corresponding to the health matching score and the third weight coefficient corresponding to the time matching score are decreased.

[0101] In step S105, the user selects a set of target recommendations from the recommendation list via the vehicle's central control screen or voice interaction. This set of target recommendations includes the target restaurant and its corresponding dishes. In response to this confirmation, the vehicle sends a reservation order to the cloud server or directly to the merchant terminal of the target restaurant via the vehicle communication unit.

[0102] The reservation order must include at least the estimated arrival time and the desired dish. The estimated arrival time is calculated by the vehicle's navigation system based on the current location, navigation route, and real-time traffic conditions. The desired dish is the specific dish selected by the user from the recommended list. The reservation order may also include the user's pick-up person's information and contact number.

[0103] After receiving a reservation order, the target restaurant will plan its food preparation according to the estimated arrival time, completing the preparation of the target dish before the user's vehicle arrives to avoid long waits upon arrival. If the target dish requires advance preparation and the preparation time is longer than the remaining time before the estimated arrival time, the target restaurant will begin preparation immediately upon receiving the reservation order; if the preparation time is shorter than the remaining time before the estimated arrival time, the target restaurant will begin preparation at the start of the preparation period before the estimated arrival time.

[0104] In step S106, the vehicle obtains its current location in real time through the positioning system and calculates the real-time distance between the current location and the target restaurant location. When this real-time distance is less than or equal to a preset distance threshold, a food pickup reminder is triggered. The preset distance threshold can be configured according to the actual scenario, for example, set to 500 meters or 1 kilometer.

[0105] The food pickup reminder information is output through at least one of the following methods: display on the in-vehicle central control screen and voice broadcast through the in-vehicle speaker. The content of the food pickup reminder information includes at least the name of the target restaurant, the name of the target dish, and pickup instructions. The pickup instructions may include directions to the entrance of the target restaurant, the location of the pickup window, or the parking location.

[0106] After the vehicle outputs the food pickup reminder, it can also automatically switch the navigation route to the parking location of the target restaurant, or display parking information around the target restaurant on the central control screen to assist users in completing the final food pickup and parking operation.

[0107] In this embodiment of the application, the execution of step S101 is premised on the activation of the vehicle's navigation function, the arrival of a preset meal time, or the occurrence of abnormalities in the real-time physiological monitoring data.

[0108] The navigation function is activated as a trigger because it signifies that the user is about to begin or is already on a trip. Obtaining navigation trip information at this time allows for the acquisition of a complete navigation route, estimated arrival time, and real-time traffic conditions at the user's departure point. This enables subsequent restaurant selection and recommendations to be planned along the entire trip route from the beginning, avoiding the need to initiate the recommendation process only after the user has traveled a considerable distance or is about to reach their destination, which would significantly reduce the usability and range of options in the recommendations.

[0109] Reaching the preset meal time is used as a trigger because users' dining needs exhibit clear temporal patterns. The preset meal time is pre-set based on users' daily eating habits; when the system time reaches the preset meal time, it indicates that the user has entered their mealtime window. At this point, automatically triggering the recommendation process proactively provides recommendations before the user realizes they need to eat, preventing them from neglecting their dining needs due to focusing on driving, or from being distracted by searching for a restaurant.

[0110] Anomalies in real-time physiological monitoring data are used as a trigger because when a user's physical condition fluctuates during travel, their dietary needs may change instantly. For example, low blood sugar requires rapid sugar replenishment, high blood pressure necessitates strict control of sodium intake, and abnormal heart rate requires avoiding irritating foods. The automatic recommendation process can then promptly generate appropriate dietary constraints based on the current abnormal signs, ensuring that the recommended dishes reflect the user's current physical condition rather than past health records. This prevents users from responding to abnormal signs with their normal eating habits, thus avoiding health risks.

[0111] In this embodiment of the application, the vehicle-side control method further includes:

[0112] S107, After the user finishes eating, obtain the dining feedback information and post-dinner physiological monitoring data;

[0113] S108, the dining feedback information and the post-dinner physiological monitoring data are uploaded to the cloud server so that the cloud server updates the user's health record information based on the dining feedback information and the post-dinner physiological monitoring data.

[0114] After a user completes their meal, feedback is obtained through the in-vehicle interface. This feedback includes at least the user's satisfaction rating, level of satiety, and subjective physical sensations after the meal, such as bloating, drowsiness, or taste discomfort. Simultaneously, post-meal physiological monitoring data is acquired via in-vehicle sensors or wearable devices. This data includes measurements of heart rate, blood pressure, blood sugar, and blood oxygen levels at a preset time after the meal. This post-meal physiological monitoring data, along with the real-time physiological monitoring data acquired before the meal, forms a complete physiological data chain, reflecting changes in the user's physical condition after consuming the recommended dishes.

[0115] When post-meal physiological monitoring data shows an unfavorable trend and is correlated with discomfort symptoms reported in the meal feedback, the cloud server marks the recommended dish or its category as having low suitability and reduces its priority in calculating health match scores in subsequent recommendations. Conversely, when post-meal physiological monitoring data shows a positive trend and the meal feedback is positive, the cloud server marks the recommended dish or its category as having high suitability and increases its recommendation weight in subsequent recommendations. The continuous accumulation of meal feedback data allows the cloud server to gradually build a personalized dietary response database for each user. Subsequent dietary constraints no longer rely entirely on general medical guidelines but gradually incorporate the user's individual physical responses to different dishes, thus continuously improving recommendation accuracy with increased usage.

[0116] While the user is driving, the vehicle's central control screen displays as follows: Figure 2 The recommended interface shown is divided into a map area and a recommended list area.

[0117] Reference Figure 2 The map area displays the vehicle's current navigation route, the vehicle's current location icon, and the location markers for each recommended restaurant. The location markers for each recommended restaurant are distinguished by different colors or numbers according to their recommendation priority, with the highest priority restaurant's marker highlighted.

[0118] The recommendation list area displays recommendations in a list format. Each set of recommendations corresponds to a recommended restaurant and its recommended dish, arranged in descending order of overall recommendation score. Each set of recommendations displays the restaurant name, the dish name, a summary of the dish's nutrition label, and the overall recommendation score for that set. The nutrition label summary includes at least the calorie count.

[0119] like Figure 3 The user performs a touch selection operation on a set of target recommendations from the recommendation list. For example... Figure 4 The central control screen displays a confirmation interface, which includes detailed information about the target restaurant, the target dish, the estimated arrival time, and the preparation time from the recommended menu. The confirmation interface also includes a confirm order button and a return button. After the user touches the confirm order button, the reservation order is sent to the target restaurant. Figure 5 Once the confirmation screen is closed, the central control screen returns to the navigation screen, which displays the location marker of the target restaurant and a countdown timer for picking up the food.

[0120] When the vehicle arrives within a preset distance of the target restaurant, a pop-up reminder window appears on the central control screen, and the vehicle's speaker simultaneously broadcasts the reminder message. Figure 6 The order pickup reminder pop-up window includes the name of the target restaurant and the pickup code.

[0121] After the user completes the food pickup according to the pickup reminder, the central control screen can also display a dining feedback interface, which includes options for satisfaction rating and physical sensation. The user can complete the input of dining feedback information through touch.

[0122] In the aforementioned vehicle-side control method, the recommendation process is automatically initiated at appropriate times based on multiple triggering conditions, eliminating the need for manual user intervention. This allows for timely intervention when the user has a dining need or experiences abnormal physical conditions. After acquiring health record information and real-time physiological monitoring data, dietary constraints are generated, incorporating both the user's long-term health restrictions and current physical condition. This ensures that the subsequently selected dishes meet both static medical history and dynamic physical condition requirements. Combined with navigation route information, restaurants in the vicinity of the route are selected for ordering, ensuring the recommendations do not deviate from the driving route. Furthermore, the recommendation list is generated by weighting and sorting based on three dimensions: health matching, route matching, and time matching. This frees the user from multiple considerations, allowing them to simply select from the sorted list. Upon user confirmation, a reservation order containing the estimated arrival time is sent to the target restaurant, enabling the restaurant to prepare the meal in advance. A pickup reminder is issued when the vehicle reaches a preset distance, seamlessly integrating the pickup process with the navigation route. Finally, continuous updates to the health record are achieved through meal feedback and the uploading of post-meal physiological data, gradually improving the accuracy of subsequent recommendations with increased usage. The entire method integrates multiple factors such as the user's long-term health contraindications, real-time physical condition, driving route, meal preparation time, and meal feedback into a complete decision chain, which is automatically triggered at the start of the trip and forms a closed loop through data feedback after the meal is picked up.

[0123] Reference Figure 7 This application also provides a vehicle-side control method for recommending healthy diets for travel, wherein the vehicle-side control device includes:

[0124] The acquisition module 201 is used to acquire the user's health record information, real-time physiological monitoring data, and vehicle navigation trip information;

[0125] The dietary restriction condition generation module 202 is used to generate dietary restriction conditions based on the health record information and the real-time physiological monitoring data.

[0126] The candidate information filtering module 203 is used to filter out restaurants that meet the dietary constraints and candidate dishes of the restaurants from the candidate restaurants in the adjacent area of ​​the path corresponding to the navigation route information, based on the dietary constraints and the navigation route information.

[0127] The recommendation list generation module 204 is used to generate a recommendation list in descending order of recommendation priority based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time. Each set of recommendation information in the recommendation list includes a recommended restaurant and the corresponding recommended dishes.

[0128] Recommendation module 205 is used to send a reservation order to the target restaurant in the target recommendation information in response to the user's confirmation operation on a set of target recommendation information in the recommendation list. The reservation order includes the expected arrival time and the target dishes in the target recommendation information.

[0129] The reminder module 206 is used to output a food pickup reminder message when the vehicle arrives within a preset distance range of the target restaurant.

[0130] Thirdly, this application also provides a vehicle including the aforementioned vehicle-side control device for recommending healthy diets for travel.

[0131] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Those skilled in the art can understand that implementing all or part of the processes of the above embodiments and making equivalent changes according to the claims of this application still fall within the scope of this application.

Claims

1. A vehicle-side control method for recommending healthy diets during travel, characterized in that, The vehicle-side control method includes: Obtain user health record information, real-time physiological monitoring data, and vehicle navigation trip information; Dietary restrictions are generated based on the health record information and the real-time physiological monitoring data. Based on the dietary constraints and the navigation route information, select restaurants that meet the dietary constraints and their candidate dishes from the candidate restaurants in the vicinity of the path corresponding to the navigation route information. Based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time, a recommendation list is generated in descending order of recommendation priority. Each set of recommendation information in the recommendation list includes a recommended restaurant and the corresponding recommended dishes. In response to the user's confirmation of a set of target recommendation information in the recommendation list, a reservation order is sent to the target restaurant in the target recommendation information, the reservation order including the expected arrival time and the target dishes in the target recommendation information; When the vehicle arrives within a preset distance of the target restaurant, a food pickup reminder message is output.

2. The vehicle-side control method for recommending healthy diets for travel according to claim 1, characterized in that, The steps for generating dietary restrictions based on the health record information and the real-time physiological monitoring data include: Based on the chronic disease history, allergy history and medication history in the health record information, a list of prohibited food ingredients and restricted nutrient indicators are determined. Based on the real-time physiological monitoring data, the user's real-time health tag is determined; The dietary restrictions are generated based on the list of prohibited ingredients, the restricted nutrient indicators, and the real-time health label.

3. The vehicle-side control method for recommending healthy diets for travel according to claim 1, characterized in that, Based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time, a recommendation list is generated in descending order of recommendation priority. The steps for generating a recommendation list that includes a recommended restaurant and its corresponding recommended dishes for each set of recommendations include: Based on the degree of matching between the nutrition labels of the candidate dishes and the dietary constraints, calculate the health matching score of each candidate dish; Calculate the path matching score for each restaurant based on the degree to which it deviates from the navigation route. Based on the estimated arrival time and the preparation time of each restaurant, calculate the time matching score for each restaurant. Based on the health matching score, the path matching score, and the time matching score, calculate the comprehensive recommendation score for each restaurant that can be ordered; Based on the comprehensive recommendation scores from highest to lowest, the restaurants offering à la carte dishes and their corresponding candidate dishes are arranged into the recommendation list.

4. The vehicle-side control method according to claim 1, characterized in that, The step of outputting a food pickup reminder message when the vehicle arrives within a preset distance range of the target restaurant includes: The current location of the vehicle can be obtained in real time; Calculate the real-time distance between the vehicle and the target restaurant based on the current location and the location of the target restaurant; When the real-time distance is less than or equal to the preset distance range, the meal pickup reminder information is output via voice and / or display on the central control screen.

5. The vehicle-side control method according to claim 1, characterized in that, The vehicle-side control method further includes: in response to the activation of the vehicle's navigation function, arrival at a preset meal time, or an abnormality in the real-time physiological monitoring data, triggering the steps of acquiring the health record information, the real-time physiological monitoring data, and the navigation trip information.

6. The vehicle-side control method according to claim 3, characterized in that, The steps for calculating the comprehensive recommendation score for each available restaurant based on the health matching score, the path matching score, and the time matching score include: Obtain the first weight coefficient corresponding to the health matching score, the second weight coefficient corresponding to the path matching score, and the third weight coefficient corresponding to the time matching score; Calculate the first product of the first weight coefficient and the health matching score, the second product of the second weight coefficient and the path matching score, and the third product of the third weight coefficient and the time matching score; The sum of the first product, the second product, and the third product is calculated as the comprehensive recommendation score.

7. The vehicle-side control method according to claim 6, characterized in that, When the blood glucose, blood pressure, or heart rate values ​​in the real-time physiological monitoring data exceed a preset threshold, the first weight coefficient corresponding to the health matching score is increased, and the second weight coefficient corresponding to the path matching score and the third weight coefficient corresponding to the time matching score are decreased. When the estimated arrival time is less than the minimum preparation time of the restaurant that offers the meal, the third weight coefficient corresponding to the time matching score is increased and the first weight coefficient corresponding to the health matching score and the second weight coefficient corresponding to the path matching score are decreased. When the real-time traffic indication congestion level in the navigation trip information exceeds a preset congestion threshold, the second weight coefficient corresponding to the path matching score is increased, and the first weight coefficient corresponding to the health matching score and the third weight coefficient corresponding to the time matching score are decreased.

8. The vehicle-side control method according to claim 1, characterized in that, The vehicle-side control method further includes: After the user finishes their meal, obtain their meal feedback information and post-meal physiological monitoring data; The dining feedback information and the post-meal physiological monitoring data are uploaded to the cloud server so that the cloud server can update the user's health record information based on the dining feedback information and the post-meal physiological monitoring data.

9. A vehicle-side control device for recommending healthy diets during travel, characterized in that, The vehicle-end control device includes: The acquisition module is used to acquire the user's health record information, real-time physiological monitoring data, and vehicle navigation trip information; The dietary constraint generation module is used to generate dietary constraints based on the health record information and the real-time physiological monitoring data. The candidate information filtering module is used to filter out restaurants that meet the dietary constraints and candidate dishes of the restaurants from the candidate restaurants in the vicinity of the path corresponding to the navigation trip information, based on the dietary constraints and the navigation trip information. The recommendation list generation module is used to generate a recommendation list in descending order of recommendation priority based on the degree of matching between the nutritional labels of the candidate dishes and the dietary constraints, the degree of deviation of the restaurants available for ordering from the navigation route, and the estimated arrival time. Each set of recommendation information in the recommendation list includes a recommended restaurant and the corresponding recommended dishes. The recommendation module is used to send a reservation order to the target restaurant in the target recommendation information in response to the user's confirmation operation on a set of target recommendation information in the recommendation list. The reservation order includes the expected arrival time and the target dishes in the target recommendation information. The reminder module is used to output a food pickup reminder message when the vehicle arrives within a preset distance range of the target restaurant.

10. A vehicle, characterized in that, Includes the vehicle-side control device for recommending healthy diets for travel as described in claim 9.

Images