Restaurant data processing method and device and computer readable storage medium

Abstract

The embodiment of the invention relates to the technical field of data analysis, and discloses a restaurant data processing method and device and a computer readable storage medium, and the method comprises the steps: obtaining the driving behavior data and diet requirements of a user; calculating the driving behavior data through a preset weighted summation algorithm to obtain the proportion of each emotion of the user; according to the sequence of the proportions, arranging the emotions to obtain an emotion arrangement sequence, and determining a taste arrangement sequence based on the emotion arrangement sequence; wherein each emotion is in one-to-one correspondence with each taste; and determining a restaurant list recommended to the user according to the taste arrangement sequence and the diet requirements. By applying the technical scheme of the invention, restaurants can be recommended from the two dimensions of taste and diet requirements, and the accuracy of restaurant recommendation is greatly improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of data analysis, and specifically to a restaurant data processing method, device and computer-readable storage medium. Background Art

[0002] Currently, in the on-board systems of new energy vehicles, intelligent technology is gradually penetrating into all aspects of life, providing drivers with more convenient and personalized services.

[0003] Users usually face the problem of restaurant selection while driving. Generally speaking, users will randomly select a restaurant based on the distance. However, this method may easily lead to the situation that the restaurant does not match the user's taste, resulting in low accuracy of existing technologies when recommending restaurants to users. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention provide a food recommendation control method, device and computer-readable storage medium, which are used to solve the problem of low accuracy in the prior art when recommending restaurants to users.

[0005] According to one aspect of an embodiment of the present invention, a restaurant data processing method is provided, the method comprising:

[0006] Obtain users’ driving behavior data and dietary requirements;

[0007] The driving behavior data is calculated by a preset weighted sum algorithm to obtain the proportion of each emotion of the user;

[0008] Arrange each emotion according to the order of proportion to obtain the emotional arrangement order, and determine the taste arrangement order based on the emotional arrangement order; wherein each emotion corresponds to each taste one by one;

[0009] Based on taste ranking and dietary requirements, a list of restaurants recommended to the user is determined.

[0010] According to another aspect of an embodiment of the present invention, there is provided a restaurant data processing device, comprising:

[0011] A data acquisition module, used to obtain the user's driving behavior data and dietary requirements;

[0012] The emotion analysis module is used to calculate the driving behavior data by a preset weighted sum algorithm to obtain the proportion of each emotion of the user;

[0013] The taste analysis module is used to arrange each emotion according to the order of proportion, obtain the emotion arrangement order, and determine the taste arrangement order based on the emotion arrangement order; wherein each emotion corresponds to each taste one by one;

[0014] The data recommendation module is used to determine the list of restaurants recommended to users based on taste ranking and dietary requirements.

[0015] In an optional manner, the driving behavior data includes: in-vehicle user information data, in-vehicle music analysis data, schedule analysis data, driving route analysis data, and driving analysis data;

[0016] The obtaining of the user's driving behavior data includes:

[0017] Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car;

[0018] Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data;

[0019] Collecting the user's schedule data and obtaining schedule analysis data;

[0020] Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data;

[0021] Collect users' driving operation data and obtain driving analysis data.

[0022] In an optional manner, the driving behavior data is calculated using a preset weighted sum algorithm to obtain the proportion of each emotion of the user, including:

[0023] Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results;

[0024] Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result;

[0025] Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result;

[0026] Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results;

[0027] Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result;

[0028] Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion;

[0029] Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

[0030] In an optional manner, each emotion corresponds to each taste, including:

[0031] Identify each emotion;

[0032] If the mood is low, the taste is determined to be spicy;

[0033] If the emotion is irritability, the taste is determined to be sweet;

[0034] If the emotion is anxiety, the taste is determined to be sour;

[0035] If the mood is lethargy, the taste is determined to be salty;

[0036] If the emotion is tiredness, the taste will definitely be bitter.

[0037] In an optional manner, determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes:

[0038] Get the candidate restaurants corresponding to each taste;

[0039] Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening;

[0040] Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

[0041] In an optional manner, after determining the restaurant list recommended to the user, the method further includes:

[0042] Display the restaurant list on the car computer;

[0043] Responding to the user's operation on the restaurant list, determining the selected restaurant;

[0044] A navigation route is generated according to the location of the selected restaurant and the current sentiment ratio of the user, and navigation to the selected restaurant is performed based on the navigation route.

[0045] In an optional manner, generating a navigation route according to the location of the selected restaurant and the current emotion ratio of the user includes:

[0046] Determine a number of routes to be selected according to the location of the selected restaurant;

[0047] Determine the emotion that accounts for the largest proportion of user emotions;

[0048] If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route;

[0049] Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

[0050] According to another aspect of an embodiment of the present invention, there is provided a restaurant data processing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0051] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the following operations:

[0052] Obtain users’ driving behavior data and dietary requirements;

[0053] The driving behavior data is calculated by a preset weighted sum algorithm to obtain the proportion of each emotion of the user;

[0054] Arrange each emotion according to the order of proportion to obtain the emotional arrangement order, and determine the taste arrangement order based on the emotional arrangement order; wherein each emotion corresponds to each taste one by one;

[0055] Based on taste ranking and dietary requirements, a list of restaurants recommended to the user is determined.

[0056] In an optional manner, the driving behavior data includes: in-vehicle user information data, in-vehicle music analysis data, schedule analysis data, driving route analysis data, and driving analysis data;

[0057] The obtaining of the user's driving behavior data includes:

[0058] Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car;

[0059] Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data;

[0060] Collecting the user's schedule data and obtaining schedule analysis data;

[0061] Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data;

[0062] Collect users' driving operation data and obtain driving analysis data.

[0063] In an optional manner, the driving behavior data is calculated using a preset weighted sum algorithm to obtain the proportion of each emotion of the user, including:

[0064] Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results;

[0065] Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result;

[0066] Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result;

[0067] Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results;

[0068] Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result;

[0069] Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion;

[0070] Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

[0071] In an optional manner, each emotion corresponds to each taste, including:

[0072] Identify each emotion;

[0073] If the mood is low, the taste is determined to be spicy;

[0074] If the emotion is irritability, the taste is determined to be sweet;

[0075] If the emotion is anxiety, the taste is determined to be sour;

[0076] If the mood is lethargy, the taste is determined to be salty;

[0077] If the emotion is tiredness, the taste will definitely be bitter.

[0078] In an optional manner, determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes:

[0079] Get the candidate restaurants corresponding to each taste;

[0080] Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening;

[0081] Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

[0082] In an optional manner, after determining the restaurant list recommended to the user, the method further includes:

[0083] Display the restaurant list on the car computer;

[0084] Responding to the user's operation on the restaurant list, determining the selected restaurant;

[0085] A navigation route is generated according to the location of the selected restaurant and the current sentiment ratio of the user, and navigation to the selected restaurant is performed based on the navigation route.

[0086] In an optional manner, generating a navigation route according to the location of the selected restaurant and the current emotion ratio of the user includes:

[0087] Determine a number of routes to be selected according to the location of the selected restaurant;

[0088] Determine the emotion that accounts for the largest proportion of user emotions;

[0089] If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route;

[0090] Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

[0091] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a restaurant data processing device / apparatus to perform the following operations:

[0092] Obtain users’ driving behavior data and dietary requirements;

[0093] The driving behavior data is calculated by a preset weighted sum algorithm to obtain the proportion of each emotion of the user;

[0094] Arrange each emotion according to the order of proportion to obtain the emotional arrangement order, and determine the taste arrangement order based on the emotional arrangement order; wherein each emotion corresponds to each taste one by one;

[0095] Based on taste ranking and dietary requirements, a list of restaurants recommended to the user is determined.

[0096] In an optional manner, the driving behavior data includes: in-vehicle user information data, in-vehicle music analysis data, schedule analysis data, driving route analysis data, and driving analysis data;

[0097] The obtaining of the user's driving behavior data includes:

[0098] Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car;

[0099] Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data;

[0100] Collecting the user's schedule data and obtaining schedule analysis data;

[0101] Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data;

[0102] Collect users' driving operation data and obtain driving analysis data.

[0103] In an optional manner, the driving behavior data is calculated using a preset weighted sum algorithm to obtain the proportion of each emotion of the user, including:

[0104] Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results;

[0105] Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result;

[0106] Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result;

[0107] Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results;

[0108] Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result;

[0109] Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion;

[0110] Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

[0111] In an optional manner, each emotion corresponds to each taste, including:

[0112] Identify each emotion;

[0113] If the mood is low, the taste is determined to be spicy;

[0114] If the emotion is irritability, the taste is determined to be sweet;

[0115] If the emotion is anxiety, the taste is determined to be sour;

[0116] If the mood is lethargy, the taste is determined to be salty;

[0117] If the emotion is tiredness, the taste will definitely be bitter.

[0118] In an optional manner, determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes:

[0119] Get the candidate restaurants corresponding to each taste;

[0120] Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening;

[0121] Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

[0122] In an optional manner, after determining the restaurant list recommended to the user, the method further includes:

[0123] Display the restaurant list on the car computer;

[0124] Responding to the user's operation on the restaurant list, determining the selected restaurant;

[0125] A navigation route is generated according to the location of the selected restaurant and the current sentiment ratio of the user, and navigation to the selected restaurant is performed based on the navigation route.

[0126] In an optional manner, generating a navigation route according to the location of the selected restaurant and the current emotion ratio of the user includes:

[0127] Determine a number of routes to be selected according to the location of the selected restaurant;

[0128] Determine the emotion that accounts for the largest proportion of user emotions;

[0129] If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route;

[0130] Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

[0131] The embodiment of the present invention performs emotion analysis on the user's driving behavior, taking into account that the same driving behavior corresponds to multiple emotions, and analyzes the proportion of each emotion through multiple driving behaviors, thereby improving the accuracy of emotion analysis. Taste is acquired by analyzing emotions, and based on the user's dietary requirements, restaurants can be recommended from two dimensions: taste and dietary requirements, greatly improving the accuracy of restaurant recommendations. The present invention arranges the proportion of each taste by the order of taste, taking into account the occurrence of all tastes, and can reduce the risk of incorrect restaurant recommendations due to taste judgment errors, further ensuring the accuracy of restaurant recommendations.

[0132] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0134] Figure 1 A schematic diagram showing a flow chart of a first embodiment of a restaurant data processing method provided by the present invention;

[0135] Figure 2 A schematic diagram showing a flow chart of a second embodiment of the restaurant data processing method provided by the present invention;

[0136] Figure 3 A schematic structural diagram of a first embodiment of a restaurant data processing device provided by the present invention is shown;

[0137] Figure 4 A schematic diagram showing the structure of an embodiment of a restaurant data processing device provided by the present invention;

[0138] Figure 5 A schematic diagram showing an embodiment of emotion recognition provided by the present invention;

[0139] Figure 6 A schematic diagram showing an embodiment of matching emotions and tastes provided by the present invention;

[0140] Figure 7 A schematic diagram showing a flow chart of a second embodiment of the restaurant data processing method provided by the present invention;

[0141] Figure 8 A flowchart of a third embodiment of the restaurant data processing method provided by the present invention is shown. DETAILED DESCRIPTION

[0142] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0143] Figure 1 FIG. 1 is a flow chart showing a first embodiment of a restaurant data processing method of the present invention, wherein the method is executed by a restaurant data processing device. Figure 1 As shown, the method comprises the following steps:

[0144] Step 110: Obtain the user's driving behavior data and dietary requirements.

[0145] The driving behavior data includes: in-car user information data, in-car music analysis data, schedule analysis data, driving route analysis data and driving analysis data;

[0146] The obtaining of the user's driving behavior data includes:

[0147] Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car;

[0148] Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data;

[0149] Collecting the user's schedule data and obtaining schedule analysis data;

[0150] Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data;

[0151] Collect users' driving operation data and obtain driving analysis data.

[0152] In a specific embodiment, vehicle-mounted sensors (such as heart rate monitoring, voice analysis, etc.) are used to collect heart rate data and voice data, and facial expression recognition technology (needs to be used in conjunction with the in-vehicle camera) is used to collect expression data to capture the emotional changes of the driver and passengers; based on the user's driving habits (collecting driving operation data), daily notes, and travel routes (collecting driving time data, vehicle condition data, travel location data, and driving distance data), etc., the current emotional state is accurately judged.

[0153] Step 120: Calculate the driving behavior data using a preset weighted sum algorithm to obtain the proportion of each emotion of the user.

[0154] The driving behavior data is calculated by a preset weighted summation algorithm to obtain the proportion of each emotion of the user, including:

[0155] Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results;

[0156] Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result;

[0157] Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result;

[0158] Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results;

[0159] Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result;

[0160] Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion;

[0161] Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

[0162] In a specific embodiment, the weight corresponding to the in-vehicle user information data is 20, the weight corresponding to the in-vehicle music analysis data is 10, the weight corresponding to the schedule analysis data is 40, the weight corresponding to the driving route analysis data is 20, and the weight corresponding to the driving analysis data is 10.

[0163] In a specific embodiment, the weighted summation formula is specifically:

[0164] Y=20a+10b+40c+20d+10e

[0165] Where Y is the score corresponding to emotion, a is the user index, b is the music index, c is the arrangement index, d is the route index and e is the operation index.

[0166] In a specific embodiment, in order to better explain the analysis process of emotion types, it is provided Figure 5 Description is given; wherein the heart rate detection device can be installed on a steering wheel or a watch. Figure 5 This is an implementation example of a correlation table between user behavior information and emotions. This correlation table maintains a set of data correspondence tables and is continuously updated and iterated.

[0167] In a specific embodiment, the calculation formula of the proportion is specifically:

[0168] Z=X÷(A+B+C+D+E+F)×100%

[0169] In the formula, Z is the proportion of the selected emotion, X is the corresponding score of the selected emotion, A is the corresponding score of depression, B is the corresponding score of irritability, C is the corresponding score of anxiety, D is the corresponding score of mental depression, and E is the corresponding score of fatigue.

[0170] It should be noted that a series of information is collected in the car and the current user's emotional state is summarized and judged. All information collection is designed into the user's privacy agreement and requires the user's consent before it can be carried out:

[0171] For in-car user information data, the user’s consent to collect in-car voice data, video data, etc. is required; for in-car music, the user’s consent to collect music information data is required; for schedule notepad, the user’s consent to obtain schedule information data is required; for driving route, the user’s consent to obtain driving route information data is required; for driving habits, the user’s consent to collect driving habit information data is required.

[0172] Step 130: Arrange each emotion according to the order of proportion to obtain the emotion arrangement order, and determine the taste arrangement order based on the emotion arrangement order; wherein each emotion corresponds to each taste one by one.

[0173] Wherein, each emotion corresponds to each taste, including:

[0174] Identify each emotion;

[0175] If the mood is low, the taste is determined to be spicy;

[0176] If the emotion is irritability, the taste is determined to be sweet;

[0177] If the emotion is anxiety, the taste is determined to be sour;

[0178] If the mood is lethargy, the taste is determined to be salty;

[0179] If the emotion is tiredness, the taste will definitely be bitter.

[0180] In a specific embodiment, see Figure 6 , an example of matching emotions and tastes: according to the collected user information, the total score of the user's emotional instability, anxiety, and restlessness is 60 points, and the total score of depression, mental depression, and lack of energy is 20 points. Therefore, it is calculated that the user's taste needs at this time are 60% sour, 20% spicy, and 20% salty.

[0181] In a specific embodiment, each taste sense corresponds to multiple cuisines:

[0182] If you feel depressed or frustrated, you can produce endorphins by eating spicy food, such as Hunan cuisine, Sichuan cuisine, Malatang, grilled fish and beef hot pot.

[0183] When you are upset and want to lose your temper, you can eat some sweets to make your body produce dopamine, such as: Shanghai cuisine, Jiangsu and Zhejiang cuisine, dessert shops, cakes, sweet and sour fish, ice cream, honey barbecued pork and milk tea;

[0184] If you feel anxious, uneasy and excited at the same time, you can eat some sour food to calm your mind and concentrate better, such as Thai food, Japanese food, Northeastern pickled cabbage, pickled cabbage stewed with vermicelli and lemon tea;

[0185] When you are depressed, eat some salty food, which will make you more appetizing, such as Shandong cuisine, Cantonese cuisine, Northwest cuisine, Northeast cuisine, pickles, stewed food, seafood, salty porridge and noodles;

[0186] When you are emotionally exhausted and full of resentment, you can eat some bitter foods to guide the fire downward and clear the heart fire, such as: coffee, tea, dark chocolate, matcha and bitter melon.

[0187] When you are in a good mood, recommend favorite restaurants, newly opened restaurants, and restaurants you usually like to eat.

[0188] It should be noted that based on the captured emotional data, the relationship between emotions and taste is used to derive the taste needs of users under the emotions. Deep learning algorithms are used to analyze the types of food, taste, and environment that users may prefer, so as to recommend restaurant options that can relieve stress, boost spirits, or enhance family harmony.

[0189] Step 140: Determine a list of restaurants recommended to the user based on the taste ranking order and dietary requirements.

[0190] The step of determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes:

[0191] Get the candidate restaurants corresponding to each taste;

[0192] Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening;

[0193] Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

[0194] It should be noted that based on dietary requirements, users can set personal taste preferences, food allergy information, and emotional coping strategies for specific situations to further refine the accuracy of emotional taste matching.

[0195] In a specific embodiment, as the mood fluctuates during the trip, the intelligent recommendation will update the recommendation list in real time to ensure that the recommended restaurants always match the user's current mood and provide the most considerate service.

[0196] It is understandable that users share their experiences after meals, including changes in emotions before and after meals, food satisfaction, etc., forming a positive cycle and continuously optimizing the recommendation algorithm. At the same time, the present invention strictly abides by the privacy policy, ensures that all emotional data collection and processing are explicitly agreed by users, and takes encryption measures to ensure information security.

[0197] In a specific embodiment, to better illustrate the restaurant recommendation control process of the present application, see Figure 7, wherein the dietary requirements include: dish requirements, score requirements, environment requirements, location requirements, taste preferences and family preferences; dish requirements can be: non-dietary dishes entered in advance by the user, score requirements can be: higher than the score entered in advance by the user, environment requirements can be: non-fly restaurants or elegant environments, location requirements can be: specified locations or specified ranges, taste preferences can be: taste types entered in advance by the user (spicy, numbing), and family preferences can be: baby seats or private rooms. If the restaurant data list obtained after filtering through the user's personalized preference settings (i.e., the dietary requirements described in the present invention) does not meet the recommended list data quantity, the missing data part bypasses the user's personalized preference settings to filter out the restaurant data, which is added to the recommended list and noted.

[0198] Beneficial Effects

[0199] A navigation route is generated based on the location of the selected restaurant and the current proportion of the user's emotions, and navigation to the selected restaurant is performed based on the navigation route. It is considered that the same driving behavior corresponds to multiple emotions, and the proportion of each emotion is analyzed through multiple driving behaviors, thereby improving the accuracy of emotion analysis. Taste is acquired by analyzing emotions, and based on the user's dietary requirements, restaurants can be recommended from two dimensions: taste and dietary requirements, which greatly improves the accuracy of restaurant recommendations. The present invention arranges the proportion of each taste by the order of taste arrangement, taking into account the occurrence of all tastes, and can reduce the risk of incorrect restaurant recommendations due to taste judgment errors, further ensuring the accuracy of restaurant recommendations.

[0200] Figure 2 FIG. 2 is a flow chart showing another embodiment of the restaurant data processing method of the present invention, which is executed by a restaurant data processing device. Figure 2 As shown, the method comprises the following steps:

[0201] Step 210: Display the restaurant list on the vehicle computer.

[0202] Step 220: In response to the user's operation on the restaurant list, determine the selected restaurant.

[0203] Step 230: Generate a navigation route based on the location of the selected restaurant and the current sentiment ratio of the user, and navigate to the selected restaurant based on the navigation route.

[0204] The generating of the navigation route according to the location of the selected restaurant and the current emotion proportion of the user includes:

[0205] Determine a number of routes to be selected according to the location of the selected restaurant;

[0206] Determine the emotion that accounts for the largest proportion of user emotions;

[0207] If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route;

[0208] Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

[0209] In a specific embodiment, to better illustrate the restaurant recommendation control process of the present application, see Figure 8 It should be noted that Figure 8 Combining the application scenarios within the car and intelligently interacting with users, it realizes intelligent restaurant recommendations and improves the accuracy of restaurant recommendations based on emotion recognition.

[0210] Beneficial Effects

[0211] The technical solution of selecting a navigation route with the least impact on the user's current mood can improve the accuracy of navigation route recommendations and help improve the safety of users navigating to restaurants. Through accurate emotion recognition and personalized recommendations, it ensures that each user can get the most suitable dining recommendations for themselves, while relieving the emotional pressure of users when going to restaurants.

[0212] Figure 3 FIG. 2 is a schematic diagram showing the structure of an embodiment of a restaurant data processing device of the present invention. Figure 3 As shown, the device 300 includes: a data acquisition module 310, a sentiment analysis module 320, a taste analysis module 330 and a data recommendation module 340;

[0213] A data acquisition module, used to obtain the user's driving behavior data and dietary requirements;

[0214] The emotion analysis module is used to calculate the driving behavior data by a preset weighted sum algorithm to obtain the proportion of each emotion of the user;

[0215] The taste analysis module is used to arrange each emotion according to the order of proportion, obtain the emotion arrangement order, and determine the taste arrangement order based on the emotion arrangement order; wherein each emotion corresponds to each taste one by one;

[0216] The data recommendation module is used to determine the list of restaurants recommended to users based on taste ranking and dietary requirements.

[0217] In an optional manner, the driving behavior data includes: in-vehicle user information data, in-vehicle music analysis data, schedule analysis data, driving route analysis data, and driving analysis data;

[0218] The obtaining of the user's driving behavior data includes:

[0219] Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car;

[0220] Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data;

[0221] Collecting the user's schedule data and obtaining schedule analysis data;

[0222] Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data;

[0223] Collect users' driving operation data and obtain driving analysis data.

[0224] In an optional manner, the driving behavior data is calculated using a preset weighted sum algorithm to obtain the proportion of each emotion of the user, including:

[0225] Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results;

[0226] Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result;

[0227] Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result;

[0228] Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results;

[0229] Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result;

[0230] Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion;

[0231] Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

[0232] In an optional manner, each emotion corresponds to each taste, including:

[0233] Identify each emotion;

[0234] If the mood is low, the taste is determined to be spicy;

[0235] If the emotion is irritability, the taste is determined to be sweet;

[0236] If the emotion is anxiety, the taste is determined to be sour;

[0237] If the mood is lethargy, the taste is determined to be salty;

[0238] If the emotion is tiredness, the taste will definitely be bitter.

[0239] In an optional manner, determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes:

[0240] Get the candidate restaurants corresponding to each taste;

[0241] Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening;

[0242] Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

[0243] In an optional manner, after determining the restaurant list recommended to the user, the method further includes:

[0244] Display the restaurant list on the car computer;

[0245] Responding to the user's operation on the restaurant list, determining the selected restaurant;

[0246] A navigation route is generated according to the location of the selected restaurant and the current sentiment ratio of the user, and navigation to the selected restaurant is performed based on the navigation route.

[0247] In an optional manner, generating a navigation route according to the location of the selected restaurant and the current emotion ratio of the user includes:

[0248] Determine a number of routes to be selected according to the location of the selected restaurant;

[0249] Determine the emotion that accounts for the largest proportion of user emotions;

[0250] If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route;

[0251] Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

[0252] Beneficial Effects

[0253] The embodiment of the present invention performs emotion analysis on the user's driving behavior, taking into account that the same driving behavior corresponds to multiple emotions, and analyzes the proportion of each emotion through multiple driving behaviors, thereby improving the accuracy of emotion analysis. Taste is acquired by analyzing emotions, and based on the user's dietary requirements, restaurants can be recommended from two dimensions: taste and dietary requirements, greatly improving the accuracy of restaurant recommendations. The present invention arranges the proportion of each taste by the order of taste, taking into account the occurrence of all tastes, and can reduce the risk of incorrect restaurant recommendations due to taste judgment errors, further ensuring the accuracy of restaurant recommendations.

[0254] Figure 4 A schematic diagram of the structure of an embodiment of a restaurant data processing device of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the restaurant data processing device.

[0255] like Figure 4 As shown, the restaurant data processing device may include: a processor (processor) 402, a communication interface (Communications Interface) 404, a memory (memory) 406, and a communication bus 408.

[0256] The processor 402, the communication interface 404, and the memory 406 communicate with each other via the communication bus 408. The communication interface 404 is used to communicate with other devices such as a client or other server network elements. The processor 402 is used to execute the program 410, which can specifically execute the relevant steps in the above-mentioned restaurant data processing method embodiment.

[0257] Specifically, the program 410 may include program code including computer executable instructions.

[0258] The processor 402 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention. The one or more processors included in the restaurant data processing device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0259] The memory 406 is used to store the program 410. The memory 406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0260] Program 410 can be specifically called by processor 402 to enable restaurant data processing device to perform the following operations:

[0261] Obtain users’ driving behavior data and dietary requirements;

[0262] The driving behavior data is calculated by a preset weighted sum algorithm to obtain the proportion of each emotion of the user;

[0263] Arrange each emotion according to the order of proportion to obtain the emotional arrangement order, and determine the taste arrangement order based on the emotional arrangement order; wherein each emotion corresponds to each taste one by one;

[0264] Based on taste ranking and dietary requirements, a list of restaurants recommended to the user is determined.

[0265] In an optional manner, the driving behavior data includes: in-vehicle user information data, in-vehicle music analysis data, schedule analysis data, driving route analysis data, and driving analysis data;

[0266] The obtaining of the user's driving behavior data includes:

[0267] Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car;

[0268] Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data;

[0269] Collecting the user's schedule data and obtaining schedule analysis data;

[0270] Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data;

[0271] Collect users' driving operation data and obtain driving analysis data.

[0272] In an optional manner, the driving behavior data is calculated using a preset weighted sum algorithm to obtain the proportion of each emotion of the user, including:

[0273] Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results;

[0274] Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result;

[0275] Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result;

[0276] Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results;

[0277] Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result;

[0278] Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion;

[0279] Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

[0280] In an optional manner, each emotion corresponds to each taste, including:

[0281] Identify each emotion;

[0282] If the mood is low, the taste is determined to be spicy;

[0283] If the emotion is irritability, the taste is determined to be sweet;

[0284] If the emotion is anxiety, the taste is determined to be sour;

[0285] If the mood is lethargy, the taste is determined to be salty;

[0286] If the emotion is tiredness, the taste will definitely be bitter.

[0287] In an optional manner, determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes:

[0288] Get the candidate restaurants corresponding to each taste;

[0289] Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening;

[0290] Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

[0291] In an optional manner, after determining the restaurant list recommended to the user, the method further includes:

[0292] Display the restaurant list on the car computer;

[0293] Responding to the user's operation on the restaurant list, determining the selected restaurant;

[0294] A navigation route is generated according to the location of the selected restaurant and the current sentiment ratio of the user, and navigation to the selected restaurant is performed based on the navigation route.

[0295] In an optional manner, generating a navigation route according to the location of the selected restaurant and the current emotion ratio of the user includes:

[0296] Determine a number of routes to be selected according to the location of the selected restaurant;

[0297] Determine the emotion that accounts for the largest proportion of user emotions;

[0298] If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route;

[0299] Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

[0300] Beneficial Effects

[0301] The embodiment of the present invention performs emotion analysis on the user's driving behavior, taking into account that the same driving behavior corresponds to multiple emotions, and analyzes the proportion of each emotion through multiple driving behaviors, thereby improving the accuracy of emotion analysis. Taste is acquired by analyzing emotions, and based on the user's dietary requirements, restaurants can be recommended from two dimensions: taste and dietary requirements, greatly improving the accuracy of restaurant recommendations. The present invention arranges the proportion of each taste by the order of taste, taking into account the occurrence of all tastes, and can reduce the risk of incorrect restaurant recommendations due to taste judgment errors, further ensuring the accuracy of restaurant recommendations.

[0302] An embodiment of the present invention provides a computer-readable storage medium, which stores at least one executable instruction. When the executable instruction runs on a restaurant data processing device / apparatus, the restaurant data processing device / apparatus executes the restaurant data processing method in any of the above-mentioned method embodiments.

[0303] The executable instructions can be specifically used to enable the restaurant data processing device / apparatus to perform the following operations:

[0304] Obtain users’ driving behavior data and dietary requirements;

[0305] The driving behavior data is calculated by a preset weighted sum algorithm to obtain the proportion of each emotion of the user;

[0306] Arrange each emotion according to the order of proportion to obtain the emotional arrangement order, and determine the taste arrangement order based on the emotional arrangement order; wherein each emotion corresponds to each taste one by one;

[0307] Based on taste ranking and dietary requirements, a list of restaurants recommended to the user is determined.

[0308] In an optional manner, the driving behavior data includes: in-vehicle user information data, in-vehicle music analysis data, schedule analysis data, driving route analysis data, and driving analysis data;

[0309] The obtaining of the user's driving behavior data includes:

[0310] Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car;

[0311] Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data;

[0312] Collecting the user's schedule data and obtaining schedule analysis data;

[0313] Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data;

[0314] Collect users' driving operation data and obtain driving analysis data.

[0315] In an optional manner, the driving behavior data is calculated using a preset weighted sum algorithm to obtain the proportion of each emotion of the user, including:

[0316] Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results;

[0317] Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result;

[0318] Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result;

[0319] Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results;

[0320] Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result;

[0321] Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion;

[0322] Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

[0323] In an optional manner, each emotion corresponds to each taste, including:

[0324] Identify each emotion;

[0325] If the mood is low, the taste is determined to be spicy;

[0326] If the emotion is irritability, the taste is determined to be sweet;

[0327] If the emotion is anxiety, the taste is determined to be sour;

[0328] If the mood is lethargy, the taste is determined to be salty;

[0329] If the emotion is tiredness, the taste will definitely be bitter.

[0330] In an optional manner, determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes:

[0331] Get the candidate restaurants corresponding to each taste;

[0332] Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening;

[0333] Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

[0334] In an optional manner, after determining the restaurant list recommended to the user, the method further includes:

[0335] Display the restaurant list on the car computer;

[0336] Responding to the user's operation on the restaurant list, determining the selected restaurant;

[0337] A navigation route is generated according to the location of the selected restaurant and the current sentiment ratio of the user, and navigation to the selected restaurant is performed based on the navigation route.

[0338] In an optional manner, generating a navigation route according to the location of the selected restaurant and the current emotion ratio of the user includes:

[0339] Determine a number of routes to be selected according to the location of the selected restaurant;

[0340] Determine the emotion that accounts for the largest proportion of user emotions;

[0341] If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route;

[0342] Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

[0343] Beneficial Effects

[0344] The embodiment of the present invention performs emotion analysis on the user's driving behavior, taking into account that the same driving behavior corresponds to multiple emotions, and analyzes the proportion of each emotion through multiple driving behaviors, thereby improving the accuracy of emotion analysis. Taste is acquired by analyzing emotions, and based on the user's dietary requirements, restaurants can be recommended from two dimensions: taste and dietary requirements, greatly improving the accuracy of restaurant recommendations. The present invention arranges the proportion of each taste by the order of taste, taking into account the occurrence of all tastes, and can reduce the risk of incorrect restaurant recommendations due to taste judgment errors, further ensuring the accuracy of restaurant recommendations.

[0345] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present invention are not directed to any particular programming language.

[0346] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself is a separate embodiment of the present invention.

[0347] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0348] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A restaurant data processing method, characterized in that: The method comprises: Obtain users’ driving behavior data and dietary requirements; The driving behavior data is calculated by a preset weighted sum algorithm to obtain the proportion of each emotion of the user; Arrange each emotion according to the order of proportion to obtain the emotional arrangement order, and determine the taste arrangement order based on the emotional arrangement order; wherein each emotion corresponds to each taste one by one; Based on taste ranking and dietary requirements, a list of restaurants recommended to the user is determined.

2. The method according to claim 1, characterized in that The driving behavior data includes: in-car user information data, in-car music analysis data, schedule analysis data, driving route analysis data and driving analysis data; The obtaining of the user's driving behavior data includes: Collect the user's facial expression data, voice data and heart rate data in the car to obtain the user's information data in the car; Collect data on the types of music played by users in the car and their playing behavior to obtain in-car music analysis data; Collecting the user's schedule data and obtaining schedule analysis data; Collect the user's driving time data, vehicle condition data, travel location data and driving distance data to obtain driving route analysis data; Collect users' driving operation data and obtain driving analysis data.

3. The method according to claim 2, characterized in that The driving behavior data is calculated by a preset weighted sum algorithm to obtain the proportion of each emotion of the user, including: Matching the facial expression data, voice data and heart rate data corresponding to the in-vehicle user information data with the facial expression samples, voice samples and heart rate samples corresponding to each emotion, and obtaining the user index corresponding to the in-vehicle user information data based on the matching results; Matching the music category data and playback behavior data corresponding to the in-vehicle music analysis data with the music category samples and playback behavior samples corresponding to each emotion, and obtaining the music index corresponding to the in-vehicle music analysis data based on the matching result; Matching the schedule data corresponding to the schedule analysis data with the schedule samples corresponding to each emotion, and obtaining the schedule index corresponding to the schedule analysis data based on the matching result; Matching the driving time data, vehicle condition data, travel location data and driving distance data corresponding to the driving route analysis data with the driving time samples, vehicle condition samples, travel location samples and driving distance samples corresponding to each emotion, and obtaining the route index corresponding to the driving route analysis data based on the matching results; Matching the driving operation data corresponding to the driving analysis data with the driving operation samples corresponding to each emotion, and obtaining the operation index corresponding to the driving operation data based on the matching result; Substitute the user index, music index, arrangement index, route index, operation index and corresponding weights into the weighted summation formula to calculate the score of each emotion; Based on the score of each emotion and the total score of all emotions, the proportion of each emotion of the user is calculated.

4. The method according to claim 1, characterized in that: Each emotion corresponds to each taste, including: Identify each emotion; If the mood is low, the taste is determined to be spicy; If the emotion is irritability, the taste is determined to be sweet; If the emotion is anxiety, the taste is determined to be sour; If the mood is lethargy, the taste is determined to be salty; If the emotion is tiredness, the taste will definitely be bitter.

5. The method according to claim 1, characterized in that: Determining a list of restaurants recommended to the user based on the taste ranking order and dietary requirements includes: Get the candidate restaurants corresponding to each taste; Screening the candidate restaurants corresponding to each taste based on dietary requirements, and generating a sub-list of recommended restaurants corresponding to each taste based on the candidate restaurants that pass the screening; Based on the taste ranking order, each sub-list to be recommended is ranked to obtain a list of restaurants recommended to the user.

6. The method according to any one of claims 1 to 5, characterized in that: After determining the restaurant list recommended to the user, the method further includes: Display the restaurant list on the car computer; Responding to the user's operation on the restaurant list, determining the selected restaurant; A navigation route is generated according to the location of the selected restaurant and the current sentiment ratio of the user, and navigation to the selected restaurant is performed based on the navigation route.

7. The method according to claim 6, characterized in that The generating of a navigation route according to the location of the selected restaurant and the current emotion ratio of the user includes: Determine a number of routes to be selected according to the location of the selected restaurant; Determine the emotion that accounts for the largest proportion of user emotions; If the user's emotion with the largest proportion is anxiety or irritability, then the candidate route with the least congestion is selected as the navigation route; Otherwise, the candidate route with the shortest travel time is selected as the navigation route.

8. A restaurant data processing device, characterized in that: The device comprises: A data acquisition module, used to obtain the user's driving behavior data and dietary requirements; The emotion analysis module is used to calculate the driving behavior data by a preset weighted sum algorithm to obtain the proportion of each emotion of the user; The taste analysis module is used to arrange each emotion according to the order of proportion, obtain the emotion arrangement order, and determine the taste arrangement order based on the emotion arrangement order; wherein each emotion corresponds to each taste one by one; The data recommendation module is used to determine the list of restaurants recommended to users based on taste ranking and dietary requirements.

9. A restaurant data processing device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the restaurant data processing method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one executable instruction, and when the executable instruction runs on the restaurant data processing device / apparatus, the restaurant data processing device / apparatus performs the operation of the restaurant data processing method as described in any one of claims 1-7.

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