Method, system and computing device for intelligent makeup touch-up reminder for vehicles

Abstract

The present invention discloses a method, system and computing device for intelligent makeup touch-up reminder for vehicles, the method comprising: after a user enters a vehicle, obtaining multiple data related to makeup touch-up; using different data from the multiple data to construct one or more feature vectors; using a trained makeup touch-up reminder model to determine whether the user needs to touch up makeup based on the one or more feature vectors; and when it is determined that the user needs to touch up makeup, generating makeup touch-up reminder information and presenting the makeup touch-up reminder information to the user. The present invention can intelligently determine whether the user needs to touch up makeup based on multiple factors that affect makeup touch-up and remind the user, without the need for manual detection by the user, is easy to use, has high detection efficiency, and as the number of times the user uses it increases, the reminder becomes more accurate, greatly improving the user experience of the user.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a method, system and computing device for intelligent makeup touch-up reminder for a vehicle. Background Art

[0002] In modern society, makeup is a way of showing respect and courtesy to each other in interpersonal relationships. Many women and even men pay great attention to their makeup. However, due to many external factors, such as hot weather, rainy weather, or the body's own sweaty constitution, people's makeup is likely to be smudged after a certain period of time. When attending certain public occasions, if you do not check your makeup in time or do not realize that you need to check your makeup, it will affect your appearance.

[0003] One of the current solutions is the sweat detector. When using it, you need to stick the sweat detector on the sweating part. It is very inconvenient and impossible to keep the instrument on your face while driving. It is also unsafe to use the instrument to detect in the car. Moreover, the degree of sweating and oil production in different parts of the human body is different. The sweat detector can only provide results for the degree of sweating, not the degree of oil production. Moreover, the user can only consciously detect it, and there is no problem of reminding the user to touch up the makeup.

[0004] Therefore, a new method, system and computing device for intelligent makeup touch-up reminder for vehicles are needed to solve the above problems. Summary of the invention

[0005] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0006] According to one embodiment of the present invention, a method for intelligent makeup touch-up reminder for a vehicle is provided, the method comprising: acquiring multiple data related to makeup touch-up after a user enters a vehicle; constructing one or more feature vectors using different data among the multiple data; determining whether the user needs makeup touch-up based on the one or more feature vectors using a trained makeup touch-up reminder model; and when it is determined that the user needs makeup touch-up, generating makeup touch-up reminder information and presenting the makeup touch-up reminder information to the user.

[0007] In one embodiment, the one or more feature vectors include one or more of a time feature vector, a user facial state feature vector, a cosmetics state feature vector, and a vehicle environment feature vector.

[0008] In one embodiment, the one or more feature vectors further include a comprehensive feature vector, and the comprehensive feature vector is obtained by fusing the time feature vector, the user's facial state feature vector, the cosmetics state feature vector and the vehicle environment feature vector.

[0009] In one embodiment, the multiple data related to makeup touch-up include: midnight time, default makeup touch-up reminder time, time interval from the user getting in the car, user's facial temperature, car's indoor / outdoor ambient temperature / humidity, and the type of cosmetics used by the user.

[0010] In one embodiment, one or more feature vectors are constructed using different data from the multiple data, including: constructing the time feature vector using a time period identifier, a default makeup touch-up reminder time, and a time interval from the user getting in the car, wherein the time period identifier indicates the time interval from the zero time; constructing the user facial state feature vector using the time period identifier, the user facial temperature, the in-car ambient temperature, and the in-car ambient humidity; constructing the cosmetic state feature vector using the time period identifier, the cosmetics category, and the user facial temperature or the in-car ambient temperature; and constructing the vehicle environment feature vector using the time period identifier, the in-car ambient temperature, the in-car ambient humidity, the outside vehicle ambient temperature, and the outside vehicle ambient humidity.

[0011] In one embodiment, the zero o'clock time can be preset as the user's working hours or appointment time.

[0012] In one embodiment, the default makeup touch-up reminder time can be preset as a time before the midnight time and a certain amount of time from the midnight time.

[0013] In one embodiment, the use of a trained makeup touch-up reminder model to determine whether the user needs to touch up makeup based on the one or more feature vectors includes: using the trained makeup touch-up reminder model to calculate the distance between each feature vector of the one or more feature vectors and a corresponding stored average feature vector; normalizing the distance to obtain a normalized distance; and determining whether the user needs to touch up makeup based on the normalized distance.

[0014] In one embodiment, the distance is a chi-square distance weighted by an information gain factor.

[0015] In one embodiment, the makeup touch-up reminder model is constructed using a back-propagation neural network.

[0016] According to another embodiment of the present invention, a system for intelligent makeup touch-up reminder for vehicles is provided, the system comprising: a processor for using a trained neural network to: after a user enters a vehicle, obtain multiple data related to makeup touch-up; construct one or more feature vectors using different data among the multiple data; use a trained makeup touch-up reminder model to determine whether the user needs makeup touch-up based on the one or more feature vectors; and when it is determined that the user needs makeup touch-up, generate makeup touch-up reminder information and present the makeup touch-up reminder information to the user; a memory for storing network parameters of the neural network.

[0017] According to another embodiment of the present invention, a computing device is provided. The computing device includes a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the method described above.

[0018] According to yet another embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed, the method described above is executed.

[0019] According to the method, system and computing device for intelligent makeup touch-up reminder for vehicles according to the embodiments of the present invention, it is possible to intelligently determine whether the user needs to touch up makeup and remind the user based on multiple factors that affect makeup touch-up, without the need for manual detection by the user. The method is easy to use and has high detection efficiency. Moreover, as the number of times the user uses it increases, the reminder becomes more accurate, thereby greatly improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The following drawings of the present invention are used to understand the present invention as part of the present invention. The embodiments of the present invention are shown in the drawings and the description thereof is used to explain the principle of the present invention.

[0021] In the attached figure:

[0022] Figure 1 The present invention is a schematic structural block diagram of an electronic device for implementing a method, system and computing device for implementing an intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention.

[0023] Figure 2 The figure is a flowchart of exemplary steps of a method for intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention.

[0024] Figure 3 The figure is a schematic structural block diagram of a system for intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention.

[0025] Figure 4A schematic structural block diagram of a computing device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

[0027] As mentioned above, the existing sweat detector cannot detect oiliness, is very inconvenient to use, and cannot automatically remind the user to touch up their makeup.

[0028] Therefore, in order to provide intelligent makeup touch-up reminders, the present invention provides a method for intelligent makeup touch-up reminders for vehicles, the method comprising: after a user enters a vehicle, acquiring multiple data related to makeup touch-up; constructing one or more feature vectors using different data among the multiple data; determining whether the user needs makeup touch-up based on the one or more feature vectors using a trained makeup touch-up reminder model; and when it is determined that the user needs makeup touch-up, generating makeup touch-up reminder information and presenting the makeup touch-up reminder information to the user.

[0029] According to the method for intelligent makeup touch-up reminder for vehicles of the present invention, it is possible to intelligently determine whether the user needs to touch up their makeup and remind the user based on multiple factors that affect makeup touch-up, without the need for manual detection by the user. The method is easy to use and has high detection efficiency. Moreover, as the number of times the user uses the method increases, the reminder becomes more accurate, thereby greatly improving the user experience.

[0030] The method, system and computing device for intelligent makeup touch-up reminder for a vehicle according to the present invention are described in detail below in conjunction with specific embodiments.

[0031] First, refer to Figure 1 An electronic device 100 for implementing a method, system, and computing device for implementing an intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention is described.

[0032] In one embodiment, the electronic device 100 can be, for example, a laptop computer, a desktop computer, a tablet computer, a learning machine, a mobile device (such as a smart phone, a phone watch, etc.), an embedded computer, a tower server, a rack server, a blade server, or any other suitable electronic device.

[0033] In one embodiment, the electronic device 100 may include at least one processor 102 and at least one memory 104 .

[0034] Among them, the memory 104 can be a volatile memory, such as a random access memory (RAM), a cache memory (cache), a dynamic random access memory (DRAM) (including stacked DRAM) or a high bandwidth memory (HBM), etc., or it can be a non-volatile memory, such as a read-only memory (ROM), a flash memory, 3D Xpoint, etc. In one embodiment, some parts of the memory 104 can be volatile memory, while another part can be a non-volatile memory (for example, using a two-level memory hierarchy). The memory 104 is used to store a computer program, which, when executed, can implement the client functions (implemented by the processor) and / or other desired functions in the embodiments of the present invention described below.

[0035] Processor 102 may be a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, or other processing unit having data processing capabilities and / or instruction execution capabilities. Processor 102 may be communicatively coupled to any suitable number or type of components, peripherals, modules, or devices via a communication bus. In one embodiment, the communication bus may be implemented using any suitable protocol, such as Peripheral Component Interconnect (PCI), Peripheral Component Interconnect Express (PCIe), Accelerated Graphics Port (AGP), HyperTransport, or any other bus or one or more point-to-point communication protocols.

[0036] The electronic device 100 may further include an input device 106 and an output device 108. The input device 106 is a device for receiving user input, which may include a keyboard, a mouse, a touch pad, a microphone, etc. In addition, the input device 106 may also be any interface for receiving information. The output device 108 may output various information (such as images or sounds) to the outside (such as a user), which may include one or more of a display, a speaker, etc. In addition, the output device 108 may also be any other device with an output function, such as a printer, etc.

[0037] Reference below Figure 2 A flowchart of exemplary steps of a method 200 for intelligent makeup touch-up reminder for a vehicle according to one embodiment of the present invention is described.

[0038] like Figure 2 As shown, the method 200 for intelligent makeup touch-up reminder for a vehicle may include the following steps:

[0039] In step S210, after the user enters the vehicle, a plurality of data related to makeup touch-up are obtained.

[0040] In step S220 , one or more feature vectors are constructed using different data among the plurality of data.

[0041] In step S230 , the trained makeup touch-up reminder model is used to determine whether the user needs to touch up makeup based on the one or more feature vectors.

[0042] In step S240, when it is determined that the user needs to touch up makeup, makeup touch up prompt information is generated and presented to the user.

[0043] In one embodiment, the multiple data related to makeup touch-up may include the time interval from the user getting on the car, the user's facial temperature, the temperature / humidity of the environment inside / outside the car, and the type of cosmetics used by the user, etc. It may also include data on other factors that may affect the user's facial condition (e.g., sweating, oiliness), or may cause cosmetics to dissolve, such as the user's skin type (e.g., dry skin, oily skin, mixed skin, etc.), weather conditions (e.g., rain, windy), etc. In one embodiment, the multiple data related to makeup touch-up may also include the midnight time and the default reminder makeup touch-up time, etc.

[0044] In one embodiment, the time interval to the time when the user gets on the vehicle can be obtained by calculating the time when the data is obtained and the time when the user gets on the vehicle. The time when the user gets on the vehicle can be input by the user, or can be automatically obtained as the time when the user starts the vehicle, etc., and the present invention is not limited to this.

[0045] In one embodiment, the user's facial temperature may be measured by a sensor installed in the vehicle (eg, an infrared sensor, etc.).

[0046] In one embodiment, the interior / exterior ambient temperature / humidity can be obtained directly from the vehicle system.

[0047] In one embodiment, the cosmetics category used by the user can be obtained from the user (for example, input by the user), or can be pre-set by the user as a default cosmetics category. In one embodiment, the cosmetics category may include toner, lotion, eye cream, face cream, eye shadow, eyeliner, mascara, blush, lipstick, sunscreen, primer, liquid foundation, setting powder, etc. Since various cosmetics have different solubility at different temperatures, the time for cosmetics to dissolve can be calculated based on the solubility of different chemical components at different temperatures, thereby dividing the cosmetics category into a makeup removal cosmetics category and a non-makeup cosmetics category to represent the cosmetics category. For example, skin care products containing VC and protein will dissolve at 40 degrees. When the user's facial temperature or the ambient temperature reaches 40 degrees, the user's face using cosmetics containing this ingredient will lose makeup and need to be reminded to touch up.

[0048] In one embodiment, the zero time can be preset as the user's working hours or appointment time, for example, 8 a.m., 7 p.m., etc. In one embodiment, the zero time can include a fixed zero time, and the user does not need to set it every time, such as 8 a.m. In one embodiment, the zero time can also include other zero times, which are set by the user as needed each time after getting on the bus. For example, if the appointment time is 7 p.m., the user can set other zero times to 7 p.m. after getting on the bus.

[0049] In one embodiment, the default makeup touch-up reminder time refers to the time when the user is first reminded to touch up the makeup, which can be preset as a time before the midnight time and a certain amount of time from the midnight time. In one embodiment, the amount of time can be set according to user needs, such as half an hour, one hour, 45 minutes, etc. For example, if the midnight time is 8 o'clock in the morning, the default makeup touch-up reminder time can be 7:30 in the morning, etc.

[0050] In one embodiment, one or more feature vectors may include one or more of a time feature vector, a user facial state feature vector, a cosmetic state feature vector, and a vehicle environment feature vector. The time feature vector may be used to characterize the time after the user gets on the vehicle or the time from zero time, which may affect the makeup touch-up. The user facial state feature vector may be used to characterize the facial state of the user, such as oiliness, sweating, etc., which may affect the makeup touch-up. The cosmetic state feature vector may be used to characterize whether the cosmetics used by the user, which may affect the makeup touch-up, have dissolved. The vehicle environment feature vector may be used to characterize the environmental factors inside and outside the vehicle that may affect the makeup touch-up.

[0051] In one embodiment, the time feature vector can be constructed using a time period identifier, a default makeup touch-up reminder time, and a time interval from the user getting on the bus, and can be expressed as [time period identifier, default makeup touch-up reminder time, time interval from the user getting on the bus], which is not limited in the present invention. The time period identifier indicates the time interval from the zero o'clock time.

[0052] In one embodiment, the above quantity can be quantified to construct a time feature vector. Among them, the zero time can be taken as 0, the time period of every 30 minutes can be identified as 0.5, and the time before zero can be negative, so as to quantify the time period identification. For example, taking 8 o'clock in the morning as the zero time, the time period identification of the time period from 7:30 to 8 o'clock is -0.5, and the time period identification of the time period from 7 o'clock to 8 o'clock can be quantified as -1. Among them, the time interval of every 30 minutes after the user gets on the bus can be taken as 1. If the user gets on the bus at 7 o'clock, there are two 30 minutes at 8 o'clock, that is, there are two time intervals, and the time interval from the user getting on the bus can be quantified as 2. Therefore, an exemplary time feature vector can be: [-0.5 (time period identification), 0 (default reminder makeup touch-up time), 2 (time interval from the user getting on the bus)].

[0053] In one embodiment, the user facial state feature vector can be constructed using time period identifier, user facial temperature, in-vehicle ambient temperature, and in-vehicle ambient humidity, and can be expressed as [time period identifier, user facial temperature, in-vehicle ambient temperature, in-vehicle ambient humidity], which is not limited in the present invention. An exemplary user facial state feature vector can be: [-0.5 (time period identifier), 38 (user facial temperature), 40 (in-vehicle ambient temperature), 46 (in-vehicle ambient humidity)].

[0054] In one embodiment, the cosmetic state feature vector can be constructed using a time period identifier, a cosmetic category, and the user's facial temperature or the in-car ambient temperature, and can be expressed as [time period identifier, cosmetic category, user's facial temperature / in-car ambient temperature], which is not limited in the present invention. Among them, the cosmetic category can be quantified, for example, the makeup removal cosmetic category is quantized to 1, and the makeup non-removal cosmetic category is quantized to 0. Then an exemplary user facial state feature vector can be: [-0.5 (time period identifier), 1 (cosmetic category), 40 (user facial temperature / in-car ambient temperature)].

[0055] In one embodiment, the vehicle environment feature vector can be constructed using time period identifier, in-vehicle environment temperature, in-vehicle environment humidity, outside vehicle environment temperature and outside vehicle environment humidity, and can be expressed as [time period identifier, in-vehicle environment temperature, in-vehicle environment humidity, outside vehicle environment temperature, outside vehicle environment humidity], which is not limited in the present invention. An exemplary vehicle environment feature vector can be: [-0.5 (time period identifier), 45 (in-vehicle environment temperature), 30 (in-vehicle environment humidity), 40 (outside vehicle environment temperature), 46 (outside vehicle environment humidity), 38 (user face temperature)].

[0056] In one embodiment, one or more feature vectors also include a comprehensive feature vector, which can be obtained by fusing a time feature vector, a user facial state feature vector, a cosmetic state feature vector, and a vehicle environment feature vector. An exemplary comprehensive feature vector can be expressed as [time period identifier, default reminder time for touch-up makeup, time interval from the user getting on the car, user facial temperature, cosmetic category, in-car ambient temperature, in-car ambient humidity, outside vehicle ambient temperature, outside vehicle ambient humidity]. An exemplary comprehensive feature vector can be: [-0.5 (time period identifier), 0 (default reminder time for touch-up makeup), 2 (time interval from the user getting on the car), 38 (user facial temperature), 1 (cosmetic category), 45 (in-car ambient temperature), 30 (in-car ambient humidity), 40 (out-car ambient temperature), 46 (out-car ambient humidity)].

[0057] In one embodiment, the step S230 of using the trained makeup touch-up reminder model to determine whether the user needs to touch up makeup based on the one or more feature vectors may include the following steps: using the trained makeup touch-up reminder model,

[0058] In step a, the distance between each of the one or more feature vectors and the corresponding stored average feature vector is calculated.

[0059] In step b, the distance is normalized to obtain a normalized distance.

[0060] In step c, it is determined whether the user needs to touch up her makeup based on the normalized distance.

[0061] In one embodiment, the distance between each feature vector and the corresponding stored average feature vector may be a chi-square distance, or other distances, such as Mahalanobis distance, Euclidean distance, etc., which is not limited in the present invention.

[0062] The following uses the chi-square distance as an example to illustrate the method for calculating the distance between each eigenvector and the corresponding stored average eigenvector.

[0063] The calculation formula of chi-square distance is as follows (1):

[0064]

[0065] Where n is the dimension of x and y; x i is the value of the i-th dimension of vector x, E(x i ) is the expected value (average value) of vector x in the i-th dimension; y i is the value of the i-th dimension of vector y, E(y i ) is the expected value (average value) of vector y in the i-th dimension.

[0066] Since one of the compared vectors y is the average eigenvector, y i =E(x i )=E(y i ), so the chi-square distance formula can be simplified to the following formula (2).

[0067]

[0068] The chi-square distance is used to characterize the difference between each eigenvector and the corresponding average eigenvector. The smaller the difference, the closer the eigenvector is to the corresponding average eigenvector, which means that the possibility of touch-up is greater.

[0069] When using the chi-square distance formula to calculate the difference, the weights of each dimension are the same, but in fact, the factors that determine the time for each user to touch up their makeup are different, and the impact of different feature vectors on the prediction results should also be different. Therefore, the present invention can use the chi-square distance weighted by the information gain factor, that is, before accumulation, each dimension is multiplied by the corresponding weight, that is, the information gain of the feature.

[0070] Information gain (i.e., weight) is for each eigenvector. For a certain eigenvector, the difference in the overall information entropy when it is included and when it is not included is the amount of information that this eigenvector brings to the whole. For a eigenvector T, the calculation formula for its information gain IG(T) is as follows (3).

[0071] IG(T)=H(C)-H(C|T) (3)

[0072] Among them, H(C) is the entropy of category C (in this case, the categories are only two categories: touch-up and no touch-up); H(C|T) is the conditional entropy of category C when the feature vector is T. Assume that category C has n different values ​​C1, C2, C3, …, C n , then the calculation formula of H(C) can be as follows (4).

[0073]

[0074] Among them, P(C i ) is C i The probability of occurrence. According to the conditional probability formula, the calculation formula of H(C|T) can be obtained as follows (5).

[0075]

[0076] Among them, P(t) is the probability of the feature vector T appearing, is the probability that the feature vector T does not appear, P(C i |t) is the case when the eigenvector T appears C i Probability of occurrence; C is the case when the eigenvector T does not appeari Probability of occurrence.

[0077] In summary, the information gain calculation formula of the feature vector T is as follows (6).

[0078]

[0079] For the present invention, there are only two sample categories: touch-up C1 and no touch-up C2. P(C1|t) and P(C2|t) are the probabilities of the user touching-up and no touch-up respectively when considering the feature vector. are the probabilities of the user touching up makeup and not touching up makeup when the feature vector is not considered.

[0080] The improved formula combined with information gain is expressed as the following formula (7).

[0081]

[0082] Among them, IG(x i ) is x i The information gain of the corresponding feature subsequence.

[0083] In one embodiment, a distance threshold may be preset, such as 0.5, and the obtained normalized distance may be compared with the distance threshold. A distance greater than the threshold distance (e.g., 0.6, 0.7, 0.8, 0.9) may be regarded as 1, indicating that a reminder to touch up the makeup is required; a distance less than the threshold distance (e.g., 0.4, 0.3, 0.2, 0.1) may be regarded as 0, indicating that a reminder to touch up the makeup is not required.

[0084] In one embodiment, the trained makeup touch-up reminder model can be constructed using a back propagation neural network. According to the number of feature vectors set, a corresponding number of input layer nodes and hidden layer nodes are set. For example, if the feature vectors are time feature vectors, user facial state feature vectors, cosmetics state feature vectors, vehicle environment feature vectors, and comprehensive feature vectors, a back propagation neural network with 5 input layer nodes, 5 hidden layer nodes, and 1 output layer node can be set, and the back propagation neural network is iteratively trained using labeled training data (i.e., 1 for reminder to touch up and 0 for no reminder to touch up), until the neural network can correctly predict whether makeup touch-up is needed, thereby obtaining a trained makeup touch-up reminder model.

[0085] In one embodiment, the makeup touch-up reminder information may take various forms, such as sound, warning light, music, text, etc., which is not limited in the present invention.

[0086] According to the method for intelligent makeup touch-up reminder for vehicles of the present invention, it is possible to intelligently determine whether the user needs to touch up their makeup and remind the user based on multiple factors that affect makeup touch-up, without the need for manual detection by the user. The method is easy to use and has high detection efficiency. Moreover, as the number of times the user uses the method increases, the reminder becomes more accurate, thereby greatly improving the user experience.

[0087] In yet another embodiment, the present invention provides a system for intelligent makeup touch-up reminder for a vehicle. Figure 3 A schematic structural block diagram of a system 300 for intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention is described. Figure 3 As shown, the system 300 for intelligent makeup touch-up reminder for a vehicle may include a processor 310 and a memory 320 .

[0088] Among them, the processor 310 is used to use one or more trained neural networks to implement the following processing steps: after the user enters the vehicle, obtain multiple data related to makeup touch-up; use different data among the multiple data to construct one or more feature vectors; use a trained makeup touch-up reminder model to determine whether the user needs to touch up makeup based on the one or more feature vectors; and when it is determined that the user needs to touch up makeup, generate makeup touch-up reminder information and present the makeup touch-up reminder information to the user.

[0089] Exemplarily, the processor 310 can be any processing device known in the art, such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a microcontroller, a field programmable gate array (FPGA), etc., and the present invention is not limited to this.

[0090] The memory 320 is used to store the network parameters of one or more trained neural networks. Exemplarily, the memory 320 can be RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage device, tape cassette, tape, magnetic disk storage device or other magnetic storage device, or any other medium that can be used to store the desired information and can be accessed by the processor 310.

[0091] The system 300 for intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention can implement the method 200 for intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention described above. Those skilled in the art can understand the specific operation of the system 300 for intelligent makeup touch-up reminder for a vehicle according to an embodiment of the present invention in combination with the above content, and for the sake of brevity, it will not be repeated here.

[0092] According to the system for intelligent makeup touch-up reminder for vehicles of the present invention, it is possible to intelligently determine whether the user needs to touch up their makeup and remind the user based on multiple factors that affect makeup touch-up, without the need for manual detection by the user. It is easy to use and has high detection efficiency. Moreover, as the number of times the user uses it increases, the reminder becomes more accurate, thereby greatly improving the user experience.

[0093] In yet another embodiment, the present invention provides a computing device. Figure 4 , Figure 4 FIG. 4 shows a schematic structural block diagram of a computing device 400 according to an embodiment of the present invention. Figure 4 As shown, the computing device 400 may include a memory 410 and a processor 420 , wherein the memory 410 stores a computer program, and when the computer program is executed by the processor 420 , the processor 420 executes the method 200 for intelligent makeup touch-up reminder for a vehicle as described above.

[0094] Those skilled in the art can understand the specific operation of the computing device 400 according to the embodiment of the present invention in combination with the above contents. For the sake of brevity, the specific details are not repeated here, and only some main operations of the processor 420 are described as follows:

[0095] After the user enters the vehicle, multiple data related to makeup touch-up are obtained;

[0096] constructing one or more feature vectors using different data among the plurality of data;

[0097] Determining whether the user needs to touch up makeup based on the one or more feature vectors using the trained makeup touch-up reminder model; and

[0098] When it is determined that the user needs to touch up makeup, makeup touch up prompt information is generated and presented to the user.

[0099] The computing device 400 according to the embodiment of the present invention can implement the method 200 for intelligent makeup touch-up reminder for a vehicle according to the embodiment of the present invention described above. Those skilled in the art can understand the specific operation of the computing device 400 according to the embodiment of the present invention in combination with the above content, and for the sake of brevity, it will not be repeated here.

[0100] The computing device according to the present invention can intelligently determine whether the user needs to touch up their makeup based on multiple factors that affect touch-up and remind the user without the need for manual detection by the user. It is easy to use and has high detection efficiency. Moreover, as the number of times the user uses it increases, the reminder becomes more accurate, thereby greatly improving the user experience.

[0101] In another embodiment, the present invention provides a computer-readable medium having a computer program stored thereon, wherein the computer program executes the method 200 for intelligent makeup touch-up reminder for a vehicle as described in the above embodiment when running. Any tangible, non-temporary computer-readable medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, Blu-ray disks, etc.), flash memory, and / or the like. These computer program instructions may be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing device to form a machine, so that these instructions executed on a computer or other programmable data processing device may generate a device that implements a specified function. These computer program instructions may also be stored in a computer-readable memory, which may instruct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory may form a manufactured product, including an implementation device that implements a specified function. The computer program instructions may also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device may provide steps for implementing a specified function.

[0102] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as required by the appended claims.

[0103] 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. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0104] Similarly, it should be understood that in order to streamline the present invention and help understand one or more of the various inventive aspects, in the description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present invention should not be interpreted as reflecting the following intention: the claimed invention requires more features than the features explicitly stated in each claim. More specifically, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0105] It will be understood by those skilled in the art that, except for mutually exclusive features, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this specification may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0106] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0107] 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.

[0108] The above is only a specific embodiment of the present invention or an explanation of a specific embodiment. The protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for intelligent makeup touch-up reminder for a vehicle, characterized in that: The method comprises: After the user enters the vehicle, multiple data related to makeup touch-up are obtained; constructing one or more feature vectors using different data among the plurality of data, wherein the one or more feature vectors include a user facial state feature vector and a cosmetics state feature vector; Determining whether the user needs to touch up makeup based on the one or more feature vectors using the trained makeup touch-up reminder model; and When it is determined that the user needs to touch up makeup, generating makeup touch up prompt information and presenting the makeup touch up prompt information to the user; The user facial state feature vector is used to characterize the facial state of the user that affects the makeup touch-up; the cosmetics state feature vector is used to characterize whether the cosmetics used by the user that affects the makeup touch-up have been dissolved; wherein the one or more feature vectors further include a time feature vector and a vehicle environment feature vector; The multiple data related to makeup touch-up include: midnight time, default makeup touch-up reminder time, time interval from the user getting on the car, user's facial temperature, temperature / humidity inside / outside the car, and the type of cosmetics used by the user; The step of constructing one or more feature vectors using different data from the plurality of data comprises: The time feature vector is constructed using a time period identifier, a default makeup touch-up reminder time, and a time interval from the user getting on the bus, wherein the time period identifier indicates the time interval from the zero time; Constructing the user's facial state feature vector using the time period identifier, the user's facial temperature, the in-vehicle ambient temperature, and the in-vehicle ambient humidity; Constructing the cosmetic state feature vector using the time period identifier, cosmetic category, and user facial temperature or vehicle ambient temperature; and The vehicle environment feature vector is constructed using the time period identifier, the in-vehicle environment temperature, the in-vehicle environment humidity, the out-vehicle environment temperature and the out-vehicle environment humidity.

2. The method according to claim 1, characterized in that The one or more feature vectors further include a comprehensive feature vector, which is obtained by fusing the time feature vector, the user's facial state feature vector, the cosmetics state feature vector and the vehicle environment feature vector.

3. The method according to claim 1, characterized in that The zero o'clock time may be preset as the user's working hours or appointment time.

4. The method according to claim 1, characterized in that The default makeup touch-up reminder time can be preset as a certain amount of time before the midnight time.

5. The method according to claim 1, characterized in that The step of using a trained makeup touch-up reminder model to determine whether the user needs to touch up makeup based on the one or more feature vectors comprises: using the trained makeup touch-up reminder model, calculating a distance between each of the one or more feature vectors and a corresponding stored average feature vector; Normalizing the distance to obtain a normalized distance; and It is determined whether the user needs to touch up makeup based on the normalized distance.

6. The method according to claim 5, characterized in that The distance is the chi-square distance weighted by the information gain factor.

7. The method according to claim 1, characterized in that The makeup touch-up reminder model is constructed using a back-propagation neural network.

8. A system for intelligent makeup touch-up reminder for vehicles, characterized in that: The system comprises: Processor for using trained neural networks: After the user enters the vehicle, multiple data related to makeup touch-up are obtained; constructing one or more feature vectors using different data among the plurality of data, wherein the one or more feature vectors include a user facial state feature vector and a cosmetics state feature vector; Determining whether the user needs to touch up makeup based on the one or more feature vectors using the trained makeup touch-up reminder model; and When it is determined that the user needs to touch up makeup, generate makeup touch-up prompt information and present the makeup touch-up prompt information to the user, The one or more feature vectors further include a time feature vector and a vehicle environment feature vector; wherein the makeup touch-up reminder model is constructed using a back propagation neural network; The multiple data related to makeup touch-up include: midnight time, default makeup touch-up reminder time, time interval from the user getting on the car, user's facial temperature, temperature / humidity inside / outside the car, and the type of cosmetics used by the user; The step of constructing one or more feature vectors using different data from the plurality of data comprises: The time feature vector is constructed using a time period identifier, a default makeup touch-up reminder time, and a time interval from the user getting on the bus, wherein the time period identifier indicates the time interval from the zero time; Constructing the user's facial state feature vector using the time period identifier, the user's facial temperature, the in-vehicle ambient temperature, and the in-vehicle ambient humidity; Constructing the cosmetic state feature vector using the time period identifier, cosmetic category, and user facial temperature or vehicle ambient temperature; and Constructing the vehicle environment feature vector using the time period identifier, the in-vehicle environment temperature, the in-vehicle environment humidity, the outside environment temperature and the outside environment humidity; A memory is used to store the network parameters of the neural network.

9. A computing device, characterized in that The computing device comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 is performed.

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