CN115295123B - Diet recommendation method, device, equipment and medium based on sleep big data - Google Patents

Diet recommendation method, device, equipment and medium based on sleep big data Download PDF

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CN115295123B
CN115295123B CN202210985162.XA CN202210985162A CN115295123B CN 115295123 B CN115295123 B CN 115295123B CN 202210985162 A CN202210985162 A CN 202210985162A CN 115295123 B CN115295123 B CN 115295123B
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王炳坤
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De Rucci Healthy Sleep Co Ltd
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Abstract

The application discloses a diet recommendation method, device, equipment and medium based on sleep big data, wherein the method acquires physical characteristic information of a target user and target sleep quality scoring data; the target sleep quality score data is greater than a preset threshold; inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, and displaying the diet recommendation data to the target user. According to the method, based on the physical characteristic data and the target sleep quality scoring data of the user, a proper diet scheme is recommended to the user through a diet recommendation model, so that the user is helped to obtain a good sleep effect through scientific diet, and the physical state of the user is improved. The application can be widely applied to the technical field of big data.

Description

Diet recommendation method, device, equipment and medium based on sleep big data
Technical Field
The application relates to the technical field of big data, in particular to a diet recommendation method, device, equipment and medium based on sleep big data.
Background
In recent years, with the improvement of the living standard of people, more people pay more attention to health, and improving the sleeping condition of users is an important way to improve the health condition.
Among related applications, there are applications for evaluating sleep quality of a user based on a machine learning algorithm, for example, analyzing time and proportion of light sleep, deep sleep and rapid eye movement periods, and scoring the sleep quality of the user, thereby helping the user to understand the sleep state of the user. However, the application is often only to obtain the sleep quality evaluation result of the user, and is difficult to give a targeted improvement suggestion, belongs to post analysis evaluation, and cannot really and effectively help the user to achieve a better sleep effect.
In view of the foregoing, there is a need for solving the technical problems in the related art.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the related art to a certain extent.
Therefore, an object of the embodiment of the application is to provide a diet recommendation method based on sleep big data.
It is another object of embodiments of the present application to provide a sleep big data based dietary recommendation device.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:
in a first aspect, an embodiment of the present application provides a method for recommending a diet based on sleep big data, including the steps of:
acquiring physical characteristic information and target sleep quality scoring data of a target user; the target sleep quality score data is greater than a preset threshold;
inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, and displaying the diet recommendation data to the target user;
the diet recommendation model is obtained through training the following steps:
acquiring physical characteristic sample information, diet data and first sleep quality scoring data of a batch of sample users; wherein the acquisition time periods corresponding to the physical characteristic sample information, the diet data and the first sleep quality score data are the same;
inputting the physical characteristic sample information and the first sleep quality score data into an initialized diet recommendation model to obtain diet prediction data output by the diet recommendation model;
Determining a trained loss value based on the diet data and the diet forecast data;
and according to the loss value, carrying out back propagation update on the parameters of the diet recommendation model to obtain a trained diet recommendation model.
In addition, the sleep big data-based diet recommendation method according to the above embodiment of the present application may further have the following additional technical features:
further, in an embodiment of the present application, the acquiring physical characteristic information of the target user includes:
at least one of weight data, height data, average heart rate over a preset time period, average respiratory rate data over a preset time period, or body movement data of the user is acquired.
Further, in one embodiment of the present application, the acquiring physical characteristic sample information, diet data, and first sleep quality score data of a batch of sample users includes:
collecting physical characteristic sample information and diet data of the sample user through terminal equipment, and sending first identification information of the terminal equipment, the physical characteristic sample information and the diet data to a server;
collecting first sleep quality scoring data of the sample user through a sleep mattress, and sending second identification information of the sleep mattress and the first sleep quality scoring data to a server;
And establishing a corresponding relation among the physical characteristic sample information, the diet data and the first sleep quality scoring data in a server according to the first identification information and the second identification information.
Further, in one embodiment of the present application, the collecting the first sleep quality score data of the sample user by the sleep mattress comprises:
recording the deep sleep time, the shallow sleep time and the snoring times of the sample user sleeping on the sleeping mattress;
and generating the first sleep quality scoring data according to at least one of the deep sleep time, the shallow sleep time or the snoring times.
Further, in one embodiment of the present application, the acquiring the target sleep quality score data:
acquiring average sleep quality scoring data of the target user in a historical time period;
determining target sleep quality score data according to the average sleep quality score data; the target sleep quality score data is greater than the average sleep quality score data.
Further, in one embodiment of the application, the diet recommendation model comprises a first encoder, a second encoder, a feature fusion device, and a decoder; inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, wherein the method comprises the following steps of:
Inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model, encoding the user identity characteristic information by the first encoder to obtain first characteristic data, and encoding the target sleep quality scoring data by the second encoder to obtain second characteristic data;
the first characteristic data and the second characteristic data are fused through the characteristic fusion device, so that fused characteristic data are obtained;
and decoding the fusion characteristic data through the decoder to obtain diet recommended data.
Further, in an embodiment of the present application, the fusing, by the feature fusion device, the first feature data and the second feature data to obtain fused feature data includes:
and carrying out weighting processing or splicing processing on the first characteristic data and the second characteristic data through the characteristic fusion device to obtain the fusion characteristic data.
In a second aspect, an embodiment of the present application provides a sleep big data-based diet recommendation apparatus, including:
the acquisition module is used for acquiring physical characteristic information of the target user and target sleep quality scoring data; the target sleep quality score data is greater than a preset threshold;
The prediction module is used for inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model, obtaining diet recommendation data output by the diet recommendation model, and displaying the diet recommendation data to the target user;
the diet recommendation model is obtained through training the following steps:
acquiring physical characteristic sample information, diet data and first sleep quality scoring data of a batch of sample users; wherein the acquisition time periods corresponding to the physical characteristic sample information, the diet data and the first sleep quality score data are the same;
inputting the physical characteristic sample information and the first sleep quality score data into an initialized diet recommendation model to obtain diet prediction data output by the diet recommendation model;
determining a trained loss value based on the diet data and the diet forecast data;
and according to the loss value, carrying out back propagation update on the parameters of the diet recommendation model to obtain a trained diet recommendation model.
In a third aspect, an embodiment of the present application provides a computer apparatus, including:
At least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the sleep big data based diet recommendation method of the first aspect.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, in which a processor executable program is stored, which when executed by a processor is configured to implement the sleep big data based diet recommendation method according to the first aspect.
The advantages and benefits of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
The embodiment of the application provides a diet recommendation method based on big sleep data, which comprises the steps of obtaining physical characteristic information of a target user and target sleep quality scoring data; the target sleep quality score data is greater than a preset threshold; inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, and displaying the diet recommendation data to the target user. According to the method, based on the physical characteristic data and the target sleep quality scoring data of the user, a proper diet scheme is recommended to the user through a diet recommendation model, so that the user is helped to obtain a good sleep effect through scientific diet, and the physical state of the user is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made with reference to the accompanying drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without the need of inventive labor for those skilled in the art.
FIG. 1 is a schematic diagram of an implementation environment of a method for recommending diet based on big sleep data according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a diet recommendation method based on sleep big data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a sleep big data-based diet recommendation device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
In recent years, with the improvement of the living standard of people, more people pay more attention to health, and improving the sleeping condition of users is an important way to improve the health condition.
Among related applications, there are applications for evaluating sleep quality of a user based on a machine learning algorithm, for example, analyzing time and proportion of light sleep, deep sleep and rapid eye movement periods, and scoring the sleep quality of the user, thereby helping the user to understand the sleep state of the user. However, the application is often only to obtain the sleep quality evaluation result of the user, and is difficult to give a targeted improvement suggestion, belongs to post analysis evaluation, and cannot really and effectively help the user to achieve a better sleep effect.
In view of this, the embodiment of the application provides a diet recommendation method based on big sleep data, and the method in the embodiment of the application recommends a proper diet scheme for a user through a diet recommendation model based on physical characteristic data and target sleep quality scoring data of the user, thereby helping the user obtain a better sleep effect through scientific diet and being beneficial to improving the physical state of the user.
First, referring to fig. 1, fig. 1 is a schematic diagram of an implementation environment of a diet recommendation method based on sleep big data according to an embodiment of the present application. Referring to fig. 1, the main body of the implementation environment mainly includes a sleep mattress 101, a server 102, and a terminal device 103, the sleep mattress 101 is communicatively connected to the server 102, and the terminal device 103 is also communicatively connected to the server 102. The sleep big data based diet recommendation method may be executed through interaction among the sleep mattress 101, the server 102 and the terminal device 103, and may be specifically selected appropriately according to the actual application, which is not specifically limited in this embodiment.
In some embodiments, the sleep mattress 101 side of the present application may include various physiological data monitoring devices, communication units, processors, etc., and the sleep mattress 101 side may monitor and obtain data related to sleep quality of a user, and send the data to the server 102 or the terminal device 103. The server 102 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligent platforms, and the like. The terminal device 103 may be an electronic device such as a mobile phone, a smart bracelet, a smart watch, etc. Communication connections between the sleep mattress 101 and the server 102, between the terminal device 103 and the server 102 may be established through a wireless network or a wired network using standard communication techniques and/or protocols, which may be provided as the internet, but may be any other network including, for example, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wired or wireless network, private network, or any combination of virtual private networks.
In the embodiment of the application, the sleep mattress 101 and the terminal equipment 103 side can collect physical characteristic information of a user in real time, and the terminal equipment 103 side can be provided with related APP software, and the server 102 can be a background server of the APP software. In APP software, the user may set target sleep quality score data that is expected to be achieved, based on the collected physical feature information of the user and the set target sleep quality score data, diet recommendation data may be predicted and obtained in the server 102 through a diet recommendation model, and the diet recommendation data is returned to the terminal device 103 side to be displayed to the user.
Next, a description will be given of a method for recommending diet based on big sleep data according to an embodiment of the present application, with reference to an implementation environment shown in fig. 1.
Referring to fig. 2, fig. 2 is a schematic diagram of a sleep big data-based diet recommendation method according to an embodiment of the present application, where the sleep big data-based diet recommendation method includes, but is not limited to:
step 110, acquiring physical characteristic information of a target user and target sleep quality scoring data; the target sleep quality score data is greater than a preset threshold;
In this step, when a diet recommendation is performed for a user, physical characteristic information and target sleep quality score data of the user may be obtained. Here, the physical characteristic information is used to characterize the physiological state of the user and related parameters, for example, in particular, in some embodiments, the physical characteristic information may include at least one of weight data, height data, average heart rate over a preset period of time, average respiratory rate data over a preset period of time, or body movement data of the user. However, the present application is not limited thereto, and for example, data such as pulse rate and blood oxygen content index may be acquired as physical characteristic information of the user.
In the embodiment of the application, because the recommended diet scheme has strong timeliness, in order to improve the accuracy of recommendation and improve the effect of improving the sleep quality as much as possible, the acquired physical characteristic information of the user should be close to the current actual time. For example, when it is desired to predict recommended food data for a certain day, it is preferable to collect physical characteristic information of users adjacent to the same day.
In this step, in addition to the above-mentioned physical characteristic information of the user, it is also necessary to acquire target sleep quality score data. Here, the target sleep quality score data is used to characterize a score corresponding to the sleep quality that the user desires to achieve. It should be noted that, in the embodiment of the present application, the sleep quality score data may be used to represent the quality of sleep, and the specific data form and the actually corresponding sleep quality may be set according to the needs. For example, in some embodiments, sleep quality may be pre-classified into four classes, sleep quality score data may be set as a percentile, locating more than 80 as sleep quality belonging to the "excellent" type, locating between 70 and 80 as sleep quality belonging to the "good" type, locating between 60 and 70 as sleep quality belonging to the "medium" type, and locating less than 60 as sleep quality belonging to the "poor" type. Of course, the above setting of the sleep quality score data is only for illustration, and is not meant to limit the practical implementation of the present application, for example, in other embodiments, 5 types of sleep quality may be set, and the sleep quality score data are respectively assigned to be 5, 4, 3, 2, and 1 in order from good to bad.
In the embodiment of the application, the target sleep quality scoring data can be set by a user or can be set by a program. For example, in some embodiments, the user may be presented with a setting field for the target sleep quality score data via the APP interface, and the target sleep quality score data may be determined by obtaining user input. It should be noted that, because the target sleep quality score data represents the score data of the sleep quality expected to be achieved by the user, the value of the data obviously cannot be too low for the health consideration of the user, so that a preset threshold value can be set in the embodiment of the application, and when the target sleep quality score data set by the user is greater than the preset threshold value, the setting can be considered to be completed; otherwise, when the target sleep quality scoring data set by the user is smaller than the preset threshold value, a corresponding feedback instruction can be output to inform the user of resetting.
In other embodiments, average sleep quality score data of the target user in a historical time period may be obtained, where the historical time period may be flexibly selected according to needs, for example, a time period of one week may be selected from the current time as the historical time period. Then, sleep quality score data of the target user detected in the historical time period can be obtained, and an average value is obtained to obtain average sleep quality score data. The target sleep quality score data may then be automatically determined from the average sleep quality score data. It should be noted that, since the purpose of the present application is to improve the sleep quality of the user, the size of the target sleep quality score data should be correspondingly larger than the average sleep quality score data. For example, a value such as sleep quality score data includes 5 levels, 5, 4, 3, 2, 1 in order of good to bad sleep quality. When it is determined that the average sleep quality score data to the user is 3 minutes, the target sleep quality score data may be automatically determined to be 4 minutes or 5 minutes. Of course, the above embodiments are merely illustrative of the implementation of the present application, and the target sleep quality score data and the average sleep quality score data may also be determined by other functions, and the present application is not limited thereto.
Step 120, inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, and displaying the diet recommendation data to the target user;
in the step, user identity characteristic information of a target user and target sleep quality scoring data are input into a trained diet recommendation model, corresponding diet recommendation data can be output based on the two types of information through the diet recommendation model, and then the data can be displayed to the user, so that the user can conveniently adjust a diet structure, and an ideal sleep effect is obtained. Here, the diet recommendation model may be built using any machine learning algorithm, which the present application is not limited to. Machine learning is a multi-domain interdisciplinary, and relates to multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like, and the machine learning is used for specially researching how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills, and reorganizing the existing knowledge structure to continuously improve the performance of the machine learning. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. In the embodiment of the application, a machine learning model is built by adopting a machine learning algorithm and is used for recommending diet data, and the machine learning model is recorded as a diet recommending model.
Specifically, in the embodiment of the application, the diet recommendation model is obtained through training by the following steps:
acquiring physical characteristic sample information, diet data and first sleep quality scoring data of a batch of sample users; wherein the acquisition time periods corresponding to the physical characteristic sample information, the diet data and the first sleep quality score data are the same;
inputting the physical characteristic sample information and the first sleep quality score data into an initialized diet recommendation model to obtain diet prediction data output by the diet recommendation model;
determining a trained loss value based on the diet data and the diet forecast data;
and according to the loss value, carrying out back propagation update on the parameters of the diet recommendation model to obtain a trained diet recommendation model.
In the embodiment of the application, before the diet recommendation model is put into use, training is needed to adjust parameters in the diet recommendation model. Specifically, bulk sample user physical characteristic sample information, diet data, and first sleep quality score data may be obtained as it is trained. Here, the sample user refers to a user who provides related information for participating in model training, which may include users of various ages, sexes. The body characteristic sample information of the sample user is identical to the body characteristic information of the target user in the acquisition mode, and the application is not limited to this. The diet data of the sample user can comprise data such as the type and weight of food, and the first sleep quality scoring data of the sample user can be acquired through sleep mattress acquisition.
It should be noted that, since the physical characteristic sample information, the diet data, and the first sleep quality score data of the sample user have time matching, these data corresponding to each sample user need to be collected in the same time period, for example, the physical characteristic sample information, the diet data, and the first sleep quality score data of the same day may be collected.
In some embodiments, the obtaining physical characteristic sample information, diet data, and first sleep quality score data for a batch of sample users comprises:
collecting physical characteristic sample information and diet data of the sample user through terminal equipment, and sending first identification information of the terminal equipment, the physical characteristic sample information and the diet data to a server;
collecting first sleep quality scoring data of the sample user through a sleep mattress, and sending second identification information of the sleep mattress and the first sleep quality scoring data to a server;
and establishing a corresponding relation among the physical characteristic sample information, the diet data and the first sleep quality scoring data in a server according to the first identification information and the second identification information.
In the embodiment of the application, when the information and the data related to the sample user are acquired, the information and the data can be acquired through different equipment, so that the corresponding relation between the data needs to be established, and the corresponding training data of the same sample user can be conveniently determined in the subsequent training process. Specifically, for example, in some embodiments, the body characteristic sample information and the diet data of the sample user may be collected by the terminal device, and after the data is collected by the terminal device, the data may be sent to the server, and the first identification information of the terminal device is sent synchronously with the data. The first sleep quality score data of the sample user can be collected through the sleep mattress, and the second identification information of the sleep mattress and the first sleep quality score data can be sent to the server after the collection is completed. On the server side, an association relationship between the terminal device corresponding to the same user and the sleeping mattress can be established in advance. Thus, according to the first identification information and the second identification information, body characteristic sample information, diet data and first sleep quality scoring data belonging to the same sample user can be determined, and then the corresponding relation of the data can be established.
Specifically, in some embodiments, the collecting first sleep quality score data of the sample user by the sleep mattress comprises:
recording the deep sleep time, the shallow sleep time and the snoring times of the sample user sleeping on the sleeping mattress;
and generating the first sleep quality scoring data according to at least one of the deep sleep time, the shallow sleep time or the snoring times.
In the embodiment of the application, the first sleep quality score data can be determined by at least one of deep sleep time, shallow sleep time or snoring frequency of a user sleeping on the sleep mattress. Here, the data such as the deep sleep time, the shallow sleep time or the snoring frequency of the user may be collected during the process of using the sleep mattress by the user, and then the first sleep quality score data may be generated according to some or all of the data.
In the embodiment of the application, as described above, for the diet recommendation model, training is required before use, so that a better prediction effect is achieved. Specifically, when the model is trained, body characteristic sample information, diet data and first sleep quality score data of each sample user can be used as a set of training data, input data of the model is body characteristic sample information and first sleep quality score data, and output data of the model is diet prediction data. After obtaining the prediction result output by the model, namely diet prediction data, the accuracy of model prediction can be evaluated according to the diet data and the diet prediction data, so that parameters of the model are updated.
For machine learning models, the accuracy of model predictions can be measured by a Loss Function (Loss Function) defined on a single training data to measure the prediction error of a training data, specifically, determining the Loss value of the training data from the label of the single training data and the model's predictions of the training data. In actual training, one training data set has a lot of training data, so that a Cost Function (Cost Function) is generally adopted to measure the overall error of the training data set, and the Cost Function is defined on the whole training data set and is used for calculating the average value of the prediction errors of all the training data, so that the prediction effect of the model can be better measured. For a general machine learning model, based on the cost function, a regular term for measuring the complexity of the model can be used as a training objective function, and based on the objective function, the loss value of the whole training data set can be obtained. There are many kinds of common loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc., which can be used as the loss function of the machine learning model, and will not be described in detail herein. In embodiments of the present application, a loss function may be selected from among which to determine a trained loss value, such as a cross entropy loss function. Based on the trained loss value, updating the parameters of the model by adopting a back propagation algorithm, and iterating for several rounds to obtain the trained diet recommendation model. The specific number of iteration rounds may be preset or training may be deemed complete when the test set meets the accuracy requirements.
In some embodiments, the diet recommendation model comprises a first encoder, a second encoder, a feature fusion device, and a decoder; inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, wherein the method comprises the following steps of:
inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model, encoding the user identity characteristic information by the first encoder to obtain first characteristic data, and encoding the target sleep quality scoring data by the second encoder to obtain second characteristic data;
the first characteristic data and the second characteristic data are fused through the characteristic fusion device, so that fused characteristic data are obtained;
and decoding the fusion characteristic data through the decoder to obtain diet recommended data.
In the embodiment of the application, an optional structure of the diet recommendation model comprises a first encoder, a second encoder, a feature fusion device and a decoder. When the model is used, the user identity characteristic information can be input into the first encoder, so that the user identity characteristic information is encoded, and first characteristic data are obtained. Similarly, the target sleep quality scoring data can be encoded by a second encoder to obtain second characteristic data, and then the first characteristic data and the second characteristic data can be fused by a characteristic fusion device to obtain fused characteristic data. The fusion manner used herein may include any one of weighting or stitching, as the application is not limited in this regard. And then, inputting the obtained fusion characteristic data into a coder for decoding to obtain diet recommended data.
The following describes a sleep big data-based food recommendation device according to an embodiment of the present application with reference to the accompanying drawings.
Referring to fig. 3, a sleep big data-based diet recommendation device according to an embodiment of the present application includes:
an acquisition module 201, configured to acquire physical characteristic information of a target user and target sleep quality score data; the target sleep quality score data is greater than a preset threshold;
the prediction module 202 is configured to input the user identity information and the target sleep quality score data into a trained diet recommendation model, obtain diet recommendation data output by the diet recommendation model, and display the diet recommendation data to the target user;
the diet recommendation model is obtained through training the following steps:
acquiring physical characteristic sample information, diet data and first sleep quality scoring data of a batch of sample users; wherein the acquisition time periods corresponding to the physical characteristic sample information, the diet data and the first sleep quality score data are the same;
inputting the physical characteristic sample information and the first sleep quality score data into an initialized diet recommendation model to obtain diet prediction data output by the diet recommendation model;
Determining a trained loss value based on the diet data and the diet forecast data;
and according to the loss value, carrying out back propagation update on the parameters of the diet recommendation model to obtain a trained diet recommendation model.
It can be understood that the content in the above method embodiment is applicable to the embodiment of the present device, and the specific functions implemented by the embodiment of the present device are the same as those of the embodiment of the above method, and the achieved beneficial effects are the same as those of the embodiment of the above method.
Referring to fig. 4, an embodiment of the present application provides a computer apparatus including:
at least one processor 301;
at least one memory 302 for storing at least one program;
the at least one program, when executed by the at least one processor 301, causes the at least one processor 301 to implement a sleep big data based diet recommendation method.
Similarly, the content in the above method embodiment is applicable to the embodiment of the present computer device, and the functions specifically implemented by the embodiment of the present computer device are the same as those of the embodiment of the above method, and the achieved beneficial effects are the same as those achieved by the embodiment of the above method.
The embodiment of the present application also provides a computer-readable storage medium in which a program executable by the processor 301 is stored, the program executable by the processor 301 being for performing the above-described sleep big data based diet recommendation method when executed by the processor 301.
Similarly, the content in the above method embodiment is applicable to the present computer-readable storage medium embodiment, and the functions specifically implemented by the present computer-readable storage medium embodiment are the same as those of the above method embodiment, and the beneficial effects achieved by the above method embodiment are the same as those achieved by the above method embodiment.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the application is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features may be integrated in a single physical device and/or software module or may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and the equivalent modifications or substitutions are intended to be included in the scope of the present application as defined in the appended claims.

Claims (7)

1. A sleep big data based diet recommendation method, comprising:
acquiring physical characteristic information and target sleep quality scoring data of a target user; the target sleep quality score data is greater than a preset threshold;
inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, and displaying the diet recommendation data to the target user;
The diet recommendation model is obtained through training the following steps:
acquiring physical characteristic sample information, diet data and first sleep quality scoring data of a batch of sample users; wherein the acquisition time periods corresponding to the physical characteristic sample information, the diet data and the first sleep quality score data are the same;
inputting the physical characteristic sample information and the first sleep quality score data into an initialized diet recommendation model to obtain diet prediction data output by the diet recommendation model;
determining a trained loss value based on the diet data and the diet forecast data;
according to the loss value, carrying out back propagation update on the parameters of the diet recommendation model to obtain a trained diet recommendation model;
the obtaining physical characteristic sample information, diet data and first sleep quality score data of a batch of sample users comprises:
collecting physical characteristic sample information and diet data of the sample user through terminal equipment, and sending first identification information of the terminal equipment, the physical characteristic sample information and the diet data to a server;
collecting first sleep quality scoring data of the sample user through a sleep mattress, and sending second identification information of the sleep mattress and the first sleep quality scoring data to a server;
Establishing a corresponding relation among the physical characteristic sample information, the diet data and the first sleep quality scoring data in a server according to the first identification information and the second identification information;
the collecting, by the sleep mattress, first sleep quality score data of the sample user, comprising:
recording the deep sleep time, the shallow sleep time and the snoring times of the sample user sleeping on the sleeping mattress;
generating the first sleep quality score data according to at least one of the deep sleep duration, the shallow sleep duration, or the number of snores;
the obtaining the target sleep quality score data includes:
acquiring average sleep quality scoring data of the target user in a historical time period;
determining target sleep quality score data according to the average sleep quality score data; the target sleep quality score data is greater than the average sleep quality score data.
2. The sleep big data based diet recommendation method according to claim 1, wherein the acquiring physical characteristic information of the target user comprises:
at least one of weight data, height data, average heart rate over a preset time period, average respiratory rate data over a preset time period, or body movement data of the user is acquired.
3. The sleep big data based diet recommendation method according to any one of claims 1-2, wherein the diet recommendation model comprises a first encoder, a second encoder, a feature fusion device and a decoder; inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model to obtain diet recommendation data output by the diet recommendation model, wherein the method comprises the following steps of:
inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model, encoding the user identity characteristic information by the first encoder to obtain first characteristic data, and encoding the target sleep quality scoring data by the second encoder to obtain second characteristic data;
the first characteristic data and the second characteristic data are fused through the characteristic fusion device, so that fused characteristic data are obtained;
and decoding the fusion characteristic data through the decoder to obtain diet recommended data.
4. The sleep big data-based diet recommendation method according to claim 3, wherein the fusing processing of the first feature data and the second feature data by the feature fusion device to obtain fused feature data comprises:
And carrying out weighting processing or splicing processing on the first characteristic data and the second characteristic data through the characteristic fusion device to obtain the fusion characteristic data.
5. A sleep big data based diet recommendation device, comprising:
the acquisition module is used for acquiring physical characteristic information of the target user and target sleep quality scoring data; the target sleep quality score data is greater than a preset threshold;
the prediction module is used for inputting the user identity characteristic information and the target sleep quality scoring data into a trained diet recommendation model, obtaining diet recommendation data output by the diet recommendation model, and displaying the diet recommendation data to the target user;
the diet recommendation model is obtained through training the following steps:
acquiring physical characteristic sample information, diet data and first sleep quality scoring data of a batch of sample users; wherein the acquisition time periods corresponding to the physical characteristic sample information, the diet data and the first sleep quality score data are the same;
inputting the physical characteristic sample information and the first sleep quality score data into an initialized diet recommendation model to obtain diet prediction data output by the diet recommendation model;
Determining a trained loss value based on the diet data and the diet forecast data;
according to the loss value, carrying out back propagation update on the parameters of the diet recommendation model to obtain a trained diet recommendation model;
the obtaining physical characteristic sample information, diet data and first sleep quality score data of a batch of sample users comprises:
collecting physical characteristic sample information and diet data of the sample user through terminal equipment, and sending first identification information of the terminal equipment, the physical characteristic sample information and the diet data to a server;
collecting first sleep quality scoring data of the sample user through a sleep mattress, and sending second identification information of the sleep mattress and the first sleep quality scoring data to a server;
establishing a corresponding relation among the physical characteristic sample information, the diet data and the first sleep quality scoring data in a server according to the first identification information and the second identification information;
the collecting, by the sleep mattress, first sleep quality score data of the sample user, comprising:
recording the deep sleep time, the shallow sleep time and the snoring times of the sample user sleeping on the sleeping mattress;
Generating the first sleep quality score data according to at least one of the deep sleep duration, the shallow sleep duration, or the number of snores;
the obtaining the target sleep quality score data includes:
acquiring average sleep quality scoring data of the target user in a historical time period;
determining target sleep quality score data according to the average sleep quality score data; the target sleep quality score data is greater than the average sleep quality score data.
6. A computer device, comprising:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the sleep big data based diet recommendation method as claimed in any one of claims 1-4.
7. A computer-readable storage medium having stored therein a program executable by a processor, characterized in that: the processor-executable program for implementing the sleep big data based diet recommendation method as claimed in any one of claims 1-4 when executed by a processor.
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