CN112148758A - Community diet health management method and system based on big data - Google Patents
Community diet health management method and system based on big data Download PDFInfo
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Abstract
The invention discloses a community diet health management method and a community diet health management system based on big data, which are applied to an electronic device of a first user, wherein the electronic device of the first user is connected with a central management platform, and the method comprises the following steps: obtaining personal information of the first user through the electronic equipment of the first user; acquiring the eating habits and the work and rest time information of the first user according to the personal information; obtaining location information of the first user; inputting the eating habits and the position information into a training model for training; obtaining that the output result of the training model contains at least one store recommendation information; and adjusting the output result according to the work and rest time information, and sending the adjusted shop recommendation information to the first user. The technical problem that in the prior art, the diet of a user is not reasonable and healthy enough and better dining experience cannot be obtained according to the dietary habits is solved.
Description
Technical Field
The invention relates to the field of intelligent community health management, in particular to a community diet health management method and system based on big data.
Background
With the improvement of living standard and the rapid development of the internet, the internet-based smart community moves into thousands of households. The construction and development of the intelligent community can not be separated from each community user, the requirements and the hobbies of the diet habits of the community users and diet products are different at present, and how to provide the diet products suitable for the users is a problem to be solved urgently.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the technical problem that the diet of a user is not reasonable and healthy enough and better dining experience cannot be obtained according to the dietary habits exists in the prior art.
Disclosure of Invention
The embodiment of the application provides a community diet health management method and system based on big data, and solves the technical problems that in the prior art, diet of a user is not reasonable and healthy enough, and better dining experience cannot be obtained according to diet habits. The technical effects of updating the diet information of community users in real time, providing reasonable and healthy diet products and high-quality dining experience are achieved.
In view of the foregoing problems, embodiments of the present application provide a community diet health management method and system based on big data.
In a first aspect, an embodiment of the present application provides a big data-based community diet health management method, which is applied to a health management system of an intelligent community, where the health management system is in communication connection with an electronic device of a first user, and the method includes: obtaining personal information of the first user through the electronic equipment of the first user; acquiring the eating habits and the work and rest time information of the first user according to the personal information; obtaining location information of the first user; inputting the eating habits and the position information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: the eating habits, the position information and shop recommendation identification information for identification; obtaining an output result of the training model, wherein the output result comprises at least one shop recommendation information; and adjusting the output result according to the work and rest time information, and sending the adjusted shop recommendation information to the first user.
In another aspect, the present application further provides a big data based community diet health management system, wherein the system includes: a first obtaining unit configured to obtain personal information of the first user through an electronic device of the first user; a second obtaining unit, configured to obtain eating habits and work and rest time information of the first user according to the personal information; a third obtaining unit, configured to obtain location information of the first user; a first input unit, configured to input the eating habits and the position information into a training model, where the training model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data includes: the eating habits, the position information and shop recommendation identification information for identification; a fourth obtaining unit, configured to obtain an output result of the training model, where the output result includes at least one store recommendation information; and the first sending unit is used for adjusting the output result according to the work and rest time information and sending the adjusted shop recommendation information to the first user.
In a third aspect, the present invention provides a big data based community dietary health management system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
due to the fact that the mode that the shop recommendation information is obtained by inputting the training model according to the diet information of the first user is adopted, the proper and healthy shop is recommended to the dining habit of the first user, and therefore the diet information of community users is updated timely, and reasonable and healthy diet products and high-quality dining experience are provided.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart illustrating a big data-based method for managing the dietary health of a community in accordance with an embodiment of the present application;
FIG. 2 is a schematic diagram of a big data based community diet health management system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first input unit 14, a fourth obtaining unit 15, a first adjusting unit 16, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the application provides a community diet health management method and system based on big data, and solves the technical problems that in the prior art, diet of a user is not reasonable and healthy enough, and better dining experience cannot be obtained according to diet habits. The technical effects of updating the diet information of community users in real time, providing reasonable and healthy diet products and high-quality dining experience are achieved. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
With the improvement of living standard and the rapid development of the internet, the internet-based smart community moves into thousands of households. The construction and development of the intelligent community can not be separated from each community user, and the diet habits and the requirements and the preferences of diet products of the community users are different at present. However, the technical problems that the diet of a user is not reasonable and healthy enough and better dining experience cannot be obtained according to the dietary habits exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a community diet health management method based on big data, which is applied to a health management system of an intelligent community, wherein the health management system is in communication connection with electronic equipment of a first user, and the method comprises the following steps: obtaining personal information of the first user through the electronic equipment of the first user; acquiring the eating habits and the work and rest time information of the first user according to the personal information; obtaining location information of the first user; inputting the eating habits and the position information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: the eating habits, the position information and shop recommendation identification information for identification; obtaining an output result of the training model, wherein the output result comprises at least one shop recommendation information; and adjusting the output result according to the work and rest time information, and sending the adjusted shop recommendation information to the first user.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a big data based community diet health management method applied to a health management system of an intelligent community, the health management system being communicatively connected to an electronic device of a first user, the method including:
step S100: obtaining personal information of the first user through the electronic equipment of the first user;
specifically, the electronic device of the first user is a device including personal information, such as a mobile phone or a computer. And acquiring the personal information of the first user by establishing network communication between the health management system and the electronic equipment of the user.
Step S200: acquiring the eating habits and the work and rest time information of the first user according to the personal information;
specifically, the eating habits of the first user refer to the eating preferences of the first user, such as taste preferences, dish preferences, Chinese and western preferences, and other personal habits. And obtaining the work and rest time of the first user, so that the dining time of the first user in one day can be obtained, and the shop capable of dining in time is recommended. And inputting the eating habit information and the work and rest time information of the first user into a health management system, and providing a data source for providing high-quality dining service in the future.
Step S300: obtaining location information of the first user;
specifically, the location information is the location information of the current location or the dining destination area of the first user. The health management system can provide suitable dining stores in the area according to the obtained location information.
Step S400: inputting the eating habits and the position information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: the eating habits, the position information and shop recommendation identification information for identification;
specifically, the eating habits and the position information are input into a training model, which is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. Training based on a large amount of training data, wherein each set of training data of the training data comprises: the eating habits, the position information and the shop recommended identification information playing a role in identification, the neural network model is continuously corrected by self, and when the output information of the neural network model reaches a preset accuracy rate/reaches a convergence state, the supervised learning process is ended. Through data training of the neural network model, the neural network model can process the input data more accurately, and the output shop information is more accurate. And obtaining output information of the training model, wherein the output result is a recommended shop. Based on the characteristic that the data is more accurate after training is carried out on the basis of the training model, the eating habits, the position information and the shop recommendation identification information playing a role in identification are used, so that the output shop information is more accurate, the diet information of community users can be updated in real time, and reasonable and healthy diet products and high-quality dining experience are provided.
Step S500: obtaining an output result of the training model, wherein the output result comprises at least one shop recommendation information;
specifically, the first user may select a store to go to a meal from a recommended store based on the results output by the training model, the output including at least one store for selection by the first user.
Step S500: adjusting the output result according to the work and rest time information, and sending the adjusted shop recommendation information to the first user;
specifically, the work and rest time includes the first user habit dining time, and the first user waiting time is different in the shop outputting the result because of the difference of the passenger flow. The adjusted recommended shop information is an optimal result according to the first user information.
Further, adjusting the output result according to the work and rest time information, in an embodiment S510 of the present application, further includes:
step S511: obtaining first feedback information of the first user;
step S512: obtaining second feedback information of a second user;
step S513: weighting the first feedback information and the second feedback information according to the relationship between the first user and the second user to obtain third feedback information;
step S514: and adjusting the output result according to the third feedback information.
Specifically, the first feedback information of the first user is personal intention evaluation information of the second user, and the second feedback information of the second user is personal intention evaluation information of the second user. In order to ensure the accuracy of the output result, the third feedback information may be obtained by performing weighting processing according to the first feedback information of the first user and the second feedback information of the second user. And adjusting the output result according to the weighted third feedback information, so that the accuracy of the output result is improved, and the adjusted shop recommendation information is close to the user requirement.
Further, before inputting the eating habits and the location information into the training model, an embodiment S410 of the present application further includes:
step S411: obtaining first evaluation information of the shop, wherein the first evaluation information is online evaluation information obtained based on Internet big data;
step S412: obtaining second evaluation information of the shop, wherein the second evaluation information is offline evaluation information of the shop;
step S413: classifying and ranking the stores according to the first evaluation information and the second evaluation information;
step S414: and updating the shop recommendation identification information according to the classification ranking result.
Specifically, the first evaluation information of the store is online evaluation information obtained based on internet big data, and the second evaluation information is offline evaluation information based on the store. Wherein the second evaluation information is higher in ranking percentage than the first evaluation information. And performing classification ranking on the stores according to the obtained first evaluation information and second evaluation information. The classification ranking means that suitable high-quality stores of different types are arranged in sequence from high to low according to evaluation. At this time, after the store recommendation information is updated according to the classification ranking result, the eating habits and the position information are input into a training model. The store information is updated according to the evaluation information, and the classification ranking is performed, so that the appropriate and high-quality store information can be accurately recommended in combination with the personal information of the user.
Further, in an embodiment S520 of the present application, obtaining the eating habits of the first user according to the personal information, further includes:
step S521: obtaining the body state information of the first user according to the personal information;
step S522: inputting the body state information and the eating habits into a convolutional neural network model, wherein the convolutional neural network model is obtained through supervised learning of a plurality of groups of data, and the supervised data comprises identification information for identifying the nutrition state of the first user;
step S523: obtaining output information of the convolutional neural network model, wherein the output information comprises nutrition state information of the first user;
step S524: and obtaining a first recommended food according to the nutrition state information of the first user.
Specifically, the body state information of the first user is obtained according to the personal information, and the body state information refers to health state information and nutrition state information of the first user. And inputting the body state information and the eating habits into a convolutional neural network model, and obtaining the convolutional neural network model through multi-group data supervised learning. Wherein the supervised data model comprises identification information of the first user nutritional status. The output information of the convolutional neural network model is obtained, the output information comprises the nutrition state information of the first user, first recommended food is obtained according to the nutrition state information of the first user, the nutrition state information output after the convolutional neural network model is trained more accurately accords with the body state of the first user, therefore, healthy food is reasonably recommended, and the technical effect of providing reasonable and healthy diet products according to the nutrition state information of the user is achieved.
Further, before inputting the physical state information and the eating habits into the convolutional neural network model, embodiment S522 of the present application further includes:
step S5221: obtaining body state information and eating habits of the first user, and generating a first verification code according to the first user;
step S5222: generating a second verification code according to a second user and the first verification code, and generating an Nth verification code according to the Nth user and the Nth-1 verification code in the same way, wherein N is a natural number greater than 1;
step S5223: respectively copying and storing the user information and the verification code on M pieces of electronic equipment, wherein M is a natural number greater than 1;
specifically, the body state information and the eating habits of the first user are obtained, and a first verification code is generated according to the first user; generating a second verification code according to a second user and the first verification code; and in the same way, generating an Nth verification code according to the Nth user and the Nth-1 verification code, wherein N is a natural number greater than 1. Respectively copying and storing all users and verification codes on M devices, wherein the first user and the first verification code are stored on one device as a first storage unit, the second user and the second verification code are stored on a device as a second storage unit, the Nth user and the Nth verification code are stored on a device as an Nth storage unit, when the user needs to be called, after each subsequent node receives the data stored by the previous node, the data is verified through a common identification mechanism and then stored, the training data is not easy to lose and damage by serially connecting the hash function to each storage unit, and a block chain technology is also called as a distributed account book technology and is an emerging technology for jointly participating in accounting by a plurality of computing devices and jointly maintaining a complete distributed database. The blockchain technology has been widely used in many fields due to its characteristics of decentralization, transparency, participation of each computing device in database records, and rapid data synchronization between computing devices. The training data is encrypted through logic of a block chain, the safety of the training data is guaranteed, the training data is stored on a plurality of devices, and the data stored on the devices are processed through a common recognition mechanism, namely a few data are subject to majority. When one or more devices are tampered, as long as the number of the devices storing correct data is larger than the number of the tampered devices, the obtained user is still correct, the safety of the user information is further guaranteed, the accuracy of a training model passing through the user information is effectively guaranteed, the body state information and the eating habits of the first user are accurately judged, the nutrition state information of the user is obtained, the eating information of the user is timely updated, and the technical effect of providing a reasonable and healthy diet product according to the body state information is achieved.
Further, the embodiment of the present application further includes:
step S610: obtaining first demand information of the first user;
step S620: obtaining a first adjusting instruction according to the first requirement information;
step S630: and adjusting the shop recommendation information according to the first adjusting instruction, and sending the adjusted shop information to the first user.
Specifically, the first demand information is obtained according to the first user, wherein the first demand information changes according to personal information of the first user, an adjustment instruction is sent to a diet health management system according to the user demand information to obtain the first adjustment instruction, and in the adjustment process, the adjusted shop information can be sent to the first user according to the diet habit, body state information, feedback information and the like of the first user and according to the shop recommendation information, so that the appropriate shop information is recommended for the first user, and the technical effect of providing a high-quality dining experience is achieved.
In summary, the community diet health management method and system based on big data provided by the embodiment of the application have the following technical effects:
1. the method for obtaining the eating habit and the work and rest time information of the first user and the position information of the first user according to the personal information is adopted, so that the first user information is accurately obtained, the technical effects of updating the personal information in real time and obtaining the eating shop identification information aiming at the user are achieved.
2. Due to the fact that the characteristic that data are processed more accurately after training based on the training model is adopted, the eating habits of the first user and the mode that the position information is input into the training model are provided for the first user shop recommendation identification information through the output information of the training model, the identification information is more accurate, the effect that the eating information of community users is updated in real time is achieved, and reasonable and healthy eating products and high-quality dining experience are provided is achieved.
3. Due to the fact that the output result is adjusted according to the work and rest time information and the output result is continuously adjusted and corrected, more accurate shop recommendation information is obtained, and then shop information is updated in real time, so that the technical effects of providing accurate dining shops and obtaining high-quality dining experience are achieved.
Example two
Based on the same inventive concept as the community diet health management method based on big data in the foregoing embodiment, the present invention further provides a community diet health management system based on big data, as shown in fig. 2, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain personal information of the first user through an electronic device of the first user;
a second obtaining unit 12, wherein the second obtaining unit 12 is configured to obtain the eating habits and the work and rest time information of the first user according to the personal information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain location information of the first user;
a first input unit 14, where the first input unit 14 is configured to input the eating habits and the position information into a training model, where the training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets of training data includes: the eating habits, the position information and shop recommendation identification information for identification;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain an output result of the training model, where the output result includes at least one store recommendation information;
a first adjusting unit 16, where the first adjusting unit 16 is configured to adjust the output result according to the work and rest time information, and send the adjusted store recommendation information to the first user.
Further, the system further comprises:
a fifth obtaining unit, configured to obtain first feedback information of the first user;
a sixth obtaining unit, configured to obtain second feedback information of a second user;
a seventh obtaining unit, configured to perform weighting processing on the first feedback information and the second feedback information according to a relationship between the first user and the second user, so as to obtain third feedback information;
and the second adjusting unit is used for adjusting the output result according to the third feedback information.
Further, the system further comprises:
an eighth obtaining unit configured to obtain first evaluation information of the store, the first evaluation information being online evaluation information obtained based on internet big data;
a ninth obtaining unit configured to obtain second evaluation information of the store, the second evaluation information being offline evaluation information for the store;
the first classification ranking unit is used for performing classification ranking on the stores according to the first evaluation information and the second evaluation information;
a first updating unit, configured to update the store recommendation identification information according to the classification ranking result.
Further, the system further comprises:
a tenth obtaining unit configured to obtain the physical status information of the first user according to the personal information;
the second input unit is used for inputting the body state information and the eating habits into a convolutional neural network model, the convolutional neural network model is obtained through supervised learning of multiple groups of data, and the supervised data comprise identification information of the nutrition state of the first user;
an eleventh obtaining unit for obtaining a first recommended food according to the nutritional status information of the first user.
Further, the system further comprises:
a twelfth obtaining unit, configured to obtain body state information and eating habits of the first user, and generate a first verification code according to the first user;
the first generation unit is used for generating a second verification code according to a second user and the first verification code, and generating an Nth verification code according to the Nth user and the Nth-1 verification code in the same way, wherein N is a natural number greater than 1;
the first storage unit is used for respectively copying and storing the user information and the verification code on M electronic devices, wherein M is a natural number larger than 1.
Further, the system further comprises:
a thirteenth obtaining unit, configured to obtain first demand information of the first user;
a fourteenth obtaining unit, configured to obtain a first adjustment instruction according to the first requirement information;
and the third adjusting unit is used for adjusting the shop recommendation information according to the first adjusting instruction and sending the adjusted shop information to the first user.
Various changes and specific examples of the big data based community dietary health management method in the first embodiment of fig. 1 are also applicable to the big data based community dietary health management system of this embodiment, and through the foregoing detailed description of the big data based community dietary health management method, those skilled in the art can clearly know the implementation method of the big data based community dietary health management system in this embodiment, so for the brevity of the description, detailed descriptions are omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the big data based community dietary health management method in the foregoing embodiments, the present invention further provides a big data based community dietary health management system, on which a computer program is stored, which when executed by a processor implements the steps of any one of the above-mentioned big data based community dietary health management methods.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides a community diet health management method based on big data, which is applied to an electronic device of a first user, wherein the electronic device of the first user is connected with a central management platform, and the method comprises the following steps: obtaining personal information of the first user through the electronic equipment of the first user; acquiring the eating habits and the work and rest time information of the first user according to the personal information; obtaining location information of the first user; inputting the eating habits and the position information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: the eating habits, the position information and shop recommendation identification information for identification; obtaining an output result of the training model, wherein the output result comprises at least one shop recommendation information; and adjusting the output result according to the work and rest time information, and sending the adjusted shop recommendation information to the first user. The technical problem that in the prior art, the diet of a user is not reasonable and healthy enough and better dining experience cannot be obtained according to the dietary habits is solved. The technical effects of updating the diet information of community users in real time, providing reasonable and healthy diet products and high-quality dining experience are achieved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A big data-based community diet health management method is applied to a health management system of a smart community, wherein the health management system is in communication connection with electronic equipment of a first user, and the method comprises the following steps:
obtaining personal information of the first user through the electronic equipment of the first user;
acquiring the eating habits and the work and rest time information of the first user according to the personal information;
obtaining location information of the first user;
inputting the eating habits and the position information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: the eating habits, the position information and shop recommendation identification information for identification;
obtaining an output result of the training model, wherein the output result comprises at least one shop recommendation information;
and adjusting the output result according to the work and rest time information, and sending the adjusted shop recommendation information to the first user.
2. The method of claim 1, wherein said adjusting said output based on said rest time information further comprises:
obtaining first feedback information of the first user;
obtaining second feedback information of a second user;
weighting the first feedback information and the second feedback information according to the relationship between the first user and the second user to obtain third feedback information;
and adjusting the output result according to the third feedback information.
3. The method of claim 1, wherein prior to entering the eating habits and the location information into a training model, the method comprises:
obtaining first evaluation information of the shop, wherein the first evaluation information is online evaluation information obtained based on Internet big data;
obtaining second evaluation information of the shop, wherein the second evaluation information is offline evaluation information of the shop;
classifying and ranking the stores according to the first evaluation information and the second evaluation information;
and updating the shop recommendation identification information according to the classification ranking result.
4. The method of claim 1, wherein the obtaining of the eating habits of the first user based on the personal information further comprises:
obtaining the body state information of the first user according to the personal information;
inputting the body state information and the eating habits into a convolutional neural network model, wherein the convolutional neural network model is obtained through supervised learning of a plurality of groups of data, and the supervised data comprises identification information for identifying the nutrition state of the first user;
obtaining output information of the convolutional neural network model, wherein the output information comprises nutrition state information of the first user;
and obtaining a first recommended food according to the nutrition state information of the first user.
5. The method of claim 4, wherein prior to said inputting said body state information and eating habits into a convolutional neural network model, said method comprises:
obtaining body state information and eating habits of the first user, and generating a first verification code according to the first user;
generating a second verification code according to a second user and the first verification code, and generating an Nth verification code according to the Nth user and the Nth-1 verification code in the same way, wherein N is a natural number greater than 1;
and respectively copying and storing the user information and the verification code on M electronic devices, wherein M is a natural number greater than 1.
6. The method of claim 1, wherein the method comprises:
obtaining first demand information of the first user;
obtaining a first adjusting instruction according to the first requirement information;
and adjusting the shop recommendation information according to the first adjusting instruction, and sending the adjusted shop information to the first user.
7. A big data based community dietary health management system, wherein the system comprises:
a first obtaining unit configured to obtain personal information of the first user through an electronic device of the first user;
a second obtaining unit, configured to obtain eating habits and work and rest time information of the first user according to the personal information;
a third obtaining unit, configured to obtain location information of the first user;
a first input unit, configured to input the eating habits and the position information into a training model, where the training model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data includes: the eating habits, the position information and shop recommendation identification information for identification;
a fourth obtaining unit, configured to obtain an output result of the training model, where the output result includes at least one store recommendation information;
and the first sending unit is used for adjusting the output result according to the work and rest time information and sending the adjusted shop recommendation information to the first user.
8. A big data based community dietary health management system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method of any of claims 1-6.
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