CN113192641A

CN113192641A - Stomach ill probability calculating device and system based on big data

Info

Publication number: CN113192641A
Application number: CN202110627703.7A
Authority: CN
Inventors: 朱长玲
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-06-05
Filing date: 2021-06-05
Publication date: 2021-07-30

Abstract

The embodiment of the application provides a device and a system for calculating stomach illness probability based on big data. The device for calculating the stomach disease probability based on big data comprises: the first acquisition module is used for acquiring the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information of a target user in a preset time period; the first calculation module is used for calculating the stomach function condition of the target user according to the exercise amount information, the weight information, the diet information and the toileting information; the selection module is used for selecting a corresponding neural network calculation model of the stomach illness probability according to the stomach function condition; and the second calculation module is used for inputting the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information into the neural network calculation model to obtain the stomach illness probability of the target user.

Description

Stomach ill probability calculating device and system based on big data

Technical Field

The application relates to the technical field of disease prevention, in particular to a device and a system for calculating stomach disease probability based on big data.

Background

Currently, people mainly rely on regular physical examination to find diseases in time so as to avoid the death of diseases caused by plaster without drugs for rescue. However, the periodic physical examination usually takes one year, and the physical condition of the user may change greatly within one year, so that the disease prevention and detection cannot be performed in time

In view of the above problems, no effective technical solution exists at present.

Disclosure of Invention

The embodiment of the application aims to provide a device and a system for calculating the stomach illness probability based on big data.

The embodiment of the present application further provides a device for calculating a stomach disease probability based on big data, the device includes:

the first acquisition module is used for acquiring the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information of a target user in a preset time period;

the first calculation module is used for calculating the stomach function condition of the target user according to the exercise amount information, the weight information, the diet information and the toileting information;

the selection module is used for selecting a corresponding neural network calculation model of the stomach illness probability according to the stomach function condition;

and the second calculation module is used for inputting the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information into the neural network calculation model to obtain the stomach illness probability of the target user.

Optionally, in the device for calculating a stomach prevalence probability based on big data according to the embodiment of the present application, the first calculation module includes:

a first calculation unit configured to calculate an intake energy consumption of the target user based on the exercise amount information, the diet information, and the weight information;

a second calculation unit for calculating stomach function status of the target user according to the toileting information, the diet information and the intake energy consumption; the stomach function status is divided into a plurality of grades from the state of being stolen.

Optionally, in the device for calculating a gastric disease probability based on big data according to the embodiment of the present application, the device further includes:

the second acquisition module is used for acquiring a first training sample set, wherein the first training sample set comprises a plurality of first training samples, and each first training sample comprises sample motion amount information, sample weight information, sample diet information, sample toileting information, sample emotion condition information and sample physiological condition information;

the third calculation module is used for calculating the stomach function condition corresponding to the first training sample according to the sample motion amount information, the sample weight information, the sample diet information and the sample toileting information;

and the first training module is used for selecting a corresponding neural network model according to the stomach function condition, inputting the plurality of first training samples into the neural network model for training, and obtaining a neural network calculation model under the corresponding stomach function condition.

Optionally, in the computing device for calculating a stomach prevalence probability based on big data according to the embodiment of the present application, the first training module is configured to:

obtaining a first training sample from the first training sample set as a current first training sample; inputting the current first training sample into the neural network model for training to obtain a trained first neural network model and a corresponding output result; calculating a loss value based on the output result, and judging whether the loss value is greater than a preset threshold value; if the current first training sample is larger than the preset threshold value, selecting an untrained first training sample from the first training sample set as a current first training sample, and returning to the step of inputting the current first training sample into the neural network model for training; and if the first neural network model is smaller than the preset threshold, ending the training, and taking the first neural network model as a neural network calculation model.

Optionally, in the computing device for calculating a stomach prevalence probability based on big data according to the embodiment of the present application, the second computing unit is configured to:

calculating a digestive absorption capacity status of the user from the dietary information and the intake energy expenditure;

acquiring a corresponding stomach condition calculation model according to the digestion capability condition;

and inputting the toileting information, the diet information and the intake energy consumption into the stomach condition calculation model to obtain the stomach function condition of the target user.

a third obtaining module, configured to obtain a second training sample set, where the second training sample set includes a plurality of second training samples, and each of the second training samples includes sample toileting information, sample diet information, and sample intake energy consumption;

a fourth calculation module, configured to calculate a sample digestion and absorption capacity status of the user according to the sample diet information and the sample intake energy expenditure;

the third selection module is used for selecting a corresponding stomach condition model according to the digestion and absorption capacity condition of the sample;

and the second training module is used for training the stomach condition model according to the second training sample set to obtain a corresponding stomach condition calculation model.

Optionally, in the device for calculating a gastric disease probability based on big data according to the embodiment of the present application, the second calculating module is used for calculating the gastric disease probability based on big data. The method comprises the following steps:

the fourth calculation unit is used for inputting the exercise amount information, the weight information, the diet information, the toileting information and the physiological condition information into the neural network calculation model to obtain the initial probability of stomach diseases of the target user;

and the fifth calculating unit is used for correcting the initial probability according to the emotional condition information to obtain the probability of stomach diseases of the target user.

In a second aspect, the present application further provides a system for calculating a gastric disease probability based on big data, where the system includes: a memory and a processor, wherein the memory includes a program of a calculation method of stomach illness probability based on big data, and the calculation method of stomach illness probability based on big data realizes the following steps when the processor executes the calculation method:

acquiring the amount of exercise, weight information, diet information, toileting information, emotional condition information and physiological condition information of a target user in a preset time period;

calculating the stomach function condition of the target user according to the exercise amount information, the weight information, the diet information and the toileting information;

selecting a corresponding neural network calculation model of the stomach disease probability according to the stomach function condition;

and inputting the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information into the neural network calculation model to obtain the stomach illness probability of the target user.

Optionally, in the computing system based on big data stomach illness probability according to the embodiment of the present application, when executed by the processor, the program of the computing method based on big data stomach illness probability realizes the following steps:

calculating the intake energy consumption of the target user according to the exercise amount information, the diet information and the weight information;

calculating stomach function status of the target user according to the toileting information, the diet information and the intake energy expenditure; the stomach function status is divided into a plurality of grades from the state of being stolen. .

In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes a program for calculating a stomach prevalence probability based on big data, and when the program for calculating a stomach prevalence probability based on big data is executed by a processor, the method includes the following complementary steps:

As can be seen from the above, the computing device and the computing system for gastric disease probability based on big data provided by the embodiment of the application acquire the motion amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information of the target user within a preset time period; calculating the stomach function condition of the target user according to the exercise amount information, the weight information, the diet information and the toileting information; selecting a corresponding neural network calculation model of the stomach disease probability according to the stomach function condition; inputting the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information into the neural network calculation model to obtain the stomach illness probability of the target user; therefore, the disease probability is calculated, prediction is carried out based on big data, the emotional condition of the human is combined, and the accuracy of prediction can be improved.

Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a schematic structural diagram of a device for calculating a gastric disease probability based on big data according to an embodiment of the present application.

Fig. 2 is a schematic structural diagram of a system for calculating a gastric disease probability based on big data according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a block diagram of a big data-based device for calculating a stomach disease probability according to some embodiments of the present application, the big data-based device comprising: a first obtaining module 101, a first calculating module 102, a first selecting module 103 and a second calculating module 104.

The first obtaining module 101 is configured to obtain motion amount information, weight information, diet information, toileting information, emotional condition information, and physiological condition information of a target user within a preset time period.

The first calculating module 102 is configured to calculate a stomach function status of the target user according to the exercise amount information, the weight information, the diet information, and the toileting information. The exercise amount information can be actively uploaded and acquired by a user, or detected by an exercise bracelet or a mobile phone based on the user. The diet information comprises food consumption, wherein the food consumption can be obtained based on food ordering information of food ordering software of a user. Wherein the toileting information can be entered by a user. Wherein, the stomach function status can be classified into a plurality of grades from the strong or weak stomach.

The first selection module 103 is configured to select a neural network computational model of the stomach disease probability according to the stomach function status. Different neural network models are selected for the stomach function conditions of different grades, and the different neural network models are obtained by adopting sample data of different stomach function conditions for training.

The second calculating module 104 is configured to input the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information, and the physiological condition information into the neural network calculation model, so as to obtain the probability that the stomach of the target user is ill. The exercise amount information is marked by exercise time and exercise type, and the diet information comprises meal time, meal amount and meal food types. The emotional condition information is a recent approximate emotional condition, such as a negative or positive state, or a heightened state. The physiological parameter information includes, but is not limited to, heart rate, blood pressure, weight, blood glucose level, etc. of the user.

In some embodiments, the first computing module 102 includes: a first calculation unit configured to calculate an intake energy consumption of the target user based on the exercise amount information, the diet information, and the weight information; a second calculation unit for calculating stomach function status of the target user according to the toileting information, the diet information and the intake energy consumption; the stomach function status is divided into a plurality of grades from the state of being stolen.

Wherein, in some embodiments, the apparatus further comprises: the second acquisition module is used for acquiring a first training sample set, wherein the first training sample set comprises a plurality of first training samples, and each first training sample comprises sample motion amount information, sample weight information, sample diet information, sample toileting information, sample emotion condition information and sample physiological condition information; the third calculation module is used for calculating the stomach function condition corresponding to the first training sample according to the sample motion amount information, the sample weight information, the sample diet information and the sample toileting information; and the first training module is used for selecting a corresponding neural network model according to the stomach function condition, inputting the plurality of first training samples into the neural network model for training, and obtaining a neural network calculation model under the corresponding stomach function condition.

Specifically, the first training module is configured to: obtaining a first training sample from the first training sample set as a current first training sample; inputting the current first training sample into the neural network model for training to obtain a trained first neural network model and a corresponding output result; calculating a loss value based on the output result, and judging whether the loss value is greater than a preset threshold value; if the current first training sample is larger than the preset threshold value, selecting an untrained first training sample from the first training sample set as a current first training sample, and returning to the step of inputting the current first training sample into the neural network model for training; and if the first neural network model is smaller than the preset threshold, ending the training, and taking the first neural network model as a neural network calculation model.

In some embodiments, the second computing unit is to: calculating a digestive absorption capacity status of the user from the dietary information and the intake energy expenditure; acquiring a corresponding stomach condition calculation model according to the digestion capability condition; and inputting the toileting information, the diet information and the intake energy consumption into the stomach condition calculation model to obtain the stomach function condition of the target user.

In some embodiments, the apparatus further comprises: a third obtaining module, configured to obtain a second training sample set, where the second training sample set includes a plurality of second training samples, and each of the second training samples includes sample toileting information, sample diet information, and sample intake energy consumption; a fourth calculation module, configured to calculate a sample digestion and absorption capacity status of the user according to the sample diet information and the sample intake energy expenditure; the third selection module is used for selecting a corresponding stomach condition model according to the digestion and absorption capacity condition of the sample; and the second training module is used for training the stomach condition model according to the second training sample set to obtain a corresponding stomach condition calculation model.

Wherein the market absorption capacity status is used to characterize the absorption capacity of the food product by the user. Users with different absorption capacities adopt different stomach condition models and sample data of users with corresponding types for training.

In some embodiments, the second calculation module 104. The method comprises the following steps: the fourth calculation unit is used for inputting the exercise amount information, the weight information, the diet information, the toileting information and the physiological condition information into the neural network calculation model to obtain the initial probability of stomach diseases of the target user; and the fifth calculating unit is used for correcting the initial probability according to the emotional condition information to obtain the probability of stomach diseases of the target user. Since the physical function of a person is different in different emotional states, the more active the person is, the better the physical function is, and therefore, the lower the probability that the stomach thereof is ill, and therefore, correcting the calculated initial probability based on the emotional state can improve the accuracy of the calculation. During specific calibration, specific calibration coefficients can be calculated based on large data or large batch of specific cases.

As can be seen from the above, the embodiment of the application acquires the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information of the target user within the preset time period; calculating the stomach function condition of the target user according to the exercise amount information, the weight information, the diet information and the toileting information; selecting a corresponding neural network calculation model of the stomach disease probability according to the stomach function condition; inputting the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information into the neural network calculation model to obtain the stomach illness probability of the target user; therefore, the disease probability is calculated, prediction is carried out based on big data, the emotional condition of the human is combined, and the accuracy of prediction can be improved.

According to the embodiment of the invention, the method further comprises the following steps:

a fourth calculation module calculates the digestion and absorption capacity condition of the sample according to the toilet information of the sample, the diet information of the sample and the intake energy consumption of the sample;

the first calculation module performs dynamic parameter correction according to the sample digestion and absorption capacity calculated by the fourth calculation module in combination with the sample weight information, the sample motion amount information and the sample physiological condition information to obtain a dynamic parameter index of the corrected sample digestion and absorption capacity;

and the third selection module corrects the selected stomach condition model according to the dynamic parameter index of the sample digestion and absorption capacity condition.

The fourth calculating module calculates the digestive absorption capacity of the sample according to the collected sample toileting information, sample diet information and sample intake energy consumption by a formula P ═ a1b1+ a2b2+ a3b3, and obtains an evaluation index of the digestive absorption capacity of the stomach of the sample, wherein P is the evaluation index of the digestive absorption capacity of the stomach of the sample, a1, a2 and a3 are weight coefficients of the collected sample information parameters, and b1, b2 and b3 are the collected sample toileting information, sample diet information and the information parameters of the sample intake energy consumption, respectively; and performing dynamic function correction on the calculated result of the digestive absorption capacity of the sample by combining with real-time dynamic parameters of the weight information, the motion amount information and the physiological condition information of the sample, so as to obtain a dynamic parameter index of the digestive absorption capacity of the sample, wherein the dynamic function correction can be logarithmic function transient correction or threshold coordinate function finite element correction, in short, the dynamic parameter index quantization can be performed on the digestive absorption capacity of the sample obtained in real time by a certain dynamic parameter correction method, so that more accurate index parameters under the dynamic environment of the stomach of the sample are obtained.

the second calculation module monitors whether the stomach disease probability of the target user is larger than a preset stomach disease threshold of the target user;

if the stomach illness probability of the target user is larger than a preset stomach illness threshold of the target user, the first training module reselects and trains the updated neural network calculation model, and the first training module updates the neural network calculation model according to the stomach function condition updated by the first selection module;

the first selection module acquires an updated training sample set, wherein the updated training sample set comprises updated sample motion amount information, sample weight information, sample diet information, sample toileting information, sample emotion condition information and sample physiological condition information;

and the first selection module recalculates and updates the stomach function condition according to the updated training sample set information.

It should be noted that, when it is monitored that the stomach illness probability of the target user is greater than the preset stomach illness threshold of the target user, the first selection module acquires current stomach information of the user again to update the training sample set, acquires updated training sample set information again to perform recalculation to obtain an updated stomach function status, reselects the neural network calculation model according to the updated stomach function status to obtain a corresponding neural network model, inputs the updated training information into the reselected neural network model to perform retraining, and finally obtains a latest neural network calculation model corresponding to the updated stomach function status.

the second calculation module performs risk threshold analysis on single or multiple parameter information of the stomach function condition of the target user, such as exercise amount information, weight information, diet information, toileting information, emotional condition information and physiological condition information, which influence the stomach disease probability threshold according to the stomach disease probability of the target user calculated by the neural network model and in combination with the stomach function condition grade of the target user of the first calculation module;

screening by the neural network model according to the acquired single or multiple parameter information affecting the stomach disease probability threshold to acquire threshold risk key parameters;

and the first selection module performs threshold fitting analysis in a patient database according to the screened and obtained threshold risk key parameter to obtain a sample stomach illness prescription for improving the threshold risk key parameter.

It should be noted that, the second calculation module performs risk threshold analysis on parameter information of a target user, which affects a stomach disease probability threshold greatly, to obtain parameters affecting a stomach disease probability threshold in motion amount information, weight information, diet information, toileting information, emotional condition information and physiological condition information of the target user, and performs risk threshold analysis on the parameter information in combination with a stomach function condition grade of the target user obtained by the first calculation module to screen out threshold risk key parameters, and performs threshold fitting analysis in a disease database through the first selection module on the screened key parameters, so as to search out a stomach sample with a maximum risk threshold and obtain a linked sample disease prescription for relieving stomach disease conditions of the target user by taking drugs under symptoms.

In a second aspect, the present application further provides a system for calculating a gastric disease probability based on big data, where the system includes: a memory 201 and a processor 202, wherein the memory includes a program of a method for calculating a gastric disease probability based on big data, and when the processor 202 executes the method, the method comprises the following steps:

acquiring the amount of exercise, weight information, diet information, toileting information, emotional condition information and physiological condition information of a target user in a preset time period; calculating the stomach function condition of the target user according to the exercise amount information, the weight information, the diet information and the toileting information; selecting a corresponding neural network calculation model of the stomach disease probability according to the stomach function condition; and inputting the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information into the neural network calculation model to obtain the stomach illness probability of the target user.

The exercise amount information can be actively uploaded and acquired by a user, or detected by a motion bracelet or a mobile phone based on the user. The diet information comprises food consumption, wherein the food consumption can be obtained based on food ordering information of food ordering software of a user. Wherein the toileting information can be entered by a user. Wherein, the stomach function status can be classified into a plurality of grades from the strong or weak stomach.

Different neural network models are selected for the stomach function conditions of different grades, and the different neural network models are obtained by adopting sample data of different stomach function conditions for training.

The exercise amount information is marked by exercise time and exercise type, and the diet information comprises meal time, meal amount and meal food types. The emotional condition information is a recent approximate emotional condition, such as a negative or positive state, or a heightened state. The physiological parameter information includes, but is not limited to, heart rate, blood pressure, weight, blood glucose level, etc. of the user.

In some embodiments, the calculation method of the stomach prevalence probability based on big data when executed by the processor 202 implements the following steps: calculating the intake energy consumption of the target user according to the exercise amount information, the diet information and the weight information; calculating stomach function status of the target user according to the toileting information, the diet information and the intake energy expenditure; the stomach function status is divided into a plurality of grades from the state of being stolen.

Wherein, in some embodiments, the calculation method of the stomach prevalence probability based on big data when executed by the processor 202 implements the following steps: acquiring a first training sample set, wherein the first training sample set comprises a plurality of first training samples, and each first training sample comprises sample motion amount information, sample weight information, sample diet information, sample toileting information, sample emotion condition information and sample physiological condition information; calculating stomach function conditions corresponding to a first training sample according to the sample motion amount information, the sample weight information, the sample diet information and the sample toileting information; and selecting a corresponding neural network model according to the stomach function condition, and inputting the plurality of first training samples into the neural network model for training to obtain a neural network calculation model under the corresponding stomach function condition.

In particular, the calculation method of the stomach prevalence probability based on big data, when executed by the processor 202, implements the following steps: obtaining a first training sample from the first training sample set as a current first training sample; inputting the current first training sample into the neural network model for training to obtain a trained first neural network model and a corresponding output result; calculating a loss value based on the output result, and judging whether the loss value is greater than a preset threshold value; if the current first training sample is larger than the preset threshold value, selecting an untrained first training sample from the first training sample set as a current first training sample, and returning to the step of inputting the current first training sample into the neural network model for training; and if the first neural network model is smaller than the preset threshold, ending the training, and taking the first neural network model as a neural network calculation model.

In some embodiments, the calculation method of the stomach prevalence probability based on big data when executed by the processor 202 implements the following steps: calculating a digestive absorption capacity status of the user from the dietary information and the intake energy expenditure; acquiring a corresponding stomach condition calculation model according to the digestion capability condition; and inputting the toileting information, the diet information and the intake energy consumption into the stomach condition calculation model to obtain the stomach function condition of the target user.

In some embodiments, the calculation method of the stomach prevalence probability based on big data when executed by the processor 202 implements the following steps: obtaining a second training sample set, wherein the second training sample set comprises a plurality of second training samples, and each second training sample comprises sample toileting information, sample diet information and sample intake energy consumption; calculating a sample digestive absorption capacity status of the user according to the sample diet information and the sample intake energy expenditure; selecting a corresponding stomach condition model according to the digestion and absorption capacity condition of the sample; and training the stomach condition model according to the second training sample set to obtain a corresponding stomach condition calculation model.

In some embodiments, the calculation method of the stomach prevalence probability based on big data when executed by the processor 202 implements the following steps: inputting the exercise amount information, the weight information, the diet information, the toileting information and the physiological condition information into the neural network calculation model to obtain the initial probability of stomach diseases of the target user; and correcting the initial probability according to the emotional condition information to obtain the probability of stomach diseases of the target user. Since the physical function of a person is different in different emotional states, the more active the person is, the better the physical function is, and therefore, the lower the probability that the stomach thereof is ill, and therefore, correcting the calculated initial probability based on the emotional state can improve the accuracy of the calculation. During specific calibration, specific calibration coefficients can be calculated based on large data or large batch of specific cases.

As can be seen from the above, the system provided by the embodiment of the application acquires the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information of the target user within the preset time period; calculating the stomach function condition of the target user according to the exercise amount information, the weight information, the diet information and the toileting information; selecting a corresponding neural network calculation model of the stomach disease probability according to the stomach function condition; inputting the exercise amount information, the weight information, the diet information, the toileting information, the emotional condition information and the physiological condition information into the neural network calculation model to obtain the stomach illness probability of the target user; therefore, the disease probability is calculated, prediction is carried out based on big data, the emotional condition of the human is combined, and the accuracy of prediction can be improved.

In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a program for calculating a stomach disease probability based on big data, and when the program is executed by a processor, the method for calculating a stomach disease probability based on big data implements the steps of the apparatus for calculating a stomach disease probability based on big data as described in any one of the above, for example, implements the following steps

The embodiment of the present application provides a storage medium, and when being executed by a processor, the computer program performs the method in any optional implementation manner of the above embodiment. The storage medium may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A device for calculating a gastric prevalence probability based on big data, the device comprising:

the first selection module is used for selecting a corresponding neural network calculation model of the stomach illness probability according to the stomach function condition;

2. The big data based calculating device for stomach prevalence probability according to claim 1, wherein the first calculating module comprises:

3. The big-data based calculating device of stomach prevalence probability according to claim 1, further comprising:

4. The big-data-based apparatus for calculating the prevalence of the stomach, according to claim 3, wherein the first training module is configured to:

5. The big-data-based stomach prevalence calculation apparatus according to claim 2, wherein the second calculation unit is configured to:

6. The big-data based apparatus for calculating a gastric prevalence probability according to claim 5, further comprising:

7. The big-data-based apparatus for calculating a gastric prevalence probability according to claim 1, wherein the second calculation module is configured to calculate the gastric prevalence probability. The method comprises the following steps:

8. A system for calculating a gastric prevalence probability based on big data, the system comprising: a memory and a processor, wherein the memory includes a program of a calculation method of stomach illness probability based on big data, and the calculation method of stomach illness probability based on big data realizes the following steps when the processor executes the calculation method:

9. The system for calculating the gastric prevalence probability based on big data according to claim 8, wherein the program of the method for calculating the gastric prevalence probability based on big data when executed by the processor implements the following steps:

calculating stomach function status of the target user according to the toileting information, the diet information and the intake energy expenditure; the stomach function status is divided into a plurality of grades from the state of being stolen.

10. A computer-readable storage medium, wherein the computer-readable storage medium includes a program for calculating a stomach prevalence probability based on big data, and when the program for calculating a stomach prevalence probability based on big data is executed by a processor, the method comprises the following complementary steps: