CN114388102A

CN114388102A - Diet recommendation method and device and electronic equipment

Info

Publication number: CN114388102A
Application number: CN202111617290.0A
Authority: CN
Inventors: 汪蒙; 龚辉; 练恒
Original assignee: Ali Health Technology China Co ltd
Current assignee: Ali Health Technology China Co ltd
Priority date: 2021-12-27
Filing date: 2021-12-27
Publication date: 2022-04-22

Abstract

The embodiment of the specification provides a diet recommendation method and device and electronic equipment. The method comprises the following steps: acquiring diet information uploaded by a user; inputting the diet information into the diet knowledge map, and acquiring diet basic information matched with the diet information in the diet knowledge map and diet basic information corresponding to the diet information under the diet label; inputting the diet basic information and the health information of the user into a prediction model for calculation to obtain a diet analysis result output by the prediction model; recommending a healthy diet plan to the user based on the diet analysis results.

Description

Diet recommendation method and device and electronic equipment

Technical Field

The embodiment of the specification relates to the technical field of computers, in particular to a diet recommendation method and device and electronic equipment.

Background

With the abundance of physical life, people pay more and more attention to the physical health condition of individuals. Medical treatment is the final choice, and people are still required to prevent the occurrence of diseases or the aggravation of the diseases in daily life.

The health of the diet in daily life has a great influence on the prevention of diseases.

However, most of the ordinary people do not have the relevant professional knowledge to identify their own dietary conditions, and therefore, cannot obtain a dietary plan meeting their health needs.

Disclosure of Invention

The diet recommending method and device and the electronic equipment provided by the embodiment of the specification are used for solving the problem that a user cannot obtain a diet scheme meeting the self health requirement.

According to a first aspect of embodiments herein, there is provided a diet recommendation method applied to a diet management system including a diet knowledge map for determining diet basic information, the method including:

acquiring diet information uploaded by a user;

inputting the diet information into the diet knowledge map, and acquiring diet basic information matched with the diet information in the diet knowledge map;

inputting the diet basic information and the health information of the user into a prediction model for calculation to obtain a diet analysis result output by the prediction model;

recommending a healthy diet plan to the user based on the diet analysis results.

Optionally, the acquiring the diet information uploaded by the user includes:

acquiring an image containing food uploaded by a user;

and identifying diet information corresponding to the diet food from the image based on an image identification technology.

Optionally, the diet information includes diet information obtained by the user scanning a graphic code on a diet food.

Optionally, the recommending a healthy diet scheme to the user based on the diet analysis result includes:

and inputting the diet analysis result into a deep knowledge perception network for calculation, and recommending the healthy diet scheme output by the deep knowledge perception network to the user.

Optionally, the healthy diet plan includes replacing unhealthy diet with healthy diet in the diet information uploaded by the user.

Optionally, the acquiring the diet basic information matched with the diet information in the diet knowledge map includes:

acquiring a diet label to which the diet information belongs in the diet knowledge map;

and acquiring diet basic information corresponding to the diet information under the diet label.

Optionally, the dietary label comprises at least one of category, attribute, nutrition, theme; the category refers to a diet type to which the diet information belongs, the attribute refers to basic information of the diet information, the nutrition refers to nutrient elements of the diet information, and the theme refers to a dietary therapy function of the diet information.

Optionally, the dietary type comprises at least one of staple food, vegetables, fruits, drinks; the basic information comprises at least one of food materials, cooking modes, tastes, meat and vegetables and vegetable lines; the nutrient elements comprise at least one of protein, fat, vitamins and calories; the dietotherapy function comprises at least one of healthy meal, sugar control, weight reduction, calcium supplement, blood enrichment and fat reduction.

Optionally, the health information of the user includes user sign data in a user health profile.

Optionally, the user vital sign data includes a disease history.

Optionally, the prediction model comprises a decision tree model.

According to a second aspect of embodiments herein, there is provided a diet recommendation device for use in a diet management system including a diet knowledge map for determining diet basic information, the device comprising:

the acquisition unit acquires the diet information uploaded by the user;

the matching unit is used for inputting the diet information into the diet knowledge map and acquiring diet basic information matched with the diet information in the diet knowledge map;

the calculation unit is used for inputting the diet basic information and the health information of the user into a prediction model for calculation to obtain a diet analysis result output by the prediction model;

a recommending unit recommending a healthy diet scheme to the user based on the diet analysis result.

Optionally, the obtaining unit includes:

the method comprises the steps of acquiring an image containing diet food uploaded by a user, and identifying diet information corresponding to the diet food from the image based on an image identification technology.

Optionally, the recommending unit includes:

Optionally, the matching unit includes:

the first matching subunit inputs the diet information into the diet knowledge map and acquires a diet label to which the diet information belongs in the diet knowledge map;

and the second matching subunit acquires the diet basic information corresponding to the diet information under the diet label.

Optionally, the user vital sign data includes a disease history.

Optionally, the prediction model comprises a decision tree model.

According to a third aspect of embodiments herein, there is provided an electronic apparatus including:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to any of the above diet recommendation methods.

The embodiment of the specification provides a diet recommendation scheme, which is characterized in that a diet knowledge map is constructed, diet information uploaded by a user is analyzed in a targeted manner, and the diet scheme meeting the health requirements of the user can be recommended by combining health information of the user.

Drawings

FIG. 1 is a flow chart of a diet recommendation method provided in an embodiment of the present description;

FIG. 2 is a schematic view of a meal tag provided by an embodiment of the present disclosure;

FIG. 3 is a schematic of a dietary knowledge map provided in an embodiment of the present description;

FIG. 4 is a schematic illustration of a diet analysis and diet recommendation provided in an embodiment of the present description;

fig. 5 is a hardware configuration diagram of a diet recommending apparatus according to an embodiment of the present specification;

fig. 6 is a block diagram of a diet recommending apparatus according to an embodiment of the present specification.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the specification, as detailed in the appended claims.

The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the description. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of the present specification. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

An embodiment of the diet recommendation method provided in this specification is described below with reference to fig. 1, and is applied to a diet management system including a diet knowledge map for determining diet basic information, the method including:

step 210: acquiring diet information uploaded by a user;

step 220: inputting the diet information into the diet knowledge map, and acquiring a diet label to which the diet information belongs in the diet knowledge map and diet basic information corresponding to the diet information under the diet label;

step 230: inputting the diet basic information and the health information of the user into a prediction model for calculation to obtain a diet analysis result output by the prediction model;

step 240: recommending a healthy diet plan to the user based on the diet analysis results.

In this specification, the diet management system refers to any type of machine or cluster of machines for managing a diet plan.

For example, in practical applications, the diet management system may be specifically a cloud-deployed machine or a cloud cluster for diet recommendation.

In this specification, the diet management system includes a diet knowledge map for determining diet basic information.

For example, in practical applications, the diet management system stores a diet knowledge map for determining diet basic information in a background database, and the diet management system may specifically provide a diet recommendation service to a user in a manner of a web page or a docked APP software. Such as: when a user accesses the diet management system through a terminal, the diet management system can interact with a client of the terminal held by the user, including that the user uploads diet information and the like through an operation interface displayed by the client.

In an exemplary embodiment, the step 210 of acquiring the food and drink information uploaded by the user may include:

acquiring an image containing food uploaded by a user;

For example, in practical applications, a user may capture an image including a food item by an imaging device (e.g., a camera) mounted on the terminal, and upload the captured image to a food and drink management system. And the diet management system can identify the diet information corresponding to the diet food from the received image based on the image identification technology.

In the present specification, the diet information may include names of diet meals. For example, rice, milk, egg, noodles, green vegetables, fish, meat, etc

In the present specification, the image recognition technology may be based on an image recognition model constructed by a Convolutional Neural Network (CNN) algorithm.

In an exemplary embodiment, the diet information includes diet information obtained by the user scanning a graphic code on the diet food.

In this example, instead of uploading an image, the user scans a graphic code sprayed on the food item by a scanning device (e.g., a barcode scanner, a camera, etc.) mounted on the terminal to acquire the food information of the food item, and uploads the food information to the food management system.

In this specification, the graphic code may include a bar code or a two-dimensional code.

After the above-mentioned diet information is introduced, the knowledge-graph in this specification is next introduced. For ease of understanding, a knowledge-graph is briefly introduced here, which is an information technology that maps human cognition on the objective world into the computer world. The object in the objective world is used as an entity in the computer world, and the attribute or the relationship between the objects in the objective world is used as the attribute or the relationship between the entities in the computer world, so that the object, the attribute of the object and the relationship between the objects in the objective world are described through the computer world.

In implementation, the construction core of the knowledge graph is a triple: entity (Entity), Attribute (Attribute), and relationship (relationship); the attributes of objects in the objective world, or relationships between objects, may be represented as < entity, relationship, entity > or < entity, attribute value >.

For example: "the protein content of milk is 3.5g/100 ml" is a human perception and can be expressed in the computer world by using the triple < milk, protein, 3.5g/ml > when described by the triple means of the knowledge map; wherein, the 'milk' is an entity, the 'protein' represents the entity attribute of the 'milk' entity, and the '3.5 g/ml' represents the attribute value of the 'protein' entity attribute.

Therefore, by constructing the three-element data of the massive knowledge map, the human cognition can form the knowledge map data of massive data in the computer world, the knowledge map data can be stored through a map database, and the specific type and principle of the map database refer to the technical description of the map database, which is not repeated here.

In the present specification, the diet knowledge map refers to the entity attributes as diet tags and attribute values as diet basic information.

In an exemplary embodiment, the diet label includes at least one of category, attribute, nutrition, theme;

the category refers to a diet type to which the diet information belongs, the attribute refers to basic information of the diet information, the element refers to a nutrient element of the diet information, and the theme refers to a dietary therapy function of the diet information.

Referring to fig. 2, fig. 2 is a schematic diagram of a diet label according to an embodiment of the present disclosure.

In this example, the diet tags described above can be divided into four major tags of category, attribute, nutrition and theme, and a number of sub-tags are further subdivided under each tag.

For example, the types of diets represented by the category labels in FIG. 2 may include staple foods, vegetables, fruits, drinks, dishes, etc.; the basic information represented by the attribute tag can comprise food materials, cooking modes, tastes, meat and vegetables, vegetables and the like; the nutritional elements represented by the nutritional label may include protein, fat, vitamins, calories, GI (Glycemic Index), GL (Glycemic Load), etc.; the dietary therapy functions represented by the subject label may include healthy meals, glucose control, weight loss, calcium supplementation, blood enrichment, fat reduction, and the like.

It should be noted that there is a certain relationship between the diet labels; generally, according to different categories of diet information, the attributes of the corresponding diet information are different; the nature of the diet further affects the nutrition of the diet; the attributes and nutrition of the diet are ultimately the basis for determining the subject.

In addition, the data source of the diet knowledge map can be obtained from public channels, such as recipes, scientific common knowledge or the Internet; or may be gradually improved during the operation of the system.

For example, the diet information corresponding to the diet food item is identified from the image based on the image identification technology, or the diet information uploaded by scanning a graphic code on the diet food item by a user can be recorded in a diet knowledge graph as diet information, and the missing diet basic information can be updated into the knowledge graph after being manually labeled, or can be automatically acquired from an open channel based on a related algorithm to be updated into the knowledge graph.

In an exemplary embodiment, the obtaining of the diet basic information in the diet knowledge map matching with the diet information in step 220 includes:

and acquiring a diet label to which the diet information belongs in the diet knowledge map, and further acquiring diet basic information corresponding to the diet information under the diet label.

Referring to fig. 3, fig. 3 is a schematic view of a dietary knowledge map provided in an embodiment of the present disclosure.

In fig. 3, the diet information input into the diet knowledge map is mutton buckwheat flour, the category label matched with the mutton buckwheat flour in the diet knowledge map is obtained as a dish, and further the diet basic information corresponding to the mutton buckwheat flour under the dish is wheaten food.

Similarly, the attribute labels matched with mutton buckwheat noodles in the diet knowledge map are meat and vegetables, food materials and cooking modes; further, the basic information of the diet corresponding to the mutton buckwheat noodles is meat; the food material is prepared from mutton and buckwheat flour as dietary basic information corresponding to the mutton and buckwheat flour; the basic diet information corresponding to the mutton buckwheat noodles is cooked in the cooking mode.

The nutrition label matched with mutton buckwheat flour in the diet knowledge map is calorie, GI, GE, fat, vitamins and protein; further, the basic diet information corresponding to mutton buckwheat noodles under calorie is 1200 kcal; the diet basic information corresponding to the mutton buckwheat noodles under GI is 59; the basic information of the diet corresponding to the mutton buckwheat noodles under GL is 11.3; the diet basic information corresponding to mutton buckwheat noodles under fat is 5.41; the basic diet information corresponding to mutton buckwheat noodles under the vitamin is 1.47; the basic information of diet corresponding to mutton buckwheat noodles under protein is 9.52;

the subject label matched with the mutton buckwheat flour in the diet knowledge map is a healthy meal; further, the basic diet information corresponding to the mutton buckwheat noodles under healthy meals is diabetes.

It should be noted that the labels and the numerical values in fig. 3 are only examples, and especially, the numerical values may deviate from actual values, and it should be understood that the examples do not specifically limit the embodiments provided in the present specification.

In an exemplary embodiment, after the diet basic information is obtained, the diet basic information and the health information of the user may be further input into a prediction model for calculation, and a diet analysis result output by the prediction model is obtained.

In practical applications, the health information of the user may include user sign data in a health profile of the user. For example, a physical examination report generated after a physical examination by the user.

The schematic of the diet analysis and diet recommendation described below in connection with fig. 4 is illustrated.

As seen in FIG. 4, the user vital sign data can include at least the user's disease history. In addition, basic physical information of the user such as age, weight, height, etc. may also be included.

In the implementation process, basic diet information (such as 338 kcal calorie, subject glucose and fat control, GI34 shown in fig. 4) and health information of the user (such as age 60, weight 45kg, height 170 cm, disease history diabetes shown in fig. 4) can be input into the prediction model for calculation, and the diet analysis result is obtained as follows: is not suitable for diabetic patients.

The prediction model may include a Decision Tree (Decision Tree) shown in fig. 4. By inputting the diet basic information and the health information of the user into the decision tree, the diet basic information and the health information of the user can be finally displayed in a data form due to calculation of the decision tree, and diet analysis results can be output in a fractional system or a hierarchical system.

Referring again to FIG. 4, the outputs of several different forms (in fractions, colors green, yellow, red, and grades A/B/C) are illustrated in FIG. 4.

In the example of the score form, the decision tree calculates the inputted diet basic information and the health information of the user, and a score reflecting the diet health degree can be calculated; if the score is between 100 and 70, the diet food corresponding to the diet information has no problem, and the user can eat the food at ease; if the score is between 70 and 50, it indicates that the user can only eat a small amount; if the score is below 50, it indicates that the user is not able to eat.

By applying the embodiment, diet analysis can be performed after the diet basic information is combined with the health information of the user, and whether the diet condition of the user meets the actual health requirement of the user is obtained.

In an exemplary embodiment, after determining the diet analysis results, a healthy diet regimen may further be recommended to the user based on the diet analysis results.

In this specification, a healthy diet plan may be recommended to a user based on diet analysis results in conjunction with a recommendation algorithm.

As can be seen in fig. 4, the recommendation algorithm may adopt DKN algorithm (Deep Knowledge-Aware Network).

Specifically, the diet analysis result can be input into a deep knowledge perception network for calculation, and a healthy diet scheme output by the deep knowledge perception network is recommended to the user.

As shown in fig. 4, the recommended diet plan includes diet contents in accordance with the user health information, for example, since the user has diabetes, the food recommended to the user is bean curd for which the recommended reason is that bean curd is suitable for diabetic people; for another example, the weight of the user is heavier than 85kg, so that the food recommended to the user is beef, and the recommended reason is that the beef is suitable for fat-reducing people; and because the user ages to 60 years, the recommended food for the user is the pumpkin soup, and the recommended reason is that the pumpkin soup is suitable for people over 60 years old. Therefore, the dietary schemes recommended to the users are healthy dietary contents meeting the health requirements of the users.

Applying the above example, since the DKN algorithm is a recommendation algorithm suitable for the knowledge map, the DKN algorithm used in this specification can quickly calculate the healthy diet scheme to be recommended according to the diet knowledge map, and the processing efficiency is higher compared with that of a general recommendation algorithm.

In an exemplary embodiment, the healthy diet program includes replacing unhealthy diet with healthy diet in the user uploaded diet information.

In practical application, unhealthy diet in the current diet content of the user is replaced by healthy diet, so that the user can more reasonably collocate diet meeting the health requirement, and the cost of diet management of the user is reduced; for the users with chronic diseases, the ability of rectifying the diet can be effectively improved, thereby achieving the purpose of preventing the diseases.

In correspondence with the foregoing embodiments of the diet recommendation method, the present specification also provides embodiments of a diet recommendation device. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. The software implementation is taken as an example, and is formed by reading a corresponding computer program in the nonvolatile memory into the memory for running through a processor of the device where the software implementation is located as a logical means. From a hardware aspect, as shown in fig. 5, the hardware structure diagram of the device where the diet recommendation device is located in this specification is shown, except for the processor, the network interface, the memory and the nonvolatile memory shown in fig. 5, the device where the device is located in the embodiment may generally recommend an actual function according to diet, and may further include other hardware, which is not described again.

Referring to fig. 6, a block diagram of a diet recommendation device provided in an embodiment of the present disclosure, the device corresponding to the embodiment shown in fig. 1, is applied to a diet management system including a diet knowledge map for determining diet basic information, and the device includes:

an obtaining unit 310, which obtains the diet information uploaded by the user;

the matching unit 320 is used for inputting the diet information into the diet knowledge map and acquiring diet basic information matched with the diet information in the diet knowledge map;

the calculating unit 330 is configured to input the diet basic information and the health information of the user into a prediction model for calculation, so as to obtain a diet analysis result output by the prediction model;

a recommending unit 340 recommending a healthy diet scheme to the user based on the diet analysis result.

In an exemplary embodiment, the obtaining unit 310 includes:

In an exemplary embodiment, the meal information includes meal information obtained by the user scanning a graphic code on a meal item.

In an exemplary embodiment, the recommending unit 340 includes:

In an exemplary embodiment, the healthy diet program includes replacing unhealthy diets in the user uploaded diet information with healthy diets.

In an exemplary embodiment, the matching unit 320 includes:

In an exemplary embodiment, the dietary label includes at least one of category, attribute, nutrition, theme; the category refers to a diet type to which the diet information belongs, the attribute refers to basic information of the diet information, the nutrition refers to nutrient elements of the diet information, and the theme refers to a dietary therapy function of the diet information.

In an exemplary embodiment, the dietary type includes at least one of a staple food, a vegetable, a fruit, a drink; the basic information comprises at least one of food materials, cooking modes, tastes, meat and vegetables and vegetable lines; the nutrient elements comprise at least one of protein, fat, vitamins and calories; the dietotherapy function comprises at least one of healthy meal, sugar control, weight reduction, calcium supplement, blood enrichment and fat reduction.

In an exemplary embodiment, the health information of the user includes user sign data in a health profile of the user.

In an exemplary embodiment, the user vital sign data includes a disease history.

In an exemplary embodiment, the predictive model includes a decision tree model.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.

The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.

For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the 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 modules can be selected according to actual needs to achieve the purpose of the solution in the specification. One of ordinary skill in the art can understand and implement it without inventive effort.

Fig. 6 above describes the internal functional modules and the structural schematic of the diet recommending apparatus, and the implementation subject of the diet recommending apparatus can be an electronic device, which includes:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to perform any of the above embodiments of the diet recommendation method.

In the above embodiments of the electronic device, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, and the aforementioned memory may be a read-only memory (ROM), a Random Access Memory (RAM), a flash memory, a hard disk, or a solid state disk. The steps of a method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiment of the electronic device, since it is substantially similar to the embodiment of the method, the description is simple, and for the relevant points, reference may be made to part of the description of the embodiment of the method.

Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This specification is intended to cover any variations, uses, or adaptations of the specification following, in general, the principles of the specification and including such departures from the present disclosure as come within known or customary practice within the art to which the specification pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the specification being indicated by the following claims.

It will be understood that the present description is not limited to the precise arrangements described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.

Claims

1. A diet recommendation method applied to a diet management system including a diet knowledge map for determining diet basic information, the method comprising:

acquiring diet information uploaded by a user;

2. The method of claim 1, wherein the obtaining of the dietary information uploaded by the user comprises:

acquiring an image containing food uploaded by a user;

3. The method of claim 1, the dietary information comprising dietary information obtained by the user scanning a graphical code on a dietary food item.

4. The method of claim 1, the recommending a healthy diet regimen to the user based on the diet analysis results, comprising:

5. The method of claim 1 or 4, the healthy diet program comprising replacing unhealthy diets in the user uploaded diet information with healthy diets.

6. The method of claim 1, wherein the obtaining of the dietary basis information in the dietary profile that matches the dietary information comprises:

7. The method of claim 6, the dietary label comprising at least one of a category, a property, a nutrition, a theme; the category refers to a diet type to which the diet information belongs, the attribute refers to basic information of the diet information, the nutrition refers to nutrient elements of the diet information, and the theme refers to a dietary therapy function of the diet information.

8. The method of claim 7, the dietary type comprising at least one of a staple food, a vegetable, a fruit, a beverage; the basic information comprises at least one of food materials, cooking modes, tastes, meat and vegetables and vegetable lines; the nutrient elements comprise at least one of protein, fat, vitamins and calories; the dietotherapy function comprises at least one of healthy meal, sugar control, weight reduction, calcium supplement, blood enrichment and fat reduction.

9. The method of claim 1, wherein the health information of the user comprises user vital sign data in a health profile of the user.

10. The method of claim 9, the user vital sign data comprising a disease history.

11. The method of claim 1, the predictive model comprising a decision tree model.

12. A diet recommendation device for use in a diet management system including a diet knowledge map for determining diet basis information, the device comprising:

the acquisition unit acquires the diet information uploaded by the user;

13. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method of any of the preceding claims 1-11.