CN111383731A

CN111383731A - Medicated diet recommendation method and system, electronic equipment and storage medium

Info

Publication number: CN111383731A
Application number: CN202010152791.5A
Authority: CN
Inventors: 沈靖雯
Original assignee: Ningbo Fotile Kitchen Ware Co Ltd
Current assignee: Ningbo Fotile Kitchen Ware Co Ltd
Priority date: 2020-03-06
Filing date: 2020-03-06
Publication date: 2020-07-07
Anticipated expiration: 2040-03-06
Also published as: CN111383731B

Abstract

The invention discloses a medicated diet recommendation method, a system, electronic equipment and a storage medium. The medicated diet recommendation method comprises the following steps: acquiring a disease name and a disease expression of a user; searching food materials and medicinal materials which are beneficial to the disease names to respectively construct a benign food material set and a benign medicinal material set; searching food materials and medicinal materials which are beneficial to the manifestation of the disease, so as to respectively update the benign food material set and the benign medicinal material set; searching the medicated diet comprising the food materials in the updated benign food material set and the medicated diet comprising the traditional Chinese medicinal materials in the updated benign medicinal material set to construct a recommended medicated diet set; recommending the medicated diet in the recommended medicated diet set to the user. The invention can recommend the medicated diet suitable for the body nursing of the user to the user on the basis of comprehensively acquiring the body condition of the user, and comprehensively considers the disease condition name and the disease condition expression of the user, thereby realizing accurate recommendation of the medicated diet under the conditions of the same disease and different diseases and being beneficial to the body nursing of the user.

Description

Medicated diet recommendation method and system, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of intelligent household appliances, in particular to a medicated diet recommendation method, a system, electronic equipment and a storage medium.

Background

Nowadays, with the continuous acceleration of life rhythm, people pay more and more attention to the body health of people, and traditional Chinese medicine and diet which is helpful for recuperating diseases gradually enters thousands of families.

Disclosure of Invention

The invention aims to overcome the defect that most users are difficult to correctly select medicated diet due to lack of professional knowledge in the prior art, and provides a medicated diet recommendation method, a system, electronic equipment and a storage medium.

The invention solves the technical problems through the following technical scheme:

a method for recommending medicinal meals comprises the following steps:

acquiring a disease name and a disease expression of a user;

searching food materials and medicinal materials which are beneficial to the disease names to respectively construct a benign food material set and a benign medicinal material set;

searching food materials and medicinal materials which are beneficial to the manifestation of the disease, so as to respectively update the benign food material set and the benign medicinal material set;

searching the medicated diet comprising the food materials in the updated benign food material set and the medicated diet comprising the traditional Chinese medicinal materials in the updated benign medicinal material set to construct a recommended medicated diet set;

recommending the medicated diet in the recommended medicated diet set to the user.

Preferably, the step of acquiring the disease name and disease expression of the user comprises:

collecting a sound signal of the user;

performing voiceprint recognition on the sound signal to identify the disease name of the user, and/or performing semantic recognition on the sound signal to identify the disease expression of the voice input of the user;

and/or the presence of a gas in the gas,

the step of recommending the medicated diet in the recommended medicated diet set to the user comprises:

recommending the medicated diet in the recommended medicated diet set to the user through voice.

Preferably, the step of searching for the food materials and the medicinal materials favorable for the disease name comprises:

acquiring a first map, wherein the first map comprises a disease name, food materials and a first attribute value, the food materials are the food materials beneficial to the disease name, and the first attribute value is used for representing the beneficial degree of the food materials to the disease name;

acquiring a second map, wherein the second map comprises a disease name, medicinal materials and a second attribute value, the medicinal materials are medicinal materials beneficial to the disease name, and the second attribute value is used for representing the beneficial degree of the medicinal materials to the disease name;

the step of searching for food materials and medicinal materials beneficial to the manifestation of the disease comprises the following steps:

obtaining a third map, wherein the third map comprises a disease manifestation, a food material and a third attribute value, the food material is the food material beneficial to the disease manifestation, and the third attribute value is used for representing the beneficial degree of the food material to the disease manifestation;

acquiring a fourth map, wherein the fourth map comprises disease symptoms, medicinal materials and a fourth attribute value, the medicinal materials are medicinal materials beneficial to the disease symptoms, and the fourth attribute value is used for representing the beneficial degree of the medicinal materials to the disease symptoms.

Preferably, the step of updating the benign food material set and the benign medicinal material set respectively comprises:

judging whether the food materials which are found to be beneficial to the symptom expression are included in the benign food material set;

if the benign food material set is included in the benign food material set, judging whether the third attribute value of the food material is equal to the first attribute value;

if the first attribute value is equal to the second attribute value, adding the first attribute value and a first preset value to obtain a new first attribute value;

if not, taking the larger value of the first attribute value and the third attribute value as a new first attribute value;

if the food materials are not included in the benign food material set, adding the searched food materials which are beneficial to the symptom expression into the benign food material set;

judging whether the searched medicinal materials which are beneficial to the symptom expression are included in the benign medicinal material set;

if the attribute value is included in the benign medicinal material set, judging whether the fourth attribute value and the second attribute value of the medicinal material are equal;

if the first attribute value is equal to the second attribute value, adding the second attribute value and the first preset value to obtain a new second attribute value;

if not, taking the larger value of the second attribute value and the fourth attribute value as a new second attribute value;

if the medical materials are not included in the benign medical material set, the searched medical materials which are favorable for the disease manifestation are added into the benign medical material set.

Preferably, the step of searching for the medicated diet including the food materials in the updated benign food material set and the medicated diet including the traditional Chinese medicinal materials in the updated benign medicinal material set comprises the following steps:

selecting a plurality of food materials from the updated benign food material set according to the sequence of the first attribute value from high to low so as to construct a recommended food material set;

selecting a plurality of medicinal materials from the updated benign medicinal material set according to the sequence of the second attribute value from high to low to construct a recommended medicinal material set;

searching the medicated diet comprising the food materials in the recommended food material set and the medicated diet comprising the traditional Chinese medicinal materials in the recommended medicinal material set.

Preferably, before the step of recommending the medicated diet of the recommended medicated diet set to the user, the method further comprises the following steps:

searching food materials and medicinal materials harmful to the disease names to respectively construct a malignant food material set and a malignant medicinal material set;

searching food materials and medicinal materials harmful to the symptom expression so as to respectively update the malignant food material set and the malignant medicinal material set;

judging whether the medicated diet in the recommended medicated diet set comprises a malignant medicated diet, wherein the malignant medicated diet comprises the food materials in the updated malignant food material set or the medicinal materials in the updated malignant medicinal material set;

if yes, deleting the malignant medicated diet from the recommended medicated diet set so as to update the recommended medicated diet set.

acquiring the medicated diet preference of the user;

determining user preference values of the user to all medicated food according to the medicated food preference of the user;

screening all medicated foods according to the user preference value to construct a preference medicated food set;

judging whether the intersection of the recommended medicated diet set and the preferred medicated diet set is an empty set;

if not, updating the recommended medicated diet set into the intersection.

Preferably, the user preference comprises a plurality of preference features, the initial value of the user preference value is 0, and for each preference feature, the step of determining the user preference value of the user for all medicated food according to the medicated food preference of the user comprises:

judging whether the medicated food completely accords with the preference characteristics;

if the user preference value completely accords with the second preset value, adding the user preference value and the second preset value to be used as a new user preference value;

if the preference characteristics are not completely met, judging whether the medicated food is not met with the preference characteristics completely;

if the user preference value does not meet the preset value, subtracting the second preset value from the user preference value to serve as a new user preference value;

and if the user preference value is not completely inconsistent with the third preset value, adding the user preference value and the third preset value to be used as a new user preference value.

A system for dietary recommendation, comprising:

the first acquisition module is used for acquiring the disease name and the disease expression of the user;

the first searching and constructing module is used for searching food materials and medicinal materials which are favorable for the disease names so as to respectively construct a benign food material set and a benign medicinal material set;

a first searching and updating module, configured to search for food materials and medicinal materials that are favorable for the manifestation of the disease, so as to update the benign food material set and the benign medicinal material set respectively;

the second searching and constructing module is used for searching the medicated diet comprising the food materials in the updated benign food material set and the medicated diet comprising the traditional Chinese medicinal materials in the updated benign medicinal material set so as to construct a recommended medicated diet set;

and the recommending module is used for recommending the medicated diet in the recommended medicated diet set to the user.

Preferably, the first obtaining module comprises an acquisition unit, and the first obtaining module further comprises a voiceprint recognition unit and/or a semantic recognition unit; wherein:

the acquisition unit is used for acquiring the sound signal of the user;

the voiceprint recognition unit is used for carrying out voiceprint recognition on the sound signal so as to recognize the disease name of the user;

the semantic recognition unit is used for performing semantic recognition on the sound signal to recognize the symptom expression of the user voice input;

and/or the presence of a gas in the gas,

the recommending module is specifically used for recommending the medicated diet in the recommended medicated diet set to the user through voice.

Preferably, the first search building module includes:

a first obtaining unit, configured to obtain a first graph, where the first graph includes a disease name, a food material and a first attribute value, where the food material is a food material favorable for the disease name, and the first attribute value is used to represent a degree of benefit of the food material on the disease name;

the second acquisition unit is used for acquiring a second map, wherein the second map comprises a disease name, medicinal materials and a second attribute value, the medicinal materials are the medicinal materials beneficial to the disease name, and the second attribute value is used for representing the beneficial degree of the medicinal materials to the disease name;

the first lookup update module comprises:

a third obtaining unit, configured to obtain a third map, where the third map includes a disease manifestation, a food material and a third attribute value, where the food material is a food material favorable for the disease manifestation, and the third attribute value is used to represent a degree of benefit of the food material on the disease manifestation;

the fourth acquisition unit is used for acquiring a fourth map, wherein the fourth map comprises disease symptoms, medicinal materials and a fourth attribute value, the medicinal materials are medicinal materials beneficial to the disease symptoms, and the fourth attribute value is used for representing the beneficial degree of the medicinal materials to the disease symptoms.

Preferably, the first lookup updating module further includes:

a first determining unit configured to determine whether the searched food material favorable for the manifestation of the disease is included in the benign food material set;

if the benign food material set is included in the benign food material set, calling a second judging unit for judging whether the third attribute value of the food material is equal to the first attribute value;

if the first attribute value is equal to the first preset value, calling a first updating unit for adding the first attribute value and the first preset value to be used as a new first attribute value;

if the attribute values are not equal to the first attribute value, calling a second updating unit for taking the larger value of the first attribute value and the third attribute value as a new first attribute value;

if the food materials are not included in the benign food material set, calling a first adding unit for adding the searched food materials which are beneficial to the symptom expression to the benign food material set;

a third judging unit, configured to judge whether the searched medicinal material favorable for the manifestation of the disease is included in the benign medicinal material set;

if the attribute value is included in the benign medicinal material set, calling a fourth judging unit for judging whether the fourth attribute value of the medicinal material is equal to the second attribute value;

if the attribute values are equal to the first preset value, calling a third updating unit for adding the second attribute value and the first preset value to be used as a new second attribute value;

if the attribute values are not equal to the first attribute value, calling a fourth updating unit for taking the larger value of the second attribute value and the fourth attribute value as a new second attribute value;

if the benign medicinal material collection is not included, calling a second adding unit for adding the searched medicinal material which is favorable for the symptom expression into the benign medicinal material collection.

Preferably, the second search building module includes:

the first building unit is used for selecting a plurality of food materials from the updated benign food material set according to the sequence of the first attribute value from high to low so as to build a recommended food material set;

the second construction unit is used for selecting a plurality of medicinal materials from the updated benign medicinal material set according to the sequence of the second attribute value from high to low so as to construct a recommended medicinal material set;

and the searching unit is used for searching the medicated diet comprising the food materials in the recommended food material set and the medicated diet comprising the traditional Chinese medicinal materials in the recommended medicinal material set.

Preferably, the system for recommending medicated diet further comprises:

the third searching and constructing module is used for searching the food materials and the medicinal materials harmful to the disease names so as to respectively construct a malignant food material set and a malignant medicinal material set;

the second searching and updating module is used for searching food materials and medicinal materials harmful to the symptom expression so as to respectively update the malignant food material set and the malignant medicinal material set;

the first judging module is used for judging whether the medicated diet in the recommended medicated diet set comprises a malignant medicated diet, wherein the malignant medicated diet comprises food materials in an updated malignant food material set or medicinal materials in an updated malignant medicinal material set;

if yes, calling a first updating module for deleting the malignant medicated diet from the recommended medicated diet set so as to update the recommended medicated diet set.

Preferably, the system for recommending medicated diet further comprises:

the second acquisition module is used for acquiring the medicated diet preference of the user;

the determining module is used for determining the user preference values of the user to all medicated food according to the medicated food preference of the user;

the screening and constructing module is used for screening all medicated foods according to the user preference value so as to construct a preference medicated food set;

the second judgment module is used for judging whether the intersection of the recommended medicated diet set and the preferred medicated diet set is an empty set;

if not, calling a second updating module for updating the recommended medicated diet set into the intersection.

Preferably, the user preference includes a plurality of preference features, the initial value of the user preference value is 0, and the determining module includes, for each preference feature:

a fifth judging unit, configured to judge whether the medicated diet completely meets the preference feature;

if the user preference value is completely met, calling a fifth updating unit for adding the user preference value and a second preset value to be used as a new user preference value;

if the preference characteristics do not completely meet the requirements, a sixth judging unit is called to judge whether the medicated food does not meet the preference characteristics completely;

if the user preference value does not meet the preset value, calling a sixth updating unit for subtracting the second preset value from the user preference value to serve as a new user preference value;

if the user preference value does not completely meet the requirement, a seventh updating unit is called and used for adding the user preference value and a third preset value to serve as a new user preference value.

An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements any of the above-mentioned medical diet recommendation methods when executing the computer program.

A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of any of the above-mentioned method of medical meal recommendation.

The positive progress effects of the invention are as follows: according to the invention, the food materials and the medicinal materials suitable for the specific disease names and the food materials and the medicinal materials suitable for the specific disease expressions are obtained based on the disease names and the disease expressions of the users, and then the medicated diet comprising the related food materials and the related medicinal materials are obtained and recommended to the users, so that the medicated diet suitable for the body nursing of the users can be recommended to the users on the basis of comprehensively obtaining the body conditions of the users, and the disease names and the disease expressions of the users are comprehensively considered, so that the accurate recommendation of the medicated diet under the conditions of the same disease and different diseases can be realized, and the body nursing of the users is facilitated.

Drawings

Fig. 1 is a flowchart of a method for recommending medicated diet according to embodiment 1 of the present invention.

Fig. 2 is a partial flowchart of a method for recommending herbal cuisine according to embodiment 2 of the present invention.

Fig. 3 is a block diagram of a system for recommending herbal cuisine according to embodiment 3 of the present invention.

Fig. 4 is a block diagram of a system for recommending herbal cuisine according to embodiment 4 of the present invention.

Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention.

Detailed Description

The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.

Example 1

The present embodiment provides a method for recommending medicated diet, and referring to fig. 1, the method for recommending medicated diet of the present embodiment includes:

s101, acquiring a disease name and a disease expression of a user.

In this embodiment, the disease name (e.g., respiratory diseases such as rhinitis, pharyngitis, laryngitis, tracheitis, bronchitis, asthma, pneumonia, etc.) and the disease expression (e.g., wheezing, shortness of breath, cough, nasal obstruction, hoarseness, etc.) of the user may be obtained based on the voice of the user, and specifically, first, the voice signal of the user may be collected, for example, the voice signal of the disease expression may be collected by the user, and then the voice print recognition may be performed on the voice signal to recognize the disease name of the user, and the semantic recognition may be performed on the voice signal to recognize the disease expression of the user voice input.

The voiceprint recognition is mainly based on a voiceprint acoustic detection technology, acoustic characteristic parameters are extracted to construct a voiceprint recognition model, so that standard voice (healthy voice) and pathological voice can be recognized, and disease names corresponding to the pathological voice can be recognized. Specifically, the identification of the disease name by using voiceprint recognition mainly comprises two aspects of establishment of a voiceprint recognition model and voiceprint recognition, and the establishment of the voiceprint recognition model mainly comprises two parts of collection of a voice sample and extraction of characteristic parameters of the sample.

Specifically, the sound sample collection may include standard (standard voice) sample collection and abnormal (sick voice) sample collection, and the collected sound sample set may be recorded as S_iWhere i is used to indicate the type of sound sample, e.g. when i is 0, S_iCan characterize a standard sample set corresponding to the standard voice, and the number of samples can be recorded as N₀When i ≠ 0, S_iAn abnormal sample set corresponding to the pathological voice can be characterized, and each disease type is assumed to comprise g types, namely, the name of each disease can be recorded as: s₁,S₂,…,S_gThe number of samples corresponding to each type of disease can be recorded as N_i(i ═ 1,2, …, g). Thus, each sound sample set S_iCan be recorded as S_i(s_i1,…,s_ij,…,s_iNi) Wherein s is_ijJ sample, N, representing the ith class sample set_iThe number of samples in the ith type sample set is shown.

For extracting sample characteristic parameters, wherein the characteristic parameters are used for establishing voiceprint recognitionThe parameter index of the model may cover, but is not limited to, acoustic information such as tone quality, volume, pitch, pronunciation duration, range, special tone (e.g. cough), and sample s_ijCan be denoted as s_ij＝(x₀,…,x_k,…,x_n) Wherein x is_kRepresenting a sample s_ijN characterizes the number of the characteristic parameters.

The voiceprint recognition may include acquiring a voice signal of the user, extracting a feature parameter of the voice signal, and recognizing a voiceprint of the user, where the acquired voice signal may be recorded as an object s to be measured, and the extracted feature parameter of the object s to be measured may be recorded as s ═ x'₀,…,x′_k,…,x′_n). The step of recognizing the voiceprint of the user specifically may include inputting the characteristic parameters of the target s to be detected into a voiceprint recognition model for recognition and matching, for example, a modified Hausdorff distance (LTS-HD, denoted as D) may be used_LTS) The K-nearest neighbor algorithm of (1) is used as a classification basis, and specifically, the distance D between the target s to be detected and all the sound samples in the g +1 class sound sample set is calculated firstly_LTS(s,s_ij) And then the resulting D will be calculated_LTS(s,s_ij) The sound samples are sorted in an increasing mode according to results, the first K minimum distances are taken as K neighbor, and then the K neighbor obtained through statistics belongs to the sound sample set S_iFrequency of (t)_i(i-0, 1, …, g), finally defined as p-index (max { t)₀,…,t_g}), where index denotes the index acquisition function, i.e. the index for which p is the maximum frequency, S ∈ S_pObtaining a sound sample set to which the sample to be tested belongs, wherein if p is 0, S is output₀The voice analysis result of the user is represented as healthy, and if p is not equal to 0, S is output_pAnd characterizing the corresponding disease name of the user.

S102, food materials and medicinal materials favorable for disease names are searched for, so that a benign food material set and a benign medicinal material set are respectively constructed.

Specifically, step S102 may include the step of acquiring a first map and the step of acquiring a second map. The first map may include a disease name, a food material, and a first attribute value, wherein the food material is a food material favorable for the disease name, and the second map may include a disease name, a food material, and a first attribute valueAn attribute value is used for representing the degree of interest of the food material on the disease name, for example, the first attribute value can be recorded as r_ingreMay have r_ingre1,5 where r_ingre1 is suitable for characterizing the beneficial degree of food materials on disease names, r_ingreThe beneficial degree of the food material on the disease name can be characterized as being strong and suitable as 5. The second map may include a disease name, a herb, and a second attribute value, wherein the herb is favorable for the disease name, the second attribute value is used to represent the degree of benefit of the herb for the disease name, for example, the second attribute value may be denoted as r_medMay have r_med1,3,5, where r_med1 can characterize the beneficial degree of the medicinal materials to the disease names as weak and proper, and r is_med3, the beneficial degree of the medicinal materials on the disease names is moderate and proper, and r is_medThe Chinese medicinal material has strong and proper advantages on disease names by being characterized by 5. It should be understood that the first attribute value and the second attribute value may be set by a user according to an actual application, and are not limited to the above value example.

In this embodiment, the benign food material set can be denoted as F_good＝{f₁,…,f_j,…,f_qIn which f_jIs [ food material, r ]_ingre]The obtained benign herb collection can be recorded as M_good＝{m₁,…,m_i,…,m_pIn which m is_iIs prepared from Chinese medicinal materials_med]。

S103, food materials and medicinal materials which are beneficial to disease manifestation are searched, so that the benign food material set and the benign medicinal material set are updated respectively.

Specifically, step S103 may include a step of acquiring a third map and a step of acquiring a fourth map. The third map may include a disease manifestation, a food material, and a third attribute value, wherein the food material is a food material favorable for the disease manifestation, and the third attribute value is used for representing a degree of benefit of the food material on the disease manifestation, for example, the third attribute value may be denoted as sym_ingreThere may be sym_ingre1,5, where sym_ingre1 is suitable for characterizing the beneficial degree of food material on the manifestation of the disease, sym_ingreFood material pair disease can be characterized by 5The favorable degree of the symptoms is strong and proper. The fourth map may include disease manifestation, medicinal materials, and a fourth attribute value, wherein the medicinal materials are medicinal materials beneficial to disease manifestation, the fourth attribute value is used for representing the degree of benefit of the medicinal materials on disease manifestation, for example, the fourth attribute value may be denoted as sym_medThere may be sym_med1,3,5, where sym_med1 can characterize the beneficial degree of the medicinal materials on the symptoms, and sym is weak and suitable_med3, the beneficial degree of the medicinal materials on the symptoms can be represented, and sym is suitable_medThe Chinese medicinal material is strong and suitable for representing the favorable degree of the Chinese medicinal material on the symptom. It should be understood that the third attribute value and the fourth attribute value may be set by self according to the actual application, and are not limited to the above value example.

In this step, it is also determined whether the searched food materials favorable for the manifestation of the disease are included in the benign food material set F_goodPerforming the following steps; if included in the benign food material set F_goodIf yes, the third attribute value sym of the food material is judged_ingreAnd a first attribute value r_ingreWhether the first attribute values are equal to each other or not, if so, the first attribute values r_ingreAdding the first attribute value r to a first preset value (which can be set by user according to practical application, for example, the first preset value can be 0.01) to obtain a new first attribute value r_ingreIf not, the first attribute value r is set_ingreAnd a third attribute value sym_ingreAs a new first attribute value r_ingre(ii) a If not included in the benign food material set F_goodAdding the searched food materials beneficial to disease manifestation to the benign food material set F_goodIn (1).

In this step, it is also determined whether the searched medicinal material favorable for the manifestation of the disease is included in the benign medicinal material set M_goodPerforming the following steps; if included in the benign herb set M_goodIn the above formula, the fourth attribute value sym of the herb is determined_medAnd a second attribute value r_medWhether the two attribute values are equal or not, if so, the second attribute value r is added_medAdding the first preset value as a new second attribute value r_medIf not, the second attribute value r is set_medAnd a fourth attribute value sym_medAs a new second attribute value r_med(ii) a If not included in the benign herb set M_goodAdding the searched medicinal materials beneficial to the symptom expression into the benign medicinal material set M_goodIn (1).

On the basis of the initial attribute values of the food materials and the medicinal materials, the attribute values of the food materials and the medicinal materials can be adaptively corrected according to actual conditions, so that the influences of the food materials and the medicinal materials on disease names and symptom expressions can be adaptively updated, and the accuracy of the recommended medicated diet of the embodiment is improved.

S104, searching for food materials and medicinal materials harmful to disease names to respectively construct a malignant food material set and a malignant medicinal material set.

In the first map of this embodiment, r may be further included_ingreThe benefit of the food material on the disease name can be characterized as adverse, i.e. the food material is a malignant food material that should be contraindicated for the disease name. In the second map of this embodiment, r may be_medThe beneficial degree of the herb to the disease name is not good, that is, the herb is a malignant herb which should be contraindicated in the disease name. In this embodiment, the obtained set of malignant food materials can be denoted as F_badThe obtained malignant medicinal material set can be marked as M_bad。

In this step, it is also determined whether the searched food material harmful to the symptom name is included in the benign food material set F_goodIf the food material is included in the benign food material set F_goodIn (5), the good food material set F_goodAnd deleting the searched food materials harmful to the disease names.

In this step, it is also determined whether the searched medicinal material harmful to the disease name is included in the benign medicinal material set M_goodIn the above formula, M is included in the benign herbs_goodIn middle, M is a collection of good herbs_goodAnd deleting the searched medicinal materials harmful to the disease names.

S105, food materials and medicinal materials harmful to the symptom expression are searched, so that the malignant food material set and the malignant medicinal material set are updated respectively.

In this implementationIn the third map, sym may be further included_ingreThe beneficial degree of food material on the manifestation of the disorder can be characterized as adverse, i.e. the food material is a malignant food material that should be contraindicated that is detrimental to the manifestation of the disorder. In the fourth map of this embodiment, sym may be further included_medThe beneficial degree of the medicinal material on the symptom can be characterized as unfavorable, namely, the medicinal material is a malignant medicinal material which is harmful to the symptom and should be contraindicated.

In this step, it is also determined whether the searched food materials harmful to the manifestation of the disease are included in the malignant food material set F_badIn (1), if not included in the malignant food material set F_badAdding the searched food materials harmful to the symptom expression into a malignant food material set F_badIn the above, it is also determined whether the food material harmful to the manifestation of the disease found is included in the benign food material set F_goodIf the food material is included in the benign food material set F_goodIn (5), the good food material set F_goodAnd deleting the searched food materials harmful to the symptom expression.

In this step, it is also determined whether the searched medicinal material harmful to the manifestation of the disease is included in the malignant medicinal material set M_badIn (1), if not included in the malignant medicinal material set M_badAdding the searched medicinal materials harmful to the disease manifestation into a malignant medicinal material set M_badIn the method, whether the searched medicinal materials harmful to the disease manifestation are included in the benign medicinal material set M or not is judged_goodIn the above formula, M is included in the benign herbs_goodIn middle, M is a collection of good herbs_goodThe searched medicinal materials harmful to the symptom are deleted.

S106, searching the medicated diet comprising the food materials in the updated benign food material set and the medicated diet comprising the traditional Chinese medicinal materials in the updated benign medicinal material set to construct a recommended medicated diet set.

In the step, firstly, a plurality of food materials are selected from the updated benign food material set according to the sequence of the first attribute value from high to low to construct a recommended food material set, secondly, a plurality of medicinal materials are selected from the updated benign medicinal material set according to the sequence of the second attribute value from high to low to construct a recommended medicinal material set, and finally, the medicated diet including the food materials in the recommended food material set and the medicated diet including the medicinal materials in the recommended medicinal material set are searched. The number of the selected food materials and the number of the medicinal materials can be set in a user-defined mode according to practical application.

S107, judging whether the medicated diet in the recommended medicated diet set comprises malignant medicated diet;

if yes, go to step S108; if not, go to step S109;

s108, deleting malignant medicated diet from the recommended medicated diet set to update the recommended medicated diet set, and then executing the step S109;

and S109, recommending the medicated diet in the recommended medicated diet set to the user.

In this embodiment, the malignant medicated diet includes the food materials in the updated malignant food material set or the medicinal materials in the updated malignant medicinal material set, and the malignant medicated diet is deleted from the recommended medicated diet set obtained in step S106, which is beneficial to improving the safety of the recommended medicated diet of this embodiment. In addition, in this embodiment, the recommended medical meals in the medical meal set may be specifically recommended to the user through voice.

In the embodiment, the food materials and the medicinal materials suitable for the specific disease names and the food materials and the medicinal materials suitable for the specific disease expressions are obtained based on the disease names and the disease expressions of the users, and then the medicated diet comprising the related food materials and the related medicinal materials are obtained and recommended to the users, so that the embodiment can recommend the medicated diet suitable for the body nursing of the users to the users on the basis of comprehensively obtaining the body conditions of the users, and the disease names and the disease expressions of the users are comprehensively considered, so that the accurate recommendation of the medicated diet under the conditions of the same disease and different diseases can be realized, and the body nursing of the users is facilitated.

Example 2

The present embodiment provides a method for recommending medicated diet based on embodiment 1, referring to fig. 2, the method for recommending medicated diet of the present embodiment further includes, before step S109:

s201, obtaining medicated diet preferences of a user;

s202, determining user preference values of all medicated foods of a user according to the medicated food preferences of the user;

s203, screening all medicated foods according to the user preference values to construct a preference medicated food set;

s204, judging whether the intersection of the recommended medicated diet set and the preferred medicated diet set is an empty set;

if yes, go to step S109; if not, go to step S205;

and S205, updating the recommended medicated diet set into an intersection, and then executing the step S109.

Specifically, in the present embodiment, the initial value of the user preference value is 0, and the user preference includes several preference characteristics (e.g., taste, cuisine, cooking technique, time consumption, etc.). For each preference feature, step S202 specifically includes a step of determining whether the medicated diet completely conforms to the preference feature, if the medicated diet completely conforms to the preference feature, adding the user preference value to a second preset value (which may be set by user-defined according to actual application, for example, the second preset value may take a value of 1) to obtain a new user preference value, if the medicated diet does not completely conform to the preference feature, further determining whether the medicated diet does not completely conform to the preference feature, if the medicated diet does not completely conform to the preference feature, subtracting the user preference value from the second preset value to obtain a new user preference value, and if the medicated diet does not completely conform to the second preset value, adding the user preference value to a third preset value (which may be set by user-defined according to actual application, for example, the second preset value may take a value of 0.1).

Taking the preference characteristics including the taste as an example, the value of the taste can include light, slightly spicy, heavy spicy and the like, when the taste of the medicated food is light, if the value of the taste of the user is light, the medicated food completely accords with the preference characteristics, and if the value of the taste of the user is any one of slightly spicy, spicy and heavy spicy, the medicated food does not accord with the preference characteristics. When the taste of the medicated food is slightly spicy, if the taste value of the user is slightly spicy, the medicated food completely accords with the preference characteristic, if the taste value of the user is light, the medicated food does not accord with the preference characteristic, and if the taste value of the user is spicy or heavy spicy, the medicated food does not completely accord with the preference characteristic, namely, does not accord with the preference characteristic completely.

If the medicated diet does not comprise the preference feature or the preference feature completes the calculation of the user preference value, the user preference value is continuously updated according to the other preference feature until all the preference features are traversed.

In step S203 in this embodiment, a preferred medicated diet set may be constructed by screening out medicated diets whose user preference values are greater than a preset threshold (which may be set by a user according to an actual application, for example, the preset threshold may be 0). In this embodiment, the malignant medicated diet in embodiment 1 can be deleted from the preferred medicated diet set obtained in step S203 to improve the safety of the recommended medicated diet of this embodiment.

On the basis of the embodiment 1, the medicated diet preference of the user is also considered in the embodiment, so that the medicated diet finally recommended in the embodiment can be used for accurately recuperating the body of the user by specifically aiming at the disease name and the disease expression of the user, that is, on the basis of ensuring the food therapy effect, the specific preference of the user on the medicated diet can be met, and the user experience can be further improved.

Example 3

The present embodiment provides a medicated diet recommendation system, and referring to fig. 3, the medicated diet recommendation system of the present embodiment includes:

a first obtaining module 301, configured to obtain a disease name and a disease manifestation of a user.

In this embodiment, the disease name (e.g., respiratory diseases such as rhinitis, pharyngitis, laryngitis, tracheitis, bronchitis, asthma, pneumonia, etc.) and the disease expression (e.g., asthma, shortness of breath, cough, nasal obstruction, hoarseness, etc.) of the user may be obtained based on the voice of the user, and specifically, the first obtaining module 301 may include an acquiring unit, a voiceprint recognition unit, and a semantic recognition unit, where the acquiring unit may be configured to acquire a voice signal of the user, for example, may acquire a voice signal of a disease expression input by the user, the voiceprint recognition unit may be configured to perform voiceprint recognition on the voice signal to recognize the disease name of the user, and the semantic recognition unit may be configured to perform semantic recognition on the voice signal to recognize the disease expression input by the user.

Specifically, the sound sample collection may include standard (standard voice) sample collection and abnormal (sick voice) sample collection, and the collected sound sample set may be recorded as S_iWhere i is used to indicate the type of sound sample, e.g. when i is 0, S_iCan characterize a standard sample set corresponding to the standard voice, and the number of samples can be recorded as N₀When i ≠ 0, S_iAn abnormal sample set corresponding to the pathological voice can be characterized, and each disease type is assumed to comprise g types, namely, the name of each disease can be recorded as: s₁,S₂,…,S_gThe number of samples corresponding to each type of disease can be recorded as N_i(i ═ 1,2, …, g). Thus, each sound sample set S_iCan be recorded as

Wherein s is_ijJ sample, N, representing the ith class sample set_iThe number of samples in the ith type sample set is shown.

For extracting sample characteristic parameters, wherein the parameter index for establishing the voiceprint recognition model can cover but is not limited to acoustic information such as tone quality, volume, tone, pronunciation duration, range, special sound type (such as cough) and the like, the sample s_ijCan be denoted as s_ij＝(x₀,…,x_k,…,x_n) Wherein x is_kRepresenting a sample s_ijN characterizes the number of the characteristic parameters.

The voiceprint recognition may include collecting a voice signal of a user, extracting a feature parameter of the voice signal, and recognizing a voiceprint of the user, where the collected voice signal may be recorded as an object s to be measured, and the extracted feature parameter of the object s to be measured is recorded as s ═ s(x′₀,…,x′_k,…,x′_n). The step of recognizing the voiceprint of the user specifically may include inputting the characteristic parameters of the target s to be detected into a voiceprint recognition model for recognition and matching, for example, a modified Hausdorff distance (LTS-HD, denoted as D) may be used_LTS) The K-nearest neighbor algorithm of (1) is used as a classification basis, and specifically, the distance D between the target s to be detected and all the sound samples in the g +1 class sound sample set is calculated firstly_LTS(s,s_ij) And then the resulting D will be calculated_LTS(s,s_ij) The sound samples are sorted in an increasing mode according to results, the first K minimum distances are taken as K neighbor, and then the K neighbor obtained through statistics belongs to the sound sample set S_iFrequency of (t)_i(i-0, 1, …, g), finally defined as p-index (max { t)₀,…,t_g}), where index denotes the index acquisition function, i.e. the index for which p is the maximum frequency, S ∈ S_pObtaining a sound sample set to which the sample to be tested belongs, wherein if p is 0, S is output₀The voice analysis result of the user is represented as healthy, and if p is not equal to 0, S is output_pAnd characterizing the corresponding disease name of the user.

The first search building module 302 is configured to search for food materials and medicinal materials favorable for disease names to respectively build a benign food material set and a benign medicinal material set.

Specifically, the first lookup building module 302 may include a first obtaining unit for obtaining a first map and a second obtaining unit for obtaining a second map. The first map may include a disease name, a food material favorable for the disease name, and a first attribute value used to represent a degree of interest of the food material in the disease name, for example, the first attribute value may be denoted as r_ingreMay have r_ingre1,5 where r_ingre1 is suitable for characterizing the beneficial degree of food materials on disease names, r_ingreThe beneficial degree of the food material on the disease name can be characterized as being strong and suitable as 5. The second map may include a disease name, a herb, and a second attribute value, wherein the herb is favorable for the disease name, the second attribute value is used to represent the degree of benefit of the herb for the disease name, for example, the second attribute value may be denoted as r_medCan haver_med1,3,5, where r_med1 can characterize the beneficial degree of the medicinal materials to the disease names as weak and proper, and r is_med3, the beneficial degree of the medicinal materials on the disease names is moderate and proper, and r is_medThe Chinese medicinal material has strong and proper advantages on disease names by being characterized by 5. It should be understood that the first attribute value and the second attribute value may be set by a user according to an actual application, and are not limited to the above value example.

The first searching and updating module 303 is configured to search for food materials and medicinal materials that are favorable for disease manifestation, so as to update the benign food material set and the benign medicinal material set respectively.

Specifically, the first lookup updating module 303 may include a third obtaining unit for obtaining a third map and a fourth obtaining unit for obtaining a fourth map. The third map may include a disease manifestation, a food material, and a third attribute value, wherein the food material is a food material favorable for the disease manifestation, and the third attribute value is used for representing a degree of benefit of the food material on the disease manifestation, for example, the third attribute value may be denoted as sym_ingreThere may be sym_ingre1,5, where sym_ingre1 is suitable for characterizing the beneficial degree of food material on the manifestation of the disease, sym_ingreThe beneficial degree of the food material on the disease manifestation can be characterized as being strong and suitable as 5. The fourth map may include disease manifestation, medicinal materials, and a fourth attribute value, wherein the medicinal materials are medicinal materials beneficial to disease manifestation, the fourth attribute value is used for representing the degree of benefit of the medicinal materials on disease manifestation, for example, the fourth attribute value may be denoted as sym_medThere may be sym_med1,3,5, where sym_med1 can characterize the beneficial degree of the medicinal materials on the symptoms, and sym is weak and suitable_med3 can characterize the beneficial degree of the medicinal materials to the symptoms and manifestations as followsModerate in property, sym_medThe Chinese medicinal material is strong and suitable for representing the favorable degree of the Chinese medicinal material on the symptom. It should be understood that the third attribute value and the fourth attribute value may be set by self according to the actual application, and are not limited to the above value example.

The first searching and updating module 303 may further comprise a module for determining whether the searched food materials favorable for disease manifestation are included in the benign food material set F_goodThe first judging unit of (1); if included in the benign food material set F_goodIf yes, a second judging unit is called to judge the third attribute value sym of the food material_ingreAnd a first attribute value r_ingreWhether the first attribute values are equal to the second attribute values r is judged, if so, a first updating unit is called to update the first attribute values r_ingreAdding the first attribute value r to a first preset value (which can be set by user according to practical application, for example, the first preset value can be 0.01) to obtain a new first attribute value r_ingreIf not, then call the second updating unit to update the first attribute value r_ingreAnd a third attribute value sym_ingreAs a new first attribute value r_ingre(ii) a If not included in the benign food material set F_goodAnd calling a first adding unit to add the searched food materials beneficial to the symptom expression to the benign food material set F_goodIn (1).

The first searching and updating module 303 may further comprise a module for determining whether the searched medical materials beneficial to the disease manifestation are included in the benign medical material set M_goodThe third judging unit of (1); if included in the benign herb set M_goodIf so, a fourth judging unit is called to judge the fourth attribute value sym of the medicinal material_medAnd a second attribute value r_medWhether the attribute values are equal or not, if so, calling a third updating unit to update the second attribute value r_medAdding the first preset value as a new second attribute value r_medIf the attribute values are not equal to the first attribute value r, the fourth updating unit is called to update the second attribute value r_medAnd a fourth attribute value sym_medAs a new second attribute value r_med(ii) a If not included in the benign herb set M_goodIn the method, the second adding unit is called to add the searched medicinal materials which are beneficial to the symptom expression to the benign medicinal material set M_goodIn (1).

The third searching and constructing module 304 is configured to search for food materials and medicinal materials that are harmful to the disease names, so as to respectively construct a malignant food material set and a malignant medicinal material set.

In this embodiment, the third searching and constructing module 304 may be further configured to determine whether the searched food materials harmful to the disease names are included in the benign food material set F_goodIf the food material is included in the benign food material set F_goodIn (5), the good food material set F_goodAnd deleting the searched food materials harmful to the disease names.

In this embodiment, the third searching and constructing module 304 may be further configured to determine whether the searched medicinal materials harmful to the disease condition name are included in the benign medicinal material set M_goodIn the above formula, M is included in the benign herbs_goodIn middle, M is a collection of good herbs_goodAnd deleting the searched medicinal materials harmful to the disease names.

The second searching and updating module 305 is configured to search for food materials and medicinal materials harmful to the manifestation of a disease, so as to update the malignant food material set and the malignant medicinal material set, respectively.

In the third map of this embodiment, sym may be further included_ingreThe beneficial degree of food material on the manifestation of the disorder can be characterized as adverse, i.e. the food material is a malignant food material that should be contraindicated that is detrimental to the manifestation of the disorder. In the fourth map of this embodiment, sym may be further included_medThe beneficial degree of the medicinal material on the symptom can be characterized as unfavorable, namely, the medicinal material is a malignant medicinal material which is harmful to the symptom and should be contraindicated.

In this embodiment, the second searching and updating module 305 may be further configured to determine whether the searched food materials harmful to the manifestation of the disease are included in the malignant food material set F_badIn (1), if not included in the malignant food material set F_badAdding the searched food materials harmful to the symptom expression into a malignant food material set F_badIn the above, it is also determined whether the food material harmful to the manifestation of the disease found is included in the benign food material set F_goodIf the food material is included in the benign food material set F_goodIn (5), the good food material set F_goodAnd deleting the searched food materials harmful to the symptom expression.

In this embodiment, the second searching and updating module 305 may be further configured to determine whether the searched medicinal materials harmful to the symptom are included in the malignant medicinal material set M_badIn (1), if not included in the malignant medicinal material set M_badAdding the searched medicinal materials harmful to the disease manifestation into a malignant medicinal material set M_badIn the method, whether the searched medicinal materials harmful to the disease manifestation are included in the benign medicinal material set M or not is judged_goodIn the above formula, M is included in the benign herbs_goodIn middle, M is a collection of good herbs_goodThe searched medicinal materials harmful to the symptom are deleted.

The second search building module 306 is configured to search for a medicated diet including the food materials in the updated benign food material set and a medicated diet including the traditional Chinese medicinal materials in the updated benign medicinal material set to build a recommended medicated diet set.

In this embodiment, the second search building module 306 may include a first building unit configured to select a plurality of food materials from the updated benign food material set according to a sequence from high to low of the first attribute value to build the recommended food material set, a second building unit configured to select a plurality of medicinal materials from the updated benign medicinal material set according to a sequence from high to low of the second attribute value to build the recommended medicinal material set, and a search unit configured to search for a medicated diet including the food materials in the recommended food material set and a medicated diet including the medicinal materials in the recommended medicinal material set. The number of the selected food materials and the number of the medicinal materials can be set in a user-defined mode according to practical application.

A first judging module 307, configured to judge whether the medicated diet in the recommended medicated diet set includes a malignant medicated diet;

if so, the first update module 308 is invoked; if not, the recommendation module 309 is called;

the first updating module 308 is used for deleting the malignant medicated diet from the recommended medicated diet set to update the recommended medicated diet set, and then calling the recommending module 309;

the recommending module 309 is used for recommending the medicated diet in the recommended medicated diet set to the user.

In this embodiment, the malignant medicated diet includes the food materials in the updated malignant food material set or the medicinal materials in the updated malignant medicinal material set, and the malignant medicated diet is deleted from the recommended medicated diet set obtained by the second search building module 306, which is beneficial to improving the safety of the recommended medicated diet of this embodiment. In addition, in this embodiment, the recommending module 309 can be specifically configured to recommend the medicated diet in the set of medicated diets to the user through voice.

Example 4

The present embodiment provides a medicated diet recommendation system based on embodiment 3, referring to fig. 4, the medicated diet recommendation system of the present embodiment further includes:

a second obtaining module 401, configured to obtain a medicated diet preference of a user;

a determining module 402, configured to determine user preference values of all medicated diet of a user according to the medicated diet preference of the user;

a screening and constructing module 403, configured to screen all medicated diet according to the user preference value to construct a preference medicated diet set;

a second judging module 404, configured to judge whether an intersection of the recommended medicated diet set and the preferred medicated diet set is an empty set;

if yes, calling a recommendation module 309; if not, the second update module 405 is invoked;

the second updating module 405 is used to update the set of recommended meals into an intersection, and then call the recommending module 309.

Specifically, in the present embodiment, the initial value of the user preference value is 0, and the user preference includes several preference characteristics (e.g., taste, cuisine, cooking technique, time consumption, etc.). For each preference feature, the determining module 402 may specifically include a fifth determining unit for determining whether the medicated diet completely meets the preference feature, and if so, a fifth updating unit is called to add the user preference value and a second preset value (which can be set by user according to practical application, for example, the second preset value can be 1) to obtain a new user preference value, and if the user preference value does not completely meet the new user preference value, a sixth judging unit is called to judge whether the medicated food completely does not accord with the preference characteristics, if not, then a sixth updating unit is called to subtract the user preference value from the second preset value to obtain a new user preference value, if the user preference value is not completely inconsistent with the second preset value, and calling a seventh updating unit to add the user preference value and a third preset value (which can be set by self-definition according to actual application, for example, the second preset value can be 0.1) to obtain a new user preference value.

The screening and constructing module 403 may specifically screen out medicated diet with a user preference value greater than a preset threshold (which may be set by a user according to actual application, for example, the preset threshold may be set to be 0) to construct a preferred medicated diet set. In this embodiment, the malignant medicated diet in embodiment 3 can be deleted from the preferred medicated diet set obtained by the screening and constructing module 403, so as to improve the safety of the recommended medicated diet of this embodiment.

On the basis of the embodiment 3, the medicated diet preference of the user is also considered in the embodiment, so that the medicated diet finally recommended in the embodiment can be used for accurately recuperating the body of the user by specifically aiming at the disease name and the disease expression of the user, that is, on the basis of ensuring the food therapy effect, the specific preference of the user on the medicated diet can be met, and the user experience can be further improved.

Example 5

The present embodiment provides an electronic device, which may be represented by a computing device (for example, may be a server device), and includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the method for recommending a medicated diet provided in embodiment 1 or 2.

Fig. 5 shows a schematic diagram of a hardware structure of the present embodiment, and as shown in fig. 5, the electronic device 9 specifically includes:

at least one processor 91, at least one memory 92, and a bus 93 for connecting the various system components (including the processor 91 and the memory 92), wherein:

the bus 93 includes a data bus, an address bus, and a control bus.

Memory 92 includes volatile memory, such as Random Access Memory (RAM)921 and/or cache memory 922, and can further include Read Only Memory (ROM) 923.

Memory 92 also includes a program/utility 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

The processor 91 executes computer programs stored in the memory 92 to execute various functional applications and data processing, such as the method for recommending medicated diet provided in embodiment 1 or 2 of the present invention.

The electronic device 9 may further communicate with one or more external devices 94 (e.g., a keyboard, a pointing device, etc.). Such communication may be through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 96. The network adapter 96 communicates with the other modules of the electronic device 9 via the bus 93. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module, according to embodiments of the application. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.

Example 6

The present embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of the method for recommending medical meals provided in embodiment 1 or 2.

More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.

In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code for causing a terminal device to execute the steps of implementing the method for recommending medicated meals as described in embodiment 1 or 2 when the program product is run on the terminal device.

Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.

While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims

1. A method for recommending medicinal meals, which is characterized by comprising the following steps:

acquiring a disease name and a disease expression of a user;

2. The method for recommending a medicated diet of claim 1, wherein the step of obtaining the name and the performance of the disease of the user comprises:

collecting a sound signal of the user;

and/or the presence of a gas in the gas,

3. The method for recommending herbal cuisine of claim 1, wherein the step of searching for food materials and medicinal materials favorable for the disease name comprises:

4. The method as claimed in claim 3, wherein the step of updating the benign food material set and the benign herb material set respectively comprises:

5. The method as claimed in claim 4, wherein the step of searching for a medicated diet containing the food materials in the updated benign food material set and a medicated diet containing the traditional Chinese medicinal materials in the updated benign medicinal material set comprises:

6. The method for recommending medical meals according to claim 1, further comprising, before said step of recommending said user said medical meals in said set of recommended medical meals:

7. The method for recommending medical meals according to claim 1, further comprising, before said step of recommending said user said medical meals in said set of recommended medical meals:

acquiring the medicated diet preference of the user;

if not, updating the recommended medicated diet set into the intersection.

8. A method as defined in claim 7, wherein the user preferences include a plurality of preference characteristics, the initial value of the user preference value is 0, and for each preference characteristic, the step of determining the user preference values of the user for all medicated meals according to the user's medicated meal preferences comprises:

9. A system for recommending medicinal meals, comprising:

10. The system as claimed in claim 9, wherein the first acquiring module comprises an acquiring unit, the first acquiring module further comprises a voiceprint recognition unit and/or a semantic recognition unit; wherein:

the acquisition unit is used for acquiring the sound signal of the user;

and/or the presence of a gas in the gas,

11. The system of claim 9, wherein the first search building module comprises:

the first lookup update module comprises:

12. The system as recited in claim 11, wherein said first search update module further comprises:

13. The system of claim 12, wherein the second search building module comprises:

14. The system as set forth in claim 9, further comprising:

15. The system as set forth in claim 9, further comprising:

16. The system of claim 15, wherein the user preferences comprise a plurality of preference characteristics, the initial value of the user preference value is 0, and the determining module comprises, for each preference characteristic:

17. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of recommending medical meals according to any of claims 1 to 8 when executing the computer program.

18. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of dietary recommendation according to any one of claims 1 to 8.