CN112489767A - Diet recommendation method based on big data analysis - Google Patents
Diet recommendation method based on big data analysis Download PDFInfo
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Abstract
The invention discloses a diet recommendation method based on big data analysis, which relates to the technical field of diet intelligent recommendation and solves the technical problems of inaccurate diet recommendation and small popularization range in the existing scheme; the invention is provided with the range analysis module, the range analysis module draws a diet activity curve through diet records and search records of the user, obtains the activity range of the user on the basis of the diet activity curve, and can narrow the screening range by recommending diet for the user on the basis of the activity range, thereby ensuring that the recommended diet is more in line with the taste of the user; the dish recommending module is arranged, dishes are recommended for the client by combining the household location and the living time of the user in the current living location, and the dishes can be pushed for the client more accurately; the data interaction module is arranged, and the dish screening range and the dish screening graph are deeply pushed according to the relation between the contact persons and the users, so that excellent dishes are distributed to the suitable users.
Description
Technical Field
The invention belongs to the technical field of intelligent diet recommendation, relates to a big data technology, and particularly relates to a diet recommendation method based on big data analysis.
Background
With the improvement of living standard, people pay more and more attention to health, and diet as an indispensable part of people's daily life can produce the great influence to health. If the diet is reasonable, the immunity of the human body can be improved, and the diseases can be prevented; improper diet can cause disease, even aggravation.
The invention patent with publication number CN110931110A provides a diet recommendation method and a device thereof, the diet recommendation method comprises: acquiring the type of dishes and dish information of each type of dish; acquiring diet information of a user, wherein the diet information comprises diet notes, diet records and operation records; calculating a recommended value of each dish according to the dish information of each dish and the diet cautionary items, diet records and operation records of the user; and recommending dishes to the user according to the recommended value.
The scheme can provide a diet reference for the user, and the diet reference can meet the user preference and simultaneously prevent the user from eating food which is not beneficial to self health; however, the user data referred by the scheme is single, and the recommended user is only a single user, so that the diet recommended by the scheme has low fitness with the user and a small popularization range; therefore, the above solution still needs further improvement.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a diet recommendation method based on big data analysis.
The purpose of the invention can be realized by the following technical scheme: a diet recommendation method based on big data analysis, the diet recommendation method comprising the steps of:
the method comprises the following steps: the user performs registration login through the verification information;
step two: acquiring a diet record and a search record of a user, drawing a diet activity curve of the user, and selecting an activity range according to the diet activity curve;
step three: obtaining dishes in the activity range, calculating a recommended value of the dishes, and obtaining a dish recommended sorting table;
step four: the dish recommending and sorting table is combined with verification information to provide a dish screening range for the user;
step five: and acquiring the diet relationship value between the user and the friend, and recommending the diet for the friend according to the diet relationship value.
Preferably, the registration login is completed through a registration login module, the registration login module is a component of the control system, and the control system further comprises a processor, a range analysis module, a dish recommendation module, a data interaction module, a maintenance management module and a data storage module;
the registration login module completes the registration login of the user according to the verification information, and comprises the following steps:
a user sends verification information to a registration login module through an intelligent terminal; the verification information comprises name, age, user mobile phone number, facial image, ID card photo, household location, living place and diet notice; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer; the food materials harmful to the health of the user are taken as the food cautions;
extracting a face image and an address in the identity card photo; matching the address with the household address, matching the face image with the face image when the matching is successful, and acquiring the current position of the user when the face image is successfully matched with the face image; matching the current position with the place of living, and when the current position is successfully matched with the place of living, judging that the user identity authentication is successful, generating a user account and a user password, and sending the user account and the user password to a mobile phone number of the user;
and sending the verification information, the user account and the user password to a data storage module for storage.
Preferably, the range analysis module is configured to obtain a range of motion of the user, and includes:
acquiring a diet record of a user through an intelligent terminal; the diet records comprise time, restaurant information and dishes; acquiring a search record of a user through an intelligent terminal; the search record comprises time, restaurant information and dishes; the diet record and the search record are both obtained through an application with privacy authority opened in the intelligent terminal; the restaurant information comprises a restaurant location and a restaurant name;
generating a diet activity curve according to the sequence of time in the diet record and the search record and the distance between the restaurant position and the current residence of the user;
acquiring the total distance length of the diet activity curve, and marking the total distance length as ZC; acquiring the longest distance between the restaurant position and the current residence of the user, and marking the longest distance as ZJ;
by the formula
Acquiring a radius set value BZ; wherein alpha 1 and alpha 2 are proportionality coefficients, and both alpha 1 and alpha 2 are real numbers greater than 0;
the method comprises the following steps of (1) defining a circular area by taking a place where a user live as a circle center and a radius set value BZ as a radius, and marking the circular area as a moving range;
the processor sends the diet activity curve, the radius set value and the activity range to the data storage module for storage, and meanwhile sends the activity area to the dish recommending module.
Preferably, the dish recommending module is configured to obtain a dish recommending and sorting table, and provide a dish screening range for the user in combination with the verification information, and includes:
when the dish recommending module receives the activity range of the user, acquiring restaurants in the activity range and marking the restaurants as primary-choice restaurants;
acquiring the evaluation times, the grading mean value and the starting time of the primary restaurant, and respectively marking as PC, PJ and KS; obtaining an evaluation coefficient CXPX of the primary restaurant by a formula CXPX ═ alpha 3 xKS + alpha 4 xPJ xln (PC + 1); wherein both α 3 and α 4 are proportionality coefficients, and α 3 and α 4 are real numbers greater than 0;
when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX < L1, judging that the primary selection restaurant does not meet the requirements; when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX which is not less than L1, judging that the primary selection restaurant meets the requirements, and generating a dish recommendation table according to the signboard in the primary selection restaurant meeting the requirements; the dish recommendation table comprises nine sub-tables, the nine sub-tables are respectively nine types of dishes, and the nine types are respectively corresponding to eight major cuisine and the rest types; wherein L1 is the evaluation coefficient threshold value of the primary restaurant, and L1 is more than 0; the remaining types do not contain dishes of the eight major cuisine;
acquiring the age, the household location, the diet notice and the living time of the user in the current living place, and respectively marking the age and the living time of the user as NL and JS; acquiring a time length evaluation coefficient SPX by the formula SPX ═ α 5 × (NL-JS); wherein α 5 is a proportionality coefficient and α 5 is a real number greater than 0;
when the longest evaluation coefficient SPX meets the condition that SPX is larger than L2, acquiring eight vegetable lines corresponding to the household location and marking as standard vegetable lines, selecting N1 vegetables from the corresponding standard vegetable lines in the vegetable recommendation table, selecting N2 vegetables from the remaining eight types to form a vegetable screening range, and when the household location has no corresponding vegetable lines, selecting N2 vegetables from the nine types to form a vegetable screening range; when the time length evaluation coefficient SPX meets L2 of more than 0 and less than or equal to SPX, selecting N3 dishes from the corresponding standard dishes in the dish recommendation table, and selecting N4 dishes from the remaining eight types to form a dish screening range; wherein L2 is a time length evaluation coefficient threshold, L2 >0, N1, N2, N3 and N4 are proportionality coefficients, N1, N2, N3 and N4 are integers greater than 0, and
the food materials in the food item screening range do not contain food materials in the food attention items;
obtaining the customer scores of the dishes in the dish screening range and marking the scores as GP; when the customer score GP meets that GP is more than or equal to L3, marking the corresponding restaurant in the activity range as green to generate a dish screening chart; wherein L3 is the customer score threshold, and L3 is more than or equal to 90;
and the dish screening range and the dish screening graph are sent to an intelligent terminal of a user through the processor, and are sent to the data storage module for storage.
Preferably, the data interaction module is used for realizing deep pushing of dishes, and includes:
acquiring a contact in the user intelligent terminal, and marking the contact as i, i-1, 2, … …, n;
acquiring the number of times of communication between a user and a contact i through a user terminal, and marking the number of times of communication as JCi; the communication frequency is the sum of the voice frequency and the video frequency of the user and the contact person i; acquiring the frequency of the simultaneous presence of the user and the contact i in one restaurant and marking the frequency as CFCi;
by the formula
Acquiring a diet relationship value gamma GZi; wherein beta 1 is a proportionality coefficient, and beta 1 belongs to [0.1, 0.5 ]]；
When the diet relationship value YGZi meets L4 of YGZi, the relationship between the contact i and the user is determined to be close, and the dish screening range and the dish screening graph of the user are pushed to the contact i through the data interaction module; when the diet relation value YGZi meets the condition that YGZi is more than 0 and less than L4, pushing the dish screening range of the user to the contact i through the data interaction module;
and the processor sends the dish screening range and the pushing record of the dish screening graph to the data storage module for storage.
Preferably, the maintenance management module is configured to monitor files in the data storage module, and includes:
acquiring the resource occupancy rate of the processor in real time, and marking the resource occupancy rate as ZYZ;
acquiring a digital abstract of a file in a data storage module by a Hash method, comparing the digital abstract with a digital abstract database of a corresponding file, counting the proportion of the number of the files with different comparison results of the digital abstract and the digital abstract database to the total number of the files, and marking the proportion as BL;
by the formula B ═ gamma 1 × ZYZ × eγ2×BLAcquiring a virus threat coefficient B; wherein gamma 1 and gamma 2 are proportionality coefficients, and both gamma 1 and gamma 2 are real numbers greater than 0;
when the virus threat coefficient B is less than or equal to the virus set threshold, judging the data security in the data storage module, and sending a data security signal to the maintenance management module; and when the virus threat coefficient B is larger than a virus set threshold value, judging that the file in the data storage module is attacked by the virus, sending a virus attack signal to the maintenance management module, and only reserving the access authority of the processor and the maintenance management module to the data storage module.
Preferably, the processor is respectively in communication connection with the registration login module, the range analysis module, the dish recommendation module, the data interaction module, the maintenance management module and the data storage module, and the data storage module is in communication connection with the maintenance management module.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a range analysis module, wherein the range analysis module is used for acquiring the activity range of a user; acquiring a diet record of a user through an intelligent terminal; acquiring a search record of a user through an intelligent terminal; generating a diet activity curve according to the sequence of time in the diet record and the search record and the distance between the restaurant position and the current residence of the user; acquiring the total distance length ZC of the diet activity curve; obtaining the longest distance ZJ between the restaurant position and the current residence of the user; acquiring a radius set value BZ; the method comprises the following steps of (1) defining a circular area by taking a place where a user live as a circle center and a radius set value BZ as a radius, and marking the circular area as a moving range; the range analysis module draws a diet activity curve through diet records and search records of the user, obtains the activity range of the user on the basis of the diet activity curve, and can narrow the screening range by recommending diet for the user on the basis of the activity range, thereby ensuring that the recommended diet is more in line with the taste of the user;
2. the invention is provided with a dish recommending module, which is used for acquiring a dish recommending and sorting table; the dish recommending module extracts the primary selection restaurants according to the activity range of the user, generates a dish recommending table on the basis of signboard dishes of the primary selection restaurants meeting the requirements, and recommends dishes for the user according to a set proportion from the dish recommending table; the dish recommending module is used for recommending dishes for the client in combination with the residence time of the household and the residence time of the user in the current residence place, so that the dishes can be pushed for the client more accurately;
3. the data interaction module is arranged and used for realizing deep pushing of dishes; acquiring a contact person i in a user intelligent terminal; acquiring the number of times of communication between a user and a contact i through a user terminal, and marking the number of times of communication as JCi; acquiring a diet relation value YGZi; when the diet relation value YGZi meets YGZi which is not less than L4, judging that the relation between the contact i and the user is close, and pushing the dish screening range and the dish screening graph of the user to the contact i through the data interaction module; when the diet relation value YGZi meets the condition that YGZi is more than 0 and less than L4, pushing the dish screening range of the user to the contact i through the data interaction module; the data interaction module carries out deep pushing on the dish screening range and the dish screening graph through the relation between the contact persons and the users, and is favorable for distributing excellent dishes to the suitable users.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic representation of the steps of the present invention;
fig. 2 is a schematic diagram of the control system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, a diet recommendation method based on big data analysis includes the following steps:
the method comprises the following steps: the user performs registration login through the verification information;
step two: acquiring a diet record and a search record of a user, drawing a diet activity curve of the user, and selecting an activity range according to the diet activity curve;
step three: obtaining dishes in the activity range, calculating a recommended value of the dishes, and obtaining a dish recommended sorting table;
step four: the dish recommending and sorting table is combined with verification information to provide a dish screening range for the user;
step five: and acquiring the diet relationship value between the user and the friend, and recommending the diet for the friend according to the diet relationship value.
Furthermore, the registration login is completed through a registration login module, the registration login module is a component of the control system, and the control system further comprises a processor, a range analysis module, a dish recommendation module, a data interaction module, a maintenance management module and a data storage module;
the registration login module completes the registration login of the user according to the verification information, and comprises the following steps:
a user sends verification information to a registration login module through an intelligent terminal; the verification information comprises name, age, user mobile phone number, facial image, ID card photo, household location, living place and diet notice; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer; the food materials harmful to the health of the user are taken as the food cautions;
extracting a face image and an address in the identity card photo; matching the address with the household address, matching the face image with the face image when the matching is successful, and acquiring the current position of the user when the face image is successfully matched with the face image; matching the current position with the place of living, and when the current position is successfully matched with the place of living, judging that the user identity authentication is successful, generating a user account and a user password, and sending the user account and the user password to a mobile phone number of the user;
and sending the verification information, the user account and the user password to a data storage module for storage.
Further, the range analysis module is used for acquiring the activity range of the user, and comprises:
acquiring a diet record of a user through an intelligent terminal; the diet records include time, restaurant information, and dishes; acquiring a search record of a user through an intelligent terminal; the search records comprise time, restaurant information and dishes; the diet record and the search record are both obtained through an application of opening privacy authority in the intelligent terminal; the restaurant information includes a restaurant location and a restaurant name;
generating a diet activity curve according to the sequence of time in the diet record and the search record and the distance between the restaurant position and the current residence of the user;
acquiring the total distance length of the diet activity curve, and marking the total distance length as ZC; acquiring the longest distance between the restaurant position and the current residence of the user, and marking the longest distance as ZJ;
by the formula
Acquiring a radius set value BZ; wherein alpha 1 and alpha 2 are proportionality coefficients, and both alpha 1 and alpha 2 are real numbers greater than 0;
the method comprises the following steps of (1) defining a circular area by taking a place where a user live as a circle center and a radius set value BZ as a radius, and marking the circular area as a moving range;
the processor sends the diet activity curve, the radius set value and the activity range to the data storage module for storage, and meanwhile sends the activity area to the dish recommending module.
Further, the dish recommending module is used for acquiring the dish recommending and sorting table and providing a dish screening range for the user by combining the verification information, and comprises the following steps:
when the dish recommending module receives the activity range of the user, acquiring restaurants in the activity range and marking the restaurants as primary-choice restaurants;
acquiring the evaluation times, the grading mean value and the starting time of the primary restaurant, and respectively marking as PC, PJ and KS; obtaining an evaluation coefficient CXPX of the primary restaurant by a formula CXPX ═ alpha 3 xKS + alpha 4 xPJ xln (PC + 1); wherein both α 3 and α 4 are proportionality coefficients, and α 3 and α 4 are real numbers greater than 0;
when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX < L1, judging that the primary selection restaurant does not meet the requirements; when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX which is not less than L1, judging that the primary selection restaurant meets the requirements, and generating a dish recommendation table according to the signboard in the primary selection restaurant meeting the requirements; the dish recommendation table comprises nine sub-tables, the nine sub-tables are respectively used for nine types of dishes, and the nine types are respectively corresponding to eight major cuisine and the rest types; wherein L1 is the evaluation coefficient threshold value of the primary restaurant, and L1 is more than 0;
acquiring the age, the household location, the diet notice and the living time of the user in the current living place, and respectively marking the age and the living time of the user as NL and JS; acquiring a time length evaluation coefficient SPX by the formula SPX ═ α 5 × (NL-JS); wherein α 5 is a proportionality coefficient and α 5 is a real number greater than 0;
when the longest evaluation coefficient SPX meets the condition that SPX is larger than L2, acquiring eight vegetable lines corresponding to the household location and marking as standard vegetable lines, selecting N1 vegetables from the corresponding standard vegetable lines in the vegetable recommendation table, selecting N2 vegetables from the remaining eight types to form a vegetable screening range, and when the household location has no corresponding vegetable lines, selecting N2 vegetables from the nine types to form a vegetable screening range; when the time length evaluation coefficient SPX meets L2 of more than 0 and less than or equal to SPX, selecting N3 dishes from the corresponding standard dishes in the dish recommendation table, and selecting N4 dishes from the remaining eight types to form a dish screening range; wherein L2 is a duration evaluation coefficient threshold, and L2 >0, N1, N2, N3 and N4 are proportionality coefficients, N1, N2, N3 and N4 are integers greater than 0, and N1 > N2,
obtaining the customer scores of the dishes in the dish screening range and marking the scores as GP; when the customer score GP meets that GP is more than or equal to L3, marking the corresponding restaurant in the activity range as green to generate a dish screening chart; wherein L3 is the customer score threshold, and L3 is more than or equal to 90;
and the dish screening range and the dish screening graph are sent to an intelligent terminal of a user through the processor, and are sent to the data storage module for storage.
Further, the data interaction module is used for realizing the deep pushing of dishes, and comprises:
acquiring a contact in the user intelligent terminal, and marking the contact as i, i-1, 2, … …, n;
acquiring the number of times of communication between a user and a contact i through a user terminal, and marking the number of times of communication as JCi; the communication frequency is the sum of the voice frequency and the video frequency of the user and the contact person i; acquiring the frequency of the simultaneous presence of the user and the contact i in one restaurant and marking the frequency as CFCi;
by the formula
Acquiring a diet relation value YGZi; wherein beta 1 is a proportionality coefficient, and beta 1 belongs to [0.1, 0.5 ]]；
When the diet relation value YGZi meets YGZi which is not less than L4, judging that the relation between the contact i and the user is close, and pushing the dish screening range and the dish screening graph of the user to the contact i through the data interaction module; when the diet relation value YGZi meets the condition that YGZi is more than 0 and less than L4, pushing the dish screening range of the user to the contact i through the data interaction module;
and the processor sends the dish screening range and the pushing record of the dish screening graph to the data storage module for storage.
Further, the maintenance management module is used for monitoring files in the data storage module, and comprises:
acquiring the resource occupancy rate of the processor in real time, and marking the resource occupancy rate as ZYZ;
acquiring a digital abstract of a file in a data storage module by a Hash method, comparing the digital abstract with a digital abstract database of a corresponding file, counting the proportion of the number of the files with different comparison results of the digital abstract and the digital abstract database to the total number of the files, and marking the proportion as BL;
by the formula B ═ gamma 1 × ZYZ × eγ2×BLAcquiring a virus threat coefficient B; wherein gamma 1 and gamma 2 are proportionality coefficients, and both gamma 1 and gamma 2 are real numbers greater than 0;
when the virus threat coefficient B is less than or equal to the virus set threshold, judging the data security in the data storage module, and sending a data security signal to the maintenance management module; and when the virus threat coefficient B is larger than a virus set threshold value, judging that the file in the data storage module is attacked by the virus, sending a virus attack signal to the maintenance management module, and only reserving the access authority of the processor and the maintenance management module to the data storage module.
Further, the processor is respectively in communication connection with the registration login module, the range analysis module, the dish recommendation module, the data interaction module, the maintenance management module and the data storage module, and the data storage module is in communication connection with the maintenance management module.
The above formulas are all calculated by removing dimensions and taking values thereof, the formula is one closest to the real situation obtained by collecting a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The working principle of the invention is as follows:
acquiring a diet record of a user through an intelligent terminal; acquiring a search record of a user through an intelligent terminal; generating a diet activity curve according to the sequence of time in the diet record and the search record and the distance between the restaurant position and the current residence of the user; acquiring the total distance length ZC of the diet activity curve; obtaining the longest distance ZJ between the restaurant position and the current residence of the user; acquiring a radius set value BZ; the method comprises the following steps of (1) defining a circular area by taking a place where a user live as a circle center and a radius set value BZ as a radius, and marking the circular area as a moving range;
when the dish recommending module receives the activity range of the user, acquiring restaurants in the activity range and marking the restaurants as primary-choice restaurants; obtaining the evaluation times, the score average value and the shop opening time of the primary hotel, and obtaining the evaluation coefficient CXPX of the primary hotel; when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX < L1, judging that the primary selection restaurant does not meet the requirements; when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX which is not less than L1, judging that the primary selection restaurant meets the requirements, and generating a dish recommendation table according to the signboard in the primary selection restaurant meeting the requirements; acquiring the age, the household location, the diet notice and the living time of the user in the current living place, and respectively marking the age and the living time of the user as NL and JS; acquiring a duration evaluation coefficient SPX; when the longest evaluation coefficient SPX meets the condition that SPX is larger than L2, acquiring eight vegetable lines corresponding to the household location and marking as standard vegetable lines, selecting N1 vegetables from the corresponding standard vegetable lines in the vegetable recommendation table, selecting N2 vegetables from the remaining eight types to form a vegetable screening range, and when the household location has no corresponding vegetable lines, selecting N2 vegetables from the nine types to form a vegetable screening range; when the time length evaluation coefficient SPX meets L2 of more than 0 and less than or equal to SPX, selecting N3 dishes from the corresponding standard dishes in the dish recommendation table, and selecting N4 dishes from the remaining eight types to form a dish screening range; obtaining the customer scores of the dishes in the dish screening range and marking the scores as GP; when the customer score GP meets that GP is more than or equal to L3, marking the corresponding restaurant in the activity range as green to generate a dish screening chart;
acquiring a contact person i in a user intelligent terminal; acquiring the number of times of communication between a user and a contact i through a user terminal, and marking the number of times of communication as JCi; acquiring a diet relation value YGZi; when the diet relation value YGZi meets YGZi which is not less than L4, judging that the relation between the contact i and the user is close, and pushing the dish screening range and the dish screening graph of the user to the contact i through the data interaction module; and when the diet relation value YGZi meets 0< YGZi < L4, pushing the menu screening range of the user to the contact i through the data interaction module.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. A diet recommendation method based on big data analysis is characterized by comprising the following steps:
the method comprises the following steps: the user performs registration login through the verification information;
step two: acquiring a diet record and a search record of a user, drawing a diet activity curve of the user, and selecting an activity range according to the diet activity curve;
step three: obtaining dishes in the activity range, calculating a recommended value of the dishes, and obtaining a dish recommended sorting table;
step four: the dish recommending and sorting table is combined with verification information to provide a dish screening range for the user;
step five: and acquiring the diet relationship value between the user and the friend, and recommending the diet for the friend according to the diet relationship value.
2. The big data analysis-based diet recommendation method according to claim 1, wherein the registration login is accomplished by a registration login module, the registration login module is a component of a control system, the control system further comprises a processor, a range analysis module, a dish recommendation module, a data interaction module, a maintenance management module and a data storage module;
the registration login module completes the registration login of the user according to the verification information, and comprises the following steps:
a user sends verification information to a registration login module through an intelligent terminal; the verification information comprises name, age, user mobile phone number, facial image, ID card photo, household location, living place and diet notice; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer; the food materials harmful to the health of the user are taken as the food cautions;
extracting a face image and an address in the identity card photo; matching the address with the household address, matching the face image with the face image when the matching is successful, and acquiring the current position of the user when the face image is successfully matched with the face image; matching the current position with the place of living, and when the current position is successfully matched with the place of living, judging that the user identity authentication is successful, generating a user account and a user password, and sending the user account and the user password to a mobile phone number of the user;
and sending the verification information, the user account and the user password to a data storage module for storage.
3. A diet recommendation method based on big data analysis as claimed in claim 2, wherein said range analysis module is used to obtain the activity range of the user, comprising:
acquiring a diet record of a user through an intelligent terminal; the diet records comprise time, restaurant information and dishes; acquiring a search record of a user through an intelligent terminal; the search record comprises time, restaurant information and dishes; the diet record and the search record are both obtained through an application with privacy authority opened in the intelligent terminal; the restaurant information comprises a restaurant location and a restaurant name;
generating a diet activity curve according to the sequence of time in the diet record and the search record and the distance between the restaurant position and the current residence of the user;
acquiring the total distance length of the diet activity curve, and marking the total distance length as ZC; acquiring the longest distance between the restaurant position and the current residence of the user, and marking the longest distance as ZJ;
by the formula
Acquiring a radius set value BZ; wherein alpha 1 and alpha 2 are proportionality coefficients, and both alpha 1 and alpha 2 are real numbers greater than 0;
the method comprises the following steps of (1) defining a circular area by taking a place where a user live as a circle center and a radius set value BZ as a radius, and marking the circular area as a moving range;
the processor sends the diet activity curve, the radius set value and the activity range to the data storage module for storage, and meanwhile sends the activity area to the dish recommending module.
4. The big data analysis-based diet recommendation method according to claim 2, wherein the dish recommendation module is configured to obtain a dish recommendation ranking table and provide a dish screening range for the user in combination with the verification information, and the method includes:
when the dish recommending module receives the activity range of the user, acquiring restaurants in the activity range and marking the restaurants as primary-choice restaurants;
acquiring the evaluation times, the grading mean value and the starting time of the primary restaurant, and respectively marking as PC, PJ and KS; obtaining an evaluation coefficient CXPX of the primary restaurant by a formula CXPX ═ alpha 3 xKS + alpha 4 xPJ xln (PC + 1); wherein both α 3 and α 4 are proportionality coefficients, and α 3 and α 4 are real numbers greater than 0;
when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX < L1, judging that the primary selection restaurant does not meet the requirements; when the evaluation coefficient CXPX of the primary selection restaurant meets CXPX which is not less than L1, judging that the primary selection restaurant meets the requirements, and generating a dish recommendation table according to the signboard in the primary selection restaurant meeting the requirements; the dish recommendation table comprises nine sub-tables, the nine sub-tables are respectively nine types of dishes, and the nine types are respectively corresponding to eight major cuisine and the rest types; wherein L1 is the evaluation coefficient threshold value of the primary restaurant, and L1 is more than 0;
acquiring the age, the household location, the diet notice and the living time of the user in the current living place, and respectively marking the age and the living time of the user as NL and JS; acquiring a time length evaluation coefficient SPX by the formula SPX ═ α 5 × (NL-JS); wherein α 5 is a proportionality coefficient and α 5 is a real number greater than 0;
the time length evaluation coefficient SPX satisfies SPX>At L2, acquiring eight vegetable lines corresponding to the household location and marking as standard vegetable lines, selecting N1 vegetable lines from the corresponding standard vegetable lines in the vegetable recommendation table, selecting N2 vegetable lines from the remaining eight types to form a vegetable screening range, and selecting N2 vegetable lines from the nine types to form a vegetable screening range when the household location does not have the corresponding vegetable lines; when the time length evaluation coefficient SPX satisfies 0<When the SPX is less than or equal to L2, selecting N3 dishes from the corresponding standard vegetable lines in the dish recommendation table, and selecting N4 dishes from the remaining eight types to form a dish screening range; wherein L2 is the duration evaluation coefficient threshold, and L2>0, N1, N2, N3 and N4 are proportionality coefficients, N1, N2, N3 and N4 are integers greater than 0, and N1>N2，
Obtaining the customer scores of the dishes in the dish screening range and marking the scores as GP; when the customer score GP meets that GP is more than or equal to L3, marking the corresponding restaurant in the activity range as green to generate a dish screening chart; wherein L3 is the customer score threshold, and L3 is more than or equal to 90;
and the dish screening range and the dish screening graph are sent to an intelligent terminal of a user through the processor, and are sent to the data storage module for storage.
5. The big data analysis-based diet recommendation method according to claim 2, wherein the data interaction module is used for realizing deep pushing of dishes, and comprises:
acquiring a contact in the user intelligent terminal, and marking the contact as i, i-1, 2, … …, n;
acquiring the number of times of communication between a user and a contact i through a user terminal, and marking the number of times of communication as JCi; the communication frequency is the sum of the voice frequency and the video frequency of the user and the contact person i; acquiring the frequency of the simultaneous presence of the user and the contact i in one restaurant and marking the frequency as CFCi;
by the formula
Acquiring a diet relation value YGZi; wherein beta 1 is a proportionality coefficient, and beta 1 belongs to [0.1, 0.5 ]]；
When the diet relation value YGZi meets YGZi which is not less than L4, judging that the relation between the contact i and the user is close, and pushing the dish screening range and the dish screening graph of the user to the contact i through the data interaction module; when the diet relation value YGZi meets 0< YGZi < L4, pushing the dish screening range of the user to the contact i through the data interaction module;
and the processor sends the dish screening range and the pushing record of the dish screening graph to the data storage module for storage.
6. A big data analysis-based diet recommendation method according to claim 2, wherein the maintenance management module is used for monitoring files in the data storage module, and comprises:
acquiring the resource occupancy rate of the processor in real time, and marking the resource occupancy rate as ZYZ;
acquiring a digital abstract of a file in a data storage module by a Hash method, comparing the digital abstract with a digital abstract database of a corresponding file, counting the proportion of the number of the files with different comparison results of the digital abstract and the digital abstract database to the total number of the files, and marking the proportion as BL;
by the formula B ═ gamma 1 × ZYZ × eγ2×BLAcquiring a virus threat coefficient B; wherein gamma 1 and gamma 2 are proportionality coefficients, and both gamma 1 and gamma 2 are real numbers greater than 0;
when the virus threat coefficient B is less than or equal to the virus set threshold, judging the data security in the data storage module, and sending a data security signal to the maintenance management module; and when the virus threat coefficient B is larger than a virus set threshold value, judging that the file in the data storage module is attacked by the virus, sending a virus attack signal to the maintenance management module, and only reserving the access authority of the processor and the maintenance management module to the data storage module.
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