CN112488807A - Agricultural product sale recommendation method based on big data - Google Patents
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Abstract
The invention discloses an agricultural product sale recommending method based on big data, belonging to the technical field of sale recommending, relating to big data recommending technology and solving the problems that the prior sale mode does not distinguish and manage information of users and does not distinguish products sold by the user, so that the recommended products do not accord with the purchase interest and purchase power of customers, the invention is provided with a product classifying module which clearly separates the sold products according to big types and small divisions and an evaluation feedback module which provides a feedback mode for the users when the users purchase the products and reminds the users to purchase feedback after setting a feedback period, and the product recommending module is arranged to calculate preference degree PJ and evaluation index ZJi according to the big data to recommend the users in a targeted way, the success rate of recommendation is increased.
Description
Technical Field
The invention belongs to the technical field of sales recommendation, relates to a big data recommendation technology, and particularly relates to an agricultural product sales recommendation method based on big data.
Background
Big data is continuously developed along with the rapid development of the internet and information technology, and the big data age is coming faster. Big data enables various industries to see different new views of the whole society, and meanwhile, various industries start to be aware of the huge potential value of the data, so that the application field of the big data is more and more extensive. The application of the big data in online sale can not only meet the individualized consumption demand of consumers, but also help enterprises to more accurately acquire market information and master the market demand, thereby effectively adjusting production and reducing the economic loss caused by over-production or products which can not meet the demand of the consumers.
The existing selling mode does not distinguish and manage the information of the user and does not distinguish the products sold by the user, so that the recommended products are inconsistent with the purchasing interest and purchasing power of the customer, and the recommending effect is poor.
In order to solve the problems, an agricultural product sale recommendation method based on big data is provided.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides an agricultural product sales recommendation method based on big data, which is used for solving the phenomenon that the existing sales recommendation method is inconsistent with the purchasing interest and purchasing power of customers. The product classification module is used for clearly separating sold products according to large types and small differences, the evaluation feedback module is arranged for providing a feedback mode for a user when the user purchases the products and reminding the user to purchase feedback after setting a feedback period, the commodity recommendation module is arranged for calculating preference degrees PJ and evaluation indexes ZJi according to big data to carry out targeted recommendation on the user, and the recommendation success rate is increased.
The purpose of the invention can be realized by the following technical scheme:
a big data-based agricultural product sales recommendation method comprises a server, a data acquisition module, a product classification module, a commodity recommendation module, an evaluation feedback module, a data processing module, a data storage module and a user registration module; the user registration module is used for inputting personal information to perform user registration when a user purchases agricultural products, wherein the input personal information comprises names, ages, sexes, birthdays, addresses, telephones, areas where the users are located, e-mails and the like; the user registration module is directly connected with the server, and the server sends the personal information of the user who successfully registers to the data storage module for storage; the product classification module is used for classifying sold agricultural products, the evaluation feedback module is used for feedback summary of the agricultural products after a user purchases the agricultural products and reaches a feedback period, and the specific working process comprises the following steps:
the method comprises the following steps: when a user purchases agricultural products, personal information is input through a user registration module for registration, the evaluation feedback module marks the user who successfully registers as a target user, and simultaneously starts timing and sets a feedback period T;
step two: the agricultural product Ji purchased by a user is obtained, when a feedback period T is reached, an evaluation feedback module sends an evaluation signal to a user registration module, a target user evaluates the agricultural product through a mobile terminal, and the specific evaluation mode is a scoring system, wherein the total score is 10;
step three: and the evaluation feedback module is used for grading and summarizing, and sending the summarized grades to the data storage module for storage.
Preferably, the data acquisition module is configured to acquire specific information of a user and send the specific information to the data processing module, and the data processing module processes the specific information sent by the data acquisition module, where the specific processing process includes the following steps:
step S1: the method comprises the steps that the historical times of purchasing agricultural products by a user, the family population number of the user and the education index of the user are obtained through a data acquisition module, and the historical times of purchasing agricultural products by the user, the family population number of the user and the education index of the user are sent to a data processing module;
step S2: after the data processing module receives the historical times of purchasing agricultural products by the user, the family population number of the user and the education index of the user, the data processing module is respectively marked as: CJi, Rks, Jys;
step S3: calculating the preference degree PJ of the user on agricultural products J by using a calculation formula
Wherein a1, a2, a3 are proportionality coefficients, and a1>a2>a3, a1, a2 and a3 all belong to (0, 1);
step S4: and sending the calculated preference degree PJ of the user to the agricultural product J to a commodity recommendation module.
Preferably, the commodity recommendation module is configured to obtain a preference degree PJ of the user on an agricultural product J calculated by the data processing module, and recommend a commodity to the user, where a specific recommendation process includes the following steps:
step L1: the commodity recommending module receives the preference degrees PJ of the users on the agricultural products J calculated by the data processing module, and carries out descending arrangement on the preference degrees PJ of the users on the agricultural products J calculated by the data processing module;
step L2: marking the agricultural products with the maximum PJ degree as target agricultural products, and acquiring an evaluation score FJis of the target products Ji bought by the user s;
step L3: the evaluation index ZJi of the target agricultural product Ji is calculated by the calculation formula
Wherein a4 is a scale factor and a4 belongs to (0, 1);
step L4: the commodity recommending module sets an evaluation index threshold, if the evaluation index ZJi of the target agricultural product Ji is larger than the evaluation index threshold, the target agricultural product Ji is worth recommending, the evaluation indexes ZJi of the target agricultural product Ji are arranged in a descending order, the commodity recommending module sends a recommending signal to the server, and the server sends the target agricultural products in the first three of the evaluation indexes of the target agricultural product to the mobile terminal of the user for recommending the agricultural products.
Preferably, the evaluation feedback module sets the rule of the feedback period T to be H consumed by the person who obtains the agricultural product every day, Rks family population of the user who purchases the agricultural product and Z total purchase amount, and the calculation formula of the feedback period T is
And the evaluation feedback module is also provided with an evaluation feedback buffer period t, and when the evaluation feedback buffer period t is reached, the user does not perform evaluation feedback, the default evaluation score is 6.
Preferably, the specific information of the user includes a history number of times the user purchased agricultural products, a family population number of the user, an education index of the user indicating a degree of education of the specific user, wherein the education index is equal to a highest annual number of the user when the user is at a high school and below level, and the education index is unified to 10 when the user is at a high school and above level.
Preferably, the data storage module is used for storing personal information input during user registration, scores summarized by the evaluation feedback module, evaluation index threshold set by the commodity recommendation module and a calculation formula for calculating preference degree PJ of the user on agricultural products J
Calculation formula for calculating evaluation index ZJi of target agricultural product Ji
Preferably, the data acquisition module acquires the specific information of the user in an age-based hierarchy manner, and after the user inputs personal information during registration, the server sends the personal information of the successfully registered users to the data storage module for storage, users with different age levels are stored in different storage spaces in the data storage module, wherein the data acquisition module is used for carrying out hierarchy marking when acquiring specific information of a user, the commodity recommendation module is used for acquiring the hierarchy marking and carrying out hierarchy marking identification when sending a recommendation signal to the server, and when the matching is determined, the server sends the target agricultural products with the top three of the target agricultural product evaluation index to the mobile terminal of the user of the matching level for agricultural product recommendation.
Compared with the prior art, the invention has the beneficial effects that:
1. the agricultural product feedback system is provided with an evaluation feedback module, the evaluation feedback module is used for feedback and summary of agricultural products after a user purchases the agricultural products and reaches a feedback period, and the user carries out agricultural productionWhen the product is purchased, inputting personal information through a user registration module for registration, marking the user who successfully registers as a target user through an evaluation feedback module, starting timing at the same time, and setting a feedback period T; the agricultural product Ji purchased by a user is obtained, when a feedback period T is reached, an evaluation feedback module sends an evaluation signal to a user registration module, a target user evaluates the agricultural product through a mobile terminal, and the specific evaluation mode is a scoring system, wherein the total score is 10; and the evaluation feedback module is used for grading and summarizing, and sending the summarized grades to the data storage module for storage. The evaluation feedback module sets the rule of the feedback period T to be H consumed by the people who obtain the agricultural products every day, Rks family population of the user who purchases the agricultural products and Z total purchase amount, and the calculation formula of the feedback period T is
And the evaluation feedback module is also provided with an evaluation feedback buffer period t, and when the evaluation feedback buffer period t is reached, the user does not perform evaluation feedback, the default evaluation score is 6. By obtaining the evaluation of the user on the agricultural products, the preference degree of the user on the agricultural products is better grasped.
2. The data acquisition module adopts an age-based hierarchy acquisition mode when acquiring specific information of a user, after the user inputs personal information during registration, the server sends the personal information of the successfully registered users to the data storage module for storage, users with different age levels are stored in different storage spaces in the data storage module, wherein the data acquisition module is used for carrying out hierarchy marking when acquiring specific information of a user, the commodity recommendation module is used for acquiring the hierarchy marking and carrying out hierarchy marking identification when sending a recommendation signal to the server, and when the matching is determined, the server sends the target agricultural products with the top three of the target agricultural product evaluation index to the mobile terminal of the user of the matching level for agricultural product recommendation.
3. The invention is provided with a data processing module which is used for processing the specific information sent by the data acquisition moduleThe data acquisition module is used for acquiring the historical times of purchasing agricultural products by the user, the family population number of the user and the education index of the user, and sending the historical times of purchasing agricultural products by the user, the family population number of the user and the education index of the user to the data processing module; after the data processing module receives the historical times of purchasing agricultural products by the user, the family population number of the user and the education index of the user, the data processing module is respectively marked as: CJi, Rks, Jys; calculating the preference degree PJ of the user on agricultural products J by using a calculation formula
And a1, a2 and a3 are proportionality coefficients, and the calculated preference degree PJ of the user on agricultural products J is sent to a commodity recommending module.
4. The system is provided with a commodity recommending module, wherein the commodity recommending module is used for acquiring the preference degree PJ of a user on agricultural products J calculated by the data processing module and recommending commodities to the user; marking the agricultural products with the maximum PJ degree as target agricultural products, and acquiring an evaluation score FJis of the target products Ji bought by the user s; the evaluation index ZJi of the target agricultural product Ji is calculated by the calculation formula
The a4 is a proportionality coefficient, the commodity recommending module sets an evaluation index threshold, if the evaluation index ZJi of the target agricultural product Ji is larger than the evaluation index threshold, the target agricultural product Ji is worth recommending, the evaluation indexes ZJi of the target agricultural product Ji are arranged in a descending order, the commodity recommending module sends a recommending signal to the server, and the server sends the target agricultural products in the first three of the target agricultural product evaluation indexes to the mobile terminal of the user for agricultural product recommending.
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 flow chart 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.
Example 1:
as shown in fig. 1, a big data based agricultural product sales recommendation method, taking selling rice as an example, the specific agricultural product sales recommendation method includes the following steps:
the method comprises the following steps: when a user purchases rice, personal information is input through the user registration module for registration, specific information of twenty to forty users including historical times of rice purchase, family population of the user and education index of the user is obtained through the data acquisition module, and the historical times of rice purchase, the family population of the user and the education index of the user are sent to the data processing module;
step two: the data processing module receives the historical times of rice purchase of the user, the family population of the user and the education index of the user, and the historical times are respectively marked as: CJi, Rks, Jys; wherein J represents rice; i represents the type of rice, including long grain scented rice, round grain scented rice, rice flower scented rice and other rice; s represents a user participating in the evaluation, s is 1, …, o;
step three: the data processing module calls a calculation formula from the data storage module to calculate the preference degree PJ of the user on agricultural products J,
wherein the calculation formula is
Wherein a1, a2, a3 are proportionality coefficients, and a1>a2>a3, a1, a2 and a3 all belong to (0, 1);
step four: sending the calculated preference degree PJ of the user to the rice to a commodity recommendation module;
step five: marking the rice as a target agricultural product, and acquiring an evaluation score FJis of rice Ji bought by a user s, which is collected by an evaluation feedback module; wherein i is 1,2,3, 4; FJ1s represents the evaluation score of long scented rice purchased by the user s, FJ2s represents the evaluation score of round scented rice purchased by the user s, and FJ3s represents the evaluation score of rice with flower scent purchased by the user s; FJ4s represents the evaluation scores of other rice purchased by user s;
step six: calling a calculation formula from the data storage module to calculate the evaluation index ZJi of the target agricultural product Ji, wherein the calculation formula is
Wherein a4 is a scale factor and a4 belongs to (0, 1);
step eight: the commodity recommending module sets an evaluation index threshold, if the evaluation index ZJ1 of the long grain fragrant rice is greater than the evaluation index threshold, the long grain fragrant rice is worth recommending, if the evaluation index ZJ2 of the round grain fragrant rice is greater than the evaluation index threshold, the round grain fragrant rice is worth recommending, and if the evaluation index ZJ3 of the rice flower fragrant rice is greater than the evaluation index threshold, the rice flower fragrant rice is worth recommending; if the evaluation index ZJ4 of other rice is larger than the evaluation index threshold, the other rice is worthy of recommendation;
and if the evaluation index ZJ1 of the long grain fragrant rice, the evaluation index ZJ2 of the round grain fragrant rice, the evaluation index ZJ3 of the rice flower fragrant rice and the evaluation index ZJ4 of other rice are all larger than the evaluation index threshold values, the evaluation index ZJ1 of the long grain fragrant rice, the evaluation index ZJ2 of the round grain fragrant rice, the evaluation index ZJ3 of the rice flower fragrant rice and the evaluation index ZJ4 of the other rice are arranged in a descending order, the commodity recommending module sends a recommending signal to the server, and the server sends the rice three times before the target agricultural product evaluation index to the mobile terminal of the user in the same age group for agricultural product recommendation.
The evaluation feedback module is used for feedback summary of agricultural products after a user purchases the agricultural products and reaches a feedback period, and the specific working process comprises the following steps:
step A1: when a user purchases agricultural products, personal information is input through a user registration module for registration, the evaluation feedback module marks the user who successfully registers as a target user, and simultaneously starts timing and sets a feedback period T;
step A2: the agricultural product Ji purchased by a user is obtained, when a feedback period T is reached, an evaluation feedback module sends an evaluation signal to a user registration module, a target user evaluates the agricultural product through a mobile terminal, and the specific evaluation mode is a scoring system, wherein the total score is 10;
step A3: and the evaluation feedback module is used for grading and summarizing, and sending the summarized grades to the data storage module for storage.
The evaluation feedback module sets the rule of the feedback period T to be H consumed by people who acquire agricultural products every day, Rks family population number of users who purchase agricultural products and Z total purchase amount, and the calculation formula of the feedback period T is
And the evaluation feedback module is also provided with an evaluation feedback buffer period t, and when the evaluation feedback buffer period t is reached, the user does not perform evaluation feedback, the default evaluation score is 6.
Wherein the specific information of the user includes the historical times of purchasing agricultural products by the user, the family population number of the user, and the education index of the user, the education index of the user represents the education degree of the specific user, wherein the education index is equal to the highest annual progression of the user when the user is used at a high school and below level, and the education index is unified to 10 when the user is used at a high school and above level.
Wherein,the data storage module is used for storing personal information input during user registration, scores summarized by the evaluation feedback module, evaluation index threshold set by the commodity recommendation module and a calculation formula for calculating preference degree PJ of the user on agricultural products J
Calculation formula for calculating evaluation index ZJi of target agricultural product Ji
The user registration module is used for inputting personal information to perform user registration when a user purchases agricultural products, wherein the input personal information comprises name, age, gender, birthday, address, telephone, area and e-mail.
Wherein, the data acquisition module adopts an age-based hierarchy acquisition mode when acquiring specific information of a user, after the user inputs personal information during registration, the server sends the personal information of the successfully registered users to the data storage module for storage, users with different age levels are stored in different storage spaces in the data storage module, wherein the data acquisition module is used for carrying out hierarchy marking when acquiring specific information of a user, the commodity recommendation module is used for acquiring the hierarchy marking and carrying out hierarchy marking identification when sending a recommendation signal to the server, and when the matching is determined, the server sends the target agricultural products with the top three of the target agricultural product evaluation index to the mobile terminal of the user of the matching level for agricultural product recommendation.
Example 2:
a big data-based agricultural product sale recommendation method comprises the following steps:
the method comprises the following steps: when a user purchases agricultural products, personal information is input through the user registration module for registration, specific information of the user in the same age group, including historical times for purchasing the agricultural products, the family population number of the user and the education index of the user, is acquired through the data acquisition module, and the historical times for purchasing the agricultural products, the family population number of the user and the education index of the user are sent to the data processing module;
step two: after the data processing module receives the historical times of purchasing agricultural products by the user, the family population number of the user and the education index of the user, the data processing module is respectively marked as: CJi, Rks, Jys; wherein J represents the species of agricultural product, J ═ 1, …, m; i represents the types of J agricultural products, i is 1, …, n; s represents a user participating in the evaluation, s is 1, …, o;
step three: the data processing module calls a calculation formula from the data storage module to calculate the preference degree PJ of the user on agricultural products J,
wherein the calculation formula is
Wherein a1, a2, a3 are proportionality coefficients, and a1>a2>a3, a1, a2 and a3 all belong to (0, 1);
step four: sending the calculated preference degree PJ of the user on the agricultural product J to a commodity recommendation module;
step five: after the commodity recommending module receives the preference degrees PJ of the users on the agricultural products J calculated by the data processing module, the preference degrees PJ of the users on the agricultural products J calculated by the data processing module are arranged in a descending order;
step six: obtaining an agricultural product with the maximum preference degree PJ, marking the agricultural product as a target agricultural product, and obtaining an evaluation score FJis of a target product Ji bought by a user s, which is collected by an evaluation feedback module;
step seven: calling a calculation formula from the data storage module to calculate the evaluation index ZJi of the target agricultural product Ji, wherein the calculation formula is
Wherein a4 is a scale factor and a4 belongs to (0, 1);
step eight: the commodity recommending module sets an evaluation index threshold, if the evaluation index ZJi of the target agricultural product Ji is larger than the evaluation index threshold, the target agricultural product Ji is worth recommending, the evaluation indexes ZJi of the target agricultural product Ji are arranged in a descending order, the commodity recommending module sends a recommending signal to the server, and the server sends the target agricultural products three times before the evaluation index of the target agricultural product to the mobile terminal of the user in the same age group for agricultural product recommending.
The evaluation feedback module is used for feedback summary of agricultural products after a user purchases the agricultural products and reaches a feedback period, and the specific working process comprises the following steps:
step A1: when a user purchases agricultural products, personal information is input through a user registration module for registration, the evaluation feedback module marks the user who successfully registers as a target user, and simultaneously starts timing and sets a feedback period T;
step A2: the agricultural product Ji purchased by a user is obtained, when a feedback period T is reached, an evaluation feedback module sends an evaluation signal to a user registration module, a target user evaluates the agricultural product through a mobile terminal, and the specific evaluation mode is a scoring system, wherein the total score is 10;
step A3: and the evaluation feedback module is used for grading and summarizing, and sending the summarized grades to the data storage module for storage.
The evaluation feedback module sets the rule of the feedback period T to be H consumed by people who acquire agricultural products every day, Rks family population number of users who purchase agricultural products and Z total purchase amount, and the calculation formula of the feedback period T is
And the evaluation feedback module is also provided with an evaluation feedback buffer period t, and when the evaluation feedback buffer period t is reached, the user does not perform evaluation feedback, the default evaluation score is 6.
Wherein the specific information of the user includes the historical times of purchasing agricultural products by the user, the family population number of the user, and the education index of the user, the education index of the user represents the education degree of the specific user, wherein the education index is equal to the highest annual progression of the user when the user is used at a high school and below level, and the education index is unified to 10 when the user is used at a high school and above level.
The data storage module is used for storing personal information input during user registration, scores summarized by the evaluation feedback module, evaluation index threshold set by the commodity recommendation module and a calculation formula for calculating preference degree PJ of the user on agricultural products J
Calculation formula for calculating evaluation index ZJi of target agricultural product Ji
The user registration module is used for inputting personal information to perform user registration when a user purchases agricultural products, wherein the input personal information comprises name, age, gender, birthday, address, telephone, area and e-mail.
Wherein, the data acquisition module adopts an age-based hierarchy acquisition mode when acquiring specific information of a user, after the user inputs personal information during registration, the server sends the personal information of the successfully registered users to the data storage module for storage, users with different age levels are stored in different storage spaces in the data storage module, wherein the data acquisition module is used for carrying out hierarchy marking when acquiring specific information of a user, the commodity recommendation module is used for acquiring the hierarchy marking and carrying out hierarchy marking identification when sending a recommendation signal to the server, and when the matching is determined, the server sends the target agricultural products with the top three of the target agricultural product evaluation index to the mobile terminal of the user of the matching level for agricultural product recommendation.
The product classification module is used for clearly separating sold products according to large types and small differences, the evaluation feedback module is arranged for providing a feedback mode for a user when the user purchases the products and reminding the user to purchase feedback after setting a feedback period, the commodity recommendation module is arranged for calculating preference degrees PJ and an evaluation index ZJM according to large data to carry out targeted recommendation on the user, and the recommendation success rate is increased.
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 (7)
1. A big data-based agricultural product sale recommendation method is characterized by comprising the following steps:
the method comprises the following steps: when a user purchases agricultural products, personal information is input through the user registration module for registration, specific information of the user in the same age group, including historical times for purchasing the agricultural products, the family population number of the user and the education index of the user, is acquired through the data acquisition module, and the historical times for purchasing the agricultural products, the family population number of the user and the education index of the user are sent to the data processing module;
step two: after the data processing module receives the historical times of purchasing agricultural products by the user, the family population number of the user and the education index of the user, the data processing module is respectively marked as: CJi, Rks, Jys; wherein J represents the species of agricultural product, J ═ 1, …, m; i represents the types of J agricultural products, i is 1, …, n; s represents a user participating in the evaluation, s is 1, …, o;
step three: the data processing module calls a calculation formula from the data storage module to calculate the preference degree PJ of the user on agricultural products J,
wherein the calculation formula is
Wherein a1, a2, a3 are proportionality coefficients, and a1>a2>a3, a1, a2 and a3 all belong to (0, 1);
step four: sending the calculated preference degree PJ of the user on the agricultural product J to a commodity recommendation module;
step five: after the commodity recommending module receives the preference degrees PJ of the users on the agricultural products J calculated by the data processing module, the preference degrees PJ of the users on the agricultural products J calculated by the data processing module are arranged in a descending order;
step six: obtaining an agricultural product with the maximum preference degree PJ, marking the agricultural product as a target agricultural product, and obtaining an evaluation score FJis of a target product Ji bought by a user s, which is collected by an evaluation feedback module;
step seven: calling a calculation formula from the data storage module to calculate the evaluation index ZJi of the target agricultural product Ji, wherein the calculation formula is
Wherein a4 is a scale factor and a4 belongs to (0, 1);
step eight: the commodity recommending module sets an evaluation index threshold, if the evaluation index ZJi of the target agricultural product Ji is larger than the evaluation index threshold, the target agricultural product Ji is worth recommending, the evaluation indexes ZJi of the target agricultural product Ji are arranged in a descending order, the commodity recommending module sends a recommending signal to the server, and the server sends the target agricultural products three times before the evaluation index of the target agricultural product to the mobile terminal of the user in the same age group for agricultural product recommending.
2. The big-data-based agricultural product sales recommendation method according to claim 1, wherein the evaluation feedback module is used for performing feedback summarization of agricultural products after a user purchases the agricultural products for a feedback period, and the specific working process comprises the following steps:
step A1: when a user purchases agricultural products, personal information is input through a user registration module for registration, the evaluation feedback module marks the user who successfully registers as a target user, and simultaneously starts timing and sets a feedback period T;
step A2: the agricultural product Ji purchased by a user is obtained, when a feedback period T is reached, an evaluation feedback module sends an evaluation signal to a user registration module, a target user evaluates the agricultural product through a mobile terminal, and the specific evaluation mode is a scoring system, wherein the total score is 10;
step A3: and the evaluation feedback module is used for grading and summarizing, and sending the summarized grades to the data storage module for storage.
3. The big-data-based agricultural product sales recommendation method according to claim 1, wherein the evaluation feedback module sets the feedback period T to have a rule that a person who obtains the agricultural product consumes H every day, a family population Rks of a user who purchases the agricultural product and a total purchase amount Z, and a calculation formula of the feedback period T is
And the evaluation feedback module is also provided with an evaluation feedback buffer period t, and when the evaluation feedback buffer period t is reached, the user does not perform evaluation feedback, the default evaluation score is 6.
4. The big-data-based agricultural product sales recommendation method according to claim 1, wherein the specific information of the user comprises historical times of purchasing agricultural products by the user, family population of the user, and education index of the user, the education index of the user represents education degree of a specific user, wherein the education index is equal to the highest annual level of the user when the user is used at high school and below, and the education index is unified into 10 when the user is used at high school and above.
5. According to the claimsThe agricultural product sale recommendation method based on big data is characterized in that the data storage module is used for storing personal information input during user registration, scores summarized by the evaluation feedback module, evaluation index threshold set by the commodity recommendation module and a calculation formula used for calculating preference degree PJ of a user on agricultural products J
Calculation formula for calculating evaluation index ZJi of target agricultural product Ji
6. The big data based agricultural product sales recommendation method according to claim 1, wherein the user registration module is used for inputting personal information for user registration when a user purchases an agricultural product, and the input personal information comprises name, age, gender, birthday, address, telephone, location and email.
7. The agricultural product sales recommendation method based on big data as claimed in claim 1, wherein the data acquisition module acquires specific information of a user in an age-based hierarchy manner, after the user inputs the personal information during registration, the server sends the personal information of the user who successfully registers to the data storage module for storage, the users of different age hierarchies are stored in different storage spaces in the data storage module, wherein the users under twenty years old belong to the same hierarchy, the users over twenty years old to forty years old belong to the same hierarchy, the data acquisition module carries out hierarchy marking when acquiring the specific information of the user, the commodity recommendation module sends a recommendation signal to the server, the server acquires the hierarchy marking and carries out hierarchy marking identification, and when the identification is matched, the server sends target agricultural products three before the target agricultural product evaluation index to the mobile terminal of the user of the matched hierarchy for agricultural product sales recommendation And (5) recommending products.
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| CN116385048A (en) * | 2023-06-06 | 2023-07-04 | 山东政信大数据科技有限责任公司 | Intelligent marketing method and system for agricultural products |
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