CN106600380B - Intelligent recommendation system and method based on catering O2O e-commerce platform - Google Patents
Intelligent recommendation system and method based on catering O2O e-commerce platform Download PDFInfo
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Abstract
An intelligent recommendation system and method based on a catering O2O e-commerce platform are disclosed, wherein the system comprises: the system comprises a cloud server, an e-commerce server, a merchant client and a recyclable lunch box which is provided for a merchant by an e-commerce platform, provided for a user by the merchant and recycled; the lunch box is divided into an upper layer and a lower layer, the upper layer is divided into a plurality of troughs, each trough is correspondingly provided with a trough number, the bottom of each trough is provided with a micro weighing sensor corresponding to the trough number, and the lower layer is provided with a CPU (central processing unit) processor respectively connected with the micro weighing sensors, a memory respectively connected with the CPU processor and a wireless communication module; the wireless communication module is used for carrying out wireless data transmission with the cloud server and the e-commerce server. By means of the method and the device, an intelligent recommendation mode can better meet actual requirements of users, the existing food recommendation mode aiming at the E-commerce platform is expanded, and the food preference degree of the users in the real environment can be effectively detected.
Description
Technical Field
The invention relates to the field of electronic commerce, in particular to an intelligent recommendation system and method based on a catering O2O e-commerce platform.
Background
Electronic commerce refers to the transaction activities and related service activities performed in an electronic transaction mode on the Internet (Internet), an Intranet (Intranet) and a Value Added Network (VAN), and is the electronization and networking of each link of the traditional business activities. Electronic commerce generally refers to a novel business operation mode in which, in wide commercial and trade activities worldwide, in an internet environment open to the internet, buyers and sellers conduct various commercial and trade activities without conspiracy based on a browser/server application mode, and consumer online shopping, online transactions and online electronic payments among merchants, and various commercial activities, transaction activities, financial activities, and related comprehensive service activities are realized. Electronic commerce is a business activity using microcomputer technology and network communication technology. Governments, scholars and business persons in various countries have given many different definitions according to their positions and the angle and degree of participation in electronic commerce. E-commerce is divided into ABC, B2B, B2C, C2C, B2M, M2C, B2A (i.e. B2G), C2A (i.e. C2G), O2O E-commerce modes, and the like.
Take O2O as an example; O2O, Online To Offline, refers To the concept of combining Offline business opportunities with the internet To make the internet the foreground of Offline transactions, and it originally came from the united states. The concept of O2O is very broad and is commonly referred to as O2O as long as it can be involved both on-line and off-line in the industry chain. The mainstream business administration curriculum has introduced and paid attention to a new business model, O2O. In 2013, O2O enters a high-speed development stage and starts the integration of localization and mobile equipment, so that the business model O2O is released and becomes a localization branch of the model O2O.
In the business model O2O, especially for take-away catering e-commerce platforms, in order to help users find suitable food or merchants as soon as possible and to promote the same, such e-commerce platforms integrate a recommendation system to automatically generate recommendations for customers, for example, recommendations for relevant merchants or food are made to users according to records of merchants or food that users order every time. However, the disadvantages of this recommendation are: the online recommendation method can only acquire online user data for relevant recommendation according to the ordering operation of the user based on the e-commerce platform, and cannot analyze the preference degree of the offline user for a certain food so as to recommend the online user data.
Disclosure of Invention
The purpose of the invention is as follows: in view of the above situation, in order to overcome the disadvantages in the background art, embodiments of the present invention provide an intelligent recommendation system and method based on a catering O2O e-commerce platform, which can effectively solve the problems involved in the background art.
The technical scheme is as follows: an intelligent recommendation system based on a catering O2O e-commerce platform comprises: the system comprises a cloud server, an e-commerce server, a merchant client and a recyclable lunch box which is provided for a merchant by an e-commerce platform, provided for a user by the merchant and recycled; the lunch box is divided into an upper layer and a lower layer, the upper layer is divided into a plurality of troughs, each trough is correspondingly provided with a trough number, the bottom of each trough is provided with a micro weighing sensor corresponding to the trough number, and the lower layer is provided with a CPU (central processing unit) processor respectively connected with the micro weighing sensors, a memory respectively connected with the CPU processor and a wireless communication module; the wireless communication module is used for carrying out wireless data transmission with the cloud server and the e-commerce server; the merchant client is used for sending the food names which are input by the merchant and are arranged in the food grooves and correspond to the groove numbers to the CPU through the wireless communication module; the CPU sends a first weighing signal to the micro weighing sensor corresponding to the slot number according to the food name corresponding to the slot number; the micro weighing sensor is used for weighing the crib arranged above for the first time according to the first weighing signal and returning the first weighing data to the CPU; the CPU processor stores the food name and the first weighing data corresponding to the food name in the memory; the lunch box is provided with a button switch connected with the CPU processor and used for transmitting a pressing signal to the CPU processor when being pressed; the CPU sends a second weighing signal to the micro weighing sensor corresponding to the slot number according to the pressing signal; the micro weighing sensor reweighs the feeding trough arranged above according to the second weighing signal and returns reweighed data to the CPU; the CPU processor stores re-weighing data corresponding to the slot number in the memory, so that the re-weighing data correspond to the food name, and sends the first weighing data and the re-weighing data corresponding to the food name to the cloud server through the wireless communication module; the cloud server is used for calculating the user preference degree of food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, and sending the calculation result to the e-commerce server; and the E-commerce server is used for making a corresponding food recommendation strategy according to the calculation result so as to recommend on the E-commerce platform.
As a preferred mode of the present invention, the cloud server is further configured to collect a preset number of first weighing data and second weighing data corresponding to food names, calculate a degree of liking of the user to food corresponding to the food names, determine whether the degree of liking exceeds a first preset threshold, and if so, determine that the degree of liking of the user to food corresponding to the food names is liking; if the preference degree value is not within the first preset threshold and the second preset threshold, determining that the preference degree of the user to the food corresponding to the food name is general; if not, whether the preference degree value is lower than a second preset threshold value or not is judged, and if not, the preference degree of the user to the food corresponding to the food name is determined to be annoying.
As a preferred mode of the present invention, the user adopts a calculation formula for the preference degree value of the food corresponding to the food name as follows:
wherein n is a preset number,in order to weigh the data for the first time,
in order to weigh the data again, the weighing machine,
are weight coefficients.
In a preferred embodiment of the present invention, the first preset threshold is 70% and the second preset threshold is 30%.
As a preferable mode of the present invention, the e-commerce server is further configured to recommend the food corresponding to the food name and/or the merchant containing the food to the e-commerce platform home page when the reading result indicates that the food corresponding to the food name is liked by the user.
An intelligent recommendation method based on a catering O2O e-commerce platform, which uses the intelligent recommendation system of claim 1, and comprises the following steps:
step1, the merchant client sends the food names which are input by the merchant and are arranged in the food slots and correspond to the slot numbers to the CPU processor through the wireless communication module;
step2, the CPU sends a first weighing signal to the micro weighing sensor corresponding to the slot number according to the food name corresponding to the slot number;
step3, the micro weighing sensor carries out primary weighing on the crib arranged above according to a first weighing signal and returns primary weighing data to the CPU;
step4, the CPU processor storing the food name and the first weighing data corresponding to the food name in the memory;
step5, transmitting a pressing signal to the CPU processor by a button switch according to the pressing operation of a user;
step6, the CPU sends a second weighing signal to the micro weighing sensor corresponding to the slot number according to the pressing signal;
step7, the micro weighing sensor reweighs the feeding trough arranged above according to a second weighing signal and returns reweighed data to the CPU;
step8, the CPU stores the re-weighing data corresponding to the slot number in the memory, so that the re-weighing data correspond to the food name, and sends the first weighing data and the re-weighing data corresponding to the food name to the cloud server through the wireless communication module;
step9, the cloud server calculates the user's favorite degree of the food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, and sends the calculation result to the e-commerce server;
and Step10, the E-commerce server makes a corresponding food recommendation strategy according to the calculation result so as to recommend on the E-commerce platform.
As a preferred aspect of the present invention, Step9, the cloud server calculating, according to the first weighing data and the second weighing data corresponding to the food name, a user's preference degree of the food corresponding to the food name, includes:
acquiring a preset amount of first weighing data and second weighing data corresponding to food names, calculating the preference degree value of a user to food corresponding to the food names, judging whether the preference degree value exceeds a first preset threshold value, and if so, determining the preference degree of the user to the food corresponding to the food names as favorite; if the preference degree value is not within the first preset threshold and the second preset threshold, determining that the preference degree of the user to the food corresponding to the food name is general; if not, whether the preference degree value is lower than a second preset threshold value or not is judged, and if not, the preference degree of the user to the food corresponding to the food name is determined to be annoying.
As a preferred mode of the present invention, the user adopts a calculation formula for the preference degree value of the food corresponding to the food name as follows:
wherein n is a preset number,in order to weigh the data for the first time,
in order to weigh the data again, the weighing machine,are weight coefficients.
In a preferred embodiment of the present invention, the first preset threshold is 70% and the second preset threshold is 30%.
As a preferred mode of the present invention, Step10, the e-commerce server making a corresponding food recommendation policy according to the calculation result to make a recommendation on the e-commerce platform includes:
and recommending the food corresponding to the food name and/or the merchant containing the food to the home page of the E-commerce platform when the calculation result is read that the food corresponding to the food name is loved by the user.
The invention realizes the following beneficial effects: weighing data of food in different states are acquired through the lunch box which is provided for the user and can be recycled, the preference degree of the user for the food is calculated by the cloud server, and therefore corresponding recommendation is carried out on the E-commerce platform, the intelligent recommendation mode is more in line with the actual requirements of the user, the existing food recommendation mode for the E-commerce platform is expanded, and the food preference degree of the user in the real environment is effectively detected.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a schematic structural diagram of an intelligent recommendation system provided by the present invention;
FIG. 2 is a top view of the lunch box provided by the present invention;
FIG. 3 is a left side view of the lunch box provided by the present invention;
FIG. 4 is a right side view of the lunch box provided by the present invention;
fig. 5 is a schematic flow chart of an intelligent recommendation method provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example one
Referring to fig. 1-3, fig. 1 is a schematic structural diagram of an intelligent recommendation system according to the present invention; FIG. 2 is a top view of the lunch box provided by the present invention; FIG. 3 is a left side view of the lunch box provided by the present invention; fig. 4 is a right side view of the lunch box provided by the invention. Specifically, this embodiment provides an intelligent recommendation system based on food and beverage O2O electricity merchant platform, includes: the system comprises a cloud server 1, an e-commerce server 2, a merchant client 3 and a recyclable lunch box 4 which is provided for merchants by an e-commerce platform, provided for users by the merchants and recycled; the lunch box 4 is divided into an upper layer and a lower layer, the upper layer 5 is divided into a plurality of troughs 6, each trough 6 is correspondingly provided with a trough number, the bottom of each trough 6 is provided with a micro weighing sensor 7 corresponding to the trough number, and the lower layer 8 is provided with a CPU (central processing unit) processor 9 respectively connected with the micro weighing sensors 7, a memory 10 respectively connected with the CPU processor 9 and a wireless communication module 11; the wireless communication module 11 is used for performing wireless data transmission with the cloud server 1 and the e-commerce server 2; the merchant client 3 is configured to send the food name corresponding to the slot number, which is input by the merchant and placed in each food slot 6, to the CPU processor 9 through the wireless communication module 11; the CPU processor 9 sends a first weighing signal to the micro weighing sensor 7 corresponding to the slot number according to the food name corresponding to the slot number; the micro weighing sensor 7 is used for weighing the feeding trough 6 arranged above for the first time according to the first weighing signal and returning the first weighing data to the CPU 9; the CPU processor 9 stores the food name and its corresponding first weighing data in the memory 10; the lunch box 4 is provided with a button switch 12 connected with the CPU processor 9 and used for transmitting a pressing signal to the CPU processor 9 when being pressed; the CPU 9 sends a second weighing signal to the micro weighing sensor 7 corresponding to the slot number according to the pressing signal; the micro weighing sensor 7 reweighs the feeding tray 6 arranged above according to a second weighing signal and returns reweighed data to the CPU 9; the CPU processor 9 stores the re-weighing data corresponding to the slot number in the memory 10 so as to correspond to the food name, and transmits the first weighing data and the re-weighing data corresponding to the food name to the cloud server 1 through the wireless communication module 11; the cloud server 1 is used for calculating the user's preference degree of food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, and sending the calculation result to the e-commerce server 2; and the E-commerce server 2 is used for making a corresponding food recommendation strategy according to the calculation result so as to recommend on the E-commerce platform.
The meal box 4 is commonly used by a plurality of merchants establishing a cooperative relationship with the e-commerce platform, for example, after a certain merchant distributes the meal box 4 to a user, the meal box is stored by the user, and when the user orders a meal next time, the stored meal box 4 is provided for a distributor (which may be the distributor of any one merchant) to be recycled, and the process is repeated.
In this embodiment, the upper layer 5 of the lunch box 4 is divided into 4 troughs, the numbers of the 4 troughs are 1, 2, 3 and 4 respectively, the bottoms of the troughs with the numbers 1, 2, 3 and 4 are correspondingly provided with a first micro weighing sensor 701, a second micro weighing sensor 702, a third micro weighing sensor 703 and a fourth micro weighing sensor 704, and the CPU processor 9 is connected with the first micro weighing sensor 701, the second micro weighing sensor 702, the third micro weighing sensor 703 and the fourth micro weighing sensor 704 respectively.
Before the distributor distributes the users, the merchant needs to input the food names corresponding to the slot numbers 1, 2, 3 and 4 through the merchant client 3, and after the input is finished, the merchant client 3 sends the content input by the merchant to the CPU processor 9 through the wireless communication module 11. Wherein, the food names corresponding to the slot numbers 1, 2, 3 and 4 input by the merchant are food-a, food-b, food-c and food-d respectively. The food name of the embodiment is described only by English code, and in practical application, the food name will be specific, for example, food-a is braised spare ribs in soy sauce, food-b is spicy chicken, food-c is shredded pork with fish flavor, and food-d is fried eggs in Chinese chives.
After receiving the food names corresponding to the slot numbers 1, 2, 3, 4, the CPU processor 9 sends first weighing signals to the first micro weighing sensor 701, the second micro weighing sensor 702, the third micro weighing sensor 703, and the fourth micro weighing sensor 704 corresponding to the slot numbers 1, 2, 3, 4, respectively, the first micro weighing sensor 701, the second micro weighing sensor 702, the third micro weighing sensor 703, and the fourth micro weighing sensor 704 weigh the food slot 6 disposed above according to the weighing signals for the first time and return the first weighing data to the CPU processor 9, and the first weighing data of the food names corresponding to the set slot numbers 1, 2, 3, 4 are: 100g, 95g, 110g and 115g, the CPU processor 9 stores the food name and the first weighing data corresponding thereto in the memory 10, as shown in the following table:
| food name | First weighing data |
| food-a | 100g |
| food-b | 95g |
| food-c | 110g |
| food-d | 115g |
After eating, the user presses the button switch 12 disposed on the lunch box 4, when the button switch 12 is pressed, the button switch transmits a pressing signal to the CPU processor 9, the CPU processor 9 sends a second weighing signal to the first micro weighing sensor 701, the second micro weighing sensor 702, the third micro weighing sensor 703 and the fourth micro weighing sensor 704 corresponding to the slot numbers 1, 2, 3 and 4 according to the pressing signal, the first micro weighing sensor 701, the second micro weighing sensor 702, the third micro weighing sensor 703 and the fourth micro weighing sensor 704 re-weigh the food slot 6 disposed above according to the second weighing signal and return the re-weighed data to the CPU processor 9, and the re-weighed data of the food names corresponding to the slot numbers 1, 2, 3 and 4 are set as follows: 50g, 45g, 55g and 57g, the CPU processor 9 stores re-weighing data corresponding to the slot number in the memory 10 so as to correspond to the food name, and transmits first weighing data and re-weighing data corresponding to the food name to the cloud server 1 through the wireless communication module 11, wherein the memory 10 after the re-weighing data corresponds to the food name is as shown in the following table:
| food name | First weighing data | Data for reweighing |
| food-a | 100g | 50g |
| food-b | 95g | 45g |
| food-c | 110g | 55g |
| food-d | 115g | 57g |
When the cloud server 1 calculates the user's liking degree of food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, specifically, a preset number of the first weighing data and the second weighing data corresponding to the food name are collected, the liking degree value of the user to the food corresponding to the food name is calculated, whether the liking degree value exceeds a first preset threshold value is judged, and if the liking degree value exceeds the first preset threshold value, the user's liking degree to the food corresponding to the food name is determined as liking; if the preference degree value is not within the first preset threshold and the second preset threshold, determining that the preference degree of the user to the food corresponding to the food name is general; if not, whether the preference degree value is lower than a second preset threshold value or not is judged, and if not, the preference degree of the user to the food corresponding to the food name is determined to be annoying.
Wherein, the user adopts a calculation formula for the preference degree value of the food corresponding to the food name as follows:
wherein n is a preset number,
in order to weigh the data for the first time,
in order to weigh the data again, the weighing machine,
in this embodiment, a weighting system is set for the weighting coefficients
Is 1.
Wherein the first preset threshold is 70%, and the second preset threshold is 30%. Namely, the cloud server 1 judges whether the preference degree value exceeds 70%, and if so, determines that the preference degree of the user to the food corresponding to the food name is favorite; if not, judging whether the preference degree value is within 70% and 30%, and if so, determining that the preference degree of the user to the food corresponding to the food name is general; if not, judging whether the preference degree value is lower than 30%, and if so, determining that the preference degree of the user to the food corresponding to the food name is annoying.
Describing with the food name food-a, in this embodiment, the preset data of the first weighing data and the second weighing data, which are acquired by the cloud server 1 and correspond to the food name food-a, are set to 10, where the correspondence relationship of the first example is shown in the following table:
in this data state, the cloud server 1 performs the following calculation on the food preference value corresponding to the food name food-a by the user using a calculation formula:
in the above data state, the favorite degree value of the user for the food with the food name food-a calculated by the cloud server 1 is 49%, that is, it is determined that the favorite degree value is within a first preset threshold and a second preset threshold, that is, 30% to 70%, that is, the favorite degree of the user for the food with the food name food-a is general.
The correspondence of the second example is shown in the following table:
in this data state, the cloud server 1 performs the following calculation on the food preference value corresponding to the food name food-a by the user using a calculation formula:
in the above data state, the popularity value of the user for the food with the food name food-a calculated by the cloud server 1 is 49%, that is, it is determined that the popularity value exceeds the first preset threshold value of 70%, that is, the popularity of the user for the food with the food name food-a is a favorite.
When the e-commerce platform recommends food corresponding to the food name and/or a merchant containing the food, the e-commerce server 2 recommends the food corresponding to the food name and/or the merchant containing the food to the home page of the e-commerce platform when a corresponding food recommendation strategy is formulated according to the calculation result and the food recommendation strategy is recommended on the e-commerce platform when the calculation result is read that the food corresponding to the food name is loved by the user. Thus, in the data state of the second example, the e-commerce server 2 recommends food-a corresponding food and/or the merchant containing the food to the e-commerce platform home page.
Example two
Referring to fig. 5, fig. 5 is a flowchart illustrating an intelligent recommendation method according to the present invention. Specifically, the embodiment provides an intelligent recommendation method based on a catering O2O e-commerce platform, which uses the intelligent recommendation system of claim 1, and the method includes the following steps:
step1, the merchant client sends the food names which are input by the merchant and are arranged in the food slots and correspond to the slot numbers to the CPU processor through the wireless communication module;
step2, the CPU sends a first weighing signal to the micro weighing sensor corresponding to the slot number according to the food name corresponding to the slot number;
step3, the micro weighing sensor carries out primary weighing on the crib arranged above according to a first weighing signal and returns primary weighing data to the CPU;
step4, the CPU processor storing the food name and the first weighing data corresponding to the food name in the memory;
step5, transmitting a pressing signal to the CPU processor by a button switch according to the pressing operation of a user;
step6, the CPU sends a second weighing signal to the micro weighing sensor corresponding to the slot number according to the pressing signal;
step7, the micro weighing sensor reweighs the feeding trough arranged above according to a second weighing signal and returns reweighed data to the CPU;
step8, the CPU stores the re-weighing data corresponding to the slot number in the memory, so that the re-weighing data correspond to the food name, and sends the first weighing data and the re-weighing data corresponding to the food name to the cloud server through the wireless communication module;
step9, the cloud server calculates the user's favorite degree of the food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, and sends the calculation result to the e-commerce server;
and Step10, the E-commerce server makes a corresponding food recommendation strategy according to the calculation result so as to recommend on the E-commerce platform.
As an embodiment of the present invention, Step9, the cloud server calculating, according to the first weighing data and the second weighing data corresponding to the food name, a user's preference degree of the food corresponding to the food name, includes:
acquiring a preset amount of first weighing data and second weighing data corresponding to food names, calculating the preference degree value of a user to food corresponding to the food names, judging whether the preference degree value exceeds a first preset threshold value, and if so, determining the preference degree of the user to the food corresponding to the food names as favorite; if the preference degree value is not within the first preset threshold and the second preset threshold, determining that the preference degree of the user to the food corresponding to the food name is general; if not, whether the preference degree value is lower than a second preset threshold value or not is judged, and if not, the preference degree of the user to the food corresponding to the food name is determined to be annoying.
As an embodiment of the present invention, a calculation formula adopted by a user for the preference degree value of the food corresponding to the food name is as follows:
wherein n is a preset number,in order to weigh the data for the first time,in order to weigh the data again, the weighing machine,
are weight coefficients.
In one embodiment of the present invention, the first preset threshold is 70% and the second preset threshold is 30%.
As an embodiment of the present invention, Step10, the e-commerce server making a corresponding food recommendation policy according to the calculation result to make a recommendation on the e-commerce platform includes:
and recommending the food corresponding to the food name and/or the merchant containing the food to the home page of the E-commerce platform when the calculation result is read that the food corresponding to the food name is loved by the user.
It should be understood that the implementation process of the second embodiment may correspond to the description of the above system embodiment (embodiment one), and is not described in detail here.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (6)
1. An intelligent recommendation system based on a catering O2O e-commerce platform is characterized by comprising: the system comprises a cloud server, an e-commerce server, a merchant client and a recyclable lunch box which is provided for a merchant by an e-commerce platform, provided for a user by the merchant and recycled; the lunch box is divided into an upper layer and a lower layer, the upper layer is divided into a plurality of troughs, each trough is correspondingly provided with a trough number, the bottom of each trough is provided with a micro weighing sensor corresponding to the trough number, and the lower layer is provided with a CPU (central processing unit) processor respectively connected with the micro weighing sensors, a memory respectively connected with the CPU processor and a wireless communication module; the wireless communication module is used for carrying out wireless data transmission with the cloud server and the e-commerce server; the merchant client is used for sending the food names which are input by the merchant and are arranged in the food grooves and correspond to the groove numbers to the CPU through the wireless communication module before the distribution personnel distribute the users; the CPU sends a first weighing signal to the micro weighing sensor corresponding to the slot number according to the food name corresponding to the slot number; the micro weighing sensor is used for weighing the crib arranged above for the first time according to the first weighing signal and returning the first weighing data to the CPU; the CPU processor stores the food name and the first weighing data corresponding to the food name in the memory; the meal box is provided with a button switch connected with the CPU processor and used for transmitting a pressing signal to the CPU processor when being pressed by a user after a meal is finished; the CPU sends a second weighing signal to the micro weighing sensor corresponding to the slot number according to the pressing signal; the micro weighing sensor reweighs the feeding trough arranged above according to the second weighing signal and returns reweighed data to the CPU; the CPU processor stores re-weighing data corresponding to the slot number in the memory, so that the re-weighing data correspond to the food name, and sends the first weighing data and the re-weighing data corresponding to the food name to the cloud server through the wireless communication module; the cloud server is used for calculating the user preference degree of food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, and sending the calculation result to the e-commerce server; the e-commerce server is used for making a corresponding food recommendation strategy according to the calculation result so as to recommend on the e-commerce platform;
the cloud server is further used for collecting a preset amount of first weighing data and second weighing data corresponding to the food names, calculating the degree of love of the user to the food corresponding to the food names, judging whether the degree of love exceeds a first preset threshold value or not, and if yes, determining that the degree of love of the user to the food corresponding to the food names is favorite; if the preference degree value is not within the first preset threshold and the second preset threshold, determining that the preference degree of the user to the food corresponding to the food name is general; if not, judging whether the preference degree value is lower than a second preset threshold value, and if so, determining that the preference degree of the user to the food corresponding to the food name is annoying;
the user adopts a calculation formula for the degree of love value of the food corresponding to the food name as follows:
wherein n is a preset number,
in order to weigh the data for the first time,
in order to weigh the data again, the weighing machine,
are weight coefficients.
2. The intelligent catering O2O e-commerce platform based on claim 1, wherein the first preset threshold is 70% and the second preset threshold is 30%.
3. The intelligent catering O2O e-commerce platform according to claim 1, wherein the e-commerce server is further configured to recommend food corresponding to the food name and/or a merchant containing the food to a home page of the e-commerce platform when the food corresponding to the food name is read as being liked by the user as a result of the reading.
4. An intelligent recommendation method based on a catering O2O e-commerce platform, which is characterized in that the intelligent recommendation system of claim 1 is used, and the method comprises the following steps:
step1, before the distributor distributes the user, the merchant client sends the food name corresponding to the slot number and input by the merchant into each food slot to the CPU processor through the wireless communication module;
step2, the CPU sends a first weighing signal to the micro weighing sensor corresponding to the slot number according to the food name corresponding to the slot number;
step3, the micro weighing sensor carries out primary weighing on the crib arranged above according to a first weighing signal and returns primary weighing data to the CPU;
step4, the CPU processor storing the food name and the first weighing data corresponding to the food name in the memory;
step5, after the meal is finished, the button switch transmits a pressing signal to the CPU processor according to the pressing operation of the user;
step6, the CPU sends a second weighing signal to the micro weighing sensor corresponding to the slot number according to the pressing signal;
step7, the micro weighing sensor reweighs the feeding trough arranged above according to a second weighing signal and returns reweighed data to the CPU;
step8, the CPU stores the re-weighing data corresponding to the slot number in the memory, so that the re-weighing data correspond to the food name, and sends the first weighing data and the re-weighing data corresponding to the food name to the cloud server through the wireless communication module;
step9, the cloud server calculates the user's favorite degree of the food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, and sends the calculation result to the e-commerce server;
step10, the E-commerce server makes a corresponding food recommendation strategy according to the calculation result so as to recommend on the E-commerce platform;
in Step9, the cloud server calculates the user's liking degree of the food corresponding to the food name according to the first weighing data and the second weighing data corresponding to the food name, including:
acquiring a preset amount of first weighing data and second weighing data corresponding to food names, calculating the preference degree value of a user to food corresponding to the food names, judging whether the preference degree value exceeds a first preset threshold value, and if so, determining the preference degree of the user to the food corresponding to the food names as favorite; if the preference degree value is not within the first preset threshold and the second preset threshold, determining that the preference degree of the user to the food corresponding to the food name is general; if not, judging whether the preference degree value is lower than a second preset threshold value, and if so, determining that the preference degree of the user to the food corresponding to the food name is annoying;
the user adopts a calculation formula for the degree of love value of the food corresponding to the food name as follows:
wherein n is a preset number,
in order to weigh the data for the first time,
in order to weigh the data again, the weighing machine,
are weight coefficients.
5. The intelligent catering O2O-based e-commerce platform according to claim 4, wherein the first preset threshold is 70% and the second preset threshold is 30%.
6. The intelligent recommendation method based on the catering O2O e-commerce platform according to claim 4, wherein in Step10, the e-commerce server makes a corresponding food recommendation strategy according to the calculation result to make a recommendation on the e-commerce platform, and the method comprises the following steps:
and recommending the food corresponding to the food name and/or the merchant containing the food to the home page of the E-commerce platform when the calculation result is read that the food corresponding to the food name is loved by the user.
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