CN112100499A - Sharing platform food material distribution method and system based on data model and readable storage medium - Google Patents
Sharing platform food material distribution method and system based on data model and readable storage medium Download PDFInfo
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Abstract
The invention relates to a sharing platform food material distribution method, a sharing platform food material distribution system and a readable storage medium based on a data model, wherein the sharing platform food material distribution method comprises the following steps: user preference information is obtained through big data analysis, an original database is generated, and a data model is established; analyzing the data model, automatically generating a food material proportioning scheme, identifying the food material types through images, and generating a distribution mode; according to the distribution mode, performing weighted calculation on the food material ratio information to obtain result information; comparing the result information with preset information to obtain a deviation rate; judging whether the deviation rate is greater than a preset deviation rate threshold value or not; if the weight value is larger than the preset value, the weight value of the weighted calculation is adjusted.
Description
Technical Field
The present invention relates to a sharing platform food material distribution method, and in particular, to a sharing platform food material distribution method and system based on a data model, and a readable storage medium.
Background
The sharing fruit juice mixer is matched with automatic juicing, intelligent cleaning and self-service vending intelligent hardware, common fruit juicing can be basically met according to fruit types on the market, users with different tastes can be met to the maximum extent, and the intelligent equipment is compatible with juicing of various fruits and more conveniently meets use habits of the users.
The full-automatic intelligent operation can not be realized to present fruit juice extractor, when carrying out multiple fruit and beat fruit juice, needs the manual work to put into the fruit juice extractor with fruit, and can't carry out intelligence according to different users and eat the material distribution, and eating the material distribution in-process, can't carry out the fresh degree that intelligent monitoring eaten the material, carry out automatic abandonment to the fruit that fresh degree does not reach the requirement, use the flexibility relatively poor.
In order to realize accurate control of the shared juicer, a system matched with the shared juicer needs to be developed for control, different food material distribution schemes can be recommended for different users through the system, but in the control process, when the accurate control is realized, the intelligent food material distribution of the shared platform is urgent.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a sharing platform food material distribution method and system based on a data model and a readable storage medium.
In order to achieve the purpose, the invention adopts the technical scheme that: a sharing platform food material distribution method based on a data model comprises the following steps:
user preference information is obtained through big data analysis, an original database is generated, and a data model is established;
analyzing the data model, automatically generating a food material proportioning scheme, identifying the food material types through images, and generating a distribution mode;
according to the distribution mode, performing weighted calculation on the food material ratio information to obtain result information;
comparing the result information with preset information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the weight value is larger than the preset value, the weight value of the weighted calculation is adjusted.
Preferably, user preference information is obtained through big data analysis, an original database is generated, and a data model is established; the method specifically comprises the following steps:
extracting user characteristic data and uploading the user characteristic data to a cloud server;
analyzing the characteristic data through the cloud server to obtain user crowd attribute information and generate result information;
automatically matching recommended food material proportioning information according to the result information to generate screen display information;
and displaying the screen display information according to a preset mode.
Preferably, the user population attribute information includes one or a combination of two or more of user preference, user weight information, user taboo information, and user gender information.
Preferably, according to the distribution mode, the method performs weighted calculation on the food material proportioning information to obtain result information, and further includes:
dividing a preset area into N different sub-areas;
calculating the food material type and the matching characteristic of each subregion to obtain a characteristic value;
comparing the eigenvalue difference rate for each different sub-region;
classifying the sub-regions smaller than the threshold value of the feature value difference rate into regions of the same category;
raw database information for regions of the same category is obtained,
calculating correction parameters according to the original database information;
and feeding back optimization result information according to the correction parameters.
Preferably, the method further comprises the following steps:
extracting food material image information in real time according to the food material proportioning information, and extracting image characteristics to generate food material original information;
comparing the original information with the preset value information to obtain a deviation rate,
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, discarding the corresponding food material.
Preferably, the food materials comprise watermelon, orange, kiwi fruit, tomato, cucumber and soybean.
The second aspect of the present invention further provides a data model-based sharing platform food material distribution system, including: the data model-based sharing platform food material distribution method comprises a memory and a processor, wherein the memory comprises a data model-based sharing platform food material distribution method program, and when the data model-based sharing platform food material distribution method program is executed by the processor, the following steps are realized:
user preference information is obtained through big data analysis, an original database is generated, and a data model is established;
analyzing the data model, automatically generating a food material proportioning scheme, identifying the food material types through images, and generating a distribution mode;
according to the distribution mode, performing weighted calculation on the food material ratio information to obtain result information;
comparing the result information with preset information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the weight value is larger than the preset value, the weight value of the weighted calculation is adjusted.
Preferably, user preference information is obtained through big data analysis, an original database is generated, and a data model is established; the method specifically comprises the following steps:
extracting user characteristic data and uploading the user characteristic data to a cloud server;
analyzing the characteristic data through the cloud server to obtain user crowd attribute information and generate result information;
automatically matching recommended food material proportioning information according to the result information to generate screen display information;
and displaying the screen display information according to a preset mode.
Preferably, according to the distribution mode, the method performs weighted calculation on the food material proportioning information to obtain result information, and further includes:
dividing a preset area into N different sub-areas;
calculating the food material type and the matching characteristic of each subregion to obtain a characteristic value;
comparing the eigenvalue difference rate for each different sub-region;
classifying the sub-regions smaller than the threshold value of the feature value difference rate into regions of the same category;
raw database information for regions of the same category is obtained,
calculating correction parameters according to the original database information;
and feeding back optimization result information according to the correction parameters.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a data model-based sharing platform food material allocation method program, and when the data model-based sharing platform food material allocation method program is executed by a processor, the method implements any one of the steps of the data model-based sharing platform food material allocation method.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) this system acquires user's hobby through big data analysis when carrying out the juice, then automatic generation eats material distribution scheme, carries out the ratio preparation fruit juice of different edible material types, and intelligent degree is higher to can formulate the fruit juice scheme of pertinence according to the user's health condition of difference, audience matching degree is higher.
(2) In the food material distribution process, the generated new data replaces the data in the original database, the real-time updating of the data can be realized, the system can continuously learn, the taste of the audience is more fitted, and the fruit juice type automatically recommended by the system can be loved by the audience.
(3) Aiming at different fruit types, the fruit characteristics are analyzed according to big data and are distributed into different sub-regions, and the database information is fed back and optimized through correction parameters to enable the food material distribution scheme to be more suitable for different users, so that the intelligent degree is higher.
(4) Extracting food material image information in real time according to the food material proportioning information, and extracting image characteristics to generate food material original information; and the food materials with large deviation rate are discarded, so that the freshness of the food materials is ensured.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart of a data model-based sharing platform food material distribution method according to the present invention;
FIG. 2 illustrates a flow chart of a screen display method of the present invention;
FIG. 3 is a flow chart of the food material classification method according to the present invention;
fig. 4 shows a flow chart of the food material discarding method of the present invention;
fig. 5 shows a block diagram of a data model-based sharing platform food material distribution system according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flowchart of a data model-based sharing platform food material distribution method according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides a data model-based food material distribution method for a shared platform, including:
s102, acquiring user preference information through big data analysis, generating an original database, and establishing a data model;
s104, analyzing the data model, automatically generating a food material proportioning scheme, identifying the food material types through images, and generating a distribution mode;
s106, performing weighted calculation on the food material proportioning information according to the distribution mode to obtain result information;
s108, comparing the result information with preset information to obtain a deviation rate;
s110, judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and S112, if the weight is larger than the preset value, carrying out weight adjustment of weighting calculation.
It should be noted that the food material distribution can be monitored and counted through weighting calculation, differences among effects generated by different food material distributions can be distinguished, the transaction effects under the conditions of different users, different preferences and the like can also be understood as comparison, the matching degree of the food material distribution at a certain time is reflected, the automatic food material distribution of the system can be more suitable for the taste of audiences through the weighting calculation, and the matching degree is higher.
As shown in FIG. 2, the present invention discloses a flow chart of a screen display method;
according to the embodiment of the invention, user preference information is obtained through big data analysis, an original database is generated, and a data model is established; the method specifically comprises the following steps:
s202, extracting user characteristic data and uploading the user characteristic data to a cloud server;
s204, analyzing the characteristic data through the cloud server to obtain user crowd attribute information and generate result information;
s206, automatically matching the recommended food material proportioning information according to the result information to generate screen display information;
and S208, displaying the screen display information according to a preset mode.
It should be noted that the on-screen display information is displayed in a predetermined manner, for example, the on-screen display information may be displayed in the form of a chart, a bar chart, a list, a pie chart, a popular category sorting, a purchase transaction record sorting, and the like.
According to the embodiment of the invention, the user crowd attribute information comprises one or more of user preference, user weight information, user taboo information or user gender information.
As shown in fig. 3, the present invention discloses a flow chart of a food material classification method;
according to the embodiment of the present invention, the method for performing weighted calculation on the food material ratio information according to the distribution mode to obtain result information further includes:
s302, dividing a preset area into N different sub-areas;
s304, calculating the food material type and the matching characteristic of each sub-area to obtain a characteristic value;
s306, comparing the characteristic value difference rate of each different sub-region;
s308, classifying the sub-regions smaller than the threshold of the feature value difference rate into regions of the same category;
s310, obtaining the original database information of the same category area,
s312, calculating correction parameters according to the original database information;
and S314, feeding back optimization result information according to the correction parameters.
It should be noted that, the present invention can also perform modification optimization on food material proportioning information according to modification parameters, the modified parameters are obtained by performing big data analysis according to different similar regions, and can be closer to actual values, a preset region is first determined, the preset region can be distinguished according to climate conditions, and can also be distinguished in human environments, a person skilled in the art can adjust according to actual needs, and then the region is divided into N sub-regions, the N sub-regions can be independent regions, and can also have regions with intersection, the range size can be adjusted, adaptive food material allocation is performed for different nutritional needs of users in different regions, and feedback optimization is performed through modification parameters, so that the result information is closer to actual values, and the accuracy of automatic food material allocation of the system is increased, aiming at different fruit types, the fruit characteristics are analyzed according to big data and distributed to food material libraries in different sub-regions, and the database information is fed back through correction parameters to optimize the food material distribution scheme, so that the food material distribution scheme is more suitable for different users, and the intelligent degree is higher.
As shown in fig. 4, the present invention discloses a flow chart of a food material discarding method;
according to the embodiment of the invention, the method further comprises the following steps:
s402, extracting food material image information in real time according to the food material proportioning information, extracting image characteristics, and generating food material original information;
s404, comparing the original information with the preset value information to obtain a deviation ratio,
s406, judging whether the deviation rate is greater than a preset deviation rate threshold value;
and S408, if the number is larger than the preset value, discarding the corresponding food material.
It should be noted that identifying the food material type through the image includes acquiring a multi-angle food material image through a camera as original image information of the food material, extracting characteristic values from the multi-angle image, classifying the characteristic values into the same category when the deviation rate is smaller than a predetermined value, identifying the food material type, generating different food material waste information according to different food material types, extracting food material image characteristic points, comparing the characteristic points one by one, and when the deviation is large, indicating that the food material is damaged or discolored, and then discarding the food material.
According to an embodiment of the invention, the food material comprises watermelon, orange, kiwi, tomato, cucumber, soybean.
It should be noted that the present invention is not limited to the variety of food materials, and all food materials capable of directly absorbing nutritional ingredients by juicing are within the scope of the present invention.
It should be noted that, different food materials are refrigerated in different manners to generate corresponding refrigeration information, the refrigeration information may be parameter information such as refrigeration temperature, refrigeration time, refrigeration manner, air humidity, air flow rate, etc., the type of the fruit is identified through an image, different refrigeration temperatures and refrigeration times are set for different fruit types, the refrigeration manner may be modified atmosphere refrigeration, that is, a method for prolonging the storage period of the fruit by adjusting ambient gas, the principle is that in a certain closed system, a modified gas different from normal atmosphere composition is obtained through various adjustment methods, such as carbon dioxide and hypoxia, so as to inhibit the physiological and biochemical processes of deterioration caused by the fruit itself or the microbial activity processes acting on the fruit, the refrigeration manner may also be reduced pressure refrigeration, but is not limited to these two refrigeration manners, and those skilled in the art can replace other refrigeration manners according to climate conditions or geographic environments or fruit types, for example, in different geographical environments, the refrigeration conditions of fruits and parts of vegetables are different, and for example, tropical and subtropical fruits and parts of vegetables are stored in a temperature range of 3-10 ℃ above the freezing point, and cold damage can occur.
As shown in fig. 5, the present invention discloses a block diagram of a sharing platform food material distribution system based on a data model;
the second aspect of the present invention further provides a data model-based sharing platform food material distribution system 5, which includes: the storage 51 and the processor 52, wherein the storage includes a sharing platform food material allocation method program based on a data model, and when the sharing platform food material allocation method program based on the data model is executed by the processor, the following steps are implemented:
user preference information is obtained through big data analysis, an original database is generated, and a data model is established;
analyzing the data model, automatically generating a food material proportioning scheme, identifying the food material types through images, and generating a distribution mode;
according to the distribution mode, performing weighted calculation on the food material ratio information to obtain result information;
comparing the result information with preset information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the weight value is larger than the preset value, the weight value of the weighted calculation is adjusted.
It should be noted that the food material distribution can be monitored and counted through weighting calculation, differences among effects generated by different food material distributions can be distinguished, the transaction effects under the conditions of different users, different preferences and the like can also be understood as comparison, the matching degree of the food material distribution at a certain time is reflected, the automatic food material distribution of the system can be more suitable for the taste of audiences through the weighting calculation, and the matching degree is higher.
According to the embodiment of the invention, user preference information is obtained through big data analysis, an original database is generated, and a data model is established; the method specifically comprises the following steps:
extracting user characteristic data and uploading the user characteristic data to a cloud server;
analyzing the characteristic data through the cloud server to obtain user crowd attribute information and generate result information;
automatically matching recommended food material proportioning information according to the result information to generate screen display information;
and displaying the screen display information according to a preset mode.
It should be noted that the on-screen display information is displayed in a predetermined manner, for example, the on-screen display information may be displayed in the form of a chart, a bar chart, a list, a pie chart, a popular category sorting, a purchase transaction record sorting, and the like.
According to the embodiment of the present invention, the method for performing weighted calculation on the food material ratio information according to the distribution mode to obtain result information further includes:
dividing a preset area into N different sub-areas;
calculating the food material type and the matching characteristic of each subregion to obtain a characteristic value;
comparing the eigenvalue difference rate for each different sub-region;
classifying the sub-regions smaller than the threshold value of the feature value difference rate into regions of the same category;
raw database information for regions of the same category is obtained,
calculating correction parameters according to the original database information;
and feeding back optimization result information according to the correction parameters.
It should be noted that, the present invention can also perform modification optimization on food material proportioning information according to modification parameters, the modified parameters are obtained by performing big data analysis according to different similar regions, and can be closer to actual values, a preset region is first determined, the preset region can be distinguished according to climate conditions, and can also be distinguished in human environments, a person skilled in the art can adjust according to actual needs, and then the region is divided into N sub-regions, the N sub-regions can be independent regions, and can also have regions with intersection, the range size can be adjusted, adaptive food material allocation is performed for different nutritional needs of users in different regions, and feedback optimization is performed through modification parameters, so that the result information is closer to actual values, and the accuracy of automatic food material allocation of the system is increased, aiming at different fruit types, the fruit characteristics are analyzed according to big data and distributed into food material libraries of different sub-regions, and the database information is fed back through correction parameters to optimize a food material distribution scheme, so that the food material distribution scheme is more suitable for different users, and the intelligent degree is higher.
According to the embodiment of the invention, the method further comprises the following steps:
extracting food material image information in real time according to the food material proportioning information, and extracting image characteristics to generate food material original information;
comparing the original information with the preset value information to obtain a deviation rate,
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, discarding the corresponding food material.
It should be noted that identifying the food material type through the image includes acquiring a multi-angle food material image through a camera as original image information of the food material, extracting characteristic values from the multi-angle image, classifying the characteristic values into the same category when the deviation rate is smaller than a predetermined value, identifying the food material type, generating different food material waste information according to different food material types, extracting food material image characteristic points, comparing the characteristic points one by one, and when the deviation is large, indicating that the food material is damaged or discolored, and then discarding the food material.
According to an embodiment of the invention, the food material comprises watermelon, orange, kiwi, tomato, cucumber, soybean.
It should be noted that the present invention is not limited to the variety of food materials, and all food materials capable of directly absorbing nutritional ingredients by juicing are within the scope of the present invention.
It should be noted that, different food materials are refrigerated in different manners to generate corresponding refrigeration information, the refrigeration information may be parameter information such as refrigeration temperature, refrigeration time, refrigeration manner, air humidity, air flow rate, etc., the type of the fruit is identified through an image, different refrigeration temperatures and refrigeration times are set for different fruit types, the refrigeration manner may be modified atmosphere refrigeration, that is, a method for prolonging the storage period of the fruit by adjusting ambient gas, the principle is that in a certain closed system, a modified gas different from normal atmosphere composition is obtained through various adjustment methods, such as carbon dioxide and hypoxia, so as to inhibit the physiological and biochemical processes of deterioration caused by the fruit itself or the microbial activity processes acting on the fruit, the refrigeration manner may also be reduced pressure refrigeration, but is not limited to these two refrigeration manners, and those skilled in the art can replace other refrigeration manners according to climate conditions or geographic environments or fruit types, for example, in different geographical environments, the refrigeration conditions of fruits and parts of vegetables are different, and for example, tropical and subtropical fruits and parts of vegetables are stored in a temperature range of 3-10 ℃ above the freezing point, and cold damage can occur.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a data model-based sharing platform food material allocation method program, and when the data model-based sharing platform food material allocation method program is executed by a processor, the steps of the data model-based sharing platform food material allocation method described above are implemented.
This system acquires user's hobby through big data analysis when carrying out the juice, then automatic generation eats material distribution scheme, carries out the ratio preparation fruit juice of different edible material types, and intelligent degree is higher to can formulate the fruit juice scheme of pertinence according to the user's health condition of difference, audience matching degree is higher.
In the food material distribution process, the generated new data replaces the data in the original database, the real-time updating of the data can be realized, the system can continuously learn, the taste of the audience is more fitted, and the fruit juice type automatically recommended by the system can be loved by the audience.
Aiming at different fruit types, the fruit characteristics are analyzed according to big data and are distributed into different sub-regions, and the database information is fed back and optimized through correction parameters to enable the food material distribution scheme to be more suitable for different users, so that the intelligent degree is higher.
Extracting food material image information in real time according to the food material proportioning information, and extracting image characteristics to generate food material original information; and the food materials with large deviation rate are discarded, so that the freshness of the food materials is ensured.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A sharing platform food material distribution method based on a data model is characterized by comprising the following steps:
user preference information is obtained through big data analysis, an original database is generated, and a data model is established;
analyzing the data model, automatically generating a food material proportioning scheme, identifying the food material types through images, and generating a distribution mode;
according to the distribution mode, performing weighted calculation on the food material ratio information to obtain result information;
comparing the result information with preset information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the weight value is larger than the preset value, the weight value of the weighted calculation is adjusted.
2. The data model-based sharing platform food material distribution method according to claim 1, wherein: user preference information is obtained through big data analysis, an original database is generated, and a data model is established; the method specifically comprises the following steps:
extracting user characteristic data and uploading the user characteristic data to a cloud server;
analyzing the characteristic data through the cloud server to obtain user crowd attribute information and generate result information;
automatically matching recommended food material proportioning information according to the result information to generate screen display information;
and displaying the screen display information according to a preset mode.
3. The data model-based sharing platform food material distribution method according to claim 1, wherein: the user crowd attribute information comprises one or more of user preference, user weight information, user taboo information or user gender information.
4. The data model-based shared platform food material distribution method of claim 3, wherein: according to the distribution mode, the food material ratio information is weighted and calculated to obtain result information, and the method further comprises the following steps:
dividing a preset area into N different sub-areas;
calculating the food material type and the matching characteristic of each subregion to obtain a characteristic value;
comparing the eigenvalue difference rate for each different sub-region;
classifying the sub-regions smaller than the threshold value of the feature value difference rate into regions of the same category;
raw database information for regions of the same category is obtained,
calculating correction parameters according to the original database information;
and feeding back optimization result information according to the correction parameters.
5. The data model-based shared platform food material distribution method of claim 3, wherein: further comprising:
extracting food material image information in real time according to the food material proportioning information, and extracting image characteristics to generate food material original information;
comparing the original information with the preset value information to obtain a deviation rate,
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, discarding the corresponding food material.
6. The data model-based sharing platform food material distribution method according to claim 1, wherein: the food material comprises watermelon, orange, kiwi fruit, tomato, cucumber and soybean.
7. A data model-based shared platform food material distribution system, comprising: the data model-based sharing platform food material distribution method comprises a memory and a processor, wherein the memory comprises a data model-based sharing platform food material distribution method program, and when the data model-based sharing platform food material distribution method program is executed by the processor, the following steps are realized:
user preference information is obtained through big data analysis, an original database is generated, and a data model is established;
analyzing the data model, automatically generating a food material proportioning scheme, identifying the food material types through images, and generating a distribution mode;
according to the distribution mode, performing weighted calculation on the food material ratio information to obtain result information;
comparing the result information with preset information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the weight value is larger than the preset value, the weight value of the weighted calculation is adjusted.
8. The data model-based shared platform food material distribution system of claim 7, wherein:
user preference information is obtained through big data analysis, an original database is generated, and a data model is established; the method specifically comprises the following steps:
extracting user characteristic data and uploading the user characteristic data to a cloud server;
analyzing the characteristic data through the cloud server to obtain user crowd attribute information and generate result information;
automatically matching recommended food material proportioning information according to the result information to generate screen display information;
and displaying the screen display information according to a preset mode.
9. The data model-based shared platform food material distribution system of claim 7, wherein:
according to the distribution mode, the food material ratio information is weighted and calculated to obtain result information, and the method further comprises the following steps:
dividing a preset area into N different sub-areas;
calculating the food material type and the matching characteristic of each subregion to obtain a characteristic value;
comparing the eigenvalue difference rate for each different sub-region;
classifying the sub-regions smaller than the threshold value of the feature value difference rate into regions of the same category;
raw database information for regions of the same category is obtained,
calculating correction parameters according to the original database information;
and feeding back optimization result information according to the correction parameters.
10. A computer-readable storage medium characterized by: the computer-readable storage medium comprises a data model-based sharing platform food material distribution method program, which when executed by a processor, implements the steps of the data model-based sharing platform food material distribution method of any one of claims 1 to 6.
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| CN115497601A (en) * | 2022-09-27 | 2022-12-20 | 时代特殊医学用途配方食品(深圳)有限公司 | Clinical nutrition intelligent management system, method, device, equipment and storage medium |
| CN115497601B (en) * | 2022-09-27 | 2023-05-02 | 时代特殊医学用途配方食品(深圳)有限公司 | Method, device, equipment and storage medium for intelligent management of clinical nutrition |
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