CN116049558A - Intelligent matching cooking system based on big data cloud management - Google Patents
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
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Abstract
The invention provides an intelligent matching cooking system based on big data cloud management, which comprises a cloud instruction management terminal, a dish recommendation terminal, a cooking process management terminal, a material management terminal and a cooking machine body; the cloud instruction management terminal is used for receiving and managing various operation instructions from the cloud; the operation instruction is used for controlling the dish recommending terminal, the dish cooking process management terminal and the dish cooking machine body; the menu recommendation terminal is used for providing menu references for users and acquiring menu selection information from the users; the cooking process management terminal is used for matching corresponding cooking process information according to the cooking type selection information; the material management terminal is used for detecting the material placement state according to the menu selection information and generating material state information; the cooker body is used for performing cooking operation according to the cooking flow information and the material state information. The invention has the effect of improving the cooking quality of the cooking system.
Description
Technical Field
The invention relates to the technical field of intelligent cooking equipment, in particular to an intelligent matching cooking system based on big data cloud management.
Background
Big data cloud management refers to managing a large amount of data stored in the cloud. This involves the use of cloud-based tools and techniques to store, process, and analyze large data sets with the aim of developing valuable insights and making data-driven decisions. The intelligent matching cooking system is one intelligent cooking system for selecting ingredients and guiding cooking process. It typically collects ingredient information through sensors and digitizing techniques and optimizes ingredient selection, cooking time, temperature, etc. through artificial intelligence algorithms. Aims to help the user simplify the cooking process and improve the quality and efficiency of cooking.
A number of cooking systems have been developed, and, by extensive search and reference, the prior art cooking systems have been found to have a cooking system as disclosed in publication nos. CN113287936A, CN107703830A, EP2104806B1, US20050241496A1, JP2021171435a, which generally include: a gas device providing an adjustable size fire; the frying pan is arranged above the gas device; the rotating device is connected with the frying pan; the oil fume device is arranged above the frying pan; the camera is arranged at the side of the oil fume device; the menu setting device stores a menu for the user to select; the processor determines cooking process conditions according to the images acquired by the camera and a menu selected by a user; the controller is connected with the processor, the gas device, the oil supply device and the rotating device, controls the gas device to provide fire for the frying pan, and controls the rotating device to rotate the frying pan. The menu of the cooking system has a single recommending mode, and the control mode of the controller is simple, so that the defect of reduced cooking quality of the cooking system is caused.
Disclosure of Invention
The invention aims to provide an intelligent matching cooking system based on big data cloud management aiming at the defects of the cooking system.
The invention adopts the following technical scheme:
an intelligent matching cooking system based on big data cloud management comprises a cloud instruction management terminal, a cooking recommendation terminal, a cooking process management terminal, a material management terminal and a cooking machine body;
the cloud instruction management terminal is used for receiving and managing various operation instructions from the cloud; the operation instruction is used for controlling the dish recommending terminal, the dish cooking process management terminal and the dish cooking machine body; the menu type recommendation terminal is used for providing menu type reference for a user and acquiring menu type selection information from the user; the cooking process management terminal is used for matching corresponding cooking process information according to the cooking type selection information; the material management terminal is used for detecting the material placement state according to the menu type selection information and generating material state information;
the cooker body is used for performing cooking operation according to the cooking flow information and the material state information.
Optionally, the menu type recommendation terminal comprises a regional recommendation menu type information selection module, a menu type adjustment module, a menu type display module and a menu type selection information generation module; the regional recommended menu information selection module is used for acquiring weekly recommended menu information from the cloud; the menu adjusting module is used for adjusting the recommended menu information every week; the menu display module is used for displaying the adjusted recommended menu information; the menu selection information generation module is used for generating menu selection information according to the selection operation of a user.
Optionally, the cooking process management terminal comprises a cooking process information matching module and a cooking process checking module; the dish-type cooking process information matching module is used for matching corresponding dish-type cooking process information from the cloud according to the dish-type selection information; the cooking process checking module is used for checking the cooking process information according to the local history information of the corresponding dishes in the cloud.
Optionally, the region recommended menu information selection module comprises a selection index calculation sub-module and a recommended menu information acquisition sub-module; the selection index calculation sub-module is used for calculating a weekly selection index according to the local weekly weather forecast situation and taste grade information input by a user in advance; the recommended menu information acquisition sub-module is used for acquiring corresponding recommended menu information from a cloud preset recommended menu information database according to the selection index; the preset recommended menu information database comprises a plurality of recommended menu information updated by an administrator, each recommended menu information is provided with a corresponding selection index interval, and each recommended menu information comprises all recommended menu types in a corresponding week;
when the selection index calculation sub-module calculates, the following equation is satisfied:
wherein Z represents a selection index; a represents a reference value of appetite index, which is empirically set by an administrator; d, d 1i The number of hours at which the average air temperature is greater than 25 ℃ on day i of the week for the weather forecast conditions of the local week; d, d 2i The number of precipitation hours in day i in a week representing the weather forecast conditions of the local week; mu (mu) 1 Sum mu 2 Representing different appetite index claim coefficients, empirically set by an administrator; l represents the number of grades in the taste grade information which is input by the user in advance; k (k) 1 And k 2 Respectively representing a first index conversion coefficient and a second index conversion coefficient, which are set by an administrator according to experience;
when the corresponding recommended menu information is obtained, the corresponding recommended menu information is selected according to the selection index interval corresponding to the selection index.
Optionally, the menu adjusting module includes a re-recommending sub-module, a deleting sub-module and an adding sub-module; the re-recommending sub-module is used for calculating a re-recommending index of the menu selected by the user in the menu information recommended in one week; the deletion submodule is used for deleting the menu selected by the user in the recommended menu information; the adding sub-module is used for adding the menu selected by the user in the menu information recommended in one week into the menu information recommended in one week again according to the re-recommendation index;
when the re-recommendation sub-module calculates, the following equation is satisfied:
wherein Q represents the re-recommendation index of the menu selected by the user in the menu information recommended in one week; n represents the number of days between the first selected day of the menu which has been selected by the user and the day when the re-recommendation index is calculated; f (f) 1 (p) represents a weight selection function; p represents remark label information of the menu type, which is added in advance by the userThe method comprises the steps of carrying out a first treatment on the surface of the The remark label information of the menu when p=0 indicates that the user does not like the current menu; the remark label information of the menu when p=1 indicates that the user favors the current menu;
-representing an index value adjustment function based on the remaining days of the week; u represents a daily recommended menu quantity reference value, which is empirically set by an administrator; m represents the number of the residual menu in the recommended menu information; max represents the maximum adjustment value, which is empirically set by an administrator;
when the adding sub-module works, the following formula is satisfied:
wherein R (Q) represents an addition judgment function; q ref Indicating an addition judgment threshold value, which is empirically set by an administrator; when R (Q) =1, it indicates that the current menu needs to be added to the recommended menu information again; when R (Q) =0, it means that the current menu does not need to be re-added to the recommended menu information;
when (when)
When R (Q) =1.
Optionally, the cooking process checking module comprises a checking information reading sub-module, a first checking sub-module and a second checking sub-module; the proofreading information reading submodule is used for reading proofreading information input by a user; the first proofreading submodule is used for proofreading the dish cooking flow information according to the proofreading information; the second correction submodule is used for correcting the dish cooking flow information according to the history correction information of the local user;
when the second correction submodule works, the correction score of the menu is calculated by the following formula:
wherein V represents the proofreading score of the corresponding menu; b j Representing the sum of the proofreading duration of each cooking process in the j-th proofreading information of the corresponding dish in a history week; j represents the total number of the proofreading information input by the user in one week of the history corresponding to the menu; v represents the total number of times of the corresponding dish frying process variation in one week of history; beta 1 And beta 2 Representing different weight coefficients;
the second comparison sub-module is further used for judging whether the comparison score B of the corresponding dish is larger than a preset threshold value, if yes, the comparison information with the smallest sum of the comparison duration of each dish frying process in one week of the history is adopted for comparison; the preset threshold is empirically set by an administrator.
An intelligent matching cooking method based on big data cloud management is applied to the intelligent matching cooking system based on big data cloud management, and the intelligent matching cooking method comprises the following steps:
s1, receiving and managing various operation instructions from a cloud;
s2, controlling a dish type recommending terminal, a dish cooking process management terminal and a dish cooking machine body;
s3, providing a menu reference for a user and acquiring menu selection information from the user;
s4, matching corresponding dish frying flow information according to the dish selection information;
s5, detecting the material placement state according to the menu selection information, and generating material state information;
s6, performing cooking operation according to the vegetable cooking flow information and the material state information.
The beneficial effects obtained by the invention are as follows:
1. the cloud instruction management terminal, the dish type recommendation terminal, the cooking process management terminal, the material management terminal and the cooking machine body are beneficial to enriching control operation and dish type recommendation processes, so that the process of selecting dishes by a user to the cooking machine body to finish cooking is optimized and improved, and the cooking quality of a cooking system is improved;
2. the regional recommendation menu type information selection module, the menu type adjustment module, the menu type display module and the menu type information generation module are arranged to be beneficial to acquiring weekly recommendation menu type information from the cloud and adjusting, so that accurate menu type recommendation is improved for users, and meanwhile, the process of recommending menu type is enriched, so that the cooking quality of a cooking system is improved;
3. the arrangement of the dish-type dish-frying process information matching module and the dish-type dish-frying process checking module is beneficial to matching corresponding dish-type dish-frying process information from the cloud rapidly according to dish-type selection information, so that the accuracy and cooking efficiency of the dish-type dish-frying process are improved, the dish-type dish-frying process is checked by the dish-type dish-frying process checking module, and the dish-frying quality of a dish-frying system is greatly improved;
4. the selection index calculation sub-module and the recommended menu information acquisition sub-module are matched with a selection index algorithm, so that weekly recommended menu information is optimized, the accuracy of selecting the recommended menu information is improved, and the recommended menu is more accurately recommended to a user;
5. the re-recommending sub-module, the deleting sub-module and the adding sub-module are arranged in cooperation with a re-recommending index algorithm, so that the recommended menu information is continuously optimized in a week, and the accuracy of the recommended menu information is further improved;
6. the setting of the correction information reading sub-module, the first correction sub-module and the second correction sub-module is matched with a correction score algorithm, so that the fusion of multiple correction information is realized, and the accuracy of a correction result is improved by correcting according to the correction information input by a user and the history correction information of a local user; the dynamic adjustment and the correction are realized, whether the correction information with the smallest sum of the correction duration of each dish frying process in a week of history is needed to be corrected is judged according to the correction score of the dish, and the correction efficiency is improved; flexible threshold setting is realized; thereby improving the accuracy and efficiency of the cooking process calibration;
7. the arrangement of the flaky meat food material detection module and the cooking process adjustment module is matched with an adjustment index algorithm, so that flaky meat food materials can be accurately identified, flexible cooking processes can be realized, and cooking efficiency and quality can be improved by identifying flaky meat food materials and adjusting the cooking processes; the cooking process adjusting module can be properly adjusted according to the characteristics of different food materials, so that the cooking quality is improved.
For a further understanding of the nature and the technical aspects of the present invention, reference should be made to the following detailed description of the invention and the accompanying drawings, which are provided for purposes of reference only and are not intended to limit the invention.
Drawings
FIG. 1 is a schematic diagram of the overall structure of the present invention;
FIG. 2 is a flow chart of a method for intelligent matching cooking method based on big data cloud management;
FIG. 3 is a schematic overall structure of another embodiment of the present invention;
fig. 4 is a schematic diagram of the overall structure of the sheet-shaped meat food material detection module according to the present invention.
Detailed Description
The following embodiments of the present invention are described in terms of specific examples, and those skilled in the art will appreciate the advantages and effects of the present invention from the disclosure herein. The invention is capable of other and different embodiments and its several details are capable of modification and variation in various respects, all without departing from the spirit of the present invention. The drawings of the present invention are merely schematic illustrations, and are not drawn to actual dimensions, and are stated in advance. The following embodiments will further illustrate the related art of the present invention in detail, but the disclosure is not intended to limit the scope of the present invention.
Embodiment one.
The embodiment provides an intelligent matching cooking system based on big data cloud management. Referring to fig. 1, an intelligent matching cooking system based on big data cloud management comprises a cloud instruction management terminal, a cooking recommendation terminal, a cooking process management terminal, a material management terminal and a cooking machine body;
the cloud instruction management terminal is used for receiving and managing various operation instructions from the cloud; the operation instruction is used for controlling the dish recommending terminal, the dish cooking process management terminal and the dish cooking machine body; the menu type recommendation terminal is used for providing menu type reference for a user and acquiring menu type selection information from the user; the cooking process management terminal is used for matching corresponding cooking process information according to the cooking type selection information; the material management terminal is used for detecting the material placement state according to the menu type selection information and generating material state information;
the cooker body is used for performing cooking operation according to the cooking flow information and the material state information.
Optionally, the menu type recommendation terminal comprises a regional recommendation menu type information selection module, a menu type adjustment module, a menu type display module and a menu type selection information generation module; the regional recommended menu information selection module is used for acquiring weekly recommended menu information from the cloud; the menu adjusting module is used for adjusting the recommended menu information every week; the menu display module is used for displaying the adjusted recommended menu information; the menu selection information generation module is used for generating menu selection information according to the selection operation of a user.
Optionally, the cooking process management terminal comprises a cooking process information matching module and a cooking process checking module; the dish-type cooking process information matching module is used for matching corresponding dish-type cooking process information from the cloud according to the dish-type selection information; the cooking process checking module is used for checking the cooking process information according to the local history information of the corresponding dishes in the cloud.
Optionally, the region recommended menu information selection module comprises a selection index calculation sub-module and a recommended menu information acquisition sub-module; the selection index calculation sub-module is used for calculating a weekly selection index according to the local weekly weather forecast situation and taste grade information input by a user in advance; the recommended menu information acquisition sub-module is used for acquiring corresponding recommended menu information from a cloud preset recommended menu information database according to the selection index; the preset recommended menu information database comprises a plurality of recommended menu information updated by an administrator, each recommended menu information is provided with a corresponding selection index interval, and each recommended menu information comprises all recommended menu types in a corresponding week;
when the selection index calculation sub-module calculates, the following equation is satisfied:
wherein Z represents a selection index; a represents a reference value of appetite index, which is empirically set by an administrator; d, d 1i The number of hours at which the average air temperature is greater than 25 ℃ on day i of the week for the weather forecast conditions of the local week; d, d 2i The number of precipitation hours in day i in a week representing the weather forecast conditions of the local week; mu (mu) 1 Sum mu 2 Representing different appetite index claim coefficients, empirically set by an administrator; l represents the number of grades in the taste grade information which is input by the user in advance; k (k) 1 And k 2 Respectively representing a first index conversion coefficient and a second index conversion coefficient, which are set by an administrator according to experience;
when the corresponding recommended menu information is obtained, the corresponding recommended menu information is selected according to the selection index interval corresponding to the selection index.
Optionally, the menu adjusting module includes a re-recommending sub-module, a deleting sub-module and an adding sub-module; the re-recommending sub-module is used for calculating a re-recommending index of the menu selected by the user in the menu information recommended in one week; the deletion submodule is used for deleting the menu selected by the user in the recommended menu information; the adding sub-module is used for adding the menu selected by the user in the menu information recommended in one week into the menu information recommended in one week again according to the re-recommendation index;
when the re-recommendation sub-module calculates, the following equation is satisfied:
wherein Q represents the re-recommendation index of the menu selected by the user in the menu information recommended in one week; n represents the number of days between the first selected day of the menu which has been selected by the user and the day when the re-recommendation index is calculated; f (f) 1 (p) represents a weight selection function; p represents remark label information of the menu, and is added in advance by a user; the remark label information of the menu when p=0 indicates that the user does not like the current menu; the remark label information of the menu when p=1 indicates that the user favors the current menu;
-representing an index value adjustment function based on the remaining days of the week; u represents a daily recommended menu quantity reference value, which is empirically set by an administrator; m represents the number of the residual menu in the recommended menu information; max represents the maximum adjustment value, which is empirically set by an administrator;
when the adding sub-module works, the following formula is satisfied:
wherein R (Q) represents an addition judgment function; q ref Indicating an addition judgment threshold value, which is empirically set by an administrator; when R (Q) =1, it indicates that the current menu needs to be added to the recommended menu information again; when R (Q) =0, it means that the current menu does not need to be re-added to the recommended menu information;
when (when)
When R (Q) =1.
Optionally, the cooking process checking module comprises a checking information reading sub-module, a first checking sub-module and a second checking sub-module; the proofreading information reading submodule is used for reading proofreading information input by a user; the first proofreading submodule is used for proofreading the dish cooking flow information according to the proofreading information; the second correction submodule is used for correcting the dish cooking flow information according to the history correction information of the local user;
when the second correction submodule works, the correction score of the menu is calculated by the following formula:
wherein B represents the proofreading score of the corresponding menu; bj represents the sum of the proofreading duration of each cooking process in the jth proofreading information of the corresponding dish in a history week; j represents the total number of the proofreading information input by the user in one week of the history corresponding to the menu; v represents the total number of times of the corresponding dish frying process variation in one week of history; beta 1 And beta 2 Representing different weight coefficients;
the second comparison sub-module is further used for judging whether the comparison score B of the corresponding dish is larger than a preset threshold value, if yes, the comparison information with the smallest sum of the comparison duration of each dish frying process in one week of the history is adopted for comparison; the preset threshold is empirically set by an administrator.
An intelligent matching cooking method based on big data cloud management is applied to the intelligent matching cooking system based on big data cloud management, and is shown in combination with fig. 2, and the intelligent matching cooking method comprises the following steps:
s1, receiving and managing various operation instructions from a cloud;
s2, controlling a dish type recommending terminal, a dish cooking process management terminal and a dish cooking machine body;
s3, providing a menu reference for a user and acquiring menu selection information from the user;
s4, matching corresponding dish frying flow information according to the dish selection information;
s5, detecting the material placement state according to the menu selection information, and generating material state information;
s6, performing cooking operation according to the vegetable cooking flow information and the material state information.
Embodiment two.
The embodiment includes the whole content of the first embodiment, and provides an intelligent matching cooking system based on big data cloud management, and the intelligent matching cooking system further comprises a sheet meat food material detection module and a cooking flow adjustment module, as shown in fig. 3; the flaky meat food material detection module is used for detecting flaky meat food materials in the cooking machine body and generating detection information; the flaky meat food materials comprise steaks, chicken cutlets, pork chops, fish, luncheon meat slices and the like. The cooking process adjustment module is used for generating corresponding cooking process adjustment information according to the detection information so as to facilitate the cooking process change of the cooking machine body during cooking of the flaky meat food materials according to the cooking process information.
Referring to fig. 4, the sheet-shaped meat food detection module includes a sheet-shaped meat food surface image recognition sub-module, a sheet-shaped meat food surface temperature sub-module, and a detection information generation sub-module; the flaky meat food surface image recognition submodule is used for shooting and recognizing images of the flaky meat food surface in the cooking machine body to generate flaky meat food surface image information; the flaky meat food material surface temperature submodule is used for detecting the temperature of the flaky meat food material surface and generating temperature information; the detection information generation submodule is used for calculating an adjustment index according to the sheet-shaped meat food material surface image information and the temperature information, and the adjustment index is used as detection information.
When the detection information generation sub-module calculates the adjustment index, the following equation is satisfied:
wherein T representsAdjusting an index; x is X 1 The pixel number of the part of the Maillard reaction of the flaky meat surface in the flaky meat food material surface image information is represented; the maillard reaction, also known as a non-enzymatic browning reaction, is a non-enzymatic browning that is widely found in the food industry. The reaction between carbonyl compound and amino compound can produce brown or black macromolecular melanoidin or melanoidin through complex process, so that it is also called as carbonylamino reaction. When the image recognition sub-module on the surface of the flaky meat food material shoots the surface of the flaky meat food material in the cooking machine body and performs image recognition, the brown or even black part of the surface of the flaky meat food material is recognized. X is X 2 Representing the total number of pixels on the surface of the flaky meat food material in the flaky meat food material surface image information; o represents the real-time temperature of the surface of the flaky meat food material in the temperature information; mu (mu) 1 Sum mu 2 Representing different transform coefficients, which are empirically set by an administrator.
The cooking process adjusting module judges whether an adjusting index in the detection information is larger than an adjusting threshold value, and if so, generates cooking process adjusting information for driving the cooker body to turn over the flaky meat food materials. The adjustment threshold is empirically set by an administrator.
The foregoing disclosure is only a preferred embodiment of the present invention and is not intended to limit the scope of the invention, so that all equivalent technical changes made by the application of the present invention and the accompanying drawings are included in the scope of the invention, and in addition, the elements in the invention can be updated with the technical development.
Claims (7)
1. The intelligent matching cooking system based on big data cloud management is characterized by comprising a cloud instruction management terminal, a cooking recommendation terminal, a cooking process management terminal, a material management terminal and a cooking machine body;
the cloud instruction management terminal is used for receiving and managing various operation instructions from the cloud; the operation instruction is used for controlling the dish recommending terminal, the dish cooking process management terminal and the dish cooking machine body; the menu type recommendation terminal is used for providing menu type reference for a user and acquiring menu type selection information from the user; the cooking process management terminal is used for matching corresponding cooking process information according to the cooking type selection information; the material management terminal is used for detecting the material placement state according to the menu type selection information and generating material state information;
the cooker body is used for performing cooking operation according to the cooking flow information and the material state information.
2. The intelligent matching cooking system based on big data cloud management as claimed in claim 1, wherein the dish type recommendation terminal comprises a regional recommendation dish type information selection module, a dish type adjustment module, a dish type display module and a dish type selection information generation module; the regional recommended menu information selection module is used for acquiring weekly recommended menu information from the cloud; the menu adjusting module is used for adjusting the recommended menu information every week; the menu display module is used for displaying the adjusted recommended menu information; the menu selection information generation module is used for generating menu selection information according to the selection operation of a user.
3. The intelligent matching cooking system based on big data cloud management as claimed in claim 2, wherein the cooking process management terminal comprises a cooking process information matching module and a cooking process checking module; the dish-type cooking process information matching module is used for matching corresponding dish-type cooking process information from the cloud according to the dish-type selection information; the cooking process checking module is used for checking the cooking process information according to the local history information of the corresponding dishes in the cloud.
4. The intelligent matching cooking system based on big data cloud management as claimed in claim 3, wherein the regional recommended dish information selection module comprises a selection index calculation sub-module and a recommended dish information acquisition sub-module; the selection index calculation sub-module is used for calculating a weekly selection index according to the local weekly weather forecast situation and taste grade information input by a user in advance; the recommended menu information acquisition sub-module is used for acquiring corresponding recommended menu information from a cloud preset recommended menu information database according to the selection index; the preset recommended menu information database comprises a plurality of recommended menu information updated by an administrator, each recommended menu information is provided with a corresponding selection index interval, and each recommended menu information comprises all recommended menu types in a corresponding week;
when the selection index calculation sub-module calculates, the following equation is satisfied:
wherein Z represents a selection index; a represents a reference value of appetite index, which is empirically set by an administrator; d, d 1i The number of hours at which the average air temperature is greater than 25 ℃ on day i of the week for the weather forecast conditions of the local week; d, d 2i The number of precipitation hours in day i in a week representing the weather forecast conditions of the local week; mu (mu) 1 Sum mu 2 Representing different appetite index claim coefficients, empirically set by an administrator; l represents the number of grades in the taste grade information which is input by the user in advance; k (k) 1 And k 2 Respectively representing a first index conversion coefficient and a second index conversion coefficient, which are set by an administrator according to experience;
when the corresponding recommended menu information is obtained, the corresponding recommended menu information is selected according to the selection index interval corresponding to the selection index.
5. The intelligent matching cooking system based on big data cloud management of claim 4, wherein said menu adjusting module comprises a re-recommending sub-module, a deleting sub-module and an adding sub-module; the re-recommending sub-module is used for calculating a re-recommending index of the menu selected by the user in the menu information recommended in one week; the deletion submodule is used for deleting the menu selected by the user in the recommended menu information; the adding sub-module is used for adding the menu selected by the user in the menu information recommended in one week into the menu information recommended in one week again according to the re-recommendation index;
when the re-recommendation sub-module calculates, the following equation is satisfied:
wherein Q represents the re-recommendation index of the menu selected by the user in the menu information recommended in one week; n represents the number of days between the first selected day of the menu which has been selected by the user and the day when the re-recommendation index is calculated; f (f) 1 (p) represents a weight selection function; p represents remark label information of the menu, and is added in advance by a user; the remark label information of the menu when p=0 indicates that the user does not like the current menu; the remark label information of the menu when p=1 indicates that the user favors the current menu;
-representing an index value adjustment function based on the remaining days of the week; u represents a daily recommended menu quantity reference value, which is empirically set by an administrator; m represents the number of the residual menu in the recommended menu information; max represents the maximum adjustment value, which is empirically set by an administrator;
when the adding sub-module works, the following formula is satisfied:
wherein, the liquid crystal display device comprises a liquid crystal display device,r (Q) represents an addition judgment function; q ref Indicating an addition judgment threshold value, which is empirically set by an administrator; when R (Q) =1, it indicates that the current menu needs to be added to the recommended menu information again; when R (Q) =0, it means that the current menu does not need to be re-added to the recommended menu information;
when (when)
When R (Q) =1.
6. The intelligent matching cooking system based on big data cloud management of claim 5, wherein the cooking process checking module comprises a checking information reading sub-module, a first checking sub-module and a second checking sub-module; the proofreading information reading submodule is used for reading proofreading information input by a user; the first proofreading submodule is used for proofreading the dish cooking flow information according to the proofreading information; the second correction submodule is used for correcting the dish cooking flow information according to the history correction information of the local user;
when the second correction submodule works, the correction score of the menu is calculated by the following formula:
wherein B represents the proofreading score of the corresponding menu; b j Representing the sum of the proofreading duration of each cooking process in the j-th proofreading information of the corresponding dish in a history week; j represents the total number of the proofreading information input by the user in one week of the history corresponding to the menu; v represents the total number of times of the corresponding dish frying process variation in one week of history; beta 1 And beta 2 Representing different weight coefficients;
the second comparison sub-module is further used for judging whether the comparison score B of the corresponding dish is larger than a preset threshold value, if yes, the comparison information with the smallest sum of the comparison duration of each dish frying process in one week of the history is adopted for comparison; the preset threshold is empirically set by an administrator.
7. An intelligent matching cooking method based on big data cloud management, which is applied to the intelligent matching cooking system based on big data cloud management as claimed in claim 6, and is characterized in that the intelligent matching cooking method comprises the following steps:
s1, receiving and managing various operation instructions from a cloud;
s2, controlling a dish type recommending terminal, a dish cooking process management terminal and a dish cooking machine body;
s3, providing a menu reference for a user and acquiring menu selection information from the user;
s4, matching corresponding dish frying flow information according to the dish selection information;
s5, detecting the material placement state according to the menu selection information, and generating material state information;
s6, performing cooking operation according to the vegetable cooking flow information and the material state information.
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