CN115831322B - Automatic nutrition configuration-based nutrition scheme generation method and system - Google Patents
Automatic nutrition configuration-based nutrition scheme generation method and system Download PDFInfo
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Abstract
The invention discloses a nutrition scheme generation method and system based on automatic nutrition configuration, and relates to the field of data processing, wherein the method comprises the following steps: performing feature analysis on the user diagnosis and treatment data to obtain user diagnosis and treatment features; acquiring nutrition configuration flow information; performing control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set; analyzing the control environment of the nutrition configuration robot to obtain environment change characteristics; constructing a configuration control response function; and obtaining a parameter set to be controlled according to the configuration control response function, wherein the parameter set to be controlled is used for controlling the nutrition configuration robot. The technical problems of low quality and efficiency of nutrition configuration, insufficient accuracy of a nutrition scheme generated by the nutrition configuration and low adaptation degree in the prior art are solved. The technical effects of improving the quality and efficiency of nutrition configuration, improving the accuracy, the adaptation degree and the like of a nutrition scheme generated by the nutrition configuration are achieved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a nutrition scheme generation method and system based on automatic nutrition configuration.
Background
With the improvement of life quality of people, nutrition and health problems are widely paid attention to. Meanwhile, under the influence of lack of public nutrition and health knowledge and low proportion of nutritionists, the market demand of nutrition configuration is rapidly enlarged, and the workload of nutrition configuration is continuously increased. In the prior art, the technical problems of low quality and efficiency of nutrition configuration, insufficient accuracy of a nutrition scheme generated by the nutrition configuration and low adaptation degree exist.
Disclosure of Invention
The application provides a nutrition scheme generation method and system based on automatic nutrition configuration. The technical problems of low quality and efficiency of nutrition configuration, insufficient accuracy of a nutrition scheme generated by the nutrition configuration and low adaptation degree in the prior art are solved. The intelligent and automatic degree of nutrition configuration are improved, the quality and efficiency of nutrition configuration are improved, and the technical effects of accuracy and adaptation of a nutrition scheme generated by the nutrition configuration are improved.
In view of the above, the present application provides a nutrition scheme generation method and system based on automated nutrition configuration.
In a first aspect, the present application provides a nutritional scheme generation method based on an automated nutritional configuration, wherein the method is applied to a nutritional scheme generation system based on an automated nutritional configuration, the method comprising: connecting the user diagnosis and treatment information management system to acquire user diagnosis and treatment data; performing feature analysis on the user diagnosis and treatment data to obtain user diagnosis and treatment features; the control system of the nutrition configuration robot is connected to obtain nutrition configuration flow information; performing control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set; analyzing the control environment of the nutrition-configuring robot to obtain environmental change characteristics; setting up a configuration control response function by taking the diagnosis and treatment characteristics of the user as an input variable, taking the controllable parameter index set as a response variable, taking the environmental change characteristics as an adjustment variable, and taking index values corresponding to the controllable parameter index set as response output; and obtaining a parameter set to be controlled according to the configuration control response function, wherein the parameter set to be controlled is used for controlling the nutrition configuration robot.
In a second aspect, the present application also provides a nutritional regimen generation system based on an automated nutritional configuration, wherein the system comprises: the user diagnosis and treatment data acquisition module is used for connecting the user diagnosis and treatment information management system to acquire user diagnosis and treatment data; the user diagnosis and treatment characteristic analysis module is used for carrying out characteristic analysis on the user diagnosis and treatment data to obtain user diagnosis and treatment characteristics; the nutrition configuration flow information acquisition module is used for connecting a control system of the nutrition configuration robot to acquire nutrition configuration flow information; the control index analysis module is used for carrying out control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set; the control environment analysis module is used for analyzing the control environment of the nutrition configuration robot to obtain environment change characteristics; the function construction module is used for constructing a configuration control response function by taking the diagnosis and treatment characteristics of the user as input variables, taking the controllable parameter index set as response variables, taking the environment change characteristics as adjustment variables and taking corresponding index values in the controllable parameter index set as response output; and the control module is used for controlling the response function according to the configuration to obtain a parameter set to be controlled and controlling the nutrition configuration robot.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the nutrition scheme generation method based on the automatic nutrition configuration when executing the executable instructions stored in the memory.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, implements a nutritional regimen generating method based on an automated nutritional configuration provided by the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
acquiring diagnosis and treatment data of a user by carrying out data query on a diagnosis and treatment information management system of the user; the diagnosis and treatment data of the user are subjected to feature analysis to obtain diagnosis and treatment features of the user; the control system of the nutrition configuration robot is connected to acquire nutrition configuration flow information; the method comprises the steps of obtaining a controllable parameter index set by carrying out control index analysis on each flow of nutrition configuration flow information; analyzing the control environment of the nutrition-configuring robot to obtain environmental change characteristics; taking the diagnosis and treatment characteristics of a user as an input variable, taking a controllable parameter index set as a response variable, taking the environmental change characteristics as an adjustment variable, taking corresponding index values in the controllable parameter index set as response output, and constructing a configuration control response function; and obtaining a parameter set to be controlled according to the configuration control response function, and controlling the nutrition configuration robot according to the parameter set to be controlled. The intelligent and automatic degree of nutrition configuration are improved, the quality and efficiency of nutrition configuration are improved, and the technical effects of accuracy and adaptation of a nutrition scheme generated by the nutrition configuration are improved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
FIG. 1 is a schematic flow chart of a nutritional regimen generation method based on an automated nutritional configuration of the present application;
FIG. 2 is a schematic flow chart of acquiring nutrition configuration flow information in a nutrition scheme generation method based on automatic nutrition configuration;
FIG. 3 is a schematic diagram of a nutritional scheme generation system based on an automated nutritional configuration according to the present application;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises a user diagnosis and treatment data acquisition module 11, a user diagnosis and treatment characteristic analysis module 12, a nutrition configuration flow information acquisition module 13, a control index analysis module 14, a control environment analysis module 15, a function construction module 16, a control module 17, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
The application provides a nutrition scheme generation method and system based on automatic nutrition configuration. The technical problems of low quality and efficiency of nutrition configuration, insufficient accuracy of a nutrition scheme generated by the nutrition configuration and low adaptation degree in the prior art are solved. The intelligent and automatic degree of nutrition configuration are improved, the quality and efficiency of nutrition configuration are improved, and the technical effects of accuracy and adaptation of a nutrition scheme generated by the nutrition configuration are improved.
Example 1
Referring to fig. 1, the present application provides a nutrition scheme generating method based on automatic nutrition configuration, wherein the method is applied to a nutrition scheme generating system based on automatic nutrition configuration, the system is in communication connection with a user diagnosis and treatment information management system and a nutrition configuration robot, and the method specifically includes the following steps:
step S100: connecting the user diagnosis and treatment information management system to acquire user diagnosis and treatment data;
step S200: performing feature analysis on the user diagnosis and treatment data to obtain user diagnosis and treatment features;
specifically, the user diagnosis and treatment information management system is connected, and data query is performed on the user diagnosis and treatment information management system to obtain user diagnosis and treatment data. And obtaining diagnosis and treatment characteristics of the user by carrying out characteristic analysis on the diagnosis and treatment data of the user. The user diagnosis and treatment information management system has the functions of recording, storing, inquiring and the like of diagnosis and treatment information of a plurality of users. The user diagnosis and treatment data comprise data information such as names, contact ways, height parameters, weight parameters, drug allergy conditions, illness time, disease types, doctor diagnosis conditions and the like of any user. The user diagnosis and treatment characteristics comprise user health analysis information, user nutrition demand information, user health indexes and the like corresponding to the user diagnosis and treatment data. When the user diagnosis and treatment characteristics are obtained, historical data query is conducted based on the user diagnosis and treatment data, and a plurality of historical user diagnosis and treatment data and a plurality of historical user diagnosis and treatment characteristics are obtained. And continuously self-training and learning the diagnosis and treatment data of the plurality of historical users and the diagnosis and treatment characteristics of the plurality of historical users to a convergence state to obtain a diagnosis and treatment characteristic analysis model of the users. The user diagnosis and treatment characteristic analysis model comprises an input layer, an implicit layer and an output layer. And taking the user diagnosis and treatment data as input information, and inputting the input information into a user diagnosis and treatment characteristic analysis model to obtain the user diagnosis and treatment characteristics. The technical effect of obtaining the diagnosis and treatment characteristics of the user by carrying out characteristic analysis on the diagnosis and treatment data of the user is achieved, so that the adaptation degree of nutrition configuration is improved.
Step S300: the control system of the nutrition configuration robot is connected to obtain nutrition configuration flow information;
further, as shown in fig. 2, step S300 of the present application further includes:
step S310: screening the user diagnosis and treatment data according to nutrition related indexes to obtain screening diagnosis and treatment data;
step S320: determining nutrition configuration powder according to the screening diagnosis and treatment data;
step S330: combining the nutrition-configured powder to obtain nutrition-combined powder;
step S340: inputting the nutrition combination powder into a control system of the nutrition configuration robot to obtain the nutrition configuration flow information.
Specifically, the diagnosis and treatment data of the user are screened according to the nutrition-related indexes, and the screened diagnosis and treatment data are obtained. Illustratively, when screening diagnosis and treatment data is acquired, nutrition correlation analysis is performed on a plurality of data in user diagnosis and treatment data respectively, so as to obtain a plurality of diagnosis and treatment data nutrition correlation indexes. Judging whether the nutrition-related indexes of the diagnosis and treatment data meet the nutrition-related index threshold or not respectively, and outputting user diagnosis and treatment data corresponding to the nutrition-related indexes of the diagnosis and treatment data meeting the nutrition-related index threshold as screening diagnosis and treatment data.
Further, matching the nutrition powder according to the screening diagnosis and treatment data to obtain nutrition configuration powder. The nutrition configuration powder comprises type parameters, component parameters and content parameters corresponding to a plurality of nutrition powder such as vitamin powder, dietary fiber powder and the like which are corresponding to screening diagnosis and treatment data. Illustratively, when the nutritional configured powder is obtained, historical data queries are performed based on the screening diagnostic data to obtain a plurality of historical screening diagnostic data, a plurality of historical nutritional configured powders. And carrying out matching relation analysis on the plurality of historical screening diagnosis and treatment data and the plurality of historical nutrition configuration powders to obtain diagnosis and treatment-powder mapping relation. And arranging a plurality of historical screening diagnosis and treatment data and a plurality of historical nutrition configuration powders according to the diagnosis and treatment-powder mapping relation to obtain a nutrition powder matching database. And (5) taking the screening diagnosis and treatment data as input information, and inputting the input information into a nutrition powder matching database to obtain nutrition configuration powder.
Further, the nutritional supplement powder is produced by combining the nutritional supplement powders. And taking the nutrition combination powder as input information, inputting the input information into a control system of the nutrition configuration robot, and obtaining nutrition configuration flow information. The nutrition combination powder comprises a plurality of nutrition powder component proportion parameters and a plurality of nutrition powder content parameters after the nutrition configuration powder is combined. The control system of the nutrition-configuring robot has the function of intelligently controlling the nutrition-configuring robot. The nutrition configuration flow information comprises various flows such as a container preparation configuration flow, a container marking configuration flow, a feeding flow, a water injection flow, a container sealing configuration flow, a vibration stirring flow, a verification flow, a shipment flow and the like. The technical effects of obtaining reliable nutrition configuration flow information through multidimensional feature analysis on diagnosis and treatment data of a user and a control system of the nutrition configuration robot are achieved, and therefore accuracy and adaptation degree of a nutrition scheme generated by nutrition configuration are improved.
Step S400: performing control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set;
specifically, a controllable parameter index set is obtained by performing control index analysis on each process of the nutrition configuration process information. The controllable parameter index set comprises an amount control index, a time control index, an injection control index and a mixing control index. The dosage control index comprises a configuration container material control index, a configuration container specification control index, a nutrition powder dosage control index and the like. The time control indexes comprise a configuration container preparation time control index, a charging time control index, a water injection time control index, a vibration stirring time control index, a verification time control index, a delivery time control index and the like. The injection control indexes comprise a feed amount control index, a water injection amount control index and the like. The mixing control index comprises an oscillation stirring intensity control index, an oscillation stirring frequency control index and the like. The technical effects of improving the control reliability of the nutrition configuration and improving the efficiency of the nutrition configuration are achieved by carrying out control index analysis on the nutrition configuration flow information to obtain an accurate controllable parameter index set.
Step S500: analyzing the control environment of the nutrition-configuring robot to obtain environmental change characteristics;
further, step S500 of the present application further includes:
step S510: acquiring real-time environment data, wherein the real-time environment data comprises environment temperature data, environment humidity data and dust granularity data;
step S520: performing anomaly identification on the environmental temperature data, the environmental humidity data and the dust granularity data to obtain environmental anomaly data, wherein the environmental anomaly data is the environmental temperature data and/or the environmental humidity data and/or the dust granularity data;
step S530: acquiring N configuration flows related to the environment abnormal data in each configuration flow based on the environment abnormal data;
step S540: and carrying out data monitoring on the N configuration flows.
Specifically, the control environment of the nutrition-configured robot is subjected to real-time data acquisition, and real-time environment data are obtained. And acquiring environment abnormal data by carrying out abnormal recognition on the real-time environment data. And carrying out correlation analysis on each flow of the nutrition configuration flow information based on the environment abnormal data to obtain N configuration flows, and carrying out data monitoring on the N configuration flows. And adding the environment abnormal data and the N configuration flows to the environment change characteristics. Wherein the real-time environmental data includes environmental temperature data, environmental humidity data, and dust particle size data. The environmental anomaly data includes ambient temperature data and/or ambient humidity data and/or dust particle size data. The environment change characteristics comprise environment abnormal data and N configuration flows. Illustratively, when N configuration flows are obtained, correlation analysis is performed on the environmental anomaly data and each flow of the nutritional configuration flow information, so as to obtain a plurality of environmental flow correlation coefficients. And judging whether the plurality of environment process correlation coefficients meet the preset environment process correlation coefficients or not respectively. And outputting each process corresponding to the plurality of environment process correlation coefficients meeting the preset environment process correlation coefficient as N configuration processes. The technical effects of generating environmental change characteristics by analyzing the control environment of the nutrition configuration robot and improving the accuracy and reliability of nutrition configuration are achieved.
Further, step S520 of the present application further includes:
step S521: judging whether the environment abnormal data is output of at least two data types, and if the environment abnormal data is output of at least two data types, acquiring an environment variable superposition instruction;
step S522: obtaining a first variable superposition configuration flow and b second variable superposition configuration flows according to the environment variable superposition instruction;
step S523: and carrying out abnormal superposition monitoring by using the a first variable superposition configuration flows and the b second variable superposition configuration flows.
Specifically, whether the environment abnormal data is output of at least two data types is judged, and if the environment abnormal data is output of at least two data types, an environment variable superposition instruction is obtained. And matching each flow of the nutrition configuration flow information according to the environment variable superposition instruction to obtain a first variable superposition configuration flow and b second variable superposition configuration flows. And performing abnormal superposition monitoring according to the a first variable superposition configuration flows and the b second variable superposition configuration flows. The environment variable superposition instruction is instruction information used for representing that environment abnormal data comprise a plurality of data types and is required to be subjected to abnormal superposition monitoring. The a first variable superposition configuration flows and the b second variable superposition configuration flows comprise various flows of nutrition configuration flow information corresponding to the data type of the environment abnormal data. Illustratively, the environmental anomaly data includes ambient temperature data and ambient humidity data. And the environment abnormal data are output of two data types, and an environment variable superposition instruction is obtained. The a first variable superposition configuration flows comprise a ambient temperature superposition configuration flows. The b second variable superposition configuration flows comprise b environment humidity superposition configuration flows. The technical effects of adaptively generating an environment variable superposition instruction and starting abnormal superposition monitoring by judging whether the environment abnormal data are output of at least two data types are achieved, so that the quality of nutrition configuration is improved.
Step S600: setting up a configuration control response function by taking the diagnosis and treatment characteristics of the user as an input variable, taking the controllable parameter index set as a response variable, taking the environmental change characteristics as an adjustment variable, and taking index values corresponding to the controllable parameter index set as response output;
step S700: and obtaining a parameter set to be controlled according to the configuration control response function, wherein the parameter set to be controlled is used for controlling the nutrition configuration robot.
Specifically, the diagnosis and treatment characteristics of the user are set as input variables, the controllable parameter index set is set as response variables, the environment change characteristics are set as adjustment variables, the index values corresponding to the controllable parameter index set are set as response outputs, and the configuration control response function is constructed. And generating a parameter set to be controlled based on the configuration control response function, and controlling the nutrition configuration robot according to the parameter set to be controlled. The configuration control response function comprises an input variable, a response variable, an adjustment variable and a response output. The parameter set to be controlled comprises a dosage control index value, a time control index value, an injection control index value and a mixing control index value which correspond to the controllable parameter index set. The technical effects of controlling the nutrition configuration robot through the parameter set to be controlled, improving the automation degree of nutrition configuration and improving the quality and efficiency of nutrition configuration are achieved.
Further, step S600 of the present application further includes:
step S610: performing control index analysis on each process of the nutrition configuration process information to obtain a controllable parameter index set, wherein the controllable parameter index set comprises a dosage control index, a time control index, an injection control index and a mixed control index;
step S620: acquiring the dosage control index, the time control index, the injection control index, the mixing control index and the influence coefficient of environmental change to obtain a dosage-environmental influence coefficient, a time-environmental influence coefficient, an injection-environmental influence coefficient and a mixing-environmental influence coefficient;
step S630: generating a feedback adjustment variable layer with the usage-environment influence coefficient, the time-environment influence coefficient, the injection-environment influence coefficient, and the mixed-environment influence coefficient for optimizing the configuration control response function.
Specifically, the usage control index, the time control index, the injection control index, and the mixing control index in the controllable parameter index set are respectively subjected to environmental change influence evaluation to obtain a usage-environmental influence coefficient, a time-environmental influence coefficient, an injection-environmental influence coefficient, and a mixing-environmental influence coefficient. And generating a feedback adjustment variable layer based on the consumption-environment influence coefficient, the time-environment influence coefficient, the injection-environment influence coefficient and the mixed-environment influence coefficient, and optimizing the configuration control response function through the feedback adjustment variable layer, so that the accuracy and the adaptability of the configuration control response function are improved. The usage-environment influence coefficient, the time-environment influence coefficient, the injection-environment influence coefficient, and the mixture-environment influence coefficient are parameter information for representing the influence degree of the environmental change on the usage control index, the time control index, the injection control index, and the mixture control index. The higher the environmental change affects the dosage control index, the time control index, the injection control index, and the mixing control index, the greater the corresponding dosage-environmental impact coefficient, time-environmental impact coefficient, injection-environmental impact coefficient, and mixing-environmental impact coefficient. Feedback adjustment variable layers include dose-environment influence coefficients, time-environment influence coefficients, injection-environment influence coefficients, mix-environment influence coefficients. For example, when the configuration control response function is optimized through the feedback adjustment variable layer, the weight assignment can be performed on the controllable parameter index set according to the usage-environment influence coefficient, the time-environment influence coefficient, the injection-environment influence coefficient and the mixed-environment influence coefficient, so as to obtain a plurality of controllable parameter index environment weight values. The larger the environmental weight value of the controllable parameter index is, the higher the influence of environmental change on the index value corresponding to the controllable parameter index set is, and the higher the accuracy degree requirement on the index value corresponding to the controllable parameter index set is.
Further, step S700 of the present application further includes:
step S710: acquiring a simulation control parameter set and a real-time control parameter set of the nutrition configuration robot;
step S720: outputting a phase difference control parameter set according to the simulation control parameter set and the real-time control parameter set;
step S730: generating a control precision index according to the phase difference control parameter set, wherein the control precision index is used for identifying the precision degree of parameter control executed by the nutrition configuration robot;
step S740: generating a parameter optimization conversion model according to the control precision index;
step S750: and inputting the parameter set to be controlled into the parameter optimization conversion model to obtain an optimized control parameter set.
Specifically, a control system of the nutrition-configured robot is connected, and a simulation control parameter set and a real-time control parameter set are obtained by inquiring control parameters of the control system of the nutrition-configured robot. And calculating the parameter difference value by using the analog control parameter set and the real-time control parameter set to obtain a phase difference control parameter set. Based on the phase difference control parameter set, a control accuracy index is generated. Further, based on the control precision index and the parameter set to be controlled, historical data query is carried out, and a plurality of historical control precision indexes, a plurality of historical parameter sets to be controlled and a plurality of historical optimized control parameter sets are obtained. And continuously self-training and learning the plurality of historical control precision indexes, the plurality of historical parameter sets to be controlled and the plurality of historical optimization control parameter sets to a convergence state to obtain the parameter optimization conversion model. The parameter optimization conversion model comprises an input layer, an implicit layer and an output layer. The parameter optimization conversion model has the function of optimizing and adjusting the parameter set to be controlled according to the control precision index. And taking the control precision index and the parameter set to be controlled as input information, and inputting a parameter optimization conversion model to obtain an optimized control parameter set. And controlling the nutrition configuration robot according to the optimized control parameter set. The simulation control parameter set comprises a plurality of system control parameters corresponding to the nutrition configuration robot. The real-time control parameter set comprises a plurality of actual execution control parameters corresponding to the nutrition configuration robot. The phase difference control parameter set includes a plurality of parameter differences between the analog control parameter set and the real-time control parameter set. The control precision index is used for identifying the precision degree of parameter control executed by the nutrition configuration robot. The average value corresponding to the phase difference control parameter set may be output as the control accuracy index. The method achieves the technical effects that the parameter set to be controlled is adaptively optimized and adjusted through the control precision index and the parameter optimization conversion model, and the high-precision optimized control parameter set is obtained, so that the accuracy of controlling the nutrition configuration robot is improved, and the quality of nutrition configuration is improved.
Further, step S700 of the present application further includes:
step S760: performing data monitoring on the process of controlling the nutrition configuration robot to obtain a monitoring data set of each configuration flow;
step S770: acquiring preset monitoring indexes of each configuration flow;
step S780: performing anomaly identification on the monitoring data sets of each configuration flow according to the preset monitoring indexes to obtain an anomaly data set;
step S790: and generating reminding information according to the abnormal data set.
Specifically, the monitoring data set of each configuration flow is obtained by monitoring the control process of the nutrition-configuring robot. And carrying out anomaly identification on the monitoring data sets of each configuration flow according to preset monitoring indexes to obtain an anomaly data set. The historical data query is performed based on the monitoring data set and the preset monitoring index of each configuration flow, and a standard monitoring database is obtained. The standard monitoring database comprises a plurality of historical standard monitoring data sets corresponding to preset monitoring indexes. Based on preset monitoring indexes, the monitoring data sets of each configuration flow are compared with a standard monitoring database, and an abnormal data set can be obtained. And then, generating reminding information according to the abnormal data set. For example, when abnormality recognition is performed on the monitoring data set of each configuration flow according to the preset monitoring index, it is found that uniformity of vibration stirring is poor. The anomaly data set then includes nutritional powder mix anomaly data. The reminding information comprises mixed abnormity reminding information. The monitoring data sets of each configuration flow comprise a charging flow monitoring data set, a water injection flow monitoring data set, a vibration stirring flow monitoring data set and other flow monitoring data sets corresponding to nutrition configuration flow information. The preset monitoring indexes comprise preset and determined charging flow monitoring indexes, water injection flow monitoring indexes, vibration stirring flow monitoring indexes and other flow monitoring indexes. The abnormal data sets comprise abnormal data sets of each flow corresponding to preset monitoring indexes. The technical effects of monitoring and abnormality identification of the control process of the nutrition configuration robot and adaptively generating reminding information are achieved, so that the quality of nutrition configuration is improved.
In summary, the nutrition scheme generation method based on the automatic nutrition configuration provided by the application has the following technical effects:
1. acquiring diagnosis and treatment data of a user by carrying out data query on a diagnosis and treatment information management system of the user; the diagnosis and treatment data of the user are subjected to feature analysis to obtain diagnosis and treatment features of the user; the control system of the nutrition configuration robot is connected to acquire nutrition configuration flow information; the method comprises the steps of obtaining a controllable parameter index set by carrying out control index analysis on each flow of nutrition configuration flow information; analyzing the control environment of the nutrition-configuring robot to obtain environmental change characteristics; taking the diagnosis and treatment characteristics of a user as an input variable, taking a controllable parameter index set as a response variable, taking the environmental change characteristics as an adjustment variable, taking corresponding index values in the controllable parameter index set as response output, and constructing a configuration control response function; and obtaining a parameter set to be controlled according to the configuration control response function, and controlling the nutrition configuration robot according to the parameter set to be controlled. The intelligent and automatic degree of nutrition configuration are improved, the quality and efficiency of nutrition configuration are improved, and the technical effects of accuracy and adaptation of a nutrition scheme generated by the nutrition configuration are improved.
2. By analyzing the control index of the nutrition configuration flow information, an accurate controllable parameter index set is obtained, so that the control reliability of the nutrition configuration is improved, and the efficiency of the nutrition configuration is improved.
3. The parameter set to be controlled is adaptively optimized and adjusted through the control precision index and the parameter optimization and conversion model, and the high-precision optimized control parameter set is obtained, so that the accuracy of controlling the nutrition configuration robot is improved, and the quality of nutrition configuration is improved.
Example two
Based on the same inventive concept as the nutrition scheme generation method based on the automatic nutrition configuration in the foregoing embodiment, the present invention further provides a nutrition scheme generation system based on the automatic nutrition configuration, where the system is in communication connection with a user diagnosis and treatment information management system and a nutrition configuration robot, and please refer to fig. 3, and the system includes:
the user diagnosis and treatment data acquisition module 11 is used for connecting the user diagnosis and treatment information management system to acquire user diagnosis and treatment data;
the user diagnosis and treatment characteristic analysis module 12, wherein the user diagnosis and treatment characteristic analysis module 12 is used for performing characteristic analysis on the user diagnosis and treatment data to obtain user diagnosis and treatment characteristics;
The nutrition configuration flow information acquisition module 13, wherein the nutrition configuration flow information acquisition module 13 is used for connecting a control system of the nutrition configuration robot to acquire nutrition configuration flow information;
the control index analysis module 14 is used for performing control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set;
the control environment analysis module 15 is used for analyzing the control environment of the nutrition configuration robot to obtain environment change characteristics;
the function construction module 16 is configured to construct a configuration control response function by taking the diagnosis and treatment characteristics of the user as an input variable, taking the controllable parameter index set as a response variable, taking the environmental change characteristics as an adjustment variable, and taking the index values corresponding to the controllable parameter index set as a response output;
the control module 17 is configured to control a response function according to the configuration, and obtain a parameter set to be controlled, which is used for controlling the nutrition-configured robot.
Further, the system further comprises:
the controllable parameter index set determining module is used for carrying out control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set, wherein the controllable parameter index set comprises a dosage control index, a time control index, an injection control index and a mixed control index;
The environment influence coefficient determining module is used for obtaining the consumption control index, the time control index, the injection control index and the mixed control index and the influence coefficient of environment change, and obtaining a consumption-environment influence coefficient, a time-environment influence coefficient, an injection-environment influence coefficient and a mixed-environment influence coefficient;
and the configuration function optimization module is used for generating a feedback adjustment variable layer according to the consumption-environment influence coefficient, the time-environment influence coefficient, the injection-environment influence coefficient and the mixed-environment influence coefficient and optimizing the configuration control response function.
Further, the system further comprises:
the diagnosis and treatment data screening module is used for screening the diagnosis and treatment data of the user according to nutrition related indexes to obtain screening diagnosis and treatment data;
the first execution module is used for determining nutrition configuration powder according to the screening diagnosis and treatment data;
the powder combination module is used for combining the nutrition configuration powder to obtain nutrition combination powder;
The second execution module is used for inputting the nutrition combination powder into a control system of the nutrition configuration robot to acquire the nutrition configuration flow information.
Further, the system further comprises:
the data monitoring module is used for monitoring data in the process of controlling the nutrition configuration robot to obtain a monitoring data set of each configuration flow;
the preset monitoring index acquisition module is used for acquiring preset monitoring indexes of each configuration flow;
the abnormal data set acquisition module is used for carrying out abnormal identification on the monitoring data sets of each configuration flow according to the preset monitoring indexes to acquire abnormal data sets;
and the reminding information generation module is used for generating reminding information according to the abnormal data set.
Further, the system further comprises:
the third execution module is used for acquiring real-time environment data, wherein the real-time environment data comprises environment temperature data, environment humidity data and dust granularity data;
The environment abnormal data acquisition module is used for carrying out abnormal identification on the environment temperature data, the environment humidity data and the dust granularity data to acquire environment abnormal data, wherein the environment abnormal data is the environment temperature data and/or the environment humidity data and/or the dust granularity data;
the fourth execution module is used for acquiring N configuration flows related to the environment abnormal data in the configuration flows based on the environment abnormal data;
and the fifth execution module is used for carrying out data monitoring on the N configuration flows.
Further, the system further comprises:
the instruction acquisition module is used for judging whether the environment abnormal data are output of at least two data types or not, and acquiring an environment variable superposition instruction if the environment abnormal data are output of at least two data types;
the sixth execution module is used for obtaining a first variable superposition configuration flow and b second variable superposition configuration flows according to the environment variable superposition instruction;
The abnormal superposition monitoring module is used for carrying out abnormal superposition monitoring by using a first variable superposition configuration flow and b second variable superposition configuration flows.
Further, the system further comprises:
the control parameter set acquisition module is used for acquiring a simulation control parameter set and a real-time control parameter set of the nutrition configuration robot;
the phase difference control parameter set determining module is used for outputting a phase difference control parameter set according to the simulation control parameter set and the real-time control parameter set;
the control precision index generation module is used for generating a control precision index according to the phase difference control parameter set, wherein the control precision index is used for identifying the accuracy degree of the parameter control executed by the nutrition configuration robot;
the seventh execution module is used for generating a parameter optimization conversion model according to the control precision index;
the optimal control parameter set acquisition module is used for inputting the parameter set to be controlled into the parameter optimal conversion model to obtain an optimal control parameter set.
The nutrition scheme generating system based on the automatic nutrition configuration provided by the embodiment of the invention can execute the nutrition scheme generating method based on the automatic nutrition configuration provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example III
Fig. 4 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 4, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 4, by bus connection is taken as an example.
The memory 32 is a computer readable storage medium that can be used to store software programs, computer executable programs, and modules, such as program instructions/modules corresponding to an automated nutrient profile-based nutrient profile generation method in accordance with an embodiment of the invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e. implements an automated nutritional profile-based nutritional profile generation method as described above.
The application provides a nutrition scheme generation method based on an automatic nutrition configuration, wherein the method is applied to a nutrition scheme generation system based on the automatic nutrition configuration, and the method comprises the following steps of: acquiring diagnosis and treatment data of a user by carrying out data query on a diagnosis and treatment information management system of the user; the diagnosis and treatment data of the user are subjected to feature analysis to obtain diagnosis and treatment features of the user; the control system of the nutrition configuration robot is connected to acquire nutrition configuration flow information; the method comprises the steps of obtaining a controllable parameter index set by carrying out control index analysis on each flow of nutrition configuration flow information; analyzing the control environment of the nutrition-configuring robot to obtain environmental change characteristics; taking the diagnosis and treatment characteristics of a user as an input variable, taking a controllable parameter index set as a response variable, taking the environmental change characteristics as an adjustment variable, taking corresponding index values in the controllable parameter index set as response output, and constructing a configuration control response function; and obtaining a parameter set to be controlled according to the configuration control response function, and controlling the nutrition configuration robot according to the parameter set to be controlled. The technical problems of low quality and efficiency of nutrition configuration, insufficient accuracy of a nutrition scheme generated by the nutrition configuration and low adaptation degree in the prior art are solved. The intelligent and automatic degree of nutrition configuration are improved, the quality and efficiency of nutrition configuration are improved, and the technical effects of accuracy and adaptation of a nutrition scheme generated by the nutrition configuration are improved.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. The method is applied to a nutrition diagnosis and treatment system, and the nutrition diagnosis and treatment system is in communication connection with a user diagnosis and treatment information management system and a nutrition configuration robot, and comprises the following steps:
connecting the user diagnosis and treatment information management system to acquire user diagnosis and treatment data;
performing feature analysis on the user diagnosis and treatment data to obtain user diagnosis and treatment features;
the control system of the nutrition configuration robot is connected to obtain nutrition configuration flow information;
Performing control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set;
analyzing the control environment of the nutrition-configuring robot to obtain environmental change characteristics;
setting up a configuration control response function by taking the diagnosis and treatment characteristics of the user as an input variable, taking the controllable parameter index set as a response variable, taking the environmental change characteristics as an adjustment variable, and taking index values corresponding to the controllable parameter index set as response output;
obtaining a parameter set to be controlled according to the configuration control response function, wherein the parameter set is used for controlling the nutrition configuration robot;
wherein the method further comprises:
performing control index analysis on each process of the nutrition configuration process information to obtain a controllable parameter index set, wherein the controllable parameter index set comprises a dosage control index, a time control index, an injection control index and a mixed control index;
acquiring the dosage control index, the time control index, the injection control index, the mixing control index and the influence coefficient of environmental change to obtain a dosage-environmental influence coefficient, a time-environmental influence coefficient, an injection-environmental influence coefficient and a mixing-environmental influence coefficient;
Generating a feedback adjustment variable layer with the usage-environment influence coefficient, the time-environment influence coefficient, the injection-environment influence coefficient, and the mixed-environment influence coefficient for optimizing the configuration control response function.
2. The method of claim 1, wherein the method further comprises:
screening the user diagnosis and treatment data according to nutrition related indexes to obtain screening diagnosis and treatment data;
determining nutrition configuration powder according to the screening diagnosis and treatment data;
combining the nutrition-configured powder to obtain nutrition-combined powder;
inputting the nutrition combination powder into a control system of the nutrition configuration robot to obtain the nutrition configuration flow information.
3. The method of claim 1, wherein the method further comprises:
performing data monitoring on the process of controlling the nutrition configuration robot to obtain a monitoring data set of each configuration flow;
acquiring preset monitoring indexes of each configuration flow;
performing anomaly identification on the monitoring data sets of each configuration flow according to the preset monitoring indexes to obtain an anomaly data set;
and generating reminding information according to the abnormal data set.
4. A method according to claim 3, wherein the control environment of the nutrition-deployment robot is analyzed for environmental change characteristics, the method further comprising:
acquiring real-time environment data, wherein the real-time environment data comprises environment temperature data, environment humidity data and dust granularity data;
performing anomaly identification on the environmental temperature data, the environmental humidity data and the dust granularity data to obtain environmental anomaly data, wherein the environmental anomaly data is the environmental temperature data and/or the environmental humidity data and/or the dust granularity data;
acquiring N configuration flows related to the environment abnormal data in each configuration flow based on the environment abnormal data;
and carrying out data monitoring on the N configuration flows.
5. The method of claim 4, wherein the method further comprises:
judging whether the environment abnormal data is output of at least two data types, and if the environment abnormal data is output of at least two data types, acquiring an environment variable superposition instruction;
obtaining a first variable superposition configuration flow and b second variable superposition configuration flows according to the environment variable superposition instruction;
And carrying out abnormal superposition monitoring by using the a first variable superposition configuration flows and the b second variable superposition configuration flows.
6. The method of claim 1, wherein the method further comprises:
acquiring a simulation control parameter set and a real-time control parameter set of the nutrition configuration robot;
outputting a phase difference control parameter set according to the simulation control parameter set and the real-time control parameter set;
generating a control precision index according to the phase difference control parameter set, wherein the control precision index is used for identifying the precision degree of parameter control executed by the nutrition configuration robot;
generating a parameter optimization conversion model according to the control precision index;
and inputting the parameter set to be controlled into the parameter optimization conversion model to obtain an optimized control parameter set.
7. A nutritional scheme generation system based on automated nutritional configuration, wherein the system is in communication connection with a user diagnosis and treatment information management system, a nutritional configuration robot, the system comprising:
the user diagnosis and treatment data acquisition module is used for connecting the user diagnosis and treatment information management system to acquire user diagnosis and treatment data;
The user diagnosis and treatment characteristic analysis module is used for carrying out characteristic analysis on the user diagnosis and treatment data to obtain user diagnosis and treatment characteristics;
the nutrition configuration flow information acquisition module is used for connecting a control system of the nutrition configuration robot to acquire nutrition configuration flow information;
the control index analysis module is used for carrying out control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set;
the control environment analysis module is used for analyzing the control environment of the nutrition configuration robot to obtain environment change characteristics;
the function construction module is used for constructing a configuration control response function by taking the diagnosis and treatment characteristics of the user as input variables, taking the controllable parameter index set as response variables, taking the environment change characteristics as adjustment variables and taking corresponding index values in the controllable parameter index set as response output;
the control module is used for controlling a response function according to the configuration to obtain a parameter set to be controlled and controlling the nutrition configuration robot;
The controllable parameter index set determining module is used for carrying out control index analysis on each flow of the nutrition configuration flow information to obtain a controllable parameter index set, wherein the controllable parameter index set comprises a dosage control index, a time control index, an injection control index and a mixed control index;
the environment influence coefficient determining module is used for obtaining the consumption control index, the time control index, the injection control index and the mixed control index and the influence coefficient of environment change, and obtaining a consumption-environment influence coefficient, a time-environment influence coefficient, an injection-environment influence coefficient and a mixed-environment influence coefficient;
and the configuration function optimization module is used for generating a feedback adjustment variable layer according to the consumption-environment influence coefficient, the time-environment influence coefficient, the injection-environment influence coefficient and the mixed-environment influence coefficient and optimizing the configuration control response function.
8. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
A processor for implementing an automated nutritional profile-based nutritional profile generation method according to any one of claims 1 to 6 when executing executable instructions stored in said memory.
9. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a nutrition scheme generation method based on an automated nutrition configuration as claimed in any one of claims 1 to 6.
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| CN1610919A (en) * | 2001-12-28 | 2005-04-27 | 佩尔蒂·莱赫滕迈基 | Method and arrangement for arranging an information service to determine nutrition and/or medication |
| CN103370703A (en) * | 2010-03-08 | 2013-10-23 | 健康看护股份有限公司 | Method and apparatus to monitor, analyze and optimize physiological state of nutrition |
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| EP2819557B1 (en) * | 2012-02-28 | 2020-07-29 | Gudpod Corp. | A nutraceutical compounding system and method therefore |
| RU2689423C1 (en) * | 2018-05-10 | 2019-05-28 | Айрат Мидхатович Ханов | Method for generating recommendations on nutrition of a user based on health thereof |
| CN112270995B (en) * | 2020-10-30 | 2023-07-07 | 连云港市第一人民医院 | Quality control management system and method suitable for traditional Chinese medicine preparation |
| CN114496166B (en) * | 2022-02-16 | 2022-12-20 | 上海楚动智能科技有限公司 | Tumor patient nutrition prescription system |
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| CN1610919A (en) * | 2001-12-28 | 2005-04-27 | 佩尔蒂·莱赫滕迈基 | Method and arrangement for arranging an information service to determine nutrition and/or medication |
| CN103370703A (en) * | 2010-03-08 | 2013-10-23 | 健康看护股份有限公司 | Method and apparatus to monitor, analyze and optimize physiological state of nutrition |
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