CN112905650A - Intelligent recommendation algorithm research based on big data statistics and health report analysis - Google Patents
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Abstract
The invention discloses an intelligent recommendation algorithm research based on big data statistics and health report analysis, which comprises a main server, a data acquisition module, a prediction analysis module, a communication module and an order purchase module; the main server is respectively connected with the data acquisition module, the prediction analysis module, the communication module and the order purchase module; the data acquisition module comprises face recognition, a weight sensor and a camera; the data acquisition module is used for identifying the human face through a human face identification technology. According to the invention, a set of data acquisition scheme based on the Internet of things is researched, so that various behavior data in the dining process can be effectively acquired, offline-mode data can be managed, and a data basis is provided for the follow-up development of dining room dining and user behavior research; through statistics, analysis and modeling of user behavior data, vegetable allocation of a dining room is predicted and customized, intelligent dining allocation is achieved, grain is saved, and waste is reduced.
Description
Technical Field
The invention belongs to the technical field of canteen management, and particularly relates to intelligent recommendation algorithm research based on big data statistics and health report analysis.
Background
The dining room is used as a place for gathering people, and a great deal of data including dining behaviors, information consumption behaviors, interpersonal relationship behaviors and the like are generated in the dining process. These data not only can provide intelligent support for the management in dining room, through the big data analysis of the user meal between the different units of inside moreover, service electric wire netting meal management that can be better, practice thrift grain, reduce extravagant. But a scheme for acquiring the overall dining condition of the dining room and the user behavior data is lacked at present, and the research on a data fusion processing method by establishing a dining room dining and user behavior data model through a multi-source data acquisition technology is needed at present, so that the research on the dining room dining and user behavior big data acquisition and management technology is developed.
After the data are collected, the dining room food preparation and dining optimization management technology based on the big data technology is provided by combining the analysis of user information, the dining room food information and the dining room dining data. The method has the advantages that the information and the dining habits of the dish allocation of the dining room and the dining personnel are mined and analyzed, the dish allocation of the dining room is predicted, intelligent dining allocation is realized, grain is saved, and waste is reduced.
Therefore, we propose intelligent recommendation algorithm research based on big data statistics and statement of health analysis.
Disclosure of Invention
The invention aims to: in order to solve the problem that food is wasted due to the fact that the dish of a dining room is provided with and is not matched with the information of diner and the diner habit, an intelligent food management room management system based on the Internet of things and intelligent prediction recommendation is provided.
The technical scheme adopted by the invention is as follows:
the intelligent recommendation algorithm research based on big data statistics and health report analysis comprises a main server, a data acquisition module, a prediction analysis module, a communication module and an order purchase module;
the main server is respectively connected with the data acquisition module, the prediction analysis module, the communication module and the order purchase module;
the data acquisition module comprises face recognition, a weight sensor and a camera;
the data acquisition module is used for recording the meal taking action of each user through the face identification technology and the change of the weight sensor when the user enters the dish area, and storing the meal taking action;
the prediction analysis module is used for carrying out big data statistical analysis on the original data after the dining behavior data of the user are collected, obtaining information such as the total number of people for each meal, total dish consumption, the peak time of each meal and the like, and carrying out real-time statistics on dining staff in a dining room;
the order purchasing module is used for making ordering information of the canteen food materials according to the analysis structure of the prediction analysis module;
the communication module is used for transmitting the acquired dining information of various dining rooms to the user terminal, so that a manager can control the dining information in real time.
Preferably, the prediction analysis module extracts the dining characteristics of the user by combining the total number of people for each meal and the total weight information of the consumed dishes and the time dimension, finds the association relation among the number of people in the garden, the time, the dishes and the like, and constructs a dining room dining model.
Preferably, the prediction analysis module predicts the dish allocation of the dining room through training and optimization of the dining model, for example, the number of people eating the dining room at the next meal or the next day and the number of dishes to be prepared are predicted, so that intelligent dining allocation is realized, grains are saved, and waste is reduced.
Preferably, after the prediction analysis module collects data of the number of the diners at each meal in the canteen and the total weight of the consumed dishes for a period of time, the number of the diners at each meal and the total weight of the consumed dishes in the future can be predicted by using a time-series-based deep learning prediction model — LSTM (long-short memory network). The periodic rule based on the number of the dining people per week is obtained by learning the influence of the number of the dining people near several days and the data before long on the prediction data, and then a more accurate prediction is obtained.
Preferably, the user terminal is a mobile phone or a computer.
Preferably, the main server is further connected with a display screen, and the display screen is used for displaying canteen dish information, cook making information and raw material purchasing information.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
according to the invention, a set of data acquisition scheme based on the Internet of things is researched, so that various behavior data in the dining process can be effectively acquired, offline-mode data can be managed, and a data basis is provided for the follow-up development of dining room dining and user behavior research; through statistics, analysis and modeling of user behavior data, vegetable allocation of a dining room is predicted and customized, intelligent dining allocation is achieved, grain is saved, and waste is reduced.
Drawings
FIG. 1 is a block diagram of a schematic structure of an intelligent recommendation algorithm study based on big data statistics and statement-of-health analysis;
FIG. 2 is a diagram of the chain structure of the LSTM in the intelligent recommendation algorithm research based on big data statistics and statement-of-health analysis.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention; the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance; furthermore, unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, as they may be fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1-2, the intelligent recommendation algorithm research based on big data statistics and health report analysis includes a main server, a data collection module, a prediction analysis module, a communication module, and an order purchase module;
the main server is respectively connected with the data acquisition module, the prediction analysis module, the communication module and the order purchase module;
the data acquisition module comprises face recognition, a weight sensor and a camera;
the data acquisition module is used for binding the dinner plate and the users one by one through a face recognition technology, then recording the meal taking action of each user according to the dinner plate recognition and the change of the weight sensor in the meal taking process, and storing the meal taking action;
after entering the canteen, the person enters the canteen through the face recognition device. The identity of a person is identified through face recognition in a dish designated area in the dining process so as to collect the following dining data information.
Firstly, different face images such as static images, dynamic images, different positions, different expressions and the like are collected through a camera lens. When a user is in the shooting range of the acquisition equipment, the acquisition equipment can automatically search and shoot a face image of the user, then accurately calibrate the position and size of the face in the image, then carry out preprocessing (gray level correction, noise filtration and the like) and feature extraction (transformation coefficient features, algebraic features and the like) on the detected image, then search and match the extracted feature data of the face image with a feature template stored in a database, and output the result obtained by matching when the similarity exceeds the threshold by setting a threshold.
The prediction analysis module is used for carrying out big data statistical analysis on the original data after the dining behavior data of the user are collected, obtaining information such as the total number of people for each meal, total dish consumption, peak time of each meal and the like, and carrying out real-time statistics on dining staff in a dining room;
the order purchasing module is used for making ordering information of the canteen food materials according to the analysis structure of the prediction analysis module;
the communication module is used for transmitting the acquired dining room dining information to the user terminal, and the user terminal is a mobile phone or a computer, so that a manager can control the dining room dining information in real time.
Preferably, the prediction analysis module extracts the dining characteristics of the user by combining the total number of people for each meal and the total weight information of the consumed dishes and the time dimension, finds the association relation among the number of people in the garden, the time, the dishes and the like, and constructs a dining room dining model.
Preferably, the prediction analysis module predicts the dish allocation of the dining room through the training and optimization of the dining model, for example, the number of people eating the dining room at the next meal or the next day and the number of dishes to be prepared are predicted, so that the intelligent dining allocation is realized, the food is saved, and the waste is reduced.
Preferably, after the prediction analysis module collects data of the number of the diners at each meal in the canteen and the total weight of the consumed dishes for a period of time, the number of the diners at each meal and the total weight of the consumed dishes in the future can be predicted by using a time-sequence-based deep learning prediction model — LSTM (long-short memory network). The periodic rule based on the number of the dining people per week is obtained by learning the influence of the number data of the dining people adjacent to several days and the data before long on the prediction data, and then a more accurate prediction is obtained;
firstly, according to the data of the number of dinning people in the past, the number of dinning people is arranged according to time sequence, then LSTM is input for learning prediction, and the predicted number of dinning people is displayed to a manager. And then, after a new dining behavior occurs, writing new dining number data into the database, and predicting the next time according to the new dining number data. Thus, by using cyclic time series prediction, the latest predicted number of dining people can be displayed to the manager.
Preferably, the main server is further connected with a display screen, and the display screen is used for displaying canteen dish information, cook making information and raw material purchasing information, so that consumers can know the detailed conditions of dining conveniently.
The long-term and short-term memory artificial neural network is a model based on a memory body existing on a recurrent neural network, can remember information for a long time, avoids long-term dependence, and has the capability of autonomous learning. FIG. 2 is a chain structure diagram of LSTM, where σ is the excitation function and i is the forgetting gate, and controlling whether to forget the hidden cell state of the previous layer in LSTM with a certain probability; ii is an input gate responsible for processing the input of the current sequence position; and iii is an output gate. The LSTM relies on these three "gate" structures to allow information to selectively affect each state in the recurrent neural network, enabling the recurrent neural network to retain memory for long periods of time, suitable for processing and predicting relatively long-spaced and delayed events in a time series.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. The intelligent recommendation algorithm research based on big data statistics and health report analysis is characterized by comprising a main server, a data acquisition module, a prediction analysis module, a communication module and an order purchase module;
the main server is respectively connected with the data acquisition module, the prediction analysis module, the communication module and the order purchase module;
the data acquisition module comprises face recognition, a weight sensor and a camera;
the data acquisition module is used for recording the meal taking action of each user according to the face identity identification of the specified dish area and the change of the weight sensor in the meal taking process through face identification and storing the meal taking action;
the prediction analysis module is used for carrying out big data statistical analysis on the original data after the dining behavior data of the user are collected to obtain the total number of people for each meal, total consumed dishes and information of the peak time period of each meal, and carrying out real-time statistics on dining staff in a dining room;
the order purchasing module is used for making ordering information of the canteen food materials according to the analysis structure of the prediction analysis module;
and the communication module is used for transmitting the acquired dining information of various canteens to the user terminal, so that a manager can control the dining information in real time.
2. The intelligent recommendation algorithm study based on big data statistics and statement of health analysis of claim 1, wherein: the prediction analysis module extracts the dining characteristics of the user by combining the total number of people for each meal and the total weight information of the consumed dishes and the time dimension, finds the incidence relation among the number of people in the garden, the time, the dishes and the like, and constructs a dining room dining model.
3. The intelligent recommendation algorithm study based on big data statistics and statement of health analysis of claim 1, wherein: and the prediction analysis module predicts the dish allocation of the dining hall through the training and optimization of the dining model.
4. The intelligent recommendation algorithm study based on big data statistics and statement of health analysis of claim 1, wherein: after the data of the number of the dining people and the total weight of the consumed dishes of each meal in the dining room in a period of time are collected, the prediction analysis module predicts the number of the dining people and the total weight of the consumed dishes of each meal in the future based on a time sequence deep learning prediction model.
5. The intelligent recommendation algorithm study based on big data statistics and statement of health analysis of claim 1, wherein: the user terminal is a mobile phone or a computer.
6. The intelligent recommendation algorithm study based on big data statistics and statement of health analysis of claim 1, wherein: still include the display screen, the display screen is used for showing dining room vegetable information, chef's information of making and raw and other materials purchase information.
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Cited By (3)
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| CN113255596A (en) * | 2021-06-29 | 2021-08-13 | 中运科技股份有限公司 | Intelligent video image analysis system and method based on big data |
| CN113673960A (en) * | 2021-08-26 | 2021-11-19 | 泉州市灵动信息科技有限公司 | Wisdom dining room service system based on wisdom cloud and AI technique |
| CN114926525A (en) * | 2022-05-17 | 2022-08-19 | 中国科学院地理科学与资源研究所 | Food waste assessment method and system based on image method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113255596A (en) * | 2021-06-29 | 2021-08-13 | 中运科技股份有限公司 | Intelligent video image analysis system and method based on big data |
| CN113673960A (en) * | 2021-08-26 | 2021-11-19 | 泉州市灵动信息科技有限公司 | Wisdom dining room service system based on wisdom cloud and AI technique |
| CN114926525A (en) * | 2022-05-17 | 2022-08-19 | 中国科学院地理科学与资源研究所 | Food waste assessment method and system based on image method |
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