CN119228075A - Canteen management method and system based on big data, Internet of Things and image analysis - Google Patents
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Abstract
The invention relates to the technical field of canteen management and discloses a canteen management method and system based on big data, internet of things and image analysis, wherein the canteen management method comprises the steps of collecting, preprocessing and storing link data of a canteen dining area and a dining area in real time, wherein the link data comprises image information and non-image information; and carrying out data analysis and image analysis on the link data after preprocessing, predicting the number of dining personnel, the identity information of the dining personnel and the dining dish information in a future period of time, automatically managing the operation of the canteen by combining a preset management strategy, identifying abnormal actions of the working personnel and the dining personnel and possible faults of equipment, and carrying out alarm and/or parameter adjustment after identifying the abnormal actions and/or faults. The method and the system can improve the management efficiency.
Description
Technical Field
The invention relates to the technical field of canteen management, in particular to a canteen management method and system based on big data, internet of things and image analysis.
Background
In modern canteen management, traditional management methods rely primarily on manual operations and empirical decisions. Common practices include manually recording the number of people eating each day, purchasing food according to historical data or predictions, managing inventory using manual inventory, and the like. Although these methods meet the basic operational needs of canteens to some extent, these conventional methods are becoming increasingly inadequate as canteens expand in size and users' needs become diversified.
At present, some canteens have begun to introduce informationized management systems, such as data acquisition through bar code scanning and electronic menus, but most of these systems operate independently, have limited data analysis capability, and are difficult to provide comprehensive intelligent management support. Meanwhile, the existing canteen management system can only process limited data types, is difficult to integrate information from various sources, such as dining preference of users, real-time inventory change, equipment running state and the like, and has the following defects:
The data collection is not comprehensive, and the traditional canteen management method cannot collect and analyze dining behavior and preference data of the user comprehensively and in real time. Generally, the daily meal number and the food material demand can be estimated only through historical data or manual investigation, and the actual change is difficult to flexibly cope with.
The management efficiency is low, and because the management work depends on manual operation, the efficiency is low, statistical errors are easy to occur, and excessive purchasing or insufficient supply of food materials are caused. In addition, manual decisions are also difficult to quickly respond to an emergency, such as a sudden increase in the number of people having a meal or equipment failure.
The intelligent monitoring is lacking, and in the aspect of food safety management, the prior art mainly relies on manual inspection, so that the kitchen operation environment and the equipment operation state are difficult to monitor in real time. This can lead to hygiene hazards during food storage and processing, increasing food safety risks.
Disclosure of Invention
The invention provides a canteen management method and a canteen management system based on big data, internet of things and image analysis, which realize the intellectualization, automation and refinement of canteen management by integrating big data technology, internet of things equipment and image analysis technology. Through real-time data acquisition and analysis, the invention can effectively improve the management efficiency of the canteen, reduce the waste of food materials, improve the dining experience of users and enhance the monitoring capability of food safety.
The invention is realized by the following technical scheme:
a canteen management method based on big data, internet of things and image analysis comprises the following steps:
acquiring, preprocessing and storing link data of a dining area and a meal preparation area of a dining hall in real time, wherein the link data comprises image information and non-image information;
And carrying out data analysis and image analysis on the link data after preprocessing, predicting the number of dining personnel, the identity information of the dining personnel and the dining dish information in a future period of time, automatically managing the operation of the canteen by combining a preset management strategy, identifying abnormal actions of the working personnel and the dining personnel and possible faults of equipment, and carrying out alarm and/or parameter adjustment after identifying the abnormal actions and/or faults.
As optimization, the link data comprise the temperature and humidity of the dining area and the meal preparation area, the dining records of the dining staff, the number of the dining staff, the stock quantity of the food materials, the state of kitchen equipment and the operation actions of the staff, wherein the dining records of the dining staff comprise the identity information of the dining staff, the dining time and the dining menu.
As optimization, preprocessing the link data specifically includes cleaning missing and erroneous link data by using a big data technology.
As optimization, carrying out data analysis and image analysis on the link data after pretreatment, and predicting the number of dining personnel, dining time, identity information of the dining personnel and dining dish information in a future period of time, thereby combining a preset management strategy to automatically manage the canteen operation, wherein the specific process comprises the following steps:
establishing an identity behavior recognition model based on a deep learning algorithm, and training the identity behavior recognition model through an improved dung beetle algorithm, so that the image information is input into the identity behavior recognition model to recognize the identity information and behavior information of dining personnel and staff;
Comprehensively analyzing the number of the historical dining staff, the dining time, the identity information of the dining staff and the dining dish information based on a big data technology, and predicting the number of the dining staff, the dining time, the identity information of the dining staff and the dining dish information in a future period;
Calculating the dish usage amount of the future period according to the predicted number of the dining staff of the future period and the dish information of the dining;
Adjusting a purchasing plan according to the food material stock quantity and the dish use quantity in a future period;
and judging whether the humiture of the dining area and the meal preparation area exceeds a set threshold value.
As optimization, the identity behavior recognition model takes the maximum sum of the similarity between actual identity information of the diner and the staff and the predicted identity information of the diner and the staff and the similarity between the actual behavior information of the diner and the staff and the predicted behavior information of the diner and the staff as an objective function, and takes one group of combined values of all parameters of the identity behavior recognition model as a position where a dung beetle is located, and the specific process of training the identity behavior recognition model through an improved dung beetle algorithm is as follows:
s1, initializing parameters of a dung beetle population position and a dung beetle algorithm, wherein the parameters of the dung beetle algorithm comprise mapping parameters Iteration times;
S2, calculating an adaptability value of each dung beetle, wherein the adaptability value is the similarity between actual dining data and predicted dining data;
S3, updating a dung beetle passing mode I with the fitness value smaller than a first threshold, updating a dung beetle passing mode II with the fitness value not smaller than the first threshold and smaller than a second threshold, and updating a dung beetle passing mode III with the fitness value not smaller than the second threshold;
S4, updating positions of dancing dung beetles, foraging dung beetles, stealing dung beetles and breeding dung beetles, and judging whether each dung beetle is out of boundaries;
s5, recalculating the fitness value of the current dung beetles, and updating the dung beetle population according to the ranking of the fitness value, so that the optimal position, namely the optimal solution, is obtained;
And S6, judging whether the maximum iteration times are reached, if so, outputting the current updated dung beetle population to obtain each optimal parameter in the identity behavior recognition model, and otherwise, repeating the steps S3-S6.
As optimization, initializing the position of the dung beetle population is specifically performed by the following formula:
Generating a chaotic sequence:
;
Wherein, For the state value of the i-th generation,Is the firstIs used to determine the state value of (1),Is a random number of 0 to 1,;In order to map the parameters of the data,;
According toThe obtained population initialization positions are as follows:
;
Wherein, For the initial position of the ith dung beetle,To optimize the lower bound of the problem search space in the dung beetle algorithm,The upper limit of the problem search space is optimized in the dung beetle algorithm.
As optimization, in S3, the formula corresponding to the pattern is:
;
the formula corresponding to the mode two is as follows:
;
the formula corresponding to the mode III is as follows:
;
Wherein N is the number of dung beetles, Indicating the fitness ranking of the ith dung beetle after the t-th updating, wherein the fitness ranking is carried out according to the descending order,For the current best position updated for the t time, i.e. the current local best position,Indicating the position of the ith dung beetle after the t-th updating,Indicating the position of the ith dung beetle after t+1th updating,Taking-1 or 1 as natural coefficient, wherein-1 represents deviation from original direction, 1 represents no deviation,Is a random number of (a) and (b),As a current lower limit of the number of times,Represents the current upper bound, m is a random floating point number, and,Random floating point numbers between-1 and 1.
The invention also discloses a canteen management system based on big data, the Internet of things and image analysis, so as to implement the canteen management method based on the big data, the Internet of things and the image analysis, which comprises the following steps:
the data acquisition module is used for acquiring data of each link of the dining area and the meal preparation area of the dining hall;
The data preprocessing module is used for cleaning each link data by utilizing a big data technology so as to obtain accurate link data;
The image analysis module is used for analyzing the image information so as to obtain the state of kitchen equipment, the identity information of the diner, the diner menu of the diner, the identity information of the staff and the operation action, and meanwhile, the abnormal actions of the staff and the diner are identified;
The big data analysis module is used for analyzing the non-image information so as to obtain the temperature and humidity of the dining area and the spare dining area, the state of kitchen equipment and the number of dining personnel and dining time, predicting possible faults of the equipment according to the state of the kitchen equipment, and predicting the number of dining personnel, dining time, identity information of the dining personnel and dining dish information in a future period based on a big data analysis technology, so that the number of dining personnel, dining time, identity information of the dining personnel and dining dish information in the future period are predicted as prediction results;
the intelligent decision control module is used for adjusting according to the prediction result and combining with a preset management strategy so as to obtain decision data;
the data storage module is used for storing the data of the intelligent decision control module and the data preprocessing module;
The communication module is used for sending the obtained decision data to a manager, and sending the user data stored by the data storage module and menu information of a future period of time to a diner, wherein the user data comprises the historical diner time, the amount spent by the historical diner and the dish information of the historical diner;
The user interface feedback module is used for receiving feedback and comments of the diners, and meanwhile, the staff enter the storage module through the user interface feedback module to check the stored data;
And the alarm module is used for alarming when the temperature and humidity of the dining area and the meal preparation area, the stock quantity of food materials, the state of kitchen equipment and the operation actions of staff and dining staff are abnormal.
As optimization, the data acquisition module comprises an intelligent meal selling machine, a temperature sensor, a humidity sensor, a camera, an RFID tag and a management computer.
As an optimization, the image analysis module identifies abnormal actions of staff and diners from the image information based on a deep learning algorithm.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the management efficiency is improved, namely, a plurality of links (such as data acquisition, analysis and decision making) of canteen management are automated by utilizing the Internet of things and big data technology, so that manual intervention is reduced, and the overall management efficiency is improved. For example, the system can automatically generate a purchasing plan and adjust a menu in real time, so that the complexity of manual operation is reduced, and the system can rapidly respond to emergency situations such as dining number change, equipment failure and the like through real-time data acquisition and analysis, so that the stability and the flexibility of the operation of the canteen are ensured.
2. The system can predict the food material requirement more accurately, reduce stock backlog and food material waste, improve the resource utilization rate, and optimize the equipment use, reduce the energy consumption and the operation cost by monitoring the operation state of kitchen equipment in real time.
3. The invention monitors the whole food processing process in real time by an image analysis technology, ensures that the food processing accords with the safety standard, and reduces the food safety risk; the system can automatically detect abnormal behaviors or equipment faults and immediately send out early warning to prevent potential food safety problems.
4. The system can provide personalized dish recommendation and service through analyzing the dining behaviors and preferences of the user, meet diversified demands of the user and promote user satisfaction, can accelerate the dining settlement process through an image recognition technology, reduce queuing time and provide more convenient dining experience for the user.
5. The system can optimize inventory management, reduce food waste and excessive purchasing, remarkably reduce operation cost, and can carry out predictive maintenance by monitoring the state of equipment in real time, thereby avoiding maintenance cost and shutdown loss caused by equipment sudden faults.
6. The system provides a comprehensive data analysis report and intelligent decision advice for a canteen manager, thereby providing scientific decision basis, helping the canteen manager optimize management strategy and improving operation efficiency and economic benefit.
7. The system architecture is flexible in design and easy to expand and upgrade. Whether a new functional module is added or a new Internet of things device is accessed, the system can be easily adapted to the canteen management of different scales and demands, and through continuously optimizing resource utilization and improving management efficiency, the system can generate remarkable economic and environmental benefits in long-term operation and promote sustainable development of canteens.
8. The management level of the canteen is improved, each link of canteen management is integrated into an intelligent platform, systemization and unification of management are realized, the overall management level is improved, and a manager can clearly know the operation condition of the canteen through the data visualization function of the system, so that the management and strategy adjustment are convenient.
9. The invention fully utilizes the front technologies such as big data, the Internet of things, image analysis and the like, promotes the innovation and modernization of the traditional canteen management mode, and improves the technical level of the whole industry.
In summary, by introducing an intelligent and automatic management method, the intelligent and automatic management system not only effectively improves the operation efficiency and service quality of the canteen, but also brings remarkable positive effects in the aspects of resource optimization, food safety guarantee, user experience improvement and the like, and provides a comprehensive and advanced solution for modern canteen management.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
fig. 1 is a module connection diagram of a canteen management system based on big data, internet of things and image analysis according to the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
An intelligent canteen management method based on big data, internet of things and image analysis provides a set of comprehensive system architecture and operation flow, and aims to realize real-time monitoring, data analysis and intelligent decision on each link of the canteen. By introducing big data, the Internet of things and image analysis technologies, the problems of low efficiency, incomplete data, resource waste, inadequacy of food safety monitoring and the like in the traditional canteen management method are solved, the goals of improving data acquisition and analysis capability, improving management efficiency, optimizing resource utilization, enhancing food safety monitoring, improving user experience and the like are realized, the intellectualization, refinement and safety of canteen management are realized, and the operation efficiency and service quality of canteens are comprehensively improved.
The following is a detailed description of the technical scheme:
the embodiment 1 provides a canteen management method based on big data, internet of things and image analysis, which comprises the following steps:
the method comprises the steps of T1, collecting, preprocessing and storing link data of dining areas and dining areas in real time, wherein the link data comprise image information and non-image information;
firstly, link data are collected in real time.
The system (the composition of the system will be specifically described later) collects various data through the internet of things equipment and the sensors.
In some embodiments, the link data includes temperature and humidity of the dining area and the meal preparation area, meal records of the dining personnel, number of the dining personnel, food material inventory, kitchen equipment status and operation actions of the staff, and the meal records of the dining personnel include identity information of the dining personnel, meal time and meal menus.
Here, the humiture and the stock quantity of the food in the dining area and the meal preparation area are non-image information, the humiture can be obtained according to a humiture sensor, and the stock quantity of the food can be input into a management computer through a manager.
Dining records of dining staff, number of the dining staff and operation movements of the staff are used as image information.
It should be noted that the kitchen device state may include both image information and non-image information, for example, an external phenomenon of the kitchen device captured by the camera is image information, and usage data of the kitchen device obtained by an internal sensor of the kitchen device is non-image information.
And secondly, preprocessing link data.
In some embodiments, preprocessing link data is specifically to clean missing and erroneous link data using big data techniques. Such as denoising, filtering, normalizing, etc., ensures the quality and consistency of the data.
And T2, carrying out data analysis and image analysis on the preprocessed link data, predicting the number of dining staff, dining time, identity information of the dining staff and dining dish information in a future period, automatically managing the canteen operation by combining a preset management strategy, identifying abnormal actions of the staff and the dining staff and possible faults of equipment, and carrying out alarm and/or parameter adjustment after identifying the abnormal actions and/or faults.
Through the internet of things and image analysis technology, the system can monitor key links in canteen operation in real time, such as food processing process, equipment state, user flow and the like. Upon detection of an anomaly (e.g., temperature exceeding, equipment failure, user behavior anomaly, etc.), the system will immediately issue an early warning and automatically take preset action, such as notifying a manager or directly controlling equipment shutdown.
The system can analyze the historical data and the real-time data regularly to generate various reports of canteen operation, such as user preference analysis, dining peak prediction, food material consumption prediction and the like. The intelligent decision module automatically adjusts the operation strategy based on the analysis results, such as dynamically adjusting menus, optimizing purchasing and inventory management, customizing user services, and the like.
The system collects user feedback and opinion through the user interface and incorporates these feedback into the data analysis, constantly optimizing management policies and quality of service. Meanwhile, the system can provide personalized services, such as recommending dishes, optimizing dining time and the like, according to the historical data and preferences of the user.
Next, the implementation procedure of the above-described functions will be specifically described.
In some embodiments, the pre-processed link data is subjected to data analysis and image analysis, and the number of dining personnel, the dining time, the identity information of the dining personnel and the dining dish information in a future period of time are predicted, so that the specific process of automatically managing the canteen operation by combining a preset management strategy is as follows:
establishing an identity behavior recognition model based on a deep learning algorithm, and training the identity behavior recognition model through an improved dung beetle algorithm, so that the image information is input into the identity behavior recognition model to recognize the identity information and behavior information of dining personnel and staff;
Comprehensively analyzing the number of the historical dining staff, the dining time, the identity information of the dining staff and the dining dish information based on a big data technology, and predicting the number of the dining staff, the dining time, the identity information of the dining staff and the dining dish information in a future period;
Calculating the dish usage amount of the future period according to the predicted number of the dining staff, dining time and dining dish information of the future period;
adjusting a purchasing plan according to the stock quantity of the food materials and the use quantity of dishes for a period of time in the future;
and judging whether the humiture of the dining area and the meal preparation area exceeds a set threshold value.
In some embodiments, the identity behavior recognition model takes the maximum sum of the similarity between the actual identity information of the diner and the staff and the predicted identity information of the diner and the staff and the similarity between the actual behavior information of the diner and the staff and the predicted behavior information of the diner and the staff as an objective function, and takes one group of combined values of each parameter of the identity behavior recognition model as the position of a dung beetle, and the specific process of training the identity behavior recognition model through an improved dung beetle algorithm is as follows:
S1, initializing parameters of a dung beetle population position and a dung beetle algorithm, wherein the parameters of the dung beetle algorithm comprise mapping parameters Iteration times;
In some embodiments, initializing the position of the dung beetle population is specifically performed by the following formula:
Generating a chaotic sequence:
;
Wherein, For the state value of the i-th generation,Is the firstIs used to determine the state value of (1),Is a random number of 0 to 1,;In order to map the parameters of the data,;
According toThe obtained population initialization positions are as follows:
;
Wherein, For the initial position of the ith dung beetle,To optimize the lower bound of the problem search space in the dung beetle algorithm,The upper limit of the problem search space is optimized in the dung beetle algorithm.
The wonton mapping can be used for generating a highly diversified initial population, so that the diversity of initial solutions is improved.
S2, calculating the fitness value of each dung beetle, wherein the fitness value is the similarity between actual dining data and predicted dining data;
S3, updating a dung beetle passing mode I with the fitness value smaller than a first threshold value, updating a dung beetle passing mode II with the fitness value not smaller than the first threshold value and smaller than a second threshold value, and updating a dung beetle passing mode III with the fitness value not smaller than the second threshold value;
In some embodiments, the mode-one corresponding formula is:
;
The formula corresponding to the mode two is:
;
The formula corresponding to the mode three is:
;
Wherein N is the number of dung beetles, Indicating the fitness ranking of the ith dung beetle after the t-th updating, wherein the fitness ranking is carried out according to the descending order,For the current best position updated for the t time, i.e. the current local best position,Indicating the position of the ith dung beetle after the t-th updating,Indicating the position of the ith dung beetle after t+1th updating,Taking-1 or 1 as natural coefficient, wherein-1 represents deviation from original direction, 1 represents no deviation,Is a random number of (a) and (b),As a current lower limit of the number of times,Represents the current upper bound, m is a random floating point number, and,Random floating point numbers between-1 and 1.
By setting the first threshold and the second threshold, different updating formulas are provided for different conditions, so that the current position information is fully utilized, random interference information is added, local optimization of the position jump of the dung beetles is facilitated, global search and optimization of individual dung beetles are performed in a given area range, and the optimizing capability of a dung beetle algorithm is improved. In this embodiment, the first threshold is 0.312, and the second threshold is 0.698.
S4, updating positions of dancing dung beetles, foraging dung beetles, stealing dung beetles and breeding dung beetles, and judging whether each dung beetle is out of boundaries;
the update formulas of dancing dung beetles, foraging dung beetles, stealing dung beetles and breeding dung beetles, namely the update formulas of the existing dung beetle algorithm, are not repeated here.
S5, recalculating the fitness value of the current dung beetles, and updating the dung beetle population according to the ranking of the fitness value, so that the optimal position, namely the optimal solution, is obtained;
And S6, judging whether the maximum iteration times are reached, if so, outputting the current updated dung beetle population to obtain each optimal parameter in the identification model by using the identity behavior, otherwise, repeating the steps S3-S6.
Next, a system used in the present invention will be specifically described.
Examples
A canteen management system based on big data, internet of things and image analysis, as shown in fig. 1, to implement a canteen management method based on big data, internet of things and image analysis of the foregoing embodiment, comprising:
data acquisition and preprocessing, data analysis and intelligent decision making, image analysis and monitoring, user interface and feedback.
Data acquisition and pretreatment:
the data acquisition module is used for acquiring data of each link of the dining area and the meal preparation area of the dining hall;
in some embodiments, the data acquisition module includes a smart meal dispenser, a temperature sensor, a humidity sensor, a camera, an RFID tag.
Based on the internet of things, intelligent equipment (such as intelligent meal selling machine, temperature sensor, camera, RFID tag and the like) installed in each link of the canteen collects data in real time. Such data includes, but is not limited to, a user's meal records, food inventory, kitchen equipment status, temperature and humidity of the food processing process, and the like.
The method comprises the steps of firstly, deploying the Internet of things equipment, wherein the Internet of things equipment comprises a temperature sensor, a humidity sensor, a camera, an RFID tag and the like in each key link of a canteen. These devices collect in real time dining data of the user, food stock conditions, kitchen environment data (such as temperature, humidity), device operation status, etc.
And the Internet of things and data fusion are realized by the Internet of things equipment, so that the data acquisition of the physical world is realized, and multidimensional data (such as temperature, humidity, images and the like) are fused into a unified database. The system forms a comprehensive understanding of canteen operation through a correlation analysis of these data.
The data storage module is used for storing the data of the intelligent decision control module and the data preprocessing module;
The communication module is used for sending the acquired decision data to a manager, and simultaneously sending the user data stored by the data storage module and menu information of a period of time in the future to a diner, wherein the user data comprises the historical diner time, the amount spent by the historical diner, the historical diner time and the menu information of the historical diner of the user;
The method is used for data transmission and storage, and all acquired data are transmitted to a central data processing system through a wireless network. In order to ensure the stability and security of data transmission, encryption protocols and redundancy mechanisms are employed. The data storage adopts a distributed database technology, and supports the storage and quick retrieval of large-scale data.
The data preprocessing module is used for cleaning all the link data by utilizing a big data technology so as to obtain accurate link data, and cleaning, storing, analyzing and modeling the data by utilizing the big data technology.
The method is used for preprocessing data, namely preprocessing the original data before storage, such as data cleaning (removing noise and invalid data), data formatting, data normalization and the like, so as to ensure the quality and consistency of the data.
Data analysis and intelligent decision:
the big data analysis module is used for analyzing the non-image information so as to obtain the temperature and humidity of the dining area and the meal preparation area, the kitchen equipment state and the number of the dining personnel, predicting possible faults of the equipment through the kitchen equipment state, and predicting the number of the dining personnel, the dining time, the identity information of the dining personnel and the dining dish information in a future period based on the big data analysis technology, so that the number of the dining personnel, the dining time, the identity information of the dining personnel and the dining dish information in the future period are predicted as prediction results;
The method is to comprehensively analyze the acquired mass data by combining the large data technology with the image data. The analysis content comprises user dining behavior analysis, food material consumption prediction, inventory optimization, equipment state evaluation, environment monitoring data analysis and the like. The module supports real-time data processing and historical data analysis, helping the manager make data-based decisions such as optimizing menus, adjusting purchasing plans, predicting the number of people eating, etc.
And a big data processing frame such as Hadoop and Spark is adopted to perform distributed processing on mass data, so as to mine hidden modes and trends in the data. These analysis results are used to support intelligent decisions for the system.
User behavior analysis, namely, by analyzing dining time, dish selection, consumption amount and the like of a user, the system can identify diet preference of the user so as to optimize menu configuration and service strategies.
And the food consumption prediction is based on historical data and real-time data, so that the system can predict the food consumption in a period of time in the future, help a manager optimize a purchasing plan and reduce waste.
Inputting the number of the dining personnel on the same day, the identity information of the dining personnel on the same day, the total dining dish information on the same day and the number of the history dining personnel on N days before the same day, the identity information of the history dining personnel and the history total dining dish information into a trained identity behavior recognition model, and predicting the number of the dining personnel, the identity information of the dining personnel and the dining dish information for a period of time in the future.
And (3) evaluating the state of the equipment, namely, by analyzing the operation data of the equipment, the system can predict the possible faults of the equipment in advance, carry out preventive maintenance and avoid operation interruption caused by the sudden faults of the equipment.
And the alarm module is used for alarming when the temperature and humidity of the dining area and the meal preparation area, the stock quantity of food materials, the state of kitchen equipment and the operation actions of staff and dining staff are abnormal.
And combining the result of big data analysis, the system can automatically generate the operation strategy of the canteen. The main functions include:
And optimizing the menu, namely automatically adjusting the menu by the system according to preference analysis and food consumption prediction of the user, and providing the most popular dishes meeting the health requirements.
Inventory management, namely automatically generating a purchasing plan based on consumption prediction and inventory data, and monitoring the inventory in real time to avoid the problem of backout or expiration.
And equipment control, namely according to equipment state evaluation, the system can automatically adjust the operation parameters of equipment or automatically stop or alarm when necessary.
Image analysis and monitoring:
The image analysis module is used for analyzing the image information so as to obtain the state of kitchen equipment, the identity information of the diner, the diner menu of the diner, the identity information of the staff and the operation action, and meanwhile, the abnormal actions of the staff and the diner are identified;
the image analysis module identifies abnormal actions of the staff and the diners from the image information based on a deep learning algorithm.
In some embodiments, an identity behavior recognition model is established based on a deep learning algorithm, and the identity behavior recognition model is trained through an improved dung beetle algorithm, so that the image information is input into the identity behavior recognition model to recognize identity information and behavior information of dining personnel and staff;
In some embodiments, the identity behavior recognition model takes the maximum sum of the similarity between the actual identity information of the diner and the staff and the predicted identity information of the diner and the staff and the similarity between the actual behavior information of the diner and the staff and the predicted behavior information of the diner and the staff as an objective function, and takes one group of combined values of each parameter of the identity behavior recognition model as the position of a dung beetle, and the specific process of training the identity recognition model through an improved dung beetle algorithm is as follows:
S1, initializing parameters of a dung beetle population position and a dung beetle algorithm, wherein the parameters of the dung beetle algorithm comprise mapping parameters Iteration times;
In some embodiments, initializing the position of the dung beetle population is specifically performed by the following formula:
Generating a chaotic sequence:
;
Wherein, For the state value of the i-th generation,Is the firstIs used to determine the state value of (1),Is a random number of 0 to 1,;In order to map the parameters of the data,;
According toThe obtained population initialization positions are as follows:
;
Wherein, For the initial position of the ith dung beetle,To optimize the lower bound of the problem search space in the dung beetle algorithm,The upper limit of the problem search space is optimized in the dung beetle algorithm.
The wonton mapping can be used for generating a highly diversified initial population, so that the diversity of initial solutions is improved.
S2, calculating the fitness value of each dung beetle, wherein the fitness value is the similarity between actual dining data and predicted dining data;
S3, updating a dung beetle passing mode I with the fitness value smaller than a first threshold value, updating a dung beetle passing mode II with the fitness value not smaller than the first threshold value and smaller than a second threshold value, and updating a dung beetle passing mode III with the fitness value not smaller than the second threshold value;
In some embodiments, the mode-one corresponding formula is:
;
The formula corresponding to the mode two is:
;
The formula corresponding to the mode three is:
;
Wherein N is the number of dung beetles, Indicating the fitness ranking of the ith dung beetle after the t-th updating, wherein the fitness ranking is carried out according to the descending order,For the current best position updated for the t time, i.e. the current local best position,Indicating the position of the ith dung beetle after the t-th updating,Indicating the position of the ith dung beetle after t+1th updating,Taking-1 or 1 as natural coefficient, wherein-1 represents deviation from original direction, 1 represents no deviation,Is a random number of (a) and (b),As a current lower limit of the number of times,Represents the current upper bound, m is a random floating point number, and,Random floating point numbers between-1 and 1.
By setting the first threshold and the second threshold, different updating formulas are provided for different conditions, so that the current position information is fully utilized, random interference information is added, local optimization of the position jump of the dung beetles is facilitated, global search and optimization of individual dung beetles are performed in a given area range, and the optimizing capability of a dung beetle algorithm is improved. In this embodiment, the first threshold is 0.312, and the second threshold is 0.698.
S4, updating positions of dancing dung beetles, foraging dung beetles, stealing dung beetles and breeding dung beetles, and judging whether each dung beetle is out of boundaries;
the update formulas of dancing dung beetles, foraging dung beetles, stealing dung beetles and breeding dung beetles, namely the update formulas of the existing dung beetle algorithm, are not repeated here.
S5, recalculating the fitness value of the current dung beetles, and updating the dung beetle population according to the ranking of the fitness value, so that the optimal position, namely the optimal solution, is obtained;
And S6, judging whether the maximum iteration times are reached, if so, outputting the current updated dung beetle population to obtain each optimal parameter in the identification model by using the identity behavior, otherwise, repeating the steps S3-S6.
Image acquisition and processing, namely acquiring image data of dining areas and kitchens in real time by a system through high-definition cameras deployed in canteens. These image data are used for user identification, queuing people statistics, dining behavior analysis, kitchen operation monitoring, etc.
The module is used for user identity recognition, queuing people counting, dining behavior analysis, kitchen operation monitoring and the like. Using a deep learning algorithm, the module may identify abnormal behavior, such as non-specified food processing, abnormal operation of equipment, etc.
User identity recognition, namely, through image analysis and facial recognition technology, the system can automatically recognize the identity of the user, and the functions of automatic settlement, personalized service pushing and the like are realized. At the same time, the identification technique may prevent unauthorized persons from entering the canteen or kitchen area.
And the behavior analysis and monitoring, namely, by utilizing a deep learning algorithm, the system can analyze dining behaviors of the user, such as dining time, queuing time, dining habit and the like, and help optimize the layout and service flow of the canteen.
Food safety monitoring, namely, in a kitchen area, the system monitors the food processing process in real time through image analysis, and detects whether the behavior violating the operation rules exists, such as irregular sanitary operation, equipment is not used according to regulations and the like. Upon detection of an anomaly, the system immediately alerts and informs the relevant manager.
The intelligent decision control module is used for adjusting according to the prediction result and combining with a preset management strategy so as to obtain decision data;
The module combines the data analysis result and a preset management strategy to automatically manage the canteen operation. For example, the supply of dishes is adjusted according to the number of people having meals in real time, a purchasing plan is automatically generated according to the stock condition, or an alarm is sent out according to the monitoring result of the kitchen environment and corresponding measures are taken.
The system feeds back the analysis and monitoring results to the control module to realize automatic control of the equipment, such as temperature adjustment, equipment starting or stopping, alarm giving and the like. Such a closed-loop control system ensures operational efficiency and safety of the canteen.
User interface feedback:
The user interface feedback module is used for receiving feedback and comments of the diners, enabling the staff to enter the storage module through the user interface feedback module to view the stored data, and providing an interactive interface for canteen management staff and users, including management background, mobile application and the like. The manager can view the data in real time, set management strategies, receive system suggestions and the like through the interface, and the user can view the personal dining record, acquire personalized recommendations, participate in canteen management feedback and the like through the mobile application.
And the management background provides a comprehensive management platform for canteen managers, and displays real-time data, analysis reports and system suggestions. The manager can check various operation indexes of the canteen through the background, adjust the management strategy, check the alarm record and carry out system maintenance.
The user can view the dining record of the individual, obtain personalized dish recommendation, participate in feedback investigation, reserve seats and the like through the mobile application. The system continuously optimizes the quality of service based on user feedback.
Thus, in brief, the implementation of the invention is as follows:
1. data acquisition process
And the Internet of things equipment (data acquisition module) in the canteen acquires data in real time and uploads the data to the server.
The data preprocessing module cleans, formats and stores the data.
2. Data analysis flow
The big data analysis module performs real-time and batch processing analysis on the data to generate a user behavior analysis report, food material consumption prediction, equipment state evaluation and the like.
3. Decision and execution flow
And the intelligent decision control module automatically generates and executes an operation strategy according to the analysis result.
The system monitors the execution effect in real time and adjusts the strategy according to feedback.
4. Image processing and monitoring flow
The camera (data acquisition module) acquires images in real time and transmits the images to the image analysis module.
The image analysis module performs user identification, behavior analysis and food safety monitoring.
5. User feedback and optimization flow
The user provides feedback through the mobile application (user interface feedback module) and the system gathers and analyzes the feedback.
And according to the feedback result, the system adjusts the operation strategy and the service quality.
To ensure the efficiency and reliability of the system, the following are implementations of this patent:
and optimizing the system architecture, namely adopting a distributed architecture to ensure the expandability and high availability of the system. The data processing part adopts a cloud computing technology and supports the parallel processing of large-scale data.
And the hardware configuration is that high-performance Internet of things equipment and a high-definition camera are selected, so that the accuracy of data acquisition and the definition of image processing are ensured. The server configures the high performance CPU/GPU and mass storage to meet the needs of real-time data processing.
And (3) algorithm optimization, namely adopting deep learning and machine learning algorithms in image processing and big data analysis to improve the recognition accuracy and analysis efficiency of the system. Model updating and algorithm optimization are carried out regularly, so that the system is always in an optimal state.
User experience optimization, namely a simple and visual user interface for a user, and convenience for the user and a manager to operate. The mobile application supports personalized pushing and real-time interaction, and user experience and viscosity are enhanced.
The security guarantee that the system is provided with a strict data encryption and authority management mechanism to ensure the security of user data and system operation. The canteen equipment has an emergency treatment function, and can be automatically switched to a manual operation mode when the system fails, so that the continuity of operation is ensured.
Through these technical content and realization mode, this patent can promote the intelligent level of dining room management by a wide margin, realizes operation more high-efficient, safer, more user-friendly.
The method is based on the real-time data transmission technology between the devices of the Internet of things and between the devices and the central system, ensures the rapid and reliable transmission of data, and performs instant processing (such as cleaning and formatting) on the acquired data so as to ensure the accuracy and usability of the acquired data.
According to the invention, the historical data and the real-time data are comprehensively analyzed in the canteen management by the big data technology, and the dining behavior, the food material consumption mode, the equipment operation rule and the like of the user are mined, so that an intelligent decision algorithm and method for optimizing management are generated.
According to the invention, real-time images of dining areas and kitchens are collected through cameras arranged in the dining room, and image analysis is performed by using a deep learning algorithm. The method specifically comprises the steps of user identification, queuing people counting, dining behavior analysis, kitchen operation monitoring and the like.
In summary, the present invention can achieve the following benefits:
The intelligent and automatic management system realizes comprehensive intellectualization of canteen management through the Internet of things and big data technology, reduces manual operation and improves management efficiency.
Real-time data acquisition and analysis enable the system to rapidly respond to changes and emergency conditions, and ensure stability and safety of canteen operation.
And the individuation and the user experience are improved, namely, the system can provide services closer to the user demands by analyzing the user data, and the user satisfaction is improved.
The whole-course monitoring and food safety, namely the image analysis and the Internet of things are combined, so that the whole-course monitoring of the canteen operation is realized, the food safety is ensured, and human errors are reduced.
The application scene of the invention can comprise the following scenes:
The large-scale dining halls such as universities, enterprises, hospitals and the like are suitable for places which need to process a large number of dining people and have high requirements on food safety and operation efficiency.
And the linkage catering management is used for helping a linkage canteen or a catering enterprise to optimize the whole operation through unified management and data analysis.
Personalized catering service, namely providing personalized service based on personal data for users, and improving dining experience.
In summary, the invention aims to thoroughly reform the traditional canteen management mode by the technical means, and creates a canteen management system which is more intelligent, fine and efficient.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
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