CN117710157A - Cloud intelligent restaurant service management system - Google Patents
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Abstract
The invention relates to the technical field of service management, in particular to a cloud intelligent restaurant service management system, which comprises: user interface: providing a user-friendly operation interface for menu browsing, ordering and customer feedback; a background processing unit: responsible for processing orders, managing inventory, and staff scheduling; data storage and analysis unit: storing historical transaction and customer data, and carrying out data analysis and insight extraction; automatic planning module for festival activities: the upcoming festival and special activities are automatically identified, special menus and marketing activities are planned according to historical data and market trends, the festival demands are automatically adapted in a data-driven mode, and customer attractions and sales during the festival are improved. The invention not only improves the efficiency and cost effectiveness of restaurant operation, but also greatly enhances customer experience and market adaptability, and the advantages are helpful for improving the overall competitiveness and profitability of the restaurant.
Description
Technical Field
The invention relates to the technical field of service management, in particular to a cloud intelligent restaurant service management system.
Background
Conventional restaurant service management systems typically rely on manual decisions in festival activity planning, which is not only time-consuming and labor-consuming, but also easily limited by the subjective judgment and experience of the planners, and existing systems often cannot respond quickly in the face of sudden market trends or emerging holidays. Especially for non-traditional, cultural diversification or regional specific festival activities, traditional systems are difficult to capture and effectively cope in time.
While restaurants may accumulate significant amounts of customer data and market trend information, such data is often underutilized in conventional systems to optimize planning and performance of festival activities.
Disclosure of Invention
Based on the above purpose, the invention provides a cloud intelligent restaurant service management system.
Cloud intelligent restaurant service management system includes:
user interface: providing a user-friendly operation interface for menu browsing, ordering and customer feedback;
a background processing unit: responsible for processing orders, managing inventory, and staff scheduling;
data storage and analysis unit: storing historical transaction and customer data, and carrying out data analysis and insight extraction;
automatic planning module for festival activities: automatically identifying upcoming holidays and special events, planning special menus and marketing events based on historical data and market trends;
the user interface directly displays special menus and activity information planned by the festival activity automatic planning module, so that customer experience and participation are enhanced; the background processing unit adjusts resource allocation and staff scheduling according to the output of the festival activity automatic planning module so as to cope with the increase of passenger flow during the festival; the data storage and analysis unit collects performance data of festival activities and provides support and optimization basis for future activity planning.
Further, the background processing unit specifically includes:
order processing mechanism: presetting an order processing flow, and receiving and processing order information from a user interface in real time, wherein the order processing flow comprises order verification, processing priority allocation and seamless docking with a kitchen management system;
and integrating an intelligent inventory management module, monitoring the inventory levels of food materials and consumable materials in real time, automatically predicting future inventory demands according to historical data and current sales trends, generating optimized purchasing suggestions, and automatically triggering purchasing processes or reminding management personnel when the inventory reaches a preset low level.
And the staff management module is responsible for making and optimizing work arrangement of staff, automatically adjusting scheduling according to real-time requirements and staff availability, and ensuring that enough staff is used for processing orders and customer service.
Further, the intelligent inventory management unit includes:
deep analysis is carried out on sales trends, seasonal changes, market dynamics and historical inventory data by utilizing a data analysis technology so as to predict future inventory requirements, and an inventory management unit automatically adjusts ordering amount and frequency based on the prediction so as to adapt to changes of market and business requirements;
intelligent discard prevention mechanism: by monitoring the quality guarantee period and the service condition of the food materials in real time, the inventory which is about to expire is intelligently identified, and adjustment suggestions are automatically proposed, including sales promotion activities or preferential use, so that the waste is reduced;
the adoption of environmental protection and sustainable inventory management strategies includes the preferential purchasing of local food materials, the reduction of carbon footprint, the evaluation of environmental protection indicators of food material suppliers, and the encouragement of sustainable packaging and transportation modes.
Further, the data analysis technique is based on an autoregressive integrated moving average model, denoted ARIMA (p, d, q), wherein:
p is the number of autoregressive terms, d is the number of differences, q is the number of moving average terms;
the formula is:wherein X is t Is the predicted value of time t, c is a constant term, φ i Is an autoregressive parameter, θ j Is the moving average parameter and t is the error term.
Further, the data storage and analysis unit specifically includes:
multidimensional data integration and real-time updating: integrating data from multiple sources, including sales data, inventory data, customer feedback, market trends, and supply chain information, enabling real-time updating and synchronization of the data, ensuring that all decisions are based on the latest information;
customer purchase behavior and preference patterns are identified using cluster analysis and association rule mining for optimizing menu design, marketing strategies, and inventory management.
Based on the autoregressive comprehensive moving average model, sales and inventory predictive analysis is performed, customer historical transaction and feedback data are analyzed, and personalized customer experience insight is provided.
Further, the festival activity automatic planning module specifically includes:
integrating a calendar function, automatically identifying and tracking important festival and activity dates of countries, regions and cultures, and identifying non-traditional festival activities to be popular or welcome by combining social media and network data analysis;
historical data analysis: analyzing sales data during past festivals, including most popular dishes, customer traffic, average consumption, to identify patterns and improvement points of success, analyzing customer feedback, knowing the customer's preferences and dissatisfaction aspects in past campaigns;
market trend analysis: utilizing market research data and real-time network trends, including search engine queries and social media topics, to grasp the current consumption trend, and adjusting and optimizing the activity plan of the competitor by combining the activity strategy and market response of the competitor;
special menus and marketing campaign planning: the design adapts to the special menu of upcoming holidays or special events, taking into account seasonal food materials, cultural features and customer preferences, automatically programs and adjusts marketing strategies, including special offers, theme events and customized promotional material, to attract more customers.
Further, the market trend analysis specifically includes:
data collection and integration: collecting data from a plurality of sources, including social media platforms, search engine trends, and professional market research reports, using an API interface, collecting competitor's online activity information, including by monitoring their social media posts, campaigns, and customer feedback;
text and trend analysis: analyzing the collected text data by using a natural language processing technology, extracting keywords, emotional tendency and topic tendency, and tracking and predicting the popular tendency of specific keywords or topics by using time sequence analysis;
competitor policy analysis: evaluating the activity strategy of the competitor by SWOT analysis (advantages, disadvantages, opportunities and threats), and identifying the success cases and defects of the competitor by combining market feedback;
data-driven decision-making: and integrating a decision support system, providing strategy suggestions by using the analysis results, including menu design, marketing subjects or special offers of specific holidays, predicting possible results of different strategies through simulation and prediction models, and selecting an optimal scheme.
Further, the special menu and marketing campaign planning specifically includes:
collecting historical data: analyzing sales data during past holidays or special activities, knowing which dishes are popular and which are unpopular, knowing the preference of customers to dishes through customer feedback, order history and social media data, researching the availability of seasonal food materials, and ensuring that the menu contains fresh and season-applied food materials;
researching the cultural background and tradition of upcoming holidays or activities, knowing related eating habits and special dishes, ensuring that menus reflect the cultural characteristics of upcoming holidays or special activities, such as pushing out traditional holiday dishes on christmas, adding innovative elements on the basis of respecting the tradition, attracting customers, and providing fresh and delicious dishes by utilizing foods in the season;
testing and feedback: before formally pushing out the special menu, testing in a range, collecting customer feedback, adjusting, and promoting the special menu in advance through social media, electronic mails and in-store propaganda to stimulate customer interests.
Further, the festival activity automatic planning module further comprises a real-time trend response sub-module, and the real-time trend response sub-module specifically comprises:
monitoring social media platform and network data in real time to identify the topics and holidays being raised, and predicting the sustainability and popularity of the topics and holidays being raised by using emotion analysis and keyword trend tracking of data analysis tools and machine learning;
dynamic menu adjustment: the real-time trend response sub-module after the emerging holiday or trend is identified provides menu adjustment suggestions, which comprise adding special dishes or drinks related to the trend, automatically adjusting marketing and propaganda strategies to match the trend, and cooperating with supply chain management to ensure that food materials and supplies related to the new trend are obtained in time;
and integrating the real-time trend response sub-module into the existing cloud intelligent restaurant service management system, and combining the real-time trend response sub-module with the data storage and analysis unit and the user interface.
Further, the user interface further comprises:
festival activity feature display: the method comprises the steps of dynamically displaying special festival menus and activity information generated by a festival activity automatic planning module, wherein during festival, a user interface automatically adjusts the layout and the theme of the special festival menus and the activity information to match with the festival atmosphere, so that rich visual experience is provided;
interactive user experience: interactive elements, including sliding images, dynamic videos, and pop-up windows, are provided for introducing festival activities and special offers.
The invention has the beneficial effects that:
according to the invention, through the predictive analysis and automatic ordering system, food waste and expiration are obviously reduced, and meanwhile, the stock is ensured to be always kept at the optimal level, so that not only is the stock cost reduced, but also the food utilization rate and freshness are improved, the system can intelligently arrange staff shifts according to the predicted passenger flow volume and order data, the manpower resource waste is reduced, and the service quality and response speed are improved.
According to the invention, by utilizing the real-time trend response module, the restaurant can quickly adapt to various holidays and rising trends, create unique dining atmosphere and attract more customers, and by monitoring social media and network data, the system can immediately capture the latest market trends, so that the restaurant can quickly respond and utilize the trends, thereby keeping leading in competition, not only improving the operating efficiency and cost efficiency of the restaurant, but also greatly enhancing the customer experience and market adaptability, and the advantages are helpful for improving the overall competitiveness and profitability of the restaurant.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system functional module according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an automatic planning module for festival activities according to an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1-2, the cloud smart restaurant service management system includes:
user interface: providing a user-friendly operation interface for menu browsing, ordering and customer feedback;
a background processing unit: responsible for processing orders, managing inventory, and staff scheduling;
data storage and analysis unit: storing historical transaction and customer data, and carrying out data analysis and insight extraction;
automatic planning module for festival activities: automatically identifying upcoming holidays and special activities, planning special menus and marketing activities according to historical data and market trends, automatically adapting to festival demands in a data-driven mode, and improving customer attractions and sales during festival;
the user interface directly displays special menus and activity information planned by the festival activity automatic planning module, so that customer experience and participation are enhanced; the background processing unit adjusts resource allocation and staff scheduling according to the output of the festival activity automatic planning module so as to cope with the increase of passenger flow during the festival; the data storage and analysis unit collects performance data of festival activities and provides support and optimization basis for future activity planning.
The background processing unit specifically comprises:
order processing mechanism: presetting an order processing flow, and receiving and processing order information from a user interface in real time, wherein the order processing flow comprises order verification, processing priority allocation and seamless docking with a kitchen management system;
and integrating an intelligent inventory management module, monitoring the inventory levels of food materials and consumable materials in real time, automatically predicting future inventory demands according to historical data and current sales trends, generating optimized purchasing suggestions, and automatically triggering purchasing processes or reminding management personnel when the inventory reaches a preset low level.
The staff management module is responsible for making and optimizing work arrangement of staff, automatically adjusting scheduling according to real-time requirements and staff availability, and ensuring that enough staff is used for processing orders and customer service;
staff performance monitoring and reporting functions are also included to assist restaurant managers in assessing staff performance and performing necessary training and adjustments.
The above are key functions and operating mechanisms of a Background Processing Unit (BPU). Through efficient order processing, intelligent inventory management and a flexible staff scheduling system, the BPU remarkably improves the efficiency and response speed of restaurant operation, and ensures the service quality of the restaurant in the face of high demands. These functions form the basis of the cloud intelligent restaurant service management system and lay a solid foundation for further integration of higher-level functions (such as automatic planning of festival activities).
The intelligent inventory management unit includes:
deep analysis is carried out on sales trends, seasonal changes, market dynamics and historical inventory data by utilizing a data analysis technology so as to predict future inventory requirements, and an inventory management unit automatically adjusts ordering amount and frequency based on the prediction so as to adapt to changes of market and business requirements;
intelligent discard prevention mechanism: by monitoring the quality guarantee period and the service condition of the food materials in real time, the inventory which is about to expire is intelligently identified, and adjustment suggestions are automatically proposed, including sales promotion activities or preferential use, so that the waste is reduced;
the adoption of environmental protection and sustainable inventory management strategies includes the preferential purchasing of local food materials, the reduction of carbon footprint, the evaluation of environmental protection indicators of food material suppliers, and the encouragement of sustainable packaging and transportation modes.
The intelligent inventory management unit not only improves inventory efficiency and reduces waste, but also enhances supply chain management, promotes environmental sustainability, and provides greater customization and flexibility. The innovation can obviously improve the overall operation efficiency and market adaptability of the restaurant.
The data analysis technique is based on an autoregressive integrated moving average model, denoted ARIMA (p, d, q), in which: p is the number of autoregressive terms, d is the number of differences, q is the number of moving average terms;
the formula is:wherein X is t Is the predicted value of time t, c is a constant term, φ i Is an autoregressive parameter, θ j Is a moving average parameter, t is an error term;
in the present invention;
1. parameter X t This is the predicted output of the model at time t, representing the expected inventory requirements (e.g., the demand for a particular food item) at time t.
2. A constant term reflecting the baseline level of inventory demand. This may be a historical average demand level, or a prediction based on long-term trends.
3. Parameter phi i (autoregressive) parameters that reflect the impact of past inventory requirements. For example, if phi 1 Positive values indicate that yesterday's demand level is positively correlated with today's demand.
4. Parameter θ j (moving average parameters) these parameters represent the effect of past error terms. For example, a positive θ 1 Indicating that yesterday's prediction error will positively affect today's predictions.
5. Parameter t (error term) which represents a random impact of time t or a factor not captured by the model, such as an incident or an unusual market shift.
6. Difference times d-in the context of restaurant inventory management, d represents the difference times required to translate the data into a smooth sequence. For example, if sales data fluctuates seasonally, seasonal differentiation may be required.
7. The values of the autoregressive term p and the moving average terms q: p and q determine the depth of the model in consideration of the historical data. In inventory management, this may depend on the purchasing cycle and consumption pattern of the food material.
The data storage and analysis unit comprises:
multidimensional data integration and real-time updating: integrating data from multiple sources, including sales data, inventory data, customer feedback, market trends, and supply chain information, enabling real-time updating and synchronization of the data, ensuring that all decisions are based on the latest information;
identifying purchasing behavior and preference modes of customers by utilizing cluster analysis and association rule mining, wherein the cluster analysis is used for optimizing menu design, marketing strategies and inventory management, and the formula is as follows based on a K-means algorithm:
where S is the total squared error, k is the number of clusters, C i Is the set of data points in the ith cluster, x is one data point in the cluster, μ i Is cluster C i Is defined by a center point of (2); application to the invention: the K-means algorithm is used to analyze the purchasing behavior of customers, e.g., to group customers into different groups based on their purchase frequency, amount consumed, and dish preferences. For determining which dishes are popular in a particular customer group and which combinations of dishes are typically purchased together.
Based on the autoregressive comprehensive moving average model, sales and inventory prediction analysis is performed, historical transaction and feedback data of customers are analyzed, and personalized customer experience insight is provided;
customized data visualization tools are provided to help managers to intuitively understand complex data sets, and to automate report generation functionality to facilitate periodic review of business performance and policy adjustments.
The data storage and analysis unit not only serves as a data storage center, but also serves as a key support for restaurant management decision-making, and can provide deep business insight and data-driven decision-making support, so that the operation efficiency and market competitiveness of restaurants are remarkably improved.
The festival activity automatic planning module specifically comprises:
integrating a calendar function, automatically identifying and tracking important festival and activity dates of countries, regions and cultures, and identifying non-traditional festival activities to be popular or welcome by combining social media and network data analysis;
historical data analysis: analyzing sales data during past festivals, including most popular dishes, customer traffic, average consumption, to identify patterns and improvement points of success, analyzing customer feedback, knowing the customer's preferences and dissatisfaction aspects in past campaigns;
market trend analysis: utilizing market research data and real-time network trends, including search engine queries and social media topics, to grasp the current consumption trend, and adjusting and optimizing the activity plan of the competitor by combining the activity strategy and market response of the competitor;
special menus and marketing campaign planning: the design adapts to the special menu of upcoming holidays or special events, taking into account seasonal food materials, cultural features and customer preferences, automatically programs and adjusts marketing strategies, including special offers, theme events and customized promotional material, to attract more customers.
The festival activity automatic planning module is tightly integrated with the user interface, the background processing unit and the data storage and analysis unit, so that the successful implementation of the festival activity is ensured.
The market trend analysis specifically includes:
data collection and integration: collecting data from a plurality of sources, including social media platforms, search engine trends, and professional market research reports, using an API interface, collecting competitor's online activity information, including by monitoring their social media posts, campaigns, and customer feedback;
text and trend analysis: analyzing the collected text data by using a natural language processing technology, extracting keywords, emotional tendency and topic tendency, and tracking and predicting the popular tendency of specific keywords or topics by using time sequence analysis;
competitor policy analysis: evaluating the activity strategy of the competitor by SWOT analysis (advantages, disadvantages, opportunities and threats), and identifying the success cases and defects of the competitor by combining market feedback;
data-driven decision-making: integrating a decision support system, providing strategy suggestions by using the analysis results, including menu design, marketing subjects or special offers of specific holidays, predicting possible results of different strategies through simulation and prediction models, and selecting an optimal scheme;
after implementation, market reactions and customer feedback are continuously monitored to evaluate the effect of the activity, and the machine learning technology is utilized to continuously optimize the analysis model, so that the prediction accuracy and the strategy effectiveness are improved.
Specific technical application
Web crawlers and APIs: for automatically collecting real-time data.
NLP technology: the method is used for analyzing social media and search engine data and extracting emotion and trend of the consumers.
Time series analysis: popularity changes of keywords or topics are tracked.
SWOT analysis tool: and evaluating the advantages and disadvantages of the competitor strategy.
Decision support system: all data and analysis are integrated, providing data-based policy suggestions.
Machine learning model: for optimizing the analysis process and the prediction result.
The special menu and marketing campaign planning specifically includes:
collecting historical data: analyzing sales data during past holidays or special activities, knowing which dishes are popular and which are unpopular, knowing the preference of customers to dishes through customer feedback, order history and social media data, researching the availability of seasonal food materials, and ensuring that the menu contains fresh and season-applied food materials;
researching the cultural background and tradition of upcoming holidays or activities, knowing related eating habits and special dishes, ensuring that menus reflect the cultural characteristics of upcoming holidays or special activities, such as pushing out traditional holiday dishes on christmas, adding innovative elements on the basis of respecting the tradition, attracting customers, and providing fresh and delicious dishes by utilizing foods in the season;
testing and feedback: before formally pushing out the special menu, testing in a range, collecting customer feedback, adjusting, and promoting the special menu in advance through social media, electronic mails and in-store propaganda to stimulate customer interests.
The festival celebration activity automatic planning module also comprises a real-time trend response submodule, wherein the real-time trend response submodule specifically comprises:
monitoring social media platform and network data in real time to identify the topics and holidays being raised, and predicting the sustainability and popularity of the topics and holidays being raised by using emotion analysis and keyword trend tracking of data analysis tools and machine learning;
dynamic menu adjustment: the real-time trend response sub-module after the emerging holiday or trend is identified provides menu adjustment suggestions, which comprise adding special dishes or drinks related to the trend, automatically adjusting marketing and propaganda strategies to match the trend, and cooperating with supply chain management to ensure that food materials and supplies related to the new trend are obtained in time;
and integrating the real-time trend response sub-module into the existing cloud intelligent restaurant service management system, and combining the real-time trend response sub-module with the data storage and analysis unit and the user interface.
Promote brand image: the sensitivity and adaptability of the restaurant to the latest trend are displayed, and the brand image is improved.
Attracting young customers: especially attracting a population of young customers who pursue a fresh experience and fashion trends.
Increase sales opportunities: by responding to emerging trends in time, new sales opportunities and revenue growth points are created.
By implementing the real-time trend response sub-module, the restaurant not only can rapidly adapt to the requirements of the traditional holidays, but also can flexibly cope with the new holidays and trends suddenly rising in modern life, and better meets the market requirements of diversification and dynamic change.
The user interface further comprises:
festival activity feature display: the method comprises the steps of dynamically displaying special festival menus and activity information generated by a festival activity automatic planning module, wherein during festival, a user interface automatically adjusts the layout and the theme of the special festival menus and the activity information to match with the festival atmosphere, so that rich visual experience is provided;
interactive user experience: providing interactive elements including sliding images, dynamic videos and popup windows for introducing festival activities and special offers;
multilingual support and cultural adaptation: the method supports multiple languages, is convenient for customers with different cultural backgrounds to use, and automatically adjusts contents and display modes according to festival custom of different cultures and areas so as to improve the attribution sense and satisfaction of the users.
User interfaces play an important role in enhancing customer experience, particularly in the presentation and participation of festival events. By integrating advanced interactive design and personalized recommendation, the UI becomes a key interface for connecting the customer and restaurant service, and the participation and satisfaction of the customer are improved. At the same time, multilingual support and cultural adaptation ensure that the system is able to serve a diverse group of customers.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (10)
1. Cloud wisdom dining room service management system, its characterized in that includes:
user interface: providing a user-friendly operation interface for menu browsing, ordering and customer feedback;
a background processing unit: responsible for processing orders, managing inventory, and staff scheduling;
data storage and analysis unit: storing historical transaction and customer data, and carrying out data analysis and insight extraction;
automatic planning module for festival activities: automatically identifying upcoming holidays and special events, planning special menus and marketing events based on historical data and market trends;
the user interface directly displays special menus and activity information planned by the festival activity automatic planning module, so that customer experience and participation are enhanced; the background processing unit adjusts resource allocation and staff scheduling according to the output of the festival activity automatic planning module so as to cope with the increase of passenger flow during the festival; the data storage and analysis unit collects performance data of festival activities and provides support and optimization basis for future activity planning.
2. The cloud smart restaurant service management system of claim 1, wherein the background processing unit specifically comprises:
order processing mechanism: presetting an order processing flow, and receiving and processing order information from a user interface in real time, wherein the order processing flow comprises order verification, processing priority allocation and seamless docking with a kitchen management system;
integrating an intelligent inventory management module, monitoring inventory levels of food materials and consumable materials in real time, automatically predicting future inventory demands according to historical data and current sales trends, generating optimized purchasing suggestions, and automatically triggering purchasing flows or reminding management personnel when the inventory reaches a preset low level;
and the staff management module is responsible for making and optimizing work arrangement of staff, automatically adjusting scheduling according to real-time requirements and staff availability, and ensuring that enough staff is used for processing orders and customer service.
3. The cloud intelligent restaurant service management system according to claim 2, wherein said intelligent inventory management unit comprises:
deep analysis is carried out on sales trends, seasonal changes, market dynamics and historical inventory data by utilizing a data analysis technology so as to predict future inventory requirements, and an inventory management unit automatically adjusts ordering amount and frequency based on the prediction so as to adapt to changes of market and business requirements;
intelligent discard prevention mechanism: by monitoring the quality guarantee period and the service condition of the food materials in real time, the inventory which is about to expire is intelligently identified, and adjustment suggestions are automatically proposed, including sales promotion activities or preferential use, so that the waste is reduced;
the adoption of environmental protection and sustainable inventory management strategies includes the preferential purchasing of local food materials, the reduction of carbon footprint, the evaluation of environmental protection indicators of food material suppliers, and the encouragement of sustainable packaging and transportation modes.
4. The cloud intelligent restaurant service management system according to claim 3, wherein said data analysis technique is based on an autoregressive integrated moving average model, denoted ARIMA (p, d, q), wherein:
p is the number of autoregressive terms, d is the number of differences, q is the number of moving average terms;
the formula is:wherein X is t Is the predicted value of time t, c is a constant term, φ i Is an autoregressive parameter, θ j Is the moving average parameter and t is the error term.
5. The cloud smart restaurant service management system of claim 4, wherein said data storage and analysis unit specifically comprises:
multidimensional data integration and real-time updating: integrating data from multiple sources, including sales data, inventory data, customer feedback, market trends, and supply chain information, enabling real-time updating and synchronization of the data, ensuring that all decisions are based on the latest information;
identifying customer purchasing behavior and preference modes by utilizing cluster analysis and association rule mining for optimizing menu design, marketing strategies and inventory management;
based on the autoregressive comprehensive moving average model, sales and inventory predictive analysis is performed, customer historical transaction and feedback data are analyzed, and personalized customer experience insight is provided.
6. The cloud smart restaurant service management system of claim 5, wherein the festival activity automatic planning module specifically comprises:
integrating a calendar function, automatically identifying and tracking important festival and activity dates of countries, regions and cultures, and identifying non-traditional festival activities to be popular or welcome by combining social media and network data analysis;
historical data analysis: analyzing sales data during past festivals, including most popular dishes, customer traffic, average consumption, to identify patterns and improvement points of success, analyzing customer feedback, knowing the customer's preferences and dissatisfaction aspects in past campaigns;
market trend analysis: utilizing market research data and real-time network trends, including search engine queries and social media topics, to grasp the current consumption trend, and adjusting and optimizing the activity plan of the competitor by combining the activity strategy and market response of the competitor;
special menus and marketing campaign planning: the design adapts to the special menu of upcoming holidays or special events, taking into account seasonal food materials, cultural features and customer preferences, automatically programs and adjusts marketing strategies, including special offers, theme events and customized promotional material, to attract more customers.
7. The cloud smart restaurant service management system of claim 6, wherein the market trend analysis specifically comprises:
data collection and integration: collecting data from a plurality of sources, including social media platforms, search engine trends, and professional market research reports, using an API interface, collecting competitor's online activity information, including by monitoring their social media posts, campaigns, and customer feedback;
text and trend analysis: analyzing the collected text data by using a natural language processing technology, extracting keywords, emotional tendency and topic tendency, and tracking and predicting the popular tendency of specific keywords or topics by using time sequence analysis;
competitor policy analysis: evaluating the activity strategy of the competitor by SWOT analysis, and identifying the success cases and defects of the competitor by combining market feedback;
data-driven decision-making: and integrating a decision support system, providing strategy suggestions by using the analysis results, including menu design, marketing subjects or special offers of specific holidays, predicting possible results of different strategies through simulation and prediction models, and selecting an optimal scheme.
8. The cloud smart restaurant service management system of claim 7, wherein said special menu and marketing campaign planning specifically comprises:
collecting historical data: analyzing sales data during past holidays or special activities, knowing which dishes are popular and which are unpopular, knowing the preference of customers to dishes through customer feedback, order history and social media data, researching the availability of seasonal food materials, and ensuring that the menu contains fresh and season-applied food materials;
researching the cultural background and tradition of upcoming holidays or activities, knowing related eating habits and special dishes, ensuring that menus reflect the cultural characteristics of upcoming holidays or special activities, adding innovative elements on the basis of respecting the tradition, attracting customers, and providing fresh and delicious dishes by utilizing the foods in season;
testing and feedback: before formally pushing out the special menu, testing in a range, collecting customer feedback, adjusting, and promoting the special menu in advance through social media, electronic mails and in-store propaganda to stimulate customer interests.
9. The cloud smart restaurant service management system of claim 8, wherein the festival activity automatic planning module further comprises a real-time trend response sub-module, the real-time trend response sub-module specifically comprising:
monitoring social media platform and network data in real time to identify the topics and holidays being raised, and predicting the sustainability and popularity of the topics and holidays being raised by using emotion analysis and keyword trend tracking of data analysis tools and machine learning;
dynamic menu adjustment: the real-time trend response sub-module after the emerging holiday or trend is identified provides menu adjustment suggestions, which comprise adding special dishes or drinks related to the trend, automatically adjusting marketing and propaganda strategies to match the trend, and cooperating with supply chain management to ensure that food materials and supplies related to the new trend are obtained in time;
and integrating the real-time trend response sub-module into the existing cloud intelligent restaurant service management system, and combining the real-time trend response sub-module with the data storage and analysis unit and the user interface.
10. The cloud smart restaurant service management system of claim 9, wherein said user interface further comprises:
festival activity feature display: the method comprises the steps of dynamically displaying special festival menus and activity information generated by a festival activity automatic planning module, wherein during festival, a user interface automatically adjusts the layout and the theme of the special festival menus and the activity information to match with the festival atmosphere, so that rich visual experience is provided;
interactive user experience: interactive elements, including sliding images, dynamic videos, and pop-up windows, are provided for introducing festival activities and special offers.
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