Method for leftover analysis and menu optimization based on artificial intelligence

Abstract

The invention discloses a leftover analysis and menu optimization method based on artificial intelligence. Firstly, photographing is performed to collect data; then AI judgment and analysis are started; then multi-dimensional data analysis is carried out; and finally generating a report and decision support. An image capturing technology is applied, and the condition that more than 50% of single dish remains is recorded through photographing. Certain image capturing equipment, such as a high-definition camera or a smart phone, is needed, so that pictures are ensured to be clear, and the condition of leftovers is accurately reflected. And analyzing and judging the shot leftover pictures by using an artificial intelligence technology. The technology relates to image recognition, deep learning and the like so as to recognize information such as the types of leftovers and the remaining amount. And performing multi-dimensional data analysis on an AI judgment result, wherein the multi-dimensional data analysis comprises multiple dimensions such as leftover types, surplus, remaining time, customer feedback and the like. This requires strong data processing and analysis capabilities to extract valuable information and trends.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method for leftover food analysis and menu optimization based on artificial intelligence. Background Art

[0002] With the improvement of living standards, the problem of leftovers has become increasingly prominent, which not only wastes a lot of food resources, but also pollutes the environment. These leftovers will generate a lot of garbage during the processing, polluting the environment and threatening my country's food security. Therefore, it is particularly important to scientifically and rationally analyze and deal with leftovers.

[0003] There are many reasons for the problem of leftovers. First of all, personal eating habits and waste in dining occasions are one of the main reasons. In family gatherings, banquets and other occasions, due to the large variety and large portions of dishes, a lot of food is often left over. In addition, different personal eating habits can also lead to leftovers, such as dislike for certain foods or inaccurate estimation of food intake.

[0004] There are currently a variety of methods and technologies for dealing with the problem of leftovers. First, reducing the generation of leftovers from the source is the most direct and effective measure. This includes improving the menu planning capabilities of catering companies, arranging meal portions reasonably, and avoiding oversupply; at the same time, strengthening publicity and education on food conservation and raising the public's awareness of conservation.

[0005] For leftovers that have already been produced, reasonable treatment and reuse are also key. A common treatment method is to reheat and eat, put the leftovers in the refrigerator and reheat them when needed. This method is simple and convenient, but you need to pay attention to the safety of the food and the change in taste. Another method is to make new dishes, match the ingredients in the leftovers, and create new dishes, which not only reduces waste but also increases the diversity of diet.

[0006] In addition, fermenting leftovers is also an effective way to reuse them. After fermentation, leftovers can be made into feed or fertilizer, realizing the recycling of resources. This method not only reduces environmental pollution, but also promotes the sustainable development of agricultural production.

[0007] In the process of handling leftovers, the application of technical means also plays an important role. For example, through scientific food preservation technology, the shelf life of leftovers can be extended and the waste caused by food spoilage can be reduced. At the same time, the use of advanced detection technology can accurately analyze the nutrients and harmful substances in leftovers, providing a scientific basis for reasonable treatment and reuse.

[0008] It is worth noting that the problem of leftovers is not only a problem of resource waste and environmental pollution, but also related to public health and safety. Therefore, when handling leftovers, food safety standards must be strictly followed to ensure that the processed food meets edible requirements.

[0009] In summary, the problem of leftovers is a complex social problem that requires the joint efforts of the whole society to solve. By raising the public's awareness of conservation, strengthening the menu planning capabilities of catering companies, and promoting the application of reasonable methods and technical means for handling leftovers, we can effectively reduce the generation of leftovers and achieve resource conservation and sustainable development of the environment. At the same time, this also requires cooperation and joint efforts from the government, enterprises, the public and other parties to jointly promote the effective solution of the problem of leftovers.

[0010] In today's highly competitive catering market, menus are not only an important channel for customers to understand restaurant dishes, but also a key tool for shaping restaurant brand image, improving customer satisfaction, promoting sales and optimizing operation management. A well-designed menu can clearly and accurately display dish information, guide customers to make choices, improve ordering efficiency, and at the same time convey the restaurant's positioning, characteristics and culture, and enhance brand identity. Therefore, menu optimization has become an important means for catering companies to enhance their competitiveness.

[0011] In the catering industry, the problem of leftovers has always been a difficult problem that needs to be solved urgently. Leftovers not only cause food waste and increase operating costs, but may also affect the freshness of dishes and customers' dining experience. Traditional leftover analysis methods often rely on manual statistics and experience judgment, which are highly subjective and inefficient. With the rapid development of artificial intelligence technology, it has become possible to use AI to analyze leftovers.

[0012] The present invention aims to solve the problems of strong subjectivity and low efficiency of traditional leftover analysis methods, and provides a leftover analysis and menu optimization method based on artificial intelligence. Through photo analysis, AI judgment and multi-dimensional data analysis, the causes of leftovers can be accurately analyzed, decision support can be provided for catering companies, and accurate simplification of menus can be achieved. Summary of the invention

[0013] The present invention provides a method for leftover food analysis and menu optimization based on artificial intelligence, which can solve the existing problems.

[0014] The present invention provides a method for leftover analysis and menu optimization based on artificial intelligence, comprising:

[0015] A. Take photos and collect data;

[0016] B. Start AI judgment and analysis;

[0017] C. Then multi-dimensional data analysis;

[0018] D. Finally generate reports and decision support.

[0019] Furthermore, image capture technology is used to record the situation of more than 50% of a single dish remaining by taking photos. This requires certain image capture equipment, such as a high-definition camera or a smart phone, to ensure that the photos are clear and accurately reflect the leftovers.

[0020] Furthermore, AI judgment technology is used to analyze and judge the photos of leftovers. This involves image recognition, deep learning and other technologies to identify the type of leftovers, the amount of leftovers and other information.

[0021] Furthermore, data analysis technology is used to conduct multi-dimensional data analysis on the results of AI judgment, including the type of leftovers, the remaining amount, the remaining time, customer feedback, etc. This requires strong data processing and analysis capabilities to extract valuable information and trends.

[0022] Furthermore, using report generation technology, weekly and monthly reports can be generated based on the data analysis results. These reports should include detailed analysis of leftovers, trend forecasts, and improvement suggestions to provide decision support for managers.

[0023] Furthermore, precise decision-making technology can be used to make precise decisions based on the information and suggestions in the report, such as streamlining the menu, adjusting the standards for making dishes, etc. This requires managers to have certain data interpretation and decision-making capabilities.

[0024] Furthermore, by introducing more advanced algorithms and models, AI can more accurately identify the types and amounts of leftovers, and even predict leftover trends.

[0025] Furthermore, through real-time data analysis and feedback, real-time collection and analysis of leftover data, managers can adjust food preparation and supply strategies in a timely manner to reduce the generation of leftovers.

[0026] Furthermore, the decision support system in the future will be more intelligent and personalized. The system can provide customized decision suggestions and optimization solutions based on the manager's preferences and the actual situation of the restaurant.

[0027] Furthermore, blockchain technology can be used to ensure the authenticity and immutability of leftover data. Through blockchain technology, a transparent leftover data management method can be established, allowing managers and customers to have more trust in the accuracy of the data.

[0028] Furthermore, the Internet of Things technology can be combined with the leftovers management system to achieve full monitoring and management of food preparation, supply and leftovers disposal. For example, through smart sensors and monitoring systems, the progress of food preparation, supply and leftovers disposal can be understood in real time, improving management efficiency.

[0029] Furthermore, through multi-dimensional data analysis, weekly and monthly reports are generated to accurately analyze the causes of leftovers and assist in the system of menu streamlining.

[0030] Beneficial Effects

[0031] Improve the accuracy and efficiency of leftover analysis and reduce interference from human factors.

[0032] Through multi-dimensional data analysis, we can accurately analyze the causes of leftovers and provide decision-making support for catering companies.

[0033] Help catering companies streamline menus, optimize dish structure, and reduce food waste and operating costs.

[0034] Improve customer dining experience and increase customer satisfaction and loyalty. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of a method for leftover food analysis and menu optimization based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0037] Figure 1 A flowchart of a method for leftover analysis and menu optimization based on artificial intelligence provided by an embodiment of the present invention. The method can be performed by a method for leftover analysis and menu optimization based on artificial intelligence, which specifically includes the following steps:

[0038] S110, taking photos to collect data;

[0039] Implementation details:

[0040] Equipment configuration: High-definition cameras are installed in the table collection area of ​​each restaurant to ensure that the remaining status of each dish can be clearly captured.

[0041] Operation process: When clearing the table, if the remaining quantity of a single dish exceeds 50%, the waiter needs to take a photo of the leftovers using a mobile phone or special camera equipment, and briefly record basic information such as the dish name, table number, and dining time.

[0042] Data upload: After taking photos, the photos and related information are automatically uploaded to the central database through the cloud system to provide raw data for subsequent analysis.

[0043] Challenges and responses:

[0044] Privacy protection: Ensure that the customer's privacy is not involved during the photo shooting process and only focus on the dishes themselves.

[0045] Train staff: Provide professional training to waiters to ensure that they can accurately judge the proportion of leftovers and skillfully operate photo-taking equipment.

[0046] S120: Start AI judgment and analysis

[0047] Technical principle:

[0048] Using deep learning algorithms, image recognition is performed on the uploaded photos to automatically identify the type of dish, remaining amount, and remaining ratio.

[0049] Through machine learning models, the recognition results are continuously optimized to improve the recognition accuracy.

[0050] Implementation process:

[0051] Model training: Based on historical data, train the AI ​​model to enable it to accurately identify various types of dishes and their remaining status.

[0052] Real-time analysis: After a new photo is uploaded, the system automatically calls the AI ​​model for analysis and quickly generates a leftovers report for each dish.

[0053] Technical difficulties and solutions:

[0054] Dish diversity: Faced with a wide variety of dishes, the generalization ability of the model is improved by continuously expanding the training data set.

[0055] Influence of lighting and angle: Image enhancement technology is used to reduce the impact of lighting changes and shooting angles on recognition results.

[0056] S130: Multi-dimensional Data Analysis

[0057] Analysis Dimensions:

[0058] Dish type: Analyze the leftover rates of different dish types and identify which dishes are more likely to be left over.

[0059] Taste preference: Combined with customer feedback, analyze the impact of taste preferences on leftovers, such as spiciness, sweetness, etc.

[0060] Seasonal changes: Investigate the impact of seasonal changes on the popularity of dishes, such as leftovers of cold drinks in the summer and hot dishes in the winter.

[0061] Customer groups: Analyze the differences in leftovers among customer groups of different ages, genders, consumption habits, etc.

[0062] Analytical methods:

[0063] Data visualization: Use tools such as charts and dashboards to intuitively display data of various dimensions, making it easier for managers to understand quickly.

[0064] Association analysis: Use data mining techniques to explore the association between food leftovers and other factors.

[0065] Practical significance:

[0066] Through multi-dimensional analysis, companies can gain an in-depth understanding of the root causes of the leftover food problem and provide strong support for subsequent decision-making.

[0067] S140: Report Generation and Decision Support

[0068] Report Contents:

[0069] Weekly / monthly reports: Regularly summarize leftover data, including total leftovers, leftover rate, popular leftover dishes and other key indicators.

[0070] Cause analysis: Based on the results of multi-dimensional data analysis, in-depth analysis of the causes of leftovers, such as inconsistent taste of dishes, excessive portions, etc.

[0071] Improvement suggestions: Based on the analysis results, propose specific improvement measures, such as adjusting the portion size of dishes, optimizing the menu structure, and strengthening employee training.

[0072] Decision support:

[0073] Menu optimization: Based on leftover data and customer preferences, unpopular dishes are streamlined, popular new dishes are added, and the menu structure is optimized.

[0074] Marketing strategy: Launch customized menus or promotions for specific customer groups to increase customer satisfaction and loyalty.

[0075] Cost control: Achieve cost control by reducing leftovers and food waste.

[0076] Implementation effect:

[0077] After a period of implementation, the catering company found that the amount of leftovers was significantly reduced, customer satisfaction was improved, and the cost of ingredients was effectively controlled. At the same time, based on the results of data analysis, the company successfully launched a series of new dishes that were popular with customers, further enhancing its brand competitiveness.

[0078] Example 2

[0079] 1. Data Collection Methods

[0080] In order to accurately count the remaining amount of dishes, the company first equipped each restaurant with a high-definition camera in the table clearing area and formulated strict photography standards. When clearing the table, if the remaining amount of a single dish exceeds 50%, the waiter must use a mobile phone or a special camera to take a photo of the leftovers and briefly record basic information such as the dish name, table number, and dining time. These photos and information are then automatically uploaded to the central database through the cloud system to provide raw data for subsequent analysis.

[0081] 2. AI image recognition technology After collecting the data, the company uses advanced AI image recognition technology to analyze the photos. Through deep learning algorithms, the AI ​​model can automatically identify the type of dishes in the photos, the remaining amount, and the remaining ratio. In order to ensure the accuracy of the recognition results, the company also continuously expands the training data set, optimizes the algorithm model, and improves the recognition accuracy and efficiency.

[0082] Data analysis and problem identification

[0083] After a period of data accumulation, the company found that the amount of leftovers of certain dishes was always higher than that of other dishes. In order to gain a deeper understanding of the reasons for the high amount of leftovers of these dishes, the company conducted further data analysis.

[0084] 1. Analysis of dish taste

[0085] First, the company conducted an in-depth analysis of these dishes from the perspective of taste. By comparing the taste characteristics of dishes with high leftovers with other dishes, the company found that these dishes with high leftovers are generally spicy. Combined with the taste preference data of local customers, the company found that local customers have a relatively low acceptance of spicy taste. This finding provides important clues to explain why these dishes have high leftovers.

[0086] 2. Customer feedback verification

[0087] In order to verify that the spicy taste is the main reason for the high amount of leftovers in these dishes, the company also collected feedback from customers. Through questionnaires, online reviews and other methods, the company learned that many customers said that the taste of these spicy dishes was too strong and difficult to accept. This feedback further confirmed that the spicy taste is the key factor leading to the high amount of leftovers in these dishes.

[0088] IV. Problem Solving and Implementation

[0089] After identifying the cause of the problem, the company began to develop solutions to reduce the amount of leftovers of these dishes.

[0090] 1. Adjust the spiciness of dishes

[0091] In order to solve the problem of spicy taste, the company decided to adjust the spiciness of these dishes. By reducing the amount of chili peppers or replacing them with less spicy varieties, the company successfully reduced the spiciness of these dishes, making them more in line with the taste preferences of local customers.

[0092] 2. Offer non-spicy or mildly spicy options

[0093] In addition to adjusting the spiciness of existing dishes, the company also decided to launch more non-spicy or slightly spicy dishes to meet the taste needs of different customers. By developing new dishes or improving existing dishes, the company successfully launched a series of new dishes with moderate taste that are popular with customers, effectively diversifying customers' dependence on spicy dishes.

[0094] 3. Strengthen employee training and guidance

[0095] To ensure the effective implementation of the solution, the company also strengthened the training and guidance of employees. Through the training, employees have a deeper understanding of the severity of the leftover problem and the importance of the solution, so that they can more actively guide customers to choose dishes with moderate taste in their daily work and reduce the amount of leftovers.

[0096] V. Implementation Effect and Evaluation

[0097] After taking the above measures, the company evaluated the implementation effects.

[0098] 1. The amount of leftovers is significantly reduced

[0099] After a period of observation and data statistics, the company found that the amount of leftovers of these dishes that once had a high amount of leftovers was significantly reduced after adjusting the spiciness and launching new dishes. This change not only reduced the waste of ingredients, but also improved the dining experience of customers.

[0100] 2. Improved customer satisfaction

[0101] With the improvement of food taste and the introduction of new dishes, customer satisfaction with the restaurant has also increased significantly. Through questionnaire surveys and online reviews, the company learned that customers highly praised the restaurant's food taste, service quality and dining environment. This change not only strengthened customer loyalty, but also attracted more new customers to dine.

[0102] 3. Reduced operating costs

[0103] The reduction in leftovers not only reduces food waste, but also reduces the company's operating costs. By precisely controlling the purchase and use of food ingredients, the company has successfully reduced inventory costs; at the same time, the reduction in leftovers also reduces the manpower and material costs required to handle leftovers, further improving the company's operating efficiency.

[0104] VI. Summary and Outlook

[0105] Through the in-depth analysis and practice of the remaining amount of dishes, the catering company successfully found the root cause of the problem of leftovers and took effective measures to solve it. This process not only improved customer satisfaction and loyalty, but also reduced the company's operating costs and food waste. Looking to the future, the company will continue to deepen the application of science and technology, explore more innovative management models, and contribute to the sustainable development of the catering industry.

[0106] At the same time, this practice also provides valuable experience and inspiration for enterprises: when solving the problem of leftovers, it is necessary to deeply understand the taste preferences and needs of customers, and through accurate data analysis and scientific decision-making, to find the root cause of the problem and take effective measures to solve it. In addition, strengthening employee training and guidance is also a key link to ensure the effective implementation of the solution.

[0107] In short, through this practice, the catering company not only solved the problem of leftovers, but also improved its competitiveness and sustainable development capabilities. This successful case also provides useful reference and inspiration for other catering companies.

[0108] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0110] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0112] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0113] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0114] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0115] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for leftover analysis and menu optimization based on artificial intelligence, characterized in that: include: A. First, take photos to collect data; B. Start AI judgment and analysis; C. Then multi-dimensional data analysis; D. Finally generate reports and decision support.

2. The method according to claim 1, characterized in that include: The method uses image capture technology to record the situation where more than 50% of a single dish is left by taking photos.

3. The method according to claim 1, characterized in that include: The method adopts AI judgment technology and utilizes artificial intelligence technology to analyze and judge the photos of leftovers.

4. The method according to claim 1, characterized in that: include: The method uses data analysis technology to conduct multi-dimensional data analysis on the results of AI judgment, including the type of leftovers, remaining quantity, remaining time, and customer feedback.

5. The method according to claim 1, characterized in that include: The method generates weekly and monthly reports based on the data analysis results. The reports should include detailed analysis of leftovers, trend forecasts, and improvement suggestions to provide decision support for managers.

6. The method according to claim 1, characterized in that include: The method uses precise decision-making technology to make precise decisions based on the information and suggestions in the report, including operations related to streamlining menus and adjusting food preparation standards.

7. The method according to claim 1, characterized in that include: By introducing more advanced algorithms and models, the method allows AI to more accurately identify the types and amounts of leftovers, and even predict leftover trends.

8. The method according to claim 1, characterized in that include: The method collects and analyzes leftover data in real time through real-time data analysis and feedback, so that managers can adjust dish preparation and supply strategies in a timely manner to reduce the generation of leftovers.

9. The method according to claim 1, characterized in that: include: The method uses blockchain technology to ensure the authenticity and non-tamperability of leftover data, so that managers and customers have more confidence in the accuracy of the data and ensure the authenticity and non-tamperability of the leftover data.

10. The method according to claim 1, characterized in that include: The method can be combined with a leftover food management system by utilizing the Internet of Things technology to achieve full-process monitoring and management of food preparation, supply and leftover food processing.

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