Tourist low-carbon diet guide service system for tourism and catering

Abstract

The invention discloses a tourist low-carbon diet guide service system for tourism catering, and relates to the technical field of catering guide service. The system comprises a data acquisition and demand analysis module, a design element extraction and framework construction module, a guide service execution module and a verification optimization module which are connected in sequence. Through multi-dimensional data acquisition, a low-carbon attitude-high-carbon behavior difference cause of a tourist is accurately identified, and a core demand is extracted; based on the service blueprint and the demand classification matrix, constructing a guide framework deeply fused with tourism catering regional features; guiding strategies are executed at key contact points such as ordering, dining and remaining food processing through a man-machine interaction and manual assistance assembly, and the functions of real-time measurement and feedback of food waste and carbon emission are integrated. According to the invention, the problems of inaccurate requirements, unmatched scenes, no optimized closed loop and non-quantitative carbon emission in the prior art are effectively solved, and food waste and carbon emission in tourism catering scenes can be significantly reduced.

Description

Technical Field

[0001] This invention relates to the field of catering guidance service technology, and in particular to a low-carbon dining guidance service system for tourists. Background Technology

[0002] Low-carbon diets have become an important part of sustainable development. In the tourism and dining scene, tourists often exhibit a contradiction of "friendly attitude but high-carbon behavior" due to unfamiliarity with local cuisine and mentality of "trying something new" or "saving face". For example, they may order too much food, leading to waste, or prefer high-carbon dishes.

[0003] Existing low-carbon guiding technologies have significant shortcomings: 1. Low demand matching; general promotion cannot solve the pain point of tourists "wanting to be low-carbon but not knowing how to do it"; 2. Lack of scenario adaptability; the solution is out of touch with the characteristics of tourism and catering, which are characterized by high mobility and prominent regionality of dishes. 3. Lack of a closed-loop optimization mechanism makes it difficult to iterate and optimize in order to adapt to changes in requirements; 4. The lack of quantification of carbon emissions makes it difficult to provide intuitive data feedback and reduces its persuasiveness.

[0004] 5. The lack of immediate and positive incentives for low-carbon behaviors (such as points rewards) results in insufficient sustained motivation for users to change their behavior.

[0005] Therefore, there is an urgent need for a guidance system that can accurately identify needs, fit specific scenarios, possess closed-loop optimization capabilities, and integrate carbon quantification feedback. Summary of the Invention

[0006] To address the problems mentioned in the background section, this invention provides a low-carbon dining guidance service system for tourists in the tourism catering industry.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A low-carbon dining guidance service system for tourists in the tourism catering industry includes a data collection and demand analysis module, a design element extraction and framework construction module, a guidance service execution module, and a verification and optimization module connected in sequence. The data acquisition and demand analysis module is used to acquire tourists' high-carbon behavior data, low-carbon attitude data and the causes of the attitude-behavior gap, and extract core low-carbon needs. This module serves as the system's perception layer and decision-making foundation. It systematically collects high-carbon behavior data (such as order volume and food waste) and low-carbon attitude data from tourists through various sensors (like cameras) deployed in the restaurant and survey tools (like online questionnaires). Subsequently, based on grounded theory research methods, the collected data (especially in-depth interview data) undergoes a three-level coding analysis (open coding, axial coding, and selective coding) to accurately identify the causes of attitude-behavior gaps, such as "information asymmetry" and "consumer psychology," and extract core low-carbon needs such as "portion visualization" and "carbon emission reminders."

[0008] The design element extraction and framework construction module, based on the core low-carbon requirements and combined with the tourism catering service process and regional characteristics, constructs a low-carbon diet guidance framework. The system receives a dataset of core low-carbon needs from upstream suppliers and uses a service blueprint, a service design tool, to meticulously depict the entire visitor journey from entry to exit, thereby accurately identifying key service touchpoints such as "ordering food," "dining," and "waste disposal." Next, a demand classification matrix method is employed to map each core need to its corresponding service touchpoint, extracting specific design elements (such as displaying carbon emission information on the ordering interface). Finally, all design elements are integrated to form a highly scenario-adaptable low-carbon dining guidance framework.

[0009] The guidance service execution module is used to implement the guidance strategies defined in the low-carbon diet guidance framework at the catering service touchpoints, and integrates food waste carbon emission measurement and feedback functions. The verification and optimization module is used to iteratively optimize the low-carb diet guidance framework and its implementation effect through expert evaluation and tourist feedback, forming a closed loop. The points reward and incentive module is configured to generate and accumulate personal carbon points for tourists based on user behavior data collected by the guidance service execution module and according to a preset low-carbon behavior quantification points model, and provide a points query and redemption interface.

[0010] Preferably, the guidance service execution module includes a human-computer interaction guidance component and a manual assistance guidance component; The human-computer interaction guidance component includes an electronic ordering screen and a food waste carbon emission measuring instrument; the electronic ordering screen is configured to display the portion size information of the dishes, the estimated carbon emission information, and provide low-carbon meal recommendations; The food waste carbon emission measuring instrument is configured to calculate the carbon emission by weighing the leftover food and combining it with the carbon emission coefficient of the ingredients, and to feed the results back to tourists in real time. The manual guidance component includes a waiter training mechanism and a table prompt carrier, which are used to guide low-carbon practices manually. This includes specialized training mechanisms for waiters and table-side informational tools (such as QR code cards). It ensures waiters can use standardized scripts to make low-carbon recommendations, while tourists can scan QR codes to access more detailed information about low-carbon eating.

[0011] It consists of an electronic ordering screen and a food waste carbon emission measuring instrument. The electronic ordering screen not only displays menu information but also integrates a low-carbon meal recommendation engine. The operational logic is as follows: First, a carbon emission model for each dish is established. When a customer orders, the total carbon emission is calculated in real time. If it exceeds a preset threshold (e.g., 0.8 kg CO2e per person), a collaborative filtering algorithm is triggered to generate a meal plan with similar taste but lower carbon emissions for the customer. After the meal, the carbon emission measuring instrument uses a high-precision weighing sensor to obtain the weight of the leftover food and uses pre-stored food carbon emission coefficients (as described in claim 3: 1.2-1.4 kg CO2 / kg for meat) to quickly calculate and display the carbon footprint of the meal on the feedback screen in real time.

[0012] Preferably, the carbon emission coefficients of the food ingredients used in the food waste carbon emission measuring instrument are in the following ranges: 1.2-1.4 kg CO2 / kg for meat and 0.2-0.3 kg CO2 / kg for vegetables.

[0013] Preferably, the low-carbon meal recommendation function integrated into the electronic ordering screen includes the following execution logic: Step S1: Establish a carbon emission model for dishes based on ingredient type and cooking method; Step S2: Receive the initial menu selection set from tourists; Step S3: Calculate the estimated total carbon emissions for the initial set; Step S4: Compare the estimated total carbon emissions with the preset per capita threshold. If the threshold is exceeded, a recommendation is triggered. Step S5: Generate a low-carbon optimized package plan based on the collaborative filtering algorithm and display the estimated carbon emission reduction.

[0014] Preferably, in the points reward and incentive module, the rules of the low-carbon behavior quantification points model include: obtaining a first point value by selecting a low-carbon meal recommended by the system, obtaining a second point value by having a food waste rate below a preset threshold after the meal, and obtaining a third point value by completing a feedback questionnaire.

[0015] Preferably, the points reward and incentive module is communicatively connected to the verification and optimization module, and the verification and optimization module dynamically optimizes and adjusts the first, second, and third points values ​​using an A / B testing algorithm.

[0016] Preferably, the verification optimization module includes: The expert evaluation unit is configured to organize a cross-disciplinary expert group to conduct a multi-dimensional evaluation of the system. The visitor feedback and prototype iteration unit is configured to collect visitor feedback data and use an A / B testing algorithm to compare the effects of different guidance strategies to output optimization parameters.

[0017] Preferably, the system is suitable for tourist destination restaurants that integrate regional specialty dishes, and the guidance strategy can be adjusted according to local service procedures and customer flow characteristics.

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. Through multi-dimensional data and grounded theory analysis, we guide strategies to directly address core pain points, thereby accurately matching needs.

[0019] 2. The introduction of a points-based reward mechanism effectively stimulated users' intrinsic motivation and long-term engagement. Through A / B testing and closed-loop optimization, the system acquired data-driven self-learning and adaptive capabilities, maintaining high efficiency in the long term.

[0020] 3. Through hardware integration and algorithm models, abstract carbon emissions are transformed into concrete figures, enhancing persuasiveness, providing a basis for management, and making carbon emission quantification intuitive.

[0021] 4. A standardized solution has been developed, which organically integrates user research, strategy generation, intelligent guidance, carbon tracking, incentive feedback and system optimization, providing a one-stop solution with high commercial promotion value. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the present invention; Detailed Implementation To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 Reference Figure 1It includes a data acquisition and requirements analysis module, a design element extraction and framework construction module, a guided service execution module, and a verification and optimization module, which are connected in sequence. The data acquisition and demand analysis module is used to acquire tourists' high-carbon behavior data, low-carbon attitude data and the causes of the attitude-behavior gap, and extract core low-carbon needs. This module serves as the system's perception layer and decision-making foundation. It systematically collects high-carbon behavior data (such as order volume and food waste) and low-carbon attitude data from tourists through various sensors (like cameras) deployed in the restaurant and survey tools (like online questionnaires). Subsequently, based on grounded theory research methods, the collected data (especially in-depth interview data) undergoes a three-level coding analysis (open coding, axial coding, and selective coding) to accurately identify the causes of attitude-behavior gaps, such as "information asymmetry" and "consumer psychology," and extract core low-carbon needs such as "portion visualization" and "carbon emission reminders."

[0025] The design element extraction and framework construction module, based on the core low-carbon requirements and combined with the tourism catering service process and regional characteristics, constructs a low-carbon diet guidance framework. The system receives a dataset of core low-carbon needs from upstream suppliers and uses a service blueprint, a service design tool, to meticulously depict the entire visitor journey from entry to exit, thereby accurately identifying key service touchpoints such as "ordering food," "dining," and "waste disposal." Next, a demand classification matrix method is employed to map each core need to its corresponding service touchpoint, extracting specific design elements (such as displaying carbon emission information on the ordering interface). Finally, all design elements are integrated to form a highly scenario-adaptable low-carbon dining guidance framework.

[0026] The guidance service execution module is used to implement the guidance strategies defined in the low-carbon diet guidance framework at the catering service touchpoints, and integrates food waste carbon emission measurement and feedback functions. The verification and optimization module is used to iteratively optimize the low-carb diet guidance framework and its implementation effect through expert evaluation and tourist feedback, forming a closed loop. The points reward and incentive module is configured to generate and accumulate personal carbon points for tourists based on user behavior data collected by the guidance service execution module and according to a preset low-carbon behavior quantification points model, and provide a points query and redemption interface.

[0027] The guidance service execution module includes a human-computer interaction guidance component and a manual assistance guidance component; The human-computer interaction guidance component includes an electronic ordering screen and a food waste carbon emission measuring instrument; the electronic ordering screen is configured to display the portion size information of the dishes, the estimated carbon emission information, and provide low-carbon meal recommendations; The food waste carbon emission measuring instrument is configured to calculate the carbon emission by weighing the leftover food and combining it with the carbon emission coefficient of the ingredients, and to feed the results back to tourists in real time. The manual guidance component includes a waiter training mechanism and a table prompt carrier, which are used to guide low-carbon practices manually. This includes specialized training mechanisms for waiters and table-side informational tools (such as QR code cards). It ensures waiters can use standardized scripts to make low-carbon recommendations, while tourists can scan QR codes to access more detailed information about low-carbon eating.

[0028] It consists of an electronic ordering screen and a food waste carbon emission measuring instrument. The electronic ordering screen not only displays menu information but also integrates a low-carbon meal recommendation engine. The operational logic is as follows: First, a carbon emission model for each dish is established. When a customer orders, the total carbon emission is calculated in real time. If it exceeds a preset threshold (e.g., 0.8 kg CO2e per person), a collaborative filtering algorithm is triggered to generate a meal plan with similar taste but lower carbon emissions for the customer. After the meal, the carbon emission measuring instrument uses a high-precision weighing sensor to obtain the weight of the leftover food and uses pre-stored food carbon emission coefficients (as described in claim 3: 1.2-1.4 kg CO2 / kg for meat) to quickly calculate and display the carbon footprint of the meal on the feedback screen in real time.

[0029] The carbon emission coefficients of the food waste measuring instrument used are in the following ranges: 1.2-1.4 kg CO2 / kg for meat and 0.2-0.3 kg CO2 / kg for vegetables.

[0030] The low-carbon meal recommendation function integrated into the electronic ordering screen includes the following execution logic: Step S1: Establish a carbon emission model for dishes based on ingredient type and cooking method; Step S2: Receive the initial menu selection set from tourists; Step S3: Calculate the estimated total carbon emissions for the initial set; Step S4: Compare the estimated total carbon emissions with the preset per capita threshold. If the threshold is exceeded, a recommendation is triggered. Step S5: Generate a low-carbon optimized package plan based on the collaborative filtering algorithm and display the estimated carbon emission reduction.

[0031] The rules of the low-carbon behavior quantitative points model in the points reward and incentive module include: obtaining a first point value by selecting a low-carbon meal recommended by the system, obtaining a second point value by having a food waste rate below a preset threshold after the meal, and obtaining a third point value by completing a feedback questionnaire.

[0032] The points reward and incentive module is communicatively connected to the verification and optimization module, and the verification and optimization module dynamically optimizes and adjusts the first, second, and third points values ​​using an A / B testing algorithm.

[0033] The verification optimization module includes: The expert evaluation unit is configured to organize a cross-disciplinary expert group to conduct a multi-dimensional evaluation of the system. The visitor feedback and prototype iteration unit is configured to collect visitor feedback data and use an A / B testing algorithm to compare the effects of different guidance strategies to output optimization parameters.

[0034] The system is applicable to restaurants in tourist destinations that integrate regional specialty dishes, and the guidance strategy can be adjusted according to local service processes and customer flow characteristics.

[0035] In summary, the system provided in this embodiment, through the organic coordination of five major modules, achieves full-process management from demand analysis, strategy formulation, implementation, positive incentives to effect evaluation and optimization. The system has a flexible structure, and its guidance strategies and incentive mechanisms can be adaptively adjusted according to the specific circumstances of different regions, possessing good universality and promising prospects for promotion. In this invention, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; they can refer to the internal connection of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0036] The control method of this invention is automatic control through a controller. The control circuit of the controller can be implemented by simple programming by those skilled in the art. The power supply is also common knowledge in the art. Furthermore, since this invention is mainly used to protect mechanical devices, the control method and circuit connection will not be explained in detail here.

[0037] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A low-carbon dining guidance service system for tourists in the tourism catering industry, characterized in that: It includes a data acquisition and requirements analysis module, a design element extraction and framework construction module, a guided service execution module, and a verification and optimization module, which are connected in sequence. The data acquisition and demand analysis module is used to acquire tourists' high-carbon behavior data, low-carbon attitude data and the causes of the attitude-behavior gap, and extract core low-carbon needs. The design element extraction and framework construction module, based on the core low-carbon requirements and combined with the tourism catering service process and regional characteristics, constructs a low-carbon diet guidance framework. The guidance service execution module is used to implement the guidance strategies defined in the low-carbon diet guidance framework at the catering service touchpoints, and integrates food waste carbon emission measurement and feedback functions. The verification and optimization module is used to iteratively optimize the low-carb diet guidance framework and its implementation effect through expert evaluation and tourist feedback, forming a closed loop. The points reward and incentive module is configured to generate and accumulate personal carbon points for tourists based on user behavior data collected by the guidance service execution module and according to a preset low-carbon behavior quantification points model, and provide a points query and redemption interface.

2. The tourist low-carbon dining guidance service system for tourism catering according to claim 1, characterized in that: The guidance service execution module includes a human-computer interaction guidance component and a manual-assisted guidance component; The human-computer interaction guidance component includes an electronic ordering screen and a food waste carbon emission measuring instrument; The electronic ordering screen is configured to display the portion size information of the dishes, the estimated carbon emission information, and provide low-carbon meal recommendations; The food waste carbon emission measuring instrument is configured to calculate the carbon emission by weighing the leftover food and combining it with the carbon emission coefficient of the ingredients, and to feed the results back to tourists in real time. The manual guidance component includes a waiter training mechanism and a table prompt carrier, which are used to guide low-carbon practices manually.

3. A tourist low-carbon dining guidance service system for tourism catering according to claim 2, characterized in that: The carbon emission coefficients of the food waste measuring instrument used are in the following ranges: meat 1.2-1.4 kg CO2 / kg, vegetables 0.2-0.3 kg CO2 / kg.

4. A tourist low-carbon dining guidance service system for tourism catering according to claim 1, characterized in that: The low-carbon meal recommendation function integrated into the electronic ordering screen includes the following execution logic: Step S1: Establish a carbon emission model for dishes based on ingredient type and cooking method; Step S2: Receive the initial menu selection set from tourists; Step S3: Calculate the estimated total carbon emissions for the initial set; Step S4: Compare the estimated total carbon emissions with the preset per capita threshold. If the threshold is exceeded, a recommendation is triggered. Step S5: Generate a low-carbon optimized package plan based on the collaborative filtering algorithm and display the estimated carbon emission reduction.

5. A tourist low-carbon dining guidance service system for tourism catering according to claim 1, characterized in that: In the points reward and incentive module, the rules of the low-carbon behavior quantification points model include: obtaining a first point value by selecting a low-carbon meal recommended by the system, obtaining a second point value by having a food waste rate below a preset threshold after the meal, and obtaining a third point value by completing a feedback questionnaire.

6. A tourist low-carbon dining guidance service system for tourism catering according to claim 1, characterized in that: The points reward and incentive module is communicatively connected to the verification and optimization module, and the verification and optimization module dynamically optimizes and adjusts the first, second, and third points values ​​using an A / B testing algorithm.

7. A tourist low-carbon dining guidance service system for tourism catering according to claim 1, characterized in that: The verification optimization module includes: The expert evaluation unit is configured to organize a cross-disciplinary expert group to conduct a multi-dimensional evaluation of the system. The visitor feedback and prototype iteration unit is configured to collect visitor feedback data and use an A / B testing algorithm to compare the effects of different guidance strategies to output optimization parameters.

8. A tourist low-carbon dining guidance service system for tourism catering according to claim 1, characterized in that: The system is applicable to restaurants in tourist destinations that integrate regional specialty dishes, and the guidance strategy can be adjusted according to local service processes and customer flow characteristics.

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