Coffee grinding and brewing management method, coffee grinding and brewing management system and coffee grinding and brewing management device based on swan Mongolian system

Abstract

According to the coffee grinding and brewing management method, system and device based on the swan Mongolian system, key parameters such as the balance of coffee beans and the rotating speed of the grinder are collected in real time through the sensor, parameters such as the grinding thickness, the brewing temperature and the water pressure are determined in combination with user selection requirements and historical adjustment data, manual operation errors can be reduced, and the efficiency is improved. The making conditions of each cup of coffee are more stable, so that the consistency of coffee quality is ensured, parameter adjustment is performed based on user selection requirements, personalized preferences of different users on coffee tastes can be met, user experience is improved, historical adjustment data are updated through user feedback, dynamically optimized parameter adjustment is formed, and the user experience is improved. Grinding and brewing parameters can continuously adapt to actual situation changes, the coffee making effect is continuously improved, automatic management of the grinding and brewing process is achieved through the swan Mongolian system, manual intervention is reduced, the operation efficiency and the intelligent degree of equipment are improved, and the operation management cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of coffee making technology, and in particular to a coffee grinding and brewing management method, system and device based on the Hongmeng system. Background Art

[0002] The grinding and brewing processes of traditional commercial coffee machines often rely on preset parameters, which have the following drawbacks:

[0003] 1) Grinding precision adjustment relies on manual operation, which makes it difficult to adapt to the coffee bean type and humidity in real time;

[0004] 2) The brewing water temperature and water pressure are fixed and cannot be dynamically adjusted for different coffee types such as Americano and latte;

[0005] 3) Equipment status monitoring is lagging behind, and maintenance relies on manual inspections, which can easily affect coffee quality due to component wear and tear;

[0006] 4) Lack of data analysis capabilities for user taste preferences makes it difficult to achieve personalized production.

[0007] Smart devices based on the Harmony system have the advantages of distributed collaboration, real-time data processing, and cross-device linkage, providing technical support for solving the above problems. Summary of the Invention

[0008] The present invention provides a coffee grinding and brewing management method, system and device based on the Hongmeng system to solve the problems raised in the background technology.

[0009] A coffee grinding and brewing management method based on the Hongmeng system, comprising:

[0010] S1: Based on the sensors deployed in the coffee machine, the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity are collected to obtain the collected parameters;

[0011] S2: Based on user selection requirements and combined with historical adjustment data, the collected parameters are analyzed to determine the grinding coarseness and brewing parameters;

[0012] S3: establishing a grinding instruction and a brewing instruction based on the grinding fineness and the brewing parameters, and making coffee according to the grinding instruction and the brewing instruction;

[0013] S4: Based on user feedback, the historical adjustment data is updated and processed to obtain the latest historical adjustment data.

[0014] Preferably, in S1, the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity are collected based on sensors deployed in the coffee machine to obtain the collected parameters, including:

[0015] The remaining amount of coffee beans is obtained based on the weight sensor;

[0016] The grinding machine speed is obtained based on the speed sensor;

[0017] The water temperature is obtained based on the temperature sensor, and the water pressure is obtained based on the pressure sensor;

[0018] The liquid outflow rate is obtained based on the flow meter;

[0019] The ambient temperature and humidity are collected based on the temperature and humidity sensor.

[0020] Preferably, in S2, based on the user's selection requirements and in combination with historical adjustment data, the collected parameters are analyzed to determine the grinding coarseness and brewing parameters, including:

[0021] Obtaining a coffee type and a taste preference from a user's selection requirements, determining initial brewing parameters from a preset brewing method of the coffee machine based on the coffee type, determining initial optimized parameters based on the taste preference, and optimizing the initial brewing parameters based on the initial optimized parameters to obtain first brewing parameters;

[0022] Obtaining target historical adjustment data whose difference from the first production parameter is within a preset range from the historical adjustment data, obtaining a first variation curve of the target historical adjustment data with temperature and humidity, obtaining a second variation curve of the target historical adjustment data with user satisfaction, obtaining preference similarity between the current user and historical users, and obtaining a local curve from the second variation curve whose preference similarity is greater than a preset similarity as a third variation curve;

[0023] Determining first historical adjustment data corresponding to the ambient temperature and humidity in the collected parameters from the first change curve, obtaining user satisfaction matching the first historical adjustment data from the third change curve, and determining whether the user satisfaction meets a preset requirement;

[0024] If so, the first historical adjustment data is used as reference adjustment data;

[0025] Otherwise, reacquire the first historical adjustment data corresponding to the second ambient temperature and humidity from the first change curve until the preset requirement is met, and use the latest first historical adjustment data as the reference adjustment data;

[0026] Obtaining actual effect data corresponding to the reference adjustment data from the historical adjustment data, obtaining an effect difference sequence between the reference adjustment data and the actual effect data based on time, and predicting a current effect difference of the reference adjustment data in the current coffee making process based on the effect difference sequence;

[0027] Based on the current effect difference and in combination with historical adjustment data, a correction parameter for the reference adjustment data is determined to obtain target adjustment data;

[0028] Grind coarseness and brewing parameters are determined based on the target adjustment data.

[0029] Preferably, based on the current effect difference and in combination with historical adjustment data, determining correction parameters for the reference adjustment data to obtain target adjustment data includes:

[0030] The historical adjustment data, actual historical effect data and historical correction data are used as training data to train the initial neural network model to obtain a parameter correction model;

[0031] Inputting the current effect difference and the reference adjustment data into a parameter correction model to obtain correction parameters for the reference adjustment data;

[0032] The reference adjustment data is corrected based on the correction parameters to obtain target adjustment data.

[0033] Preferably, obtaining a local curve having a preference similarity greater than a preset similarity from the second change curve as the third change curve includes:

[0034] Obtaining historical users whose preference similarity is greater than a preset similarity from the second change curve, and obtaining multiple local curves corresponding to the historical users;

[0035] The multiple local curves are fitted to obtain a third variation curve covering the complete range of historical adjustment data.

[0036] Preferably, in S3, establishing a grinding instruction and a brewing instruction based on the grinding coarseness and the brewing parameters, and making coffee according to the grinding instruction and the brewing instruction includes:

[0037] determining a target grinder speed based on the grinding coarseness, and determining a grinding instruction based on a speed difference between the current grinder speed and the target grinder speed;

[0038] determining a target brewing temperature and a target brewing pressure based on the brewing parameters, and determining brewing instructions based on a difference between a current brewing temperature and a current brewing pressure and the target brewing temperature and the target brewing pressure;

[0039] Follow the grinding and brewing instructions to prepare the coffee.

[0040] Preferably, in S4, based on user feedback, the historical adjustment data is updated and processed to obtain the latest historical adjustment data, including:

[0041] Obtaining quantitative feedback, qualitative feedback, and implicit feedback on the historical adjustment data from user feedback, and determining a feedback score for the historical adjustment data based on the quantitative feedback, qualitative feedback, and implicit feedback;

[0042] Obtaining a correspondence between user feedback and historical adjustment data, and based on the correspondence, eliminating historical adjustment data with feedback scores less than a first threshold, and uniformly assigning a high weight to historical adjustment data with feedback scores greater than a second threshold, thereby obtaining the corresponding latest historical adjustment data;

[0043] Obtaining initial historical adjustment data having a feedback score greater than a first threshold and less than a second threshold, determining, based on an association between the feedback keyword and the adjustment keyword, a correlation between each adjustment data type in the initial historical adjustment data and the feedback score, and calculating a type weight for each adjustment data type based on the correlation;

[0044] Based on the type weight, weighted processing is performed on the corresponding adjustment data types in the initial historical adjustment data to obtain the latest historical adjustment data.

[0045] Preferably, after obtaining the latest historical adjustment data, the method further includes:

[0046] Applying the latest historical adjustment data to step S2;

[0047] The latest historical adjustment data is updated every preset time period.

[0048] A coffee grinding and brewing management system based on Hongmeng system, including:

[0049] The data acquisition module is used to collect the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity based on sensors deployed in the coffee machine to obtain the collected parameters;

[0050] Parameter analysis module, which is used to analyze the collected parameters based on user selection requirements and historical adjustment data to determine the grinding coarseness and brewing parameters;

[0051] An instruction determination module, configured to establish a grinding instruction and a brewing instruction based on the grinding fineness and the brewing parameters, and to prepare coffee according to the grinding instruction and the brewing instruction;

[0052] The data update module is used to update and process the historical adjustment data based on user feedback to obtain the latest historical adjustment data.

[0053] A coffee grinding and brewing management device based on the Hongmeng system, including a coffee grinding and brewing management system based on the Hongmeng system.

[0054] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0055] Through real-time collection of key parameters such as coffee bean residue and grinder speed by sensors, and determination of parameters such as grinding fineness, brewing temperature, and water pressure based on user selection requirements and historical adjustment data, human operation errors can be reduced, making the production conditions of each cup of coffee more stable, thereby ensuring the consistency of coffee quality. Parameter adjustment based on user selection requirements can meet the personalized preferences of different users for coffee flavors and enhance user experience. Historical adjustment data is updated through user feedback to form dynamically optimized parameter adjustments, so that grinding and brewing parameters can continuously adapt to changes in actual conditions and continuously improve coffee making effects. Relying on the Hongmeng system, the grinding and brewing processes can be automated and managed, reducing manual intervention, improving the operating efficiency and intelligence of equipment, and reducing operating and management costs.

[0056] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0059] Figure 1 This is a flowchart of a coffee grinding and brewing management method based on the Hongmeng system in an embodiment of the present invention;

[0060] Figure 2 A flowchart for establishing grinding instructions and brewing instructions in an embodiment of the present invention;

[0061] Figure 3 This is a structural diagram of the coffee grinding and brewing management system based on the Hongmeng system in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0063] Example 1:

[0064] The embodiment of the present invention provides a coffee grinding and brewing management method based on the Hongmeng system, such as Figure 1 Shown, including:

[0065] S1: Based on the sensors deployed in the coffee machine, the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity are collected to obtain the collected parameters;

[0066] S2: Based on user selection requirements and combined with historical adjustment data, the collected parameters are analyzed to determine the grinding coarseness and brewing parameters;

[0067] S3: establishing a grinding instruction and a brewing instruction based on the grinding fineness and the brewing parameters, and making coffee according to the grinding instruction and the brewing instruction;

[0068] S4: Based on user feedback, the historical adjustment data is updated and processed to obtain the latest historical adjustment data.

[0069] In this embodiment, the grinding coarseness and brewing parameters include grinding coarseness, brewing temperature, and brewing water pressure.

[0070] In this embodiment, the user selection requirement includes the type of coffee and taste preference selected by the user.

[0071] The beneficial effects of the above design scheme are: through the real-time collection of key parameters such as the amount of coffee beans remaining and the grinder speed through sensors, the grinding coarseness, brewing temperature, water pressure and other parameters are determined in combination with user selection needs and historical adjustment data, which can reduce human operation errors and make the production conditions of each cup of coffee more stable, thereby ensuring the consistency of coffee quality. Parameter adjustment based on user selection needs can meet the personalized preferences of different users for coffee flavors and improve user experience. Historical adjustment data is updated through user feedback to form dynamically optimized parameter adjustment, so that the grinding and brewing parameters can continuously adapt to changes in actual conditions and continuously improve the coffee making effect. Relying on the Hongmeng system, the grinding and brewing process can be automatically managed, reducing manual intervention, improving the operating efficiency and intelligence of the equipment, and reducing operating and management costs.

[0072] Example 2:

[0073] Based on Example 1, this embodiment of the present invention provides a coffee grinding and brewing management method based on the Hongmeng system. In S1, the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity are collected based on sensors deployed in the coffee machine to obtain the collected parameters, including:

[0074] The remaining amount of coffee beans is obtained based on the weight sensor;

[0075] The grinding machine speed is obtained based on the speed sensor;

[0076] The water temperature is obtained based on the temperature sensor, and the water pressure is obtained based on the pressure sensor;

[0077] The liquid outflow rate is obtained based on the flow meter;

[0078] The ambient temperature and humidity are collected based on the temperature and humidity sensor.

[0079] The beneficial effect of the above design scheme is that it uses sensors to collect key parameters such as the amount of coffee beans remaining and the grinder speed in real time, thereby ensuring the accuracy of coffee production data from the perspective of real-time status monitoring.

[0080] Example 3:

[0081] Based on Example 1, this embodiment of the present invention provides a coffee grinding and brewing management method based on the Hongmeng system. In S2, based on user selection requirements and combined with historical adjustment data, the collected parameters are analyzed to determine the grinding fineness and brewing parameters, including:

[0082] Obtaining a coffee type and a taste preference from a user's selection requirements, determining initial brewing parameters from a preset brewing method of the coffee machine based on the coffee type, determining initial optimized parameters based on the taste preference, and optimizing the initial brewing parameters based on the initial optimized parameters to obtain first brewing parameters;

[0083] Obtaining target historical adjustment data whose difference from the first production parameter is within a preset range from the historical adjustment data, obtaining a first variation curve of the target historical adjustment data with temperature and humidity, obtaining a second variation curve of the target historical adjustment data with user satisfaction, obtaining preference similarity between the current user and historical users, and obtaining a local curve from the second variation curve whose preference similarity is greater than a preset similarity as a third variation curve;

[0084] Determining first historical adjustment data corresponding to the ambient temperature and humidity in the collected parameters from the first change curve, obtaining user satisfaction matching the first historical adjustment data from the third change curve, and determining whether the user satisfaction meets a preset requirement;

[0085] If so, the first historical adjustment data is used as reference adjustment data;

[0086] Otherwise, reacquire the first historical adjustment data corresponding to the second ambient temperature and humidity from the first change curve until the preset requirement is met, and use the latest first historical adjustment data as the reference adjustment data;

[0087] Obtaining actual effect data corresponding to the reference adjustment data from the historical adjustment data, obtaining an effect difference sequence between the reference adjustment data and the actual effect data based on time, and predicting a current effect difference of the reference adjustment data in the current coffee making process based on the effect difference sequence;

[0088] Based on the current effect difference and in combination with historical adjustment data, a correction parameter for the reference adjustment data is determined to obtain target adjustment data;

[0089] Grind coarseness and brewing parameters are determined based on the target adjustment data.

[0090] In this embodiment, the adjustment data is target data, for example, adjusting the brewing temperature to 80 degrees Celsius.

[0091] The beneficial effects of the above design scheme are: by extracting the coffee type and taste preference from the user's selection needs, first determining the initial parameters based on the preset production method, and then optimizing the first production parameters in combination with the taste preference, it can not only ensure that the coffee production meets the basic standards of the category, such as the inherent process requirements of American and latte, but also quickly integrate the user's personalized needs such as concentration and temperature preferences, thereby improving the user's satisfaction with the taste; by analyzing the first change curve of the target historical adjustment data with temperature and humidity, and combining the currently collected ambient temperature and humidity to determine the corresponding historical data, the grinding and brewing parameters can dynamically adapt to environmental changes, such as adjusting the grinding coarseness to avoid agglomeration in a high temperature and high humidity environment, and fine-tuning the water temperature in a low temperature environment to prevent a light taste, which solves the problem that traditional equipment parameters are fixed and easily affected by the environment, resulting in quality fluctuations, and ensures the stability of coffee quality in different environments. The third change curve is screened out by the similarity of user preferences, and whether the user satisfaction meets the standard is used as the reference adjustment data screening standard to ensure that parameter adjustment always revolves around user satisfaction. At the same time, combined with relevant It uses historical feedback from similar users to reduce the cost of parameter trial and error, making the final parameters more in line with the taste expectations of the target user group. By analyzing the difference sequence between the reference adjustment data and the actual effect data, it predicts the effect difference of the current production and generates correction parameters, realizing a closed loop of historical experience-current prediction-dynamic correction, which can effectively offset the parameter deviations caused by factors such as equipment aging and raw material batch differences, such as changes in powder output after grinder wear, ensuring that the final parameters are highly matched with actual production needs, and further improving the consistency of coffee quality. By systematically sorting out the rules in historical adjustment data, such as the relationship between environment, user preferences and parameters, a reusable adjustment model is formed to reduce dependence on manual experience. At the same time, data-based predictive adjustment can reduce the waste of raw materials caused by improper parameters, such as over-coarse grinding resulting in too weak coffee liquid requiring rework, and indirectly reduce operating costs. Ultimately, intelligent and precise adjustment of grinding and brewing parameters is realized, which not only ensures the stability of coffee quality, but also deeply fits user needs, while improving the equipment's adaptability and operational efficiency.

[0092] Example 4:

[0093] Based on Example 3, this embodiment of the present invention provides a coffee grinding and brewing management method based on the Hongmeng system. Based on the current effect difference and combined with historical adjustment data, correction parameters for the reference adjustment data are determined to obtain target adjustment data, including:

[0094] The historical adjustment data, actual historical effect data and historical correction data are used as training data to train the initial neural network model to obtain a parameter correction model;

[0095] Inputting the current effect difference and the reference adjustment data into a parameter correction model to obtain correction parameters for the reference adjustment data;

[0096] The reference adjustment data is corrected based on the correction parameters to obtain target adjustment data.

[0097] The beneficial effect of the above design scheme is: by training historical adjustment data, actual effect data and correction data through a neural network model, the model can autonomously learn the potential relationship between parameters and effects, such as the nonlinear relationship between grinding coarseness deviation and coffee concentration, the influence of water pressure fluctuation on liquid flow rate, etc. Compared with traditional manual experience correction or simple linear fitting, this model can capture more complex parameter influence logic, thereby outputting more accurate correction parameters for current effect differences, reducing coffee quality fluctuations caused by parameter deviations, and ensuring that coffee making parameters are always highly matched with actual needs, ultimately improving users' long-term satisfaction with coffee taste.

[0098] Example 5:

[0099] Based on Example 3, this embodiment of the present invention provides a coffee grinding and brewing management method based on the Hongmeng system, which obtains a local curve with a preference similarity greater than a preset similarity from the second change curve as a third change curve, including:

[0100] Obtaining historical users whose preference similarity is greater than a preset similarity from the second change curve, and obtaining multiple local curves corresponding to the historical users;

[0101] The multiple local curves are fitted to obtain a third variation curve covering the complete range of historical adjustment data.

[0102] The beneficial effect of the above design scheme is that by screening historical users whose preference similarity is greater than a preset threshold, it is possible to accurately locate the group that highly matches the taste needs of the current user, avoiding recommendation bias caused by the inclusion of user data with large differences. For example, for users who prefer a strong and bitter taste, the system will focus on analyzing historical user data with similar preferences, rather than being disturbed by user data that prefers sour, sweet and fruity aromas, so that the generated third change curve is more in line with the actual needs of the current user. When the local curves of multiple similar users are fitted into a third change curve covering the entire range, the originally scattered and discontinuous data points are integrated into a continuous and smooth functional relationship. This enables the system to make reasonable predictions within the parameter range that is not fully covered by historical data. For example, under specific temperature and humidity conditions, a more comprehensive inference can be made on the relationship between grinding coarseness and user satisfaction, filling in data gaps, and improving the integrity of parameter adjustment. This ensures that while ensuring the accuracy of recommendations, the system's adaptability to complex scenarios is enhanced, ultimately improving the personalization level of coffee making and user satisfaction.

[0103] Example 6:

[0104] Based on Example 1, the present invention provides a coffee grinding and brewing management method based on the Hongmeng system, such as Figure 2 As shown, in S3, a grinding instruction and a brewing instruction are established based on the grinding coarseness and the brewing parameters, and coffee is made according to the grinding instruction and the brewing instruction, including:

[0105] determining a target grinder speed based on the grinding coarseness, and determining a grinding instruction based on a speed difference between the current grinder speed and the target grinder speed;

[0106] determining a target brewing temperature and a target brewing pressure based on the brewing parameters, and determining brewing instructions based on a difference between a current brewing temperature and a current brewing pressure and the target brewing temperature and the target brewing pressure;

[0107] Follow the grinding and brewing instructions to prepare the coffee.

[0108] The beneficial effects of the above design scheme are: by determining the target grinder speed based on the grinding coarseness, determining the grinding instructions based on the speed difference between the current grinder speed and the target grinder speed, determining the target brewing temperature and target brewing pressure based on the brewing parameters, and determining the brewing instructions based on the difference between the current brewing temperature and the current brewing pressure and the target brewing temperature and the target brewing pressure, the production conditions of each cup of coffee are made more stable, thereby ensuring the consistency of coffee quality, and adjusting parameters based on user selection needs can meet the personalized preferences of different users for coffee flavors and enhance user experience.

[0109] Example 7:

[0110] Based on Example 1, this embodiment of the present invention provides a coffee grinding and brewing management method based on the Hongmeng system. In S4, based on user feedback, historical adjustment data is updated and processed to obtain the latest historical adjustment data, including:

[0111] Obtaining quantitative feedback, qualitative feedback, and implicit feedback on the historical adjustment data from user feedback, and determining a feedback score for the historical adjustment data based on the quantitative feedback, qualitative feedback, and implicit feedback;

[0112] The calculation formula for the feedback score of historical reconciliation data is as follows:

[0113] K=cos(A-σ1*K1-σ2*K2-σ3*K3)

[0114] Wherein, K represents the feedback score of historical adjustment data, A represents the reference constant, σ1 represents the quantitative feedback weight, K1 represents the normalized quantitative feedback effect value of historical adjustment data, σ1 represents the qualitative feedback weight, K2 represents the normalized qualitative feedback effect value of historical adjustment data, σ3 represents the implicit feedback weight, K3 represents the normalized implicit feedback effect value of historical adjustment data;

[0115] Obtaining a correspondence between user feedback and historical adjustment data, and based on the correspondence, eliminating historical adjustment data with feedback scores less than a first threshold, and uniformly assigning a high weight to historical adjustment data with feedback scores greater than a second threshold, thereby obtaining the corresponding latest historical adjustment data;

[0116] Obtaining initial historical adjustment data having a feedback score greater than a first threshold and less than a second threshold, determining, based on an association between the feedback keyword and the adjustment keyword, a correlation between each adjustment data type in the initial historical adjustment data and the feedback score, and calculating a type weight for each adjustment data type based on the correlation;

[0117]

[0118] Among them, F represents the type weight of the current adjustment data type, γ0 represents the medium weight of the initial historical adjustment data, β represents the correlation between the current adjustment data type and the feedback score, and F C represents the feedback score of the initial historical adjustment data, F1 represents the first threshold, and F2 represents the second threshold;

[0119] Based on the type weight, weighted processing is performed on the corresponding adjustment data types in the initial historical adjustment data to obtain the latest historical adjustment data.

[0120] In this embodiment, for example, if the feedback keyword corresponding to the feedback score is "temperature is too high," then the corresponding adjustment data type has a high correlation with temperature.

[0121] In this embodiment, the second threshold is greater than the first threshold.

[0122] In this embodiment, the quantitative feedback is, for example, star rating (1-5 stars) for parameters such as concentration and temperature.

[0123] In this embodiment, the qualitative feedback is, for example, text evaluations such as the taste is too bitter, the amount of ice is insufficient, and the temperature is too high.

[0124] In this embodiment, implicit feedback is, for example, the implicit feedback from the user's behavior automatically recorded by the system. For example, if the user adjusts the concentration parameter of the same category multiple times, it is assumed that the parameter needs to be optimized.

[0125] In this embodiment, the medium weight of the initial historical adjustment data is lower than the high weight of the historical adjustment data that is greater than the second threshold.

[0126] The beneficial effect of the above design is that it forms a comprehensive feedback evaluation mechanism by integrating quantitative, qualitative, and implicit feedback. This multi-dimensional rating model can more accurately capture users' true perceptions of coffee quality, avoiding the limitations of a single feedback type. By eliminating low-rated data, outdated or erroneous parameters are prevented from influencing subsequent decision-making. By assigning high weights to high-rated data, the reference value of high-quality parameters is strengthened. By fine-tuning the type weights of medium-rated data, historical data updates are more targeted. The specific weights are determined such that when a parameter has a high correlation and the feedback score is close to the second threshold, its type weight tends to be high. When the correlation is low or the score is close to the first threshold, the weight is appropriately reduced. This nonlinear adjustment enables the system to quickly respond to optimization needs of key parameters while avoiding over-adjustment of minor parameters. In summary, through multi-dimensional feedback integration, hierarchical data processing, correlation quantification, and dynamic weight adjustment, this solution ensures that historical adjustment data remains accurate and timely, providing solid support for intelligent decision-making on coffee grinding and brewing parameters, ultimately achieving a virtuous cycle of user demand, production parameters, and feedback optimization.

[0127] Example 8:

[0128] Based on Example 7, this embodiment of the present invention provides a coffee grinding and brewing management method based on the Hongmeng system. After obtaining the latest historical adjustment data, the method further includes:

[0129] Applying the latest historical adjustment data to step S2;

[0130] The latest historical adjustment data is updated every preset time period.

[0131] The beneficial effect of the above design scheme is that the latest historical adjustment data is transmitted back to S2, so that each coffee making can be optimized based on the latest user feedback and historical experience. By continuously updating historical data, the system can automatically adapt to factors such as changes in coffee bean batches, equipment wear, and fluctuations in ambient temperature and humidity, so that the system always remains sensitive to user needs and environmental changes. While improving the stability of coffee quality, it achieves multiple optimizations in personalized services, equipment management, and commercial operations.

[0132] Example 9:

[0133] A coffee grinding and brewing management system based on Hongmeng system, such as Figure 3 Shown, including:

[0134] The data acquisition module is used to collect the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity based on sensors deployed in the coffee machine to obtain the collected parameters;

[0135] Parameter analysis module, which is used to analyze the collected parameters based on user selection requirements and historical adjustment data to determine the grinding coarseness and brewing parameters;

[0136] An instruction determination module, configured to establish a grinding instruction and a brewing instruction based on the grinding fineness and the brewing parameters, and to prepare coffee according to the grinding instruction and the brewing instruction;

[0137] The data update module is used to update and process the historical adjustment data based on user feedback to obtain the latest historical adjustment data.

[0138] In this embodiment, the grinding coarseness and brewing parameters include grinding coarseness, brewing temperature, and brewing water pressure.

[0139] In this embodiment, the user selection requirement includes the type of coffee and taste preference selected by the user.

[0140] The beneficial effects of the above design scheme are: through the real-time collection of key parameters such as the amount of coffee beans remaining and the grinder speed through sensors, the grinding coarseness, brewing temperature, water pressure and other parameters are determined in combination with user selection needs and historical adjustment data, which can reduce human operation errors and make the production conditions of each cup of coffee more stable, thereby ensuring the consistency of coffee quality. Parameter adjustment based on user selection needs can meet the personalized preferences of different users for coffee flavors and improve user experience. Historical adjustment data is updated through user feedback to form dynamically optimized parameter adjustment, so that the grinding and brewing parameters can continuously adapt to changes in actual conditions and continuously improve the coffee making effect. Relying on the Hongmeng system, the grinding and brewing process can be automatically managed, reducing manual intervention, improving the operating efficiency and intelligence of the equipment, and reducing operating and management costs.

[0141] Example 10:

[0142] A coffee grinding and brewing management device based on the Hongmeng system, including a coffee grinding and brewing management system based on the Hongmeng system.

[0143] The beneficial effects of the above design scheme are: through the real-time collection of key parameters such as the amount of coffee beans remaining and the grinder speed through sensors, the grinding coarseness, brewing temperature, water pressure and other parameters are determined in combination with user selection needs and historical adjustment data, which can reduce human operation errors and make the production conditions of each cup of coffee more stable, thereby ensuring the consistency of coffee quality. Parameter adjustment based on user selection needs can meet the personalized preferences of different users for coffee flavors and improve user experience. Historical adjustment data is updated through user feedback to form dynamically optimized parameter adjustment, so that the grinding and brewing parameters can continuously adapt to changes in actual conditions and continuously improve the coffee making effect. Relying on the Hongmeng system, the grinding and brewing process can be automatically managed, reducing manual intervention, improving the operating efficiency and intelligence of the equipment, and reducing operating and management costs.

[0144] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of this application document and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A coffee grinding and brewing management method based on Hongmeng system, characterized in that: include: S1: Based on the sensors deployed in the coffee machine, the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity are collected to obtain the collected parameters; S2: Based on user selection requirements and combined with historical adjustment data, the collected parameters are analyzed to determine the grinding coarseness and brewing parameters; S3: establishing a grinding instruction and a brewing instruction based on the grinding fineness and the brewing parameters, and making coffee according to the grinding instruction and the brewing instruction; S4: Based on user feedback, the historical adjustment data is updated and processed to obtain the latest historical adjustment data.

2. The coffee grinding and brewing management method based on the Hongmeng system according to claim 1 is characterized in that: In S1, the sensors deployed in the coffee machine collect information about the amount of coffee beans remaining, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity to obtain the collected parameters, including: The remaining amount of coffee beans is obtained based on the weight sensor; The grinding machine speed is obtained based on the speed sensor; The water temperature is obtained based on the temperature sensor, and the water pressure is obtained based on the pressure sensor; The liquid outflow rate is obtained based on the flow meter; The ambient temperature and humidity are collected based on the temperature and humidity sensor.

3. The coffee grinding and brewing management method based on the Hongmeng system according to claim 1 is characterized in that: In S2, based on the user's selection requirements and in combination with historical adjustment data, the collected parameters are analyzed to determine the grinding fineness and brewing parameters, including: Obtaining a coffee type and a taste preference from a user's selection requirements, determining initial brewing parameters from a preset brewing method of the coffee machine based on the coffee type, determining initial optimized parameters based on the taste preference, and optimizing the initial brewing parameters based on the initial optimized parameters to obtain first brewing parameters; Obtaining target historical adjustment data whose difference from the first production parameter is within a preset range from the historical adjustment data, obtaining a first variation curve of the target historical adjustment data with temperature and humidity, obtaining a second variation curve of the target historical adjustment data with user satisfaction, obtaining preference similarity between the current user and historical users, and obtaining a local curve from the second variation curve whose preference similarity is greater than a preset similarity as a third variation curve; Determining first historical adjustment data corresponding to the ambient temperature and humidity in the collected parameters from the first change curve, obtaining user satisfaction matching the first historical adjustment data from the third change curve, and determining whether the user satisfaction meets a preset requirement; If so, the first historical adjustment data is used as reference adjustment data; Otherwise, reacquire the first historical adjustment data corresponding to the second ambient temperature and humidity from the first change curve until the preset requirement is met, and use the latest first historical adjustment data as the reference adjustment data; Obtaining actual effect data corresponding to the reference adjustment data from the historical adjustment data, obtaining an effect difference sequence between the reference adjustment data and the actual effect data based on time, and predicting a current effect difference of the reference adjustment data in the current coffee making process based on the effect difference sequence; Based on the current effect difference and in combination with historical adjustment data, a correction parameter for the reference adjustment data is determined to obtain target adjustment data; Grind coarseness and brewing parameters are determined based on the target adjustment data.

4. A coffee grinding and brewing management method based on Hongmeng system according to claim 3, characterized in that: Based on the current effect difference and in combination with historical adjustment data, a correction parameter for the reference adjustment data is determined to obtain target adjustment data, including: The historical adjustment data, actual historical effect data and historical correction data are used as training data to train the initial neural network model to obtain a parameter correction model; Inputting the current effect difference and the reference adjustment data into a parameter correction model to obtain correction parameters for the reference adjustment data; The reference adjustment data is corrected based on the correction parameters to obtain target adjustment data.

5. The coffee grinding and brewing management method based on the Hongmeng system according to claim 3 is characterized in that: Obtaining a local curve having a preference similarity greater than a preset similarity from the second change curve as a third change curve, including: Obtaining historical users whose preference similarity is greater than a preset similarity from the second change curve, and obtaining multiple local curves corresponding to the historical users; The multiple local curves are fitted to obtain a third variation curve covering the complete range of historical adjustment data.

6. The coffee grinding and brewing management method based on the Hongmeng system according to claim 1 is characterized in that: In S3, a grinding instruction and a brewing instruction are established based on the grinding fineness and the brewing parameters, and coffee is prepared according to the grinding instruction and the brewing instruction, including: determining a target grinder speed based on the grinding coarseness, and determining a grinding instruction based on a speed difference between the current grinder speed and the target grinder speed; determining a target brewing temperature and a target brewing pressure based on the brewing parameters, and determining brewing instructions based on a difference between a current brewing temperature and a current brewing pressure and the target brewing temperature and the target brewing pressure; Follow the grinding and brewing instructions to prepare the coffee.

7. The coffee grinding and brewing management method based on Hongmeng system according to claim 1 is characterized in that: In S4, based on user feedback, the historical adjustment data is updated and processed to obtain the latest historical adjustment data, including: Obtaining quantitative feedback, qualitative feedback, and implicit feedback on the historical adjustment data from user feedback, and determining a feedback score for the historical adjustment data based on the quantitative feedback, qualitative feedback, and implicit feedback; Obtaining a correspondence between user feedback and historical adjustment data, and based on the correspondence, eliminating historical adjustment data with feedback scores less than a first threshold, and uniformly assigning a high weight to historical adjustment data with feedback scores greater than a second threshold, thereby obtaining the corresponding latest historical adjustment data; Obtaining initial historical adjustment data having a feedback score greater than a first threshold and less than a second threshold, determining, based on an association between the feedback keyword and the adjustment keyword, a correlation between each adjustment data type in the initial historical adjustment data and the feedback score, and calculating a type weight for each adjustment data type based on the correlation; Based on the type weight, weighted processing is performed on the corresponding adjustment data types in the initial historical adjustment data to obtain the latest historical adjustment data.

8. The coffee grinding and brewing management method based on Hongmeng system according to claim 7 is characterized in that: After obtaining the latest historical adjustment data, it also includes: Applying the latest historical adjustment data to step S2; The latest historical adjustment data is updated every preset time period.

9. A coffee grinding and brewing management system based on Hongmeng system, specifically used in the coffee grinding and brewing management method based on Hongmeng system as described in any one of claims 1-8, characterized in that: include: The data acquisition module is used to collect the remaining coffee beans, grinder speed, water temperature, water pressure, liquid flow rate, and ambient temperature and humidity based on sensors deployed in the coffee machine to obtain the collected parameters; Parameter analysis module, which is used to analyze the collected parameters based on user selection requirements and historical adjustment data to determine the grinding coarseness and brewing parameters; An instruction determination module, configured to establish a grinding instruction and a brewing instruction based on the grinding fineness and the brewing parameters, and to prepare coffee according to the grinding instruction and the brewing instruction; The data update module is used to update and process the historical adjustment data based on user feedback to obtain the latest historical adjustment data.

10. A coffee grinding and brewing management device based on the Hongmeng system, comprising the coffee grinding and brewing management system based on the Hongmeng system as described in claim 9.

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