A zoned refrigeration system and control logic for smart drinking bars

Abstract

The present application belongs to the technical field of zoned refrigeration, and discloses a zoned refrigeration system and control logic for an intelligent drink bar, including: a beverage preparation area refrigeration module, a raw material storage area refrigeration module, a display area refrigeration module, an intelligent control module and an energy-saving optimization module, and each module is electrically connected, the beverage preparation area refrigeration module includes a first temperature sensor and a refrigeration assembly; the raw material storage area refrigeration module includes a humidity sensor and a second temperature sensor; the display area refrigeration module includes a transparent window and an air curtain arranged within the transparent window, and a third temperature sensor is arranged in the air curtain; the first temperature sensor, the second temperature sensor, the third temperature sensor and the humidity sensor monitor and transmit temperature data to the intelligent control module.

Description

Technical Field

[0001] The present application relates to the field of zoned refrigeration technology, and more specifically, to a zoned refrigeration system and control logic for a smart bar. Background Art

[0002] In traditional bar refrigeration systems, a unified refrigeration mode is often used, which cannot be accurately controlled according to the specific needs of different areas, resulting in insufficient or excessive refrigeration in some areas, affecting the quality of drinks and raw materials, and seriously wasting energy. In addition, traditional systems lack comprehensive consideration of time factors and cannot be flexibly adjusted according to different business hours, holidays, etc., and cannot achieve efficient energy management.

[0003] The data processing and analysis of the refrigeration system is also relatively simple, and it is impossible to effectively analyze and utilize the complex operating environment and environmental data, and it is impossible to predict refrigeration demand in advance and formulate long-term refrigeration plans.

[0004] In terms of data processing, the ability to handle abnormal data is limited, which can easily lead to the system operating based on inaccurate data; moreover, it is difficult for traditional systems to conduct in-depth analysis of the relationship between the cooling needs of different areas, and it is impossible to achieve overall coordinated optimization of the system.

[0005] In addition, there is a lack of comprehensive consideration of different environmental and operational factors during forecasting and decision-making, making it difficult to develop accurate and personalized refrigeration strategies, which cannot meet the needs of modern bar operations for refined operations and energy conservation and consumption reduction.

[0006] To this end, the present application proposes a zoned refrigeration system and control logic for a smart beverage bar to solve the above-mentioned problems.

[0007] Application Contents

[0008] In order to solve the above problems, the present application provides a zoned refrigeration system for a smart drink bar.

[0009] The present application provides a zoned refrigeration system for a smart drinking bar using the following technical solutions:

[0010] A zoned refrigeration system for an intelligent bar, comprising: a beverage preparation area refrigeration module, a raw material storage area refrigeration module, a display area refrigeration module, an intelligent control module and an energy-saving optimization module, and each module is electrically connected, characterized in that: the beverage preparation area refrigeration module comprises a first temperature sensor and a refrigeration assembly;

[0011] The raw material storage area refrigeration module includes a humidity sensor and a second temperature sensor;

[0012] The display area refrigeration module includes a transparent window and an air curtain arranged within the transparent window, wherein a third temperature sensor is arranged in the air curtain;

[0013] The first temperature sensor, the second temperature sensor, the third temperature sensor and the humidity sensor monitor and transmit temperature data to the intelligent control module;

[0014] The intelligent control module obtains the time factor and combines it with the obtained temperature and humidity to process data through a distributed architecture. The intelligent control module is linked with the energy-saving optimization module to analyze the refrigeration demand of each zone through an intelligent algorithm, accurately control and dynamically optimize the system energy consumption, and feed back the optimized control instructions to the refrigeration module.

[0015] Furthermore, the time factors acquired by the intelligent control module include working days, non-working days, and statutory holidays; the intelligent control system automatically adjusts the cooling power and operation mode of the refrigeration modules in each zone through a preset strategy.

[0016] The above technical solution can ensure that the bar can provide appropriate cooling effects under different business conditions, optimize energy use according to time differences, avoid unnecessary energy waste, reduce operating costs, improve the adaptability and economy of the system, and meet the operating needs of the bar at different times.

[0017] Furthermore, the wind speed of the wind curtain is between 1 m / s and 3 m / s, and the wind speed of the wind curtain is associated with the temperature data acquired by the third temperature sensor.

[0018] Through the above technical solution, the wind speed of the wind curtain can be automatically adjusted according to the actual temperature of the display area to achieve a better temperature maintenance effect. On the one hand, the drinks in the display area can always be in a suitable low-temperature display environment to ensure the quality and taste of the drinks; on the other hand, this association mechanism helps to achieve more precise temperature control, improve the stability and reliability of the refrigeration of the display area, and avoid energy waste caused by unreasonable wind speed of the wind curtain.

[0019] Furthermore, the control range of the humidity sensor is 40% to 60%.

[0020] Through the above technical scheme, it is ensured that the raw material storage area is within an appropriate humidity range. It can also effectively prevent the raw materials from becoming moldy and deteriorating due to excessively high humidity or from drying out and being damaged due to too low humidity, thereby maintaining the freshness and quality of the raw materials, thereby improving the quality of the raw materials used in the bar, ensuring the quality and taste of the beverages made, and at the same time helping to extend the storage time of the raw materials and reduce raw material waste and costs.

[0021] A control logic of a zoned refrigeration system for a smart bar, based on the zoned refrigeration system for a smart bar as claimed in claim 1, characterized in that the control logic comprises the following steps:

[0022] S1. Collect data including temperature, humidity, traffic, beverage production, raw material storage, ambient light intensity, external ambient temperature, etc. from the intelligent control module using distributed data acquisition technology; process the data using wavelet transform and anomaly detection algorithm to remove noise and outliers, and normalize the data using Z-Score standardization;

[0023] S2, forecast the cooling demand of each zone within 1-3 hours based on historical data, and adjust the forecast based on real-time feedback;

[0024] S3, using time series to decompose seasonal, holiday, working day, and non-working day factors, and analyzing the long-term cooling demand trend through dummy variables and regularization;

[0025] S4. Use multivariate statistical analysis methods to find out the correlation between cooling demands in different regions, establish a correlation matrix, and adjust system parameters based on the matrix;

[0026] S5. Develop sophisticated rules that include multiple factors, use fuzzy logic to handle factors that are difficult to quantify, and dynamically update rules based on different situations;

[0027] S6. Combine the analytic hierarchy process and entropy weight method to dynamically adjust the weights according to different factors and environmental conditions, and consider the impact between modules;

[0028] S7. Cluster the data according to different characteristics and formulate differentiated cooling strategies based on the clustering results.

[0029] Furthermore, in step S2, ARIMA and Kalman filtering need to be combined or LSTM and attention mechanism should be used for prediction.

[0030] Through the above technical solution, the combination of ARIMA and Kalman filtering can give play to the traditional advantages of ARIMA for time series data. At the same time, the prediction results can be smoothed by Kalman filtering to reduce prediction errors and improve the accuracy of short-term cooling demand prediction. LSTM and attention mechanism are more suitable for processing data with complex periodicity and volatility, and can better capture the time series characteristics in the data, so that the system can more accurately predict short-term cooling demand, make cooling preparations in advance, avoid insufficient or excessive cooling, and improve the adaptability and response speed of the system.

[0031] Furthermore, in step S7, the DBSCAN clustering algorithm and PCA dimensionality reduction are used to perform feature clustering.

[0032] Through the above technical solutions, the DBSCAN clustering algorithm can automatically determine the number of cluster centers and is suitable for processing data of different densities, while PCA dimensionality reduction can improve clustering efficiency and effect. By clustering data according to different characteristics, it is possible to explore potential patterns in the data and formulate differentiated cooling strategies for different clusters, so that the system can perform more targeted cooling operations according to different environments and operating conditions, such as different seasons and different traffic flows, optimize the system's cooling effect and energy usage, and enhance the system's adaptability and personalized service capabilities.

[0033] Furthermore, step S3 needs to use weighted moving average method or ridge regression / Lasso regression combined with machine learning model.

[0034] Through the above technical solutions, multiple factors are considered to analyze the long-term cooling demand trend. The weighted moving average method can highlight the importance of recent data, while the ridge regression / lasso regression combined with the machine learning model can avoid the overfitting problem and help to capture long-term trends more accurately. Taking into account factors such as seasons, holidays, working days and non-working days, the system can better plan long-term cooling arrangements and energy planning, prepare in advance, ensure long-term stable operation, and achieve rational use of energy and effective cost control.

[0035] In summary, the present application includes at least one of the following beneficial technical effects:

[0036] (1) The overall system can provide a personalized refrigeration environment according to the functional requirements of different areas through the cooperation of the zoned refrigeration module and the intelligent control module, ensuring the best conditions for beverage production, raw material storage and display;

[0037] (2) Sensors in each area can monitor environmental information in real time, provide data support for intelligent control, and ensure that the system can be flexibly adjusted according to actual conditions, thereby improving the system's pertinence, reliability, and adaptability, providing high-quality refrigeration guarantees for the operation of the bar and avoiding the deterioration of raw materials and beverage quality caused by improper refrigeration;

[0038] (3) Through precise data processing and the comprehensive application of multiple algorithms, not only can accurate refrigeration demand forecasting and efficient energy control be achieved, but also the refrigeration strategy can be dynamically adjusted according to different time, environment and operation factors, so as to achieve long-term stable operation of the system and cost optimization, thereby improving the sustainability and economic benefits of bar operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a logical diagram of this application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application; obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application without making creative work are within the scope of protection of the present application.

[0041] Example:

[0042] The following is combined with Figure 1 This application is described in further detail.

[0043] The embodiment of the present application discloses a zoned refrigeration system for an intelligent drink bar, comprising: a beverage preparation area refrigeration module, a raw material storage area refrigeration module, a display area refrigeration module, an intelligent control module and an energy-saving optimization module, and each module is electrically connected, characterized in that: the beverage preparation area refrigeration module comprises a first temperature sensor and a refrigeration assembly;

[0044] The raw material storage area refrigeration module includes a humidity sensor and a second temperature sensor;

[0045] The display area refrigeration module includes a transparent window and an air curtain arranged within the transparent window, wherein a third temperature sensor is arranged in the air curtain;

[0046] The first temperature sensor, the second temperature sensor, the third temperature sensor and the humidity sensor monitor and transmit temperature data to the intelligent control module;

[0047] The intelligent control module obtains the time factor and combines it with the acquired temperature and humidity to process the data through a distributed architecture. The intelligent control module works in conjunction with the energy-saving optimization module to analyze the cooling demand of each zone through an intelligent algorithm, accurately control and dynamically optimize the system energy consumption, and feed back the optimized control instructions to the cooling module.

[0048] See also Figure 1 The time factors acquired by the intelligent control module include working days, non-working days, and statutory holidays; the intelligent control system automatically adjusts the refrigeration power and operation mode of the refrigeration modules in each zone through preset strategies; it can ensure that the appropriate refrigeration effect is provided for the drink bar under different business conditions, and it can also optimize energy use according to time differences, avoid unnecessary energy waste, reduce operating costs, improve the adaptability and economy of the system, and meet the operating needs of the drink bar at different times.

[0049] See also Figure 1The wind speed of the wind curtain is between 1m / s and 3m / s, and the wind speed of the wind curtain is associated with the temperature data obtained by the third temperature sensor; the wind speed of the wind curtain can be automatically adjusted according to the actual temperature of the display area to achieve a better temperature maintenance effect. On the one hand, the drinks in the display area can always be in a suitable low-temperature display environment to ensure the quality and taste of the drinks; on the other hand, this association mechanism helps to achieve more accurate temperature control, improve the stability and reliability of the display area refrigeration, and avoid energy waste caused by unreasonable wind speed of the wind curtain.

[0050] See also Figure 1 The control range of the humidity sensor is 40% to 60%, which ensures that the raw material storage area is in an appropriate humidity range. It can also effectively prevent the raw materials from becoming moldy and deteriorating due to excessive humidity or from drying out and damaging due to low humidity, thereby maintaining the freshness and quality of the raw materials, thereby improving the quality of the raw materials used in the bar and ensuring the quality and taste of the beverages. At the same time, it helps to extend the storage time of the raw materials and reduce raw material waste and costs.

[0051] A control logic of a zoned refrigeration system for a smart drink bar, based on a zoned refrigeration system for a smart drink bar according to claim 1, characterized in that the control logic comprises the following steps:

[0052] S1. Collect data including temperature, humidity, traffic, beverage production, raw material storage, ambient light intensity, external ambient temperature, etc. from the intelligent control module using distributed data acquisition technology; process the data using wavelet transform and anomaly detection algorithm to remove noise and outliers, and normalize the data using Z-Score standardization;

[0053] S2, forecast the cooling demand of each zone within 1-3 hours based on historical data, and adjust the forecast based on real-time feedback;

[0054] S3, using time series to decompose seasonal, holiday, working day, and non-working day factors, and analyzing the long-term cooling demand trend through dummy variables and regularization;

[0055] S4. Use multivariate statistical analysis methods to find out the correlation between cooling demands in different regions, establish a correlation matrix, and adjust system parameters based on the matrix;

[0056] S5. Develop sophisticated rules that include multiple factors, use fuzzy logic to handle factors that are difficult to quantify, and dynamically update rules based on different situations;

[0057] S6. Combine the analytic hierarchy process and entropy weight method to dynamically adjust the weights according to different factors and environmental conditions, and consider the impact between modules;

[0058] S7. Cluster the data according to different characteristics and formulate differentiated cooling strategies based on the clustering results.

[0059] See also Figure 1 In step S2, ARIMA and Kalman filtering need to be combined or LSTM and attention mechanism should be used for prediction; the combination of ARIMA and Kalman filtering can give play to the traditional advantages of ARIMA for time series data, and at the same time, the prediction results can be smoothed by Kalman filtering to reduce prediction errors and improve the accuracy of short-term cooling demand prediction; LSTM and attention mechanism are more suitable for processing data with complex periodicity and volatility, and can better capture the time series characteristics in the data, so that the system can more accurately predict short-term cooling demand, make cooling preparations in advance, avoid insufficient or excessive cooling, and improve the adaptability and response speed of the system.

[0060] See also Figure 1 In step S7, the DBSCAN clustering algorithm and PCA dimensionality reduction are used for feature clustering; the DBSCAN clustering algorithm can automatically determine the number of cluster centers and is suitable for processing data of different densities, while the PCA dimensionality reduction can improve clustering efficiency and effect; by clustering data according to different features, it is possible to mine potential patterns in the data and formulate differentiated cooling strategies for different clusters, so that the system can perform more targeted cooling operations according to different environments and operating conditions, such as different seasons and different traffic flows, optimize the cooling effect and energy use of the system, and enhance the adaptability and personalized service capabilities of the system.

[0061] See also Figure 1 , step S3 also needs to use weighted moving average method or ridge regression / Lasso regression combined with machine learning model; considering multiple factors for long-term cooling demand trend analysis, weighted moving average method can highlight the importance of recent data, while ridge regression / Lasso regression combined with machine learning model can avoid overfitting problems, which helps to capture long-term trends more accurately. Considering factors such as seasons, holidays, working days and non-working days, the system can better plan long-term cooling arrangements and energy planning, prepare in advance, ensure long-term stable operation, and achieve rational use of energy and effective cost control.

[0062] The implementation principle of the intelligent zoned refrigeration system for a drink bar in the embodiment of the present application is as follows: First, the temperature sensors and humidity sensors in the refrigeration modules of the beverage preparation area, raw material storage area and display area monitor the environmental parameters of the corresponding areas in real time and transmit these data to the intelligent control module. The intelligent control module obtains the current time factors, including working days, non-working days, statutory holidays and other information, combines the temperature and humidity data received from each sensor, and processes the data through a distributed architecture.

[0063] The intelligent control module passes the processed data to the energy-saving optimization module, which uses intelligent algorithms to analyze the data, including data collection and preprocessing, short-term demand forecasting, long-term trend analysis, correlation analysis, rule-based decision-making, dynamic adjustment of weights and cluster analysis.

[0064] In the data collection and preprocessing stage, distributed data collection technology is used to collect multi-dimensional data, wavelet transform and anomaly detection algorithm are used to process the data and perform Z-Score standardization; for short-term demand forecasting, ARIMA and Kalman filtering are combined or LSTM and attention mechanism are used.

[0065] In long-term trend analysis, time series decomposition is used, combined with weighted moving average method or ridge regression / Lasso regression and machine learning models, and multiple time factors are considered for analysis. Correlation analysis uses multivariate statistical analysis methods to establish a correlation matrix.

[0066] In rule-based decision-making, fuzzy logic is used to deal with factors that are difficult to quantify, and rules are formulated and updated. When dynamically adjusting weights, the hierarchical analysis method and entropy weight method are combined, and the cluster analysis uses the DBSCAN clustering algorithm and PCA dimensionality reduction.

[0067] Finally, the energy-saving optimization module feeds back the optimized control instructions to the intelligent control module, and the intelligent control module adjusts the cooling power and operation mode of the refrigeration modules in each zone according to these instructions.

[0068] The wind speed of the air curtain is adjusted between 1m / s and 3m / s according to the temperature data of the third temperature sensor, and the humidity sensor controls the humidity of the raw material storage area between 40% and 60%. Through such a closed-loop system workflow, the zoned refrigeration, intelligent control and energy optimization of the smart drink bar are realized.

[0069] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A zoned refrigeration system for an intelligent drinking bar, comprising: The beverage making area refrigeration module, the raw material storage area refrigeration module, the display area refrigeration module, the intelligent control module and the energy-saving optimization module are electrically connected to each other, and the characteristic is that the beverage making area refrigeration module includes a first temperature sensor and a refrigeration component; The raw material storage area refrigeration module includes a humidity sensor and a second temperature sensor; The display area refrigeration module includes a transparent window and an air curtain arranged within the transparent window, wherein a third temperature sensor is arranged in the air curtain; The first temperature sensor, the second temperature sensor, the third temperature sensor and the humidity sensor monitor and transmit temperature data to the intelligent control module; The intelligent control module obtains the time factor and combines it with the obtained temperature and humidity to process data through a distributed architecture. The intelligent control module is linked with the energy-saving optimization module to analyze the refrigeration demand of each zone through an intelligent algorithm, accurately control and dynamically optimize the system energy consumption, and feed back the optimized control instructions to the refrigeration module.

2. According to claim 1, a zoned refrigeration system for an intelligent drinking bar is characterized by: The time factors acquired by the intelligent control module include working days, non-working days, and statutory holidays; the intelligent control system automatically adjusts the refrigeration power and operation mode of the refrigeration modules in each zone through a preset strategy.

3. The intelligent drinking bar zone refrigeration system according to claim 1, characterized in that: The wind speed of the wind curtain is between 1 m / s and 3 m / s, and the wind speed of the wind curtain is associated with the temperature data acquired by the third temperature sensor.

4. The intelligent drinking bar zone refrigeration system according to claim 1, characterized in that: The control range of the humidity sensor is 40% to 60%.

5. A control logic of a zoned refrigeration system for a smart bar, based on the zoned refrigeration system for a smart bar as claimed in claim 1, characterized in that: The control logic includes the following steps: S1. Collect data including temperature, humidity, traffic, beverage production, raw material storage, ambient light intensity, external ambient temperature, etc. from the intelligent control module using distributed data acquisition technology; process the data using wavelet transform and anomaly detection algorithm to remove noise and outliers, and normalize the data using Z-Score standardization; S2, forecast the cooling demand of each zone within 1-3 hours based on historical data, and adjust the forecast based on real-time feedback; S3, using time series to decompose seasonal, holiday, working day, and non-working day factors, and analyzing the long-term cooling demand trend through dummy variables and regularization; S4. Use multivariate statistical analysis methods to find out the correlation between cooling demands in different regions, establish a correlation matrix, and adjust system parameters based on the matrix; S5. Develop sophisticated rules that include multiple factors, use fuzzy logic to handle factors that are difficult to quantify, and dynamically update rules based on different situations; S6. Combine the analytic hierarchy process and entropy weight method to dynamically adjust the weights according to different factors and environmental conditions, and consider the impact between modules; S7. Cluster the data according to different characteristics and formulate differentiated cooling strategies based on the clustering results.

6. The intelligent drinking bar zone refrigeration system according to claim 5, characterized in that: In step S2, ARIMA and Kalman filtering need to be combined or LSTM and attention mechanism should be used for prediction.

7. The intelligent drinking bar zone refrigeration system according to claim 5, characterized in that: In step S7, the DBSCAN clustering algorithm and PCA dimensionality reduction are used to perform feature clustering.

8. The intelligent bar zoning refrigeration system according to claim 5, characterized in that: Step S3 also needs to use weighted moving average method or ridge regression / Lasso regression combined with machine learning model.

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