Bottle washing machine temperature deviation adjusting method and system based on intelligent temperature control

Abstract

The invention discloses a bottle washing machine temperature deviation adjusting method and system based on intelligent temperature control, and aims to solve the problems of temperature control precision, environmental adaptability and operation efficiency of a traditional bottle washing machine. The internal temperature, the water quality and the external environment of the bottle washing machine are monitored in real time through the data acquisition and monitoring module, the future temperature trend is predicted according to historical data by adopting a machine learning algorithm ARIMA, and the most appropriate control instruction is calculated in combination with a dynamic adjustment algorithm. After the control execution module receives the instruction, the working intensity of the heating or cooling device is adjusted through a controller and an actuator, and accurate temperature regulation and control are achieved. And the safety protection and self-adaptive optimization module ensures continuous improvement of equipment safety and production efficiency. Generally speaking, the intelligent temperature control bottle washing machine system remarkably improves the temperature control precision, and the operation efficiency and the safety guarantee level of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control of bottle washing machines, and in particular to a temperature deviation adjustment method and system for bottle washing machines based on intelligent temperature control. Background Art

[0002] Traditional bottle washers often suffer from large temperature fluctuations and poor stability in temperature control, especially when faced with varying environmental conditions and water quality. Intelligent temperature control systems, through real-time monitoring and intelligent adjustment, effectively control the internal temperature of the bottle washers, keeping them stable within the set target temperature range, improving temperature control accuracy and stability. Bottle washers often need to operate under varying environmental conditions, such as varying room temperature and water quality. Through machine learning and adaptive optimization, intelligent temperature control systems automatically adapt to changes in the external environment, predict future temperature trends, and make corresponding adjustments, ensuring optimal temperature control in all environments. Traditional bottle washers require operators to frequently monitor and adjust temperature control parameters, resulting in low efficiency and a high risk of operational errors. Intelligent temperature control systems, with their automated adjustment and abnormality alarms, reduce the need for operator intervention, improve efficiency, and reduce the likelihood of operational errors. Summary of the Invention

[0003] A method for adjusting temperature deviation of a bottle washing machine based on intelligent temperature control comprises the following steps: S1. Initialization: Determine the ideal temperature range for the bottle washing process, taking into account factors such as bottle material and stain type, set the initial temperature parameters of the bottle washing machine, including preheating temperature, washing temperature, and rinsing temperature, and read the current ambient temperature of the equipment; S2. Temperature Monitoring and Data Collection: High-precision temperature sensors are used to monitor the temperature of key locations within the bottle washer (such as the water inlet, outlet, and near the spray arms) in real time. This data is collected and stored by the control system and sent to the controller for analysis. S3. Deviation detection: Compare the difference between the actual measured temperature and the set ideal temperature. When the temperature is detected to be out of the preset range, the system identifies and records this deviation event, the current temperature value and the target temperature value, and calculates the temperature deviation (i.e., the temperature difference Δ T ), temperature difference formula: Δ T = T c− T a, where: T c is the current temperature, T a is the set target temperature; S4. Intelligent Regulation: Based on deviations, the intelligent control system automatically adjusts the operating state of the heating element (such as power or duty cycle) to compensate for temperature deviations. For large temperature fluctuations, more proactive measures are taken: rapid heating and cooling mechanisms are implemented, and intelligent control algorithms such as fuzzy control algorithms are introduced to dynamically adjust the temperature. Based on the temperature difference and rate of change, the output power of the heater / cooler is adjusted. When the temperature is low, the heater power is increased, gradually raising the water temperature or the temperature inside the equipment cavity. When the temperature is high, the cooling system (such as fans or coolant circulation) is activated to quickly reduce the temperature. S5. Multi-point temperature balance control: Compare temperature data from different monitoring points to analyze whether the temperature distribution within the bottle washer is uniform. If large regional temperature differences are found, optimize the hot air flow path or adjust the power distribution of the heater / cooler to balance the temperature in each area. After adjustment, continue to monitor temperature changes and make further fine-tuning based on the latest temperature information to ensure that the temperature gradually approaches the target value. S6. Learning and Optimization: Utilizes machine learning algorithms to analyze historical temperature data, automatically adapt to changes in water quality and external ambient temperature, predict future temperature trends, and make corresponding adjustments in advance. The temperature control system periodically runs a self-check mode to calibrate temperature sensors to ensure data accuracy, optimize control algorithm parameters, and improve temperature control accuracy. S7. Alarm and maintenance reminder: After cleaning is completed, the internal temperature of the bottle washing machine will be gradually reduced to a safe range, and the cooling and drying procedures will be automatically run to ensure the safety of the equipment. Regular maintenance will help keep the equipment in optimal operating condition.

[0004] Furthermore, a temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control is provided. In step S4, the working state of the heating element is automatically adjusted: power level and duty cycle to compensate for temperature deviation. An intelligent control algorithm, fuzzy control algorithm, is introduced to dynamically adjust the temperature. Based on the temperature difference and the rate of change, the output power of the heater / cooler is adjusted. The specific steps are as follows: S41. Fuzzy control algorithm design: The temperature difference Δ T The temperature difference and its rate of change (such as the first-order difference) are fuzzified into fuzzy variables: cold, slightly cold, normal, slightly hot, and hot levels. A fuzzy set of fuzzy input variables (temperature difference and rate of change) is created, and a fuzzy set of fuzzy output variables (heater power or duty cycle) is established. A set of fuzzy rules is designed to specify how to adjust the heater power or duty cycle according to different temperature difference and rate of change levels. The fuzzy rules are as follows: When the temperature difference is hot and the temperature difference increases rapidly, the heater power is increased; When the temperature difference is cold, the heater power is reduced; When the temperature difference is close to the target temperature, the existing power is maintained; Based on the current fuzzy input (temperature difference and rate of change) and fuzzy rules, reasoning is performed to determine the fuzzy set of output (heater power or duty cycle); S42. Defuzzification: Convert the fuzzy output into a specific heater power or duty cycle value, and determine the actual heater operating parameters based on the central value or weighted average of the fuzzy output; S43. Execute adjustment: adjust the actual power and duty cycle of the heater according to the defuzzification results, update the heater control system, and ensure that the new parameters take effect immediately and stabilize near the set value; S44. Continuous monitoring and feedback: Continuously monitor temperature changes and the actual response of the heater. If temperature deviation persists and the adjustment effect is poor, re-trigger the fuzzy control algorithm for further adjustment. S45. Abnormal handling and safety protection: Set the threshold for abnormal temperature deviation and establish an abnormal handling mechanism. When it exceeds the safety range, automatically initiate safety measures, including stopping the heater and alarming to notify the operator.

[0005] Furthermore, a temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control is provided. In step S6, a machine learning algorithm is used to analyze historical temperature data, automatically adapt to changes in water quality and external ambient temperature, predict future temperature trends, and make corresponding adjustments in advance. The specific steps are as follows; S61. Data Collection and Preprocessing: Collect temperature data from the bottle washer's historical operation, including temperature values at different time points, environmental conditions (e.g., water quality, room temperature), and operating status (e.g., heater power, duty cycle); S62. Data Preprocessing: Remove outliers, fill missing values, delete incomplete records, and smooth time series data. Extract useful features from the raw data, such as trends, daily and weekly average temperatures, and seasonal factors. Convert data of different scales to the same scale to facilitate model training. S63. Model Training and Development: Select the most relevant and influential features for prediction, such as current temperature, time, water quality indicators, and external ambient temperature. Split the historical dataset into training and test sets. Use the training set to train the ARIMA forecasting model, adjusting hyperparameters to optimize performance and learn the complex relationships between temperature and other variables. Evaluate the model's performance on the test set, including prediction accuracy and generalization ability, using metrics such as root mean square error (RMSE) and mean absolute error (MAE). Adjust model hyperparameters to optimize performance. S64. Predicting Future Temperature Trends: Use the trained model to predict temperatures for future time periods, generating temperature trend prediction curves, including mid-term and long-term forecasts. Based on the prediction results, formulate appropriate strategies or recommendations, such as adjusting the operating intensity of the cooling system and preparing countermeasures in advance. S65. Adaptive Adjustment and Control Strategy: This involves collecting current temperature data and external environmental information in real time, comparing actual observed values with model predictions, evaluating prediction accuracy, and developing an adaptive control strategy. This strategy adjusts the heater's operating state (e.g., power level, duty cycle) based on actual measured temperature deviations and model-predicted temperature trends. If the temperature is predicted to be too high or too low, the heater's operating parameters are adjusted in advance to prevent excessive temperature fluctuations or deviations from the target range. S66. Continuous Optimization and Updates: Adjust the model based on feedback data from actual operations, including updating the training dataset and retraining the model. Regularly evaluate and improve the machine learning model to adapt to environmental changes and system performance optimization needs.

[0006] A temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control, the temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control being used to implement a temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control; the temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control comprising: a data acquisition and monitoring module, a main control and data processing module, a heating element control module, an adaptive optimization and update module, and a safety protection module; Data acquisition and monitoring module: responsible for real-time monitoring of the bottle washing machine's internal temperature, water quality, and external ambient temperature data, and transmitting the data collected by the sensor to the main control module; Main control and data processing module: processes and analyzes real-time data from the data acquisition module, calculates temperature deviations and makes intelligent control decisions. It cleans, smoothes, and extracts features from the collected data to prepare input for machine learning models, which are used to predict future temperature trends and develop adaptive adjustment strategies. Based on model predictions and current deviations, it formulates strategies for real-time adjustment of the operating status of the heating elements. Heating element control module: controls the working status of the heating element (such as heater) inside the bottle washing machine and adjusts the water temperature to maintain the target temperature; Adaptive optimization and update module: Based on real-time feedback data, it automatically adjusts system parameters and optimizes control strategies to improve the performance and stability of the temperature control system; Safety protection module: ensures the safety of the bottle washing machine during operation and prevents problems caused by the temperature exceeding the safety threshold.

[0007] The beneficial effects of the present invention are as follows: Through real-time monitoring and intelligent adjustment, the internal temperature of the bottle washer can be precisely controlled to stabilize it within the set target temperature range, thereby improving the temperature control accuracy and stability of the bottle washer. Through machine learning and adaptive optimization functions, it can automatically identify and adapt to different external environmental changes, adjust temperature control parameters to ensure a stable cleaning effect, and greatly improve the adaptability and flexibility of the system. The establishment of safety thresholds and automatic shutdown protection measures effectively prevents the risk of equipment damage caused by abnormal temperatures, ensuring the safety of the system and the long-term service life of the equipment. The operating status and temperature changes of the bottle washer are monitored in real time, and the data is analyzed and recorded. This data can not only be used to immediately adjust and optimize system performance, but also provide valuable data support and experience accumulation for future system optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of a temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control; DETAILED DESCRIPTION

[0009] A method for adjusting temperature deviation of a bottle washing machine based on intelligent temperature control comprises the following steps: S1. Initialization: Determine the ideal temperature range for the bottle washing process, taking into account factors such as bottle material and stain type, set the initial temperature parameters of the bottle washing machine, including preheating temperature, washing temperature, and rinsing temperature, and read the current ambient temperature of the equipment; S2. Temperature Monitoring and Data Collection: High-precision temperature sensors are used to monitor the temperature of key locations within the bottle washer (such as the water inlet, outlet, and near the spray arms) in real time. This data is collected and stored by the control system and sent to the controller for analysis. S3. Deviation detection: Compare the difference between the actual measured temperature and the set ideal temperature. When the temperature is detected to be out of the preset range, the system identifies and records this deviation event, the current temperature value and the target temperature value, and calculates the temperature deviation (i.e., the temperature difference Δ T ), temperature difference formula: Δ T = T c− T a, where: T c is the current temperature, T a is the set target temperature; S4. Intelligent Regulation: Based on deviations, the intelligent control system automatically adjusts the operating state of the heating element (such as power or duty cycle) to compensate for temperature deviations. For large temperature fluctuations, more proactive measures are taken: rapid heating and cooling mechanisms are implemented, and intelligent control algorithms such as fuzzy control algorithms are introduced to dynamically adjust the temperature. Based on the temperature difference and rate of change, the output power of the heater / cooler is adjusted. When the temperature is low, the heater power is increased, gradually raising the water temperature or the temperature inside the equipment cavity. When the temperature is high, the cooling system (such as fans or coolant circulation) is activated to quickly reduce the temperature. S5. Multi-point temperature balance control: Compare temperature data from different monitoring points to analyze whether the temperature distribution within the bottle washer is uniform. If large regional temperature differences are found, optimize the hot air flow path or adjust the power distribution of the heater / cooler to balance the temperature in each area. After adjustment, continue to monitor temperature changes and make further fine-tuning based on the latest temperature information to ensure that the temperature gradually approaches the target value. S6. Learning and Optimization: Utilizes machine learning algorithms to analyze historical temperature data, automatically adapt to changes in water quality and external ambient temperature, predict future temperature trends, and make corresponding adjustments in advance. The temperature control system periodically runs a self-check mode to calibrate temperature sensors to ensure data accuracy, optimize control algorithm parameters, and improve temperature control accuracy. S7. Alarm and maintenance reminder: After cleaning is completed, the internal temperature of the bottle washing machine will be gradually reduced to a safe range, and the cooling and drying procedures will be automatically run to ensure the safety of the equipment. Regular maintenance will help keep the equipment in optimal operating condition.

[0010] Furthermore, a temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control is provided. In step S4, the working state of the heating element is automatically adjusted: power level and duty cycle to compensate for temperature deviation. An intelligent control algorithm, fuzzy control algorithm, is introduced to dynamically adjust the temperature. Based on the temperature difference and the rate of change, the output power of the heater / cooler is adjusted. The specific steps are as follows: S41. Fuzzy control algorithm design: The temperature difference Δ T The temperature difference and its rate of change (such as the first-order difference) are fuzzified into fuzzy variables: cold, slightly cold, normal, slightly hot, and hot levels. A fuzzy set of fuzzy input variables (temperature difference and rate of change) is created, and a fuzzy set of fuzzy output variables (heater power or duty cycle) is established. A set of fuzzy rules is designed to specify how to adjust the heater power or duty cycle according to different temperature difference and rate of change levels. The fuzzy rules are as follows: When the temperature difference is hot and the temperature difference increases rapidly, the heater power is increased; When the temperature difference is cold, the heater power is reduced; When the temperature difference is close to the target temperature, the existing power is maintained; Based on the current fuzzy input (temperature difference and rate of change) and fuzzy rules, reasoning is performed to determine the fuzzy set of output (heater power or duty cycle); S42. Defuzzification: Convert the fuzzy output into a specific heater power or duty cycle value, and determine the actual heater operating parameters based on the central value or weighted average of the fuzzy output; S43. Execute adjustment: adjust the actual power and duty cycle of the heater according to the defuzzification results, update the heater control system, and ensure that the new parameters take effect immediately and stabilize near the set value; S44. Continuous monitoring and feedback: Continuously monitor temperature changes and the actual response of the heater. If temperature deviation persists and the adjustment effect is poor, re-trigger the fuzzy control algorithm for further adjustment. S45. Abnormal handling and safety protection: Set the threshold for abnormal temperature deviation and establish an abnormal handling mechanism. When it exceeds the safety range, automatically initiate safety measures, including stopping the heater and alarming to notify the operator.

[0011] Furthermore, a temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control is provided. In step S6, a machine learning algorithm is used to analyze historical temperature data, automatically adapt to changes in water quality and external ambient temperature, predict future temperature trends, and make corresponding adjustments in advance. The specific steps are as follows; S61. Data Collection and Preprocessing: Collect temperature data from the bottle washer's historical operation, including temperature values at different time points, environmental conditions (e.g., water quality, room temperature), and operating status (e.g., heater power, duty cycle); S62. Data Preprocessing: Remove outliers, fill missing values, delete incomplete records, and smooth time series data. Extract useful features from the raw data, such as trends, daily and weekly average temperatures, and seasonal factors. Convert data of different scales to the same scale to facilitate model training. S63. Model Training and Development: Select the most relevant and influential features for prediction, such as current temperature, time, water quality indicators, and external ambient temperature. Split the historical dataset into training and test sets. Use the training set to train the ARIMA forecasting model, adjusting hyperparameters to optimize performance and learn the complex relationships between temperature and other variables. Evaluate the model's performance on the test set, including prediction accuracy and generalization ability, using metrics such as root mean square error (RMSE) and mean absolute error (MAE). Adjust model hyperparameters to optimize performance. S64. Predicting Future Temperature Trends: Use the trained model to predict temperatures for future time periods, generating temperature trend prediction curves, including mid-term and long-term forecasts. Based on the prediction results, formulate appropriate strategies or recommendations, such as adjusting the operating intensity of the cooling system and preparing countermeasures in advance. S65. Adaptive Adjustment and Control Strategy: This involves collecting current temperature data and external environmental information in real time, comparing actual observed values with model predictions, evaluating prediction accuracy, and developing an adaptive control strategy. This strategy adjusts the heater's operating state (e.g., power level, duty cycle) based on actual measured temperature deviations and model-predicted temperature trends. If the temperature is predicted to be too high or too low, the heater's operating parameters are adjusted in advance to prevent excessive temperature fluctuations or deviations from the target range. S66. Continuous Optimization and Updates: Adjust the model based on feedback data from actual operations, including updating the training dataset and retraining the model. Regularly evaluate and improve the machine learning model to adapt to environmental changes and system performance optimization needs.

[0012] A temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control, the temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control being used to implement a temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control; the temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control comprising: a data acquisition and monitoring module, a main control and data processing module, a heating element control module, an adaptive optimization and update module, and a safety protection module; Data acquisition and monitoring module: responsible for real-time monitoring of the bottle washing machine's internal temperature, water quality, and external ambient temperature data, and transmitting the data collected by the sensor to the main control module; Main control and data processing module: processes and analyzes real-time data from the data acquisition module, calculates temperature deviations and makes intelligent control decisions. It cleans, smoothes, and extracts features from the collected data to prepare input for machine learning models, which are used to predict future temperature trends and develop adaptive adjustment strategies. Based on model predictions and current deviations, it formulates strategies for real-time adjustment of the operating status of the heating elements. Heating element control module: controls the working status of the heating element (such as heater) inside the bottle washing machine and adjusts the water temperature to maintain the target temperature; Adaptive optimization and update module: Based on real-time feedback data, it automatically adjusts system parameters and optimizes control strategies to improve the performance and stability of the temperature control system; Safety protection module: ensures the safety of the bottle washing machine during operation and prevents problems caused by the temperature exceeding the safety threshold.

Claims

1. A temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control, characterized in that: The following steps are included: S1. Initialization: Determine the ideal temperature range for the bottle washing process, taking into account factors such as bottle material and stain type, set the initial temperature parameters of the bottle washing machine, including preheating temperature, washing temperature, and rinsing temperature, and read the current ambient temperature of the equipment; S2. Temperature Monitoring and Data Collection: High-precision temperature sensors are used to monitor the temperature of key locations within the bottle washer in real time, including the water inlet, water outlet, and near the spray arms. This data is collected and stored by the control system and sent to the controller for analysis. S3. Deviation detection: Compare the difference between the actual measured temperature and the set ideal temperature. When the temperature is detected to be outside the preset range, identify and record this deviation event: the current temperature value and the target temperature value, calculate the temperature deviation, the temperature difference formula is: Δ T = T c− T a, where: T c is the current temperature, T a is the set target temperature; S4. Intelligent Regulation: Automatically adjusts the heating element's operating state, including power and duty cycle, to compensate for temperature deviations. Intelligent fuzzy control algorithms are introduced to dynamically adjust the temperature. Based on the temperature difference and rate of change, the output power of the heater / cooler is adjusted. When the temperature is low, the heater power is increased, gradually raising the water temperature or the temperature inside the equipment cavity. When the temperature is high, the cooling system is activated for rapid cooling. S5. Multi-point temperature balance control: Compare temperature data from different monitoring points to analyze whether the temperature distribution within the bottle washer is uniform. If large regional temperature differences are found, optimize the hot air flow path and adjust the power distribution of the heater / cooler to balance the temperature in each area. After adjustment, continue to monitor temperature changes and make further fine-tuning based on the latest temperature information to ensure that the temperature gradually approaches the target value. S6. Learning and Optimization: Utilizes machine learning algorithms to analyze historical temperature data, automatically adapt to changes in water quality and external ambient temperature, predict future temperature trends, and make corresponding adjustments in advance. The temperature control system periodically runs a self-check mode to calibrate temperature sensors to ensure data accuracy, optimize control algorithm parameters, and improve temperature control accuracy. S7. Alarm and maintenance reminder: After cleaning is completed, the internal temperature of the bottle washing machine will be gradually reduced to a safe range, and the cooling and drying procedures will be automatically run to ensure the safety of the equipment. Regular maintenance will help keep the equipment in optimal operating condition.

2. A temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control according to claim 1, characterized in that ； In step S4, the working state of the heating element is automatically adjusted: power level and duty cycle to compensate for temperature deviation. An intelligent control algorithm, fuzzy control algorithm, is introduced to dynamically adjust the temperature. Based on the temperature difference and the rate of change, the output power of the heater / cooler is adjusted. The specific steps are as follows: S41. Fuzzy control algorithm design: The temperature difference Δ T The temperature difference and its rate of change are fuzzified into fuzzy variables: cold, slightly cold, normal, slightly hot, and hot levels. Fuzzy input variables are created: a fuzzy set of temperature difference and rate of change. Fuzzy output variables are established as a fuzzy set of heater power and duty cycle. A set of fuzzy rules is designed to specify different levels of temperature difference and rate of change. The fuzzy rules are: When the temperature difference is hot and the temperature difference increases rapidly, the heater power is increased; When the temperature difference is cold, the heater power is reduced; When the temperature difference is close to the target temperature, the existing power is maintained; According to the current fuzzy input: temperature difference, change rate and fuzzy rules, reasoning is performed to determine the fuzzy set of output heater power; S42. Defuzzification: Convert the fuzzy output into a specific heater power and duty cycle value, and determine the actual heater operating parameters based on the center value of the fuzzy output; S43. Execute adjustment: adjust the actual power and duty cycle of the heater according to the defuzzification results, update the heater control system, and ensure that the new parameters take effect immediately and stabilize near the set value; S44. Continuous monitoring and feedback: Continuously monitor temperature changes and the actual response of the heater. If temperature deviation persists and the adjustment effect is poor, re-trigger the fuzzy control algorithm for further adjustment. S45. Abnormal handling and safety protection: Set the threshold for abnormal temperature deviation and establish an abnormal handling mechanism. When it exceeds the safety range, automatically initiate safety measures, including stopping the heater and alarming to notify the operator.

3. A temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control according to claim 1, characterized in that ； In step S6, a machine learning algorithm is used to analyze historical temperature data, automatically adapt to changes in water quality and external ambient temperature, predict future temperature trends, and make corresponding adjustments in advance. The specific steps are as follows; S61. Data Collection and Preprocessing: Collect temperature data from the bottle washer during its historical operation, including temperature values at different time points, environmental conditions (water quality, room temperature), and operating status (heater power, duty cycle information). S62. Data Preprocessing: Remove outliers, fill missing values, delete incomplete records, and smooth time series data. Extract useful features from the raw data, including trends, daily and weekly average temperatures, and seasonal factors, and convert data from different scales to the same scale. S63. Model Training and Development: Select features that influence predictions: current temperature, time, water quality indicators, and external ambient temperature. Divide the historical dataset into training and test sets. Use the training set to train the ARIMA forecasting model, adjusting hyperparameters to optimize performance and learn the complex relationships between temperature and other variables. Evaluate model performance on the test set, including prediction accuracy and generalization ability, using metrics such as root mean square error (RMSE) and mean absolute error (MAE). Adjust model hyperparameters to optimize performance. S64. Predicting Future Temperature Trends: Use the trained model to predict temperatures for future time periods, generating temperature trend prediction curves, including mid-term and long-term forecasts. Based on the prediction results, formulate appropriate strategies or recommendations, such as adjusting the operating intensity of the cooling system and preparing countermeasures in advance. S65. Adaptive Adjustment and Control Strategy: This involves collecting current temperature data and external environmental information in real time, comparing actual observed values with model predictions, evaluating prediction accuracy, and developing an adaptive control strategy. This strategy adjusts the heater's operating state based on actual measured temperature deviations and model-predicted temperature trends. If the temperature is predicted to be too high or too low, the heater's operating parameters are adjusted in advance to prevent excessive temperature fluctuations or deviations from the target range. S66. Continuous Optimization and Updates: Adjust the model based on feedback data from actual operations, including updating the training dataset and retraining the model. Regularly evaluate and improve the machine learning model to adapt to environmental changes and system performance optimization needs.

4. A temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control, characterized in that: The temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control is used to implement the temperature deviation adjustment method for a bottle washing machine based on intelligent temperature control according to any one of claims 1 to 3; the temperature deviation adjustment system for a bottle washing machine based on intelligent temperature control comprises: a data acquisition and monitoring module, a main control and data processing module, a heating element control module, an adaptive optimization and update module, and a safety protection module; Data acquisition and monitoring module: responsible for real-time monitoring of the bottle washing machine's internal temperature, water quality, and external ambient temperature data, and transmitting the data collected by the sensor to the main control module; Main control and data processing module: processes and analyzes real-time data from the data acquisition module, calculates temperature deviations and makes intelligent control decisions. It cleans, smoothes, and extracts features from the collected data to prepare input for machine learning models, which are used to predict future temperature trends and develop adaptive adjustment strategies. Based on model predictions and current deviations, it formulates strategies for real-time adjustment of the operating status of the heating elements. Heating element control module: controls the working status of the heating element inside the bottle washer and adjusts the water temperature to maintain the target temperature; Adaptive optimization and update module: Based on real-time feedback data, it automatically adjusts system parameters and optimizes control strategies to improve the performance and stability of the temperature control system; Safety protection module: ensures the safety of the bottle washing machine during operation and prevents problems caused by the temperature exceeding the safety threshold.

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