Linkage constant temperature control method and system based on intelligent bathing equipment

Abstract

The invention relates to the technical field of equipment control, in particular to a linkage constant temperature control method and system based on intelligent bathing equipment. The method comprises the following steps: acquiring set target temperature data of bathing equipment; performing environmental data acquisition on a water inlet pipe and a water outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; performing water temperature difference identification on the set target temperature data and the real-time water temperature data of the bath equipment to generate water temperature difference identification data of the bath equipment; and performing potential fluctuation influence analysis on the real-time water pressure data based on the water temperature difference identification data of the bathing equipment to generate water pressure fluctuation data of the bathing equipment. Through the technologies of real-time data acquisition, intelligent water mixing adjustment, heat source linkage regulation and control, non-contact temperature control and the like, the accuracy and reliability of temperature control are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to a linkage constant temperature control method and system based on intelligent bathing equipment. Background Art

[0002] Early bathing systems mainly relied on manual adjustment of water temperature. Users had to test the water repeatedly to achieve the ideal temperature, which was cumbersome and time-consuming. With the advancement of technology, simple electronic temperature control devices have gradually emerged, but their stability and response speed are still insufficient. With the rise of the Internet of Things (IoT) and smart homes, smart bathing equipment has gradually entered the market. Intelligent constant temperature control technology realizes real-time monitoring and adjustment of water temperature through the combination of sensors, controllers and actuators. The initial system mainly uses a single water source for control, but it is found that the water temperature of different water sources fluctuates greatly during use, making it difficult to ensure a constant bathing experience. In recent years, with the development of artificial intelligence and big data technology, the linkage constant temperature control method of smart bathing equipment has been significantly improved. Through data analysis and learning, the system can predict the user's bathing habits and automatically adjust the water temperature. In addition, the introduction of multi-water source linkage control technology enables the system to adjust the water supply ratio of different water sources in real time, thereby achieving more accurate temperature control. However, current existing technologies fail to effectively integrate multiple heat sources, resulting in delays and instability in temperature control. At the same time, physical sensors are usually required for temperature detection, which are easily disturbed by the external environment, resulting in low accuracy and reliability of temperature control. Summary of the invention

[0003] Based on this, it is necessary to provide a linkage constant temperature control method and system based on intelligent bathing equipment to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a linkage constant temperature control method based on intelligent bathing equipment is provided, the method comprising the following steps: Step S1: Obtain target temperature data for bathing equipment; collect environmental data on the water inlet and outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identify water temperature differences between the target temperature data for bathing equipment and the real-time water temperature data to generate water temperature difference identification data for bathing equipment; perform potential fluctuation impact analysis on the real-time water pressure data based on the water temperature difference identification data for bathing equipment to generate water pressure fluctuation data for bathing equipment; Step S2: intelligently adjust the water mixing of the bathing equipment through the water temperature difference identification data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage regulation on the real-time water mixing adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage regulation data; perform non-contact temperature control based on the heat source linkage regulation data, thereby generating non-contact constant temperature control data; Step S3: Model training is performed on the heat source linkage control data through non-contact constant temperature control data to generate a heat source linkage control fluctuation model; the heat source linkage control data is imported into the heat source linkage control fluctuation model to predict the heat source control fluctuation, thereby generating heat source control fluctuation prediction data; the equipment warning threshold is set for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating a heat source control equipment warning threshold; Step S4: Based on the warning threshold of the heat source control equipment, the bathing equipment is monitored for early warning of the linked constant temperature control, and the linked constant temperature control early warning monitoring data is generated; a safety protection mechanism is constructed through the linked constant temperature control early warning monitoring data, and a bathing equipment control and protection strategy is generated to execute the linked constant temperature control operation.

[0005] The present invention obtains the set target temperature data of the bathing equipment, collects environmental data of the water inlet and outlet, and monitors the water temperature and water pressure in real time. This process can clearly identify the user's bathing needs and provide basic data for temperature adjustment. By identifying the difference between the set target temperature and the real-time water temperature data, the system can accurately determine the water temperature that needs to be adjusted. Based on these data, the impact of the potential fluctuation of water pressure on the water temperature is further analyzed, providing an important basis for subsequent adjustment. This stage ensures that the system has a comprehensive understanding of the current state and enhances the responsiveness of temperature control. The system uses the water temperature difference identification data and water pressure fluctuation data obtained in the first step to perform intelligent water mixing adjustment on the bathing equipment. This intelligent adjustment is not only fast but also effective, and can optimize the temperature of the mixed water and improve the user's comfort. Subsequently, the system combines the real-time water mixing adjustment data and the water pressure dynamic adjustment data to perform linkage regulation of the heat source. This mechanism realizes the efficient use of the heat source and improves the response speed of temperature control. Through non-contact temperature control, the system reduces the dependence on traditional physical sensors, improves the accuracy and stability of temperature control, and reduces the impact of external interference on temperature control. The system uses non-contact thermostatic control data to train the model of heat source linkage control data and generate a heat source control fluctuation model. This model can not only learn historical data, but also predict future control fluctuations and provide data support for subsequent operations. According to the prediction results, the system sets the warning threshold of the equipment to prevent abnormal situations. This link significantly improves the intelligence level of the system, can quickly adapt to changes in user needs and ensure safe operation. Finally, the fourth step establishes an early warning monitoring system for linkage thermostatic control. Through real-time monitoring based on the warning threshold of the heat source control equipment, the system can timely identify and handle abnormal situations to ensure the safety of users. At the same time, the monitoring data supports the construction of a safety protection mechanism and formulates a control and protection strategy for bathing equipment to deal with potential risks and failures. This link ensures the stability and safety of the system, reduces the probability of equipment damage, and improves the overall user experience. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data acquisition, intelligent water mixing regulation, heat source linkage control and non-contact temperature control.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire target temperature data for setting the bathing equipment; Step S12: using a temperature sensor to collect the real-time temperature of the water inlet pipe of the bathing equipment to obtain real-time water temperature data; using a water pressure sensor to collect the real-time water pressure of the water outlet of the bathing equipment to obtain real-time water pressure data; Step S13: performing data preprocessing on the real-time water temperature data and the real-time water pressure data to generate standard real-time water temperature data and standard real-time water pressure data, wherein the data preprocessing includes data cleaning, data denoising and data standardization; Step S14: Identify the water temperature difference between the target temperature data set for the bathing equipment and the standard real-time water temperature data to generate water temperature difference identification data for the bathing equipment; analyze the potential fluctuation impact of the standard real-time water pressure data based on the water temperature difference identification data for the bathing equipment to generate water pressure fluctuation data for the bathing equipment.

[0007] The present invention can timely understand the working status of the bathing equipment through real-time data collection of temperature sensors and water pressure sensors, and ensure that the equipment operates within the set target temperature and water pressure range. This real-time monitoring can help to timely discover and solve potential problems and improve the safety of the equipment. In the data preprocessing stage, including data cleaning, denoising and standardization, the data quality can be effectively improved, the influence of measurement errors and noise can be eliminated, thereby ensuring the accuracy of subsequent analysis. This optimization helps to obtain more reliable and consistent temperature and water pressure data, providing a solid foundation for decision-making. By comparing the target temperature set for the bathing equipment with the real-time water temperature data, the temperature difference can be identified, and the operating parameters of the equipment can be adjusted in time. This temperature difference recognition function can prevent users from feeling uncomfortable during use and improve user experience. Water pressure fluctuation analysis based on water temperature difference recognition data can reveal potential operational instability factors and provide guidance for equipment maintenance and optimization. Through a deep understanding of water pressure fluctuations, the incidence of equipment failures can be reduced and the service life of the equipment can be extended.

[0008] Preferably, step S14 includes the following steps: Step S141: Calculate the water temperature difference between the target temperature data set for the bathing equipment and the standard real-time water temperature data to obtain water temperature difference data; compare the water temperature difference data with a preset water temperature difference threshold value; when the water temperature difference data is greater than or equal to the preset water temperature difference threshold value, mark the water temperature difference data to generate bathing equipment water temperature difference identification data; Step S142: Analyze the water pressure time series change of the standard real-time water pressure data to generate historical water pressure data; calculate the flow rate of the bathing equipment based on the standard real-time water pressure data and the historical water pressure data to obtain the flow rate data of the bathing equipment; Step S143: performing a water pressure stability impact analysis on the standard real-time water pressure data and the water temperature difference identification data of the bathing equipment according to the bathing equipment flow data, and generating bathing equipment water pressure fluctuation data; The formula for calculating the flow rate of bathing equipment is as follows: In the formula, Expressed as flow rate, Expressed as the water flow resistance coefficient, Expressed as real-time water pressure, Expressed as historical water pressure.

[0009] The present invention can monitor the water temperature of the bathing equipment in real time by calculating the water temperature difference and comparing it with the preset threshold. When the temperature difference reaches the set alarm threshold, the system can issue a warning in time or automatically adjust the equipment to ensure that the water temperature is always maintained within a comfortable range and improve the user's bathing experience. By analyzing the standard real-time water pressure data and historical water pressure data, the system can accurately calculate the flow rate (Q) of the bathing equipment, which provides a basis for the operation efficiency of the equipment and the effective use of water resources. The flow rate calculation can help identify any flow anomalies and ensure that the equipment operates at optimal performance. By combining the flow rate data with the water temperature difference identification data to perform water pressure stability impact analysis, the root cause of water pressure fluctuations can be deeply understood. This analysis helps to identify potential water pressure fluctuation problems, so as to take preventive measures and reduce the risk of system failure. The generation of water pressure fluctuation data makes equipment maintenance and management more scientific. Through real-time monitoring of water pressure and flow, maintenance personnel can formulate more reasonable maintenance plans, optimize the operation cycle of equipment, and reduce unnecessary downtime. The generation of water pressure fluctuation data makes equipment maintenance and management more scientific. By real-time monitoring of water pressure and flow, maintenance personnel can develop more reasonable maintenance plans, optimize the equipment's operating cycle, and reduce unnecessary downtime. Real-time monitoring of water temperature and water pressure, especially through the water temperature difference marking system, can promptly discover and deal with problems that may cause safety hazards to users, reduce the risk of unexpected situations during bathing, and ensure user safety.

[0010] Preferably, step S2 comprises the following steps: Step S21: Intelligently adjust the water mixing of the bathing equipment by using the water temperature difference recognition data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; Step S22: recalculate the water temperature difference between the real-time mixed water adjustment data and the target temperature data set for the bathing equipment to obtain the positive difference data of the mixed water adjustment water temperature and the negative difference data of the mixed water adjustment water temperature; perform heat source output control based on the positive difference data of the mixed water adjustment water temperature and the negative difference data of the mixed water adjustment water temperature to generate the heat source output control data for the bathing equipment; Step S23: using the real-time water pressure dynamic adjustment data to perform heat source reaction regulation on the bathing equipment heat source regulation data, and generating the bathing equipment heat source reaction regulation data; integrating the heat source regulation data based on the bathing equipment heat source output regulation data and the bathing equipment heat source reaction regulation data, and generating the heat source linkage regulation data; Step S24: Analyze the user usage behavior pattern of the bathing equipment according to the heat source linkage control data to generate user bathing behavior pattern analysis data; perform non-contact temperature control on the heat source linkage control data based on the user bathing behavior pattern analysis data to generate non-contact constant temperature control data.

[0011] The present invention can intelligently adjust the mixed water by using water temperature difference identification data and water pressure fluctuation data. This automatic adjustment ensures that the user can always enjoy the ideal water temperature during the bathing process and avoid the discomfort caused by temperature fluctuations. By recalculating the water temperature difference, the positive and negative difference data of the mixed water adjustment water temperature are generated, so that the heat source output can be accurately regulated. This precise heat source regulation can improve the response speed of the heat source and ensure that the water temperature quickly reaches the user set value. By using real-time water pressure dynamic regulation data to react and regulate the heat source output, the working state of the heat source can be flexibly adjusted according to the actual water pressure change, avoiding the temperature instability problem caused by water pressure changes. By integrating the heat source output regulation data with the heat source reaction regulation data, the generated heat source linkage regulation data realizes the overall optimization of the heat source. This integration can effectively improve the operating efficiency of the system and reduce energy waste. According to the heat source linkage regulation data, the pattern analysis of the user's bathing behavior is helpful to understand the user's usage habits, and then optimize the heat source regulation strategy of the equipment. This analysis can help the equipment better adapt to the needs of different users and improve user satisfaction. The contactless temperature control based on the analysis data of user bathing behavior patterns enables the device to automatically adjust the water temperature without user intervention, providing a more convenient and comfortable bathing experience.

[0012] Preferably, step S21 includes the following steps: Step S211: Analyze the hot and cold water temperatures of the bathing equipment to generate cold water temperature data and hot water temperature data; perform cold and hot water ratio mixing requirements on the cold water temperature data and the hot water temperature data based on the water temperature difference identification data of the bathing equipment to obtain cold and hot water ratio mixing requirement data; Step S212: Calculate the water flow demand of the cold water temperature data and the hot water temperature data according to the cold and hot water ratio mixing demand data to obtain the cold water demand data and the hot water demand data; perform water mixing adjustment calculation on the cold water demand data and the hot water demand data through the water mixing ratio adjustment formula to obtain real-time water mixing adjustment data; Step S213: adjusting the water mixing valve control of the bathing equipment based on the real-time water mixing adjustment data, thereby generating water mixing valve control adjustment data; adjusting the valve opening water pressure of the water mixing valve control adjustment data based on the water pressure fluctuation data of the bathing equipment, thereby generating real-time water pressure dynamic adjustment data; The calculation formula of the mixing water ratio adjustment formula is as follows: In the formula, Represented as real-time mixed water regulation data, Expressed as the cold and hot water regulation ratio coefficient, Expressed as real-time temperature deviation, Expressed as the adjustment gain coefficient, Expressed as the adjustment differential coefficient, Expressed as the error between the set value and the actual value, Represents the current moment, Represented as a time variable.

[0013] The present invention can generate accurate cold water and hot water temperature data by analyzing the temperature of cold and hot water. This provides a basis for the subsequent calculation of the mixing ratio of water, ensuring that the final mixed water temperature meets the user's set requirements. Based on the cold and hot water ratio mixing requirements of the water temperature difference identification data, the ratio of cold water and hot water can be accurately adjusted. Through effective cold and hot water ratio mixing, it is ensured that the user obtains the required comfortable water temperature during the bathing process, and the user satisfaction is improved. Through the calculation of the demand for cold water and hot water, the water flow can be reasonably allocated to ensure the efficient use of water resources during the operation of the equipment. This flow management reduces water waste and promotes the sustainable use of resources. The generation of real-time mixing water adjustment data enables the bathing equipment to be dynamically adjusted according to user needs and real-time environmental conditions. This real-time response capability improves the intelligence level of the equipment and ensures that the user's experience during use is smoother. Based on the generation of mixing water valve control adjustment data, the bathing equipment can automatically adjust the mixing water valve to ensure that the mixing ratio of cold and hot water adapts to the changing water temperature requirements in real time. This intelligent control reduces the complexity of manual operation and improves the convenience of the equipment. Through real-time monitoring of water pressure fluctuation data and adjustment of valve opening water pressure, the negative impact of water pressure fluctuation on the bathing experience can be effectively avoided. Users can enjoy more stable water flow and water temperature during bathing, improving the overall user experience. The water mixing ratio adjustment formula is applied, and the real-time temperature deviation is adjusted using the feedback control mechanism, which enhances the system's automatic adjustment ability. This feedback-based adjustment method improves the system's adaptability to changes in the external environment and reduces the discomfort caused by water temperature fluctuations. This technology makes it possible to integrate bathing equipment into smart home systems. Through linkage with other smart devices, users can remotely adjust water temperature and water pressure through mobile phone applications and other methods to improve convenience in life.

[0014] Preferably, regulating the heat source output by using the positive difference data of the mixed water temperature and the negative difference data of the mixed water temperature comprises: The heat source output power of the bathing equipment is collected to obtain heat source output power collection data, wherein the heat source output power collection data includes current heat source output power data and maximum heat source output power data; Performing data detection on the mixed water adjustment water temperature positive difference data and the mixed water adjustment water temperature negative difference data, when the mixed water adjustment water temperature positive difference data is detected, the heat source output power data is reduced based on the mixed water adjustment water temperature positive difference data to generate the heat source first control data; When negative difference data of mixed water adjustment water temperature is detected, the current heat source output power data and the maximum output power data of the heat source are converted into a heat source power fluctuation curve to generate a heat source power fluctuation curve; the maximum fluctuation peak value of the heat source power fluctuation curve is extracted to obtain the maximum available power data of the heat source; the negative difference data of mixed water adjustment water temperature is increased according to the maximum available power data of the heat source to generate the second control data of the heat source; The first heat source control data and the second heat source control data are integrated to generate the bathing equipment heat source output control data.

[0015] The present invention can obtain the data of the current heat source output power and the maximum output power through the real-time collection of the heat source output power, and provide basic data support for the regulation. This precise power monitoring helps to optimize the use of the heat source and ensure that users enjoy a stable bathing experience. The system can intelligently adjust the heat source output power according to the positive and negative difference data of the mixed water temperature. When a positive difference is detected, the system will automatically reduce the heat source output power to avoid overheating; and in the case of a negative difference, the system can increase the heat source output power in a timely manner, thereby achieving a rapid temperature recovery. This adaptive regulation improves the intelligence level of the system and ensures the comfort of the user's bathing. By converting the current heat source output power data and the maximum output power data into a fluctuation curve, the maximum available power of the heat source can be effectively identified. This fluctuation management mechanism not only ensures the efficient use of the heat source, but also prevents potential power overload problems and prolongs the service life of the equipment. The first heat source regulation data and the second heat source regulation data are integrated, and the generated bathing equipment heat source output regulation data can provide a more comprehensive regulation strategy for the system. This data integration helps to improve the efficiency and accuracy of regulation, making the operation of the bathing equipment smoother. Through the reasonable adjustment of the heat source power, unnecessary energy waste is avoided. When the user does not need high-temperature water, the measure of reducing the heat source power not only saves electricity, but also reduces the operating cost. The intelligent regulation of heat source output enables users to enjoy more comfortable water temperature changes during bathing, avoiding the discomfort caused by water temperature fluctuations. This good user experience improves the satisfaction of bathing equipment and enhances the user's trust in smart devices. By real-time monitoring of the heat source power and reasonable regulation, the safety hazards caused by overheating of the equipment are reduced. This safety improvement not only protects the equipment itself, but also provides a guarantee for the safe use of users.

[0016] Preferably, step S24 includes the following steps: Step S241: classifying the non-contact user feedback mode of the bathing equipment according to the heat source linkage control data to generate an image feedback mode and an audio feedback mode; collecting user bathing images using a camera based on the image feedback mode to obtain a user bathing image set; performing user expression recognition on the user bathing image set to generate user expression recognition data; Step S242: marking the user image behavior of the user bathing image set according to the user expression recognition data to generate a user behavior marking image, wherein the user behavior marking image includes a comfort marking image and a discomfort marking image; performing a first non-contact feedback control on the bathing equipment based on the comfort marking image and the discomfort marking image until the number of the comfort marking images is greater than the number of the discomfort marking images, and generating expression feedback control data; Step S243: Based on the audio feedback mode, the recording device is used to collect the user's bathing audio to obtain the user's bathing audio; the audio acoustic feature extraction is performed on the user's bathing audio to obtain the user's bathing audio acoustic feature data; the user's bathing audio acoustic feature data is converted into text to generate the user's bathing acoustic text; the user's bathing acoustic text is semantically recognized to generate user feedback semantic recognition data; Step S244: Use the user feedback semantic recognition data to perform a second non-contact feedback regulation on the bathing equipment to generate audio feedback control data; merge the expression feedback control data and the user feedback semantic recognition data to generate user bathing behavior pattern analysis data; perform non-contact temperature control on the heat source linkage regulation data based on the user bathing behavior pattern analysis data, thereby generating non-contact constant temperature control data.

[0017] The present invention classifies image and audio feedback modes, and the system can collect user feedback information in multiple dimensions. Image feedback captures the user's emotional state through the user's facial expression, while audio feedback provides language information about user satisfaction and preferences. This multi-level feedback collection method improves the understanding of user experience. By using user expression recognition technology, the user's comfort during bathing can be identified in real time. This emotion-based feedback mechanism allows the system to perform personalized regulation, so that the bathing equipment can make corresponding adjustments according to the user's actual feelings, thereby improving the user's comfort and satisfaction. By combining the analysis of user behavior marker images with equipment regulation, the system can perform the first non-contact feedback regulation to ensure that adjustments can be made in time when the user feels uncomfortable. This dynamic feedback mechanism enhances the intelligence level of the device, enabling it to adapt to user needs. The expression feedback control data is merged with the user feedback semantic recognition data to generate the user bathing behavior pattern analysis data, which provides a comprehensive understanding of user behavior. This data fusion can provide a more accurate basis for subsequent equipment regulation and further improve the regulation effect. The acoustic feature extraction and text conversion of the user's bathing audio can deeply analyze the user's language expression and emotional state. This provides richer information for understanding the user's true feelings during the bathing process, which in turn helps optimize the device's control strategy. Non-contact temperature control based on user bathing behavior pattern analysis data ensures that users can enjoy a stable temperature when using bathing equipment. This intelligent temperature control enhances the user experience, making them feel more comfortable and satisfied during the bathing process. Through non-contact image and audio feedback collection methods, the user's privacy can be protected to a certain extent, avoiding the discomfort and embarrassment caused by traditional contact feedback methods.

[0018] Preferably, marking the user image behavior of the user bathing image set according to the user expression recognition data includes: Performing face region detection on the user bathing image set to generate user face region detection data; performing facial feature point extraction on the user face region detection data to obtain user facial feature points, wherein the user facial feature points include user eyes, user nose and user mouth; Performing facial region division on the user face region detection data through the user face feature points to generate user face division region data; performing multi-dimensional feature vector extraction on the user face division region data to obtain the user face multi-dimensional feature vector, wherein the multi-dimensional feature vector extraction includes facial geometry extraction, facial muscle movement extraction and facial texture feature extraction; Perform an emotion score on the multi-dimensional feature vector of the user's face according to the user's expression recognition data to generate the emotion score data of the user's facial region; perform an average score on the emotion score data of the user's facial region to generate the comprehensive emotion score data of the user's face; The user's facial comprehensive emotion score data is used to mark the user's image behavior on the user's bathing image set. When the user's facial comprehensive emotion score data is greater than or equal to the preset comprehensive emotion score threshold, the corresponding user's bathing image set is marked as a comfortable emotion image to generate a comfortable marked image; when the user's facial comprehensive emotion score data is less than the preset comprehensive emotion score threshold, the corresponding user's bathing image set is marked as a non-comfortable emotion image to generate a non-comfortable marked image.

[0019] The present invention detects the face area of ​​the user's bathing image set and extracts facial feature points. The system can accurately locate the user's facial area and accurately capture the user's eyes, nose, mouth and other key parts. This provides a reliable data basis for subsequent emotion recognition. The multidimensional feature vector extraction of the geometric structure, muscle movement and texture features of the user's face enriches the accuracy of emotion recognition. This multidimensional feature analysis not only improves the system's perception of subtle emotional changes, but also can comprehensively evaluate the user's emotional state based on information from different dimensions. Through the emotional scoring mechanism, the system can generate emotional scoring data for each facial area and obtain the comprehensive emotional score of the user's face through the averaging process. This scoring system ensures the accuracy and flexibility of emotion recognition and can perform emotional evaluation in real time according to the user's actual emotional changes. By using the comprehensive emotional scoring data to mark the user's image behavior, the user's emotional state can be divided into two categories: comfortable and uncomfortable. Through the preset emotional scoring threshold, the system can automatically mark the user's image set accordingly, thereby providing a direct basis for subsequent intelligent regulation. Based on the image behavior marking results, the system can dynamically adjust the settings of the bathing equipment, such as water temperature, heat source output, etc. When the user is detected to be in a comfortable state, the system will maintain the current device state; when the user is uncomfortable, the system will make corresponding adjustments and optimizations based on the tagged image to ensure that the user is always in a comfortable bathing experience. Through the linkage of emotion recognition and tagged images, the system can continuously adjust the device parameters so that users can enjoy a personalized and humanized bathing experience. This regulation method based on user emotions not only enhances the user's sense of participation, but also increases the intelligence of the device and user satisfaction. The system can analyze the emotional changes of the user's facial expressions in real time and make timely adjustments when an uncomfortable state is detected. This adaptive regulation mechanism can dynamically adjust the bathing experience based on the user's real-time feedback, providing a more accurate and immediate adjustment effect. Multidimensional feature extraction (geometry, muscle movement, texture) improves the accuracy of emotion recognition, especially when dealing with complex expressions or micro-expressions, the system can more keenly capture subtle emotional changes and ensure the accuracy of the marking.

[0020] Preferably, step S3 comprises the following steps: Step S31: performing time series analysis on the heat source linkage control data through the non-contact constant temperature control data to generate heat source linkage control time series data; Step S32: dividing the heat source linkage control time series data into data sets to generate a model training set and a model test set; training the model training set using a random forest algorithm to generate a heat source linkage control fluctuation prediction pre-model; optimizing and iterating the heat source linkage control fluctuation prediction pre-model using the model test set, thereby generating a heat source linkage control fluctuation model; Step S33: importing the heat source linkage control data into the heat source linkage control fluctuation model to perform heat source control fluctuation prediction, thereby generating heat source control fluctuation prediction data; Step S34: setting the equipment warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control equipment warning threshold.

[0021] The present invention performs time series analysis on non-contact thermostatic control data and heat source linkage control data, and the system can capture the fluctuation changes of the heat source at different time nodes and generate time-dependent control time series data. This step improves the sensitivity to changes in heat source behavior, so that subsequent control can be more accurately predicted based on historical data. By dividing the heat source linkage control time series data into a model training set and a test set, the system can effectively build a model under the background of big data. In particular, the training set is trained using a random forest algorithm to generate a heat source linkage control fluctuation prediction pre-model, which improves the robustness and generalization ability of the model. The pre-trained fluctuation prediction model is optimized and iterated using the test set, which not only improves the accuracy of the prediction, but also ensures the dynamic adjustment ability of the model. Through continuous iterative optimization, the system can adapt to different heat source fluctuation situations, so that the prediction results are more in line with actual control needs. The optimized heat source linkage control fluctuation model is applied to real-time data prediction, and heat source control fluctuation prediction data can be generated. This prediction mechanism based on machine learning provides forward-looking data support for equipment control, so that the system can respond to heat source fluctuations in advance and avoid the occurrence of emergencies. Based on the predicted data, the system can automatically set the warning threshold of the equipment to ensure that when the fluctuation of the heat source linkage control exceeds the safe range, the equipment can take timely countermeasures. This function effectively improves the safety of equipment operation and reduces the risk of equipment failure caused by abnormal fluctuations in the heat source. The entire process realizes automatic heat source control through intelligent algorithms and predictive models without manual intervention, which greatly improves the autonomous control ability of the equipment. This intelligent control method not only reduces the complexity of manual operation, but also enables the system to respond to user needs and environmental changes faster and more flexibly. Prediction-based equipment control can avoid excessive or insufficient heat sources, thereby improving energy utilization efficiency and reducing energy waste. This not only extends the service life of the equipment, but also saves energy expenses for users.

[0022] In this specification, a linkage constant temperature control system based on a smart bathing device is provided, which is used to execute the linkage constant temperature control method based on the smart bathing device. The linkage constant temperature control system based on the smart bathing device includes: The environmental analysis module is used to obtain the target temperature data set for the bathing equipment; collect environmental data from the water inlet pipe and the water outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identify the water temperature difference between the target temperature data set for the bathing equipment and the real-time water temperature data to generate water temperature difference identification data for the bathing equipment; analyze the potential fluctuation impact of the real-time water pressure data based on the water temperature difference identification data for the bathing equipment to generate water pressure fluctuation data for the bathing equipment; The constant temperature adjustment module is used to perform intelligent water mixing adjustment on the bathing equipment through the water temperature difference identification data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage adjustment on the real-time water mixing adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage adjustment data; perform non-contact temperature control based on the heat source linkage adjustment data, thereby generating non-contact constant temperature control data; The control warning module is used to perform model training on the heat source linkage control data through non-contact constant temperature control data to generate a heat source linkage control fluctuation model; import the heat source linkage control data into the heat source linkage control fluctuation model to predict the heat source control fluctuation, thereby generating heat source control fluctuation prediction data; set the equipment warning threshold value for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control equipment warning threshold value; The safety monitoring module is used to perform linkage constant temperature control early warning monitoring of bathing equipment based on the warning threshold of the heat source control equipment, and generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data, and generate a bathing equipment control and protection strategy to execute linkage constant temperature control operations.

[0023] The beneficial effect of the present invention is that by collecting environmental data of the water inlet and outlet of the bathing equipment, real-time water temperature and water pressure data are obtained, providing accurate basic data for subsequent analysis. This real-time monitoring ensures the transparency of the equipment operation status and facilitates immediate adjustment. The difference between the set target temperature and the real-time water temperature is identified, and water temperature difference identification data is generated, which helps to quickly discover temperature abnormalities and provide a scientific basis for water mixing regulation. Based on the water temperature difference, the potential fluctuation impact analysis of the real-time water pressure data can identify the impact of water pressure fluctuations on the bathing experience in advance, so as to formulate corresponding control strategies. Intelligent water mixing regulation is performed through water temperature difference identification data and water pressure fluctuation data, and real-time water mixing and water pressure dynamic regulation are realized, so that users can enjoy a more comfortable temperature experience during bathing. Based on real-time water mixing regulation data and water pressure dynamic regulation data, heat source linkage regulation is performed to ensure that the heat source output is highly matched with user needs, thereby reducing energy waste and improving the energy utilization efficiency of the equipment. The generated non-contact constant temperature control data adjusts the water temperature in an intelligent way, so that users can enjoy a constant water temperature without intervention, which improves the convenience and comfort of use. By training the model of the heat source linkage control data and generating the heat source control fluctuation model, the system can continuously learn and optimize, thereby improving the adaptability and flexibility of the system. The heat source linkage control data is imported into the fluctuation model for prediction, and the generated heat source control fluctuation prediction data can help to adjust the equipment operating parameters in time, thereby avoiding equipment failure or performance degradation caused by fluctuations. The equipment warning threshold is set according to the prediction data to ensure that the equipment can respond in time under abnormal conditions, avoid potential safety hazards, and improve the safety of the equipment. Bathing equipment is monitored based on the equipment warning threshold, and abnormal conditions of temperature or water pressure can be discovered and responded to in time, thereby enhancing the real-time monitoring capability of the equipment. A safety protection mechanism is constructed by linking the constant temperature control early warning monitoring data to ensure that protective measures can be taken quickly when the equipment is abnormal, thereby reducing the failure rate and extending the service life of the equipment. The whole process significantly improves the user's bathing experience through intelligent adjustment and monitoring, allowing users to enjoy higher comfort and security when using the equipment. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data acquisition, intelligent water mixing adjustment, heat source linkage control, and non-contact temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a linkage constant temperature control method based on intelligent bathing equipment; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a linkage constant temperature control method based on intelligent bathing equipment, the method comprising the following steps: Step S1: Obtain target temperature data for bathing equipment; collect environmental data on the water inlet and outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identify water temperature differences between the target temperature data for bathing equipment and the real-time water temperature data to generate water temperature difference identification data for bathing equipment; perform potential fluctuation impact analysis on the real-time water pressure data based on the water temperature difference identification data for bathing equipment to generate water pressure fluctuation data for bathing equipment; Step S2: intelligently adjust the water mixing of the bathing equipment through the water temperature difference identification data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage regulation on the real-time water mixing adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage regulation data; perform non-contact temperature control based on the heat source linkage regulation data, thereby generating non-contact constant temperature control data; Step S3: Model training is performed on the heat source linkage control data through non-contact constant temperature control data to generate a heat source linkage control fluctuation model; the heat source linkage control data is imported into the heat source linkage control fluctuation model to predict the heat source control fluctuation, thereby generating heat source control fluctuation prediction data; the equipment warning threshold is set for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating a heat source control equipment warning threshold; Step S4: Based on the warning threshold of the heat source control equipment, the bathing equipment is monitored for early warning of the linked constant temperature control, and the linked constant temperature control early warning monitoring data is generated; a safety protection mechanism is constructed through the linked constant temperature control early warning monitoring data, and a bathing equipment control and protection strategy is generated to execute the linked constant temperature control operation.

[0029] The present invention obtains the set target temperature data of the bathing equipment, collects environmental data of the water inlet and outlet, and monitors the water temperature and water pressure in real time. This process can clearly identify the user's bathing needs and provide basic data for temperature adjustment. By identifying the difference between the set target temperature and the real-time water temperature data, the system can accurately determine the water temperature that needs to be adjusted. Based on these data, the impact of the potential fluctuation of water pressure on the water temperature is further analyzed, providing an important basis for subsequent adjustment. This stage ensures that the system has a comprehensive understanding of the current state and enhances the responsiveness of temperature control. The system uses the water temperature difference identification data and water pressure fluctuation data obtained in the first step to perform intelligent water mixing adjustment on the bathing equipment. This intelligent adjustment is not only fast but also effective, and can optimize the temperature of the mixed water and improve the user's comfort. Subsequently, the system combines the real-time water mixing adjustment data and the water pressure dynamic adjustment data to perform linkage regulation of the heat source. This mechanism realizes the efficient use of the heat source and improves the response speed of temperature control. Through non-contact temperature control, the system reduces the dependence on traditional physical sensors, improves the accuracy and stability of temperature control, and reduces the impact of external interference on temperature control. The system uses non-contact thermostatic control data to train the model of heat source linkage control data and generate a heat source control fluctuation model. This model can not only learn historical data, but also predict future control fluctuations and provide data support for subsequent operations. According to the prediction results, the system sets the warning threshold of the equipment to prevent abnormal situations. This link significantly improves the intelligence level of the system, can quickly adapt to changes in user needs and ensure safe operation. Finally, the fourth step establishes an early warning monitoring system for linkage thermostatic control. Through real-time monitoring based on the warning threshold of the heat source control equipment, the system can timely identify and handle abnormal situations to ensure the safety of users. At the same time, the monitoring data supports the construction of a safety protection mechanism and formulates a control and protection strategy for bathing equipment to deal with potential risks and failures. This link ensures the stability and safety of the system, reduces the probability of equipment damage, and improves the overall user experience. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data acquisition, intelligent water mixing regulation, heat source linkage control and non-contact temperature control.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a linkage constant temperature control method based on an intelligent bathing device of the present invention. In this example, the linkage constant temperature control method based on an intelligent bathing device includes the following steps: Step S1: Obtain target temperature data for bathing equipment; collect environmental data on the water inlet and outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identify water temperature differences between the target temperature data for bathing equipment and the real-time water temperature data to generate water temperature difference identification data for bathing equipment; perform potential fluctuation impact analysis on the real-time water pressure data based on the water temperature difference identification data for bathing equipment to generate water pressure fluctuation data for bathing equipment; In an embodiment of the present invention, a target temperature (TargetTemperature, TT) is set by obtaining it from a bathing equipment control system. The target temperature data is recorded and stored in a data management system for subsequent analysis. The inlet temperature (InletTemperature, IT) and the outlet temperature (Outlet Temperature, OT) are collected in real time by temperature sensors installed at the inlet and outlet of the bathing equipment. The water pressure (Inlet Pressure, IP) of the inlet pipe of the bathing equipment and the water pressure (Outlet Pressure, OP) of the outlet are monitored in real time by a pressure sensor. The real-time water temperature and water pressure data are transmitted to a data storage system to form time series data. The set target temperature data (TT) is compared with the real-time water temperature data (IT and OT), and the water temperature difference (Temperature Difference, TD) is calculated: TD=TT-OT. If TD exceeds the set threshold, it is marked as abnormal, and the water temperature difference identification data (TemperatureDiscrepancy Data, TDD) of the bathing equipment is generated. Based on the water temperature difference identification data (TDD) of the bathing equipment, the real-time water pressure data (IP and OP) is subjected to fluctuation impact analysis. Apply the fluctuation analysis algorithm to identify potential sources of water pressure fluctuations, such as changes in water inlet temperature and fluctuations in water outlet flow, and generate water pressure fluctuation data (PFD) for bathing equipment for subsequent system adjustment and optimization. Store the generated water temperature difference identification data (TDD) and water pressure fluctuation data (PFD) in the database. Develop a data visualization interface to display the changes in water temperature and water pressure in real time, as well as the corresponding difference identification and fluctuation analysis results, so that users can monitor and adjust.

[0031] Step S2: intelligently adjust the water mixing of the bathing equipment through the water temperature difference identification data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage regulation on the real-time water mixing adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage regulation data; perform non-contact temperature control based on the heat source linkage regulation data, thereby generating non-contact constant temperature control data; In an embodiment of the present invention, the required water mixing ratio (Mixing Ratio, MR) is calculated based on the water temperature difference identification data (TDD) and the water pressure fluctuation data (PFD) of the bathing equipment to ensure that the outlet water temperature (OT) reaches the set target temperature (TT). MR=TT-OT / IT-OT. The water inlet valve and the hot water valve are controlled to adjust the flow rate according to the calculated water mixing ratio to achieve intelligent water mixing adjustment and generate real-time water mixing adjustment data (RMAD). The outlet water pressure (OP) after mixing is monitored and compared with the set range to ensure that the water pressure is within a safe range. According to the real-time water pressure dynamic adjustment data (RPAD), the water pressure at the water inlet is automatically adjusted to maintain a stable outlet water pressure to prevent pressure fluctuations from affecting the user. Based on the real-time water mixing adjustment data (RMAD) and the real-time water pressure dynamic adjustment data (RPAD), it is determined whether it is necessary to adjust the heat source (for example, an electric water heater or a boiler) to supplement hot water. Through the heat source control system, the heat source is linked and regulated, and the heat source linkage control data is generated to ensure that the temperature of the heat source is consistent with that of the water flow. Use a non-contact infrared temperature sensor to monitor the outlet water temperature (OT) of the bathing equipment in real time. Based on the heat source linkage control data (HSCCD), the heat source is adjusted to achieve precise control of the outlet water temperature, and non-contact constant temperature control data (NCTCD) is generated to ensure that the water temperature of the user is always kept within a comfortable range when bathing. The real-time water mixing adjustment data (RMAD), real-time water pressure dynamic adjustment data (RPAD), heat source linkage control data (HSCCD) and non-contact constant temperature control data (NCTCD) are stored in the database. Develop a user interface to display the mixing adjustment, water pressure dynamic adjustment and constant temperature control in real time so that users can monitor and adjust the settings.

[0032] Step S3: Model training is performed on the heat source linkage control data through non-contact constant temperature control data to generate a heat source linkage control fluctuation model; the heat source linkage control data is imported into the heat source linkage control fluctuation model to predict the heat source control fluctuation, thereby generating heat source control fluctuation prediction data; the equipment warning threshold is set for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating a heat source control equipment warning threshold; In an embodiment of the present invention, a training data set is formed by collecting non-contact constant temperature control data (NCTCD) and heat source linkage control data (HSCCD). The data set should include temperature, pressure and control decisions in different time periods. Select a suitable machine learning algorithm (such as regression analysis, decision tree or neural network) to train the heat source linkage control fluctuation model (Heat Source Coordination Fluctuation Model, HSCFM). Train the model and optimize the model parameters to improve the accuracy and robustness of the prediction. Use cross-validation and other methods to evaluate the model performance and make adjustments based on the evaluation results. Import the new heat source linkage control data (HSCCD) into the trained heat source linkage control fluctuation model (HSCFM). Use the model to predict heat source control fluctuations and generate heat source control fluctuation prediction data (Heat Source Control Fluctuation Prediction Data, HCFPD). The data should include temperature and pressure fluctuations in the future time period. Analyze the heat source control fluctuation prediction data (HCFPD) to determine the amplitude and frequency of fluctuations to identify potential risk areas. Set the device alarm threshold (DAT) based on the prediction data. The threshold should take into account the normal operating range and fluctuation range of the system, ensure that an alarm can be issued in time when the fluctuation exceeds the threshold, generate the Heat Source Control Device Alarm Threshold (HCDAT), and store it in the equipment monitoring system. Store the generated heat source control fluctuation prediction data (HCFPD) and equipment alarm threshold (HCDAT) in the database. Develop a real-time monitoring system to display the heat source control status and alarm threshold settings to ensure that users can respond to system alarms in a timely manner.

[0033] Step S4: Based on the warning threshold of the heat source control equipment, the bathing equipment is monitored for early warning of the linked constant temperature control, and the linked constant temperature control early warning monitoring data is generated; a safety protection mechanism is constructed through the linked constant temperature control early warning monitoring data, and a bathing equipment control and protection strategy is generated to execute the linked constant temperature control operation.

[0034] In an embodiment of the present invention, a monitoring system is integrated into the bathing equipment to monitor the heat source control equipment warning threshold (HCDAT) and actual operation data (such as water temperature, pressure, etc.) in real time. The threshold logic of the monitoring system is set. When the actual operation data exceeds the equipment warning threshold (HCDAT), the early warning mechanism is triggered to generate linked constant temperature control early warning monitoring data (Linked Constant Temperature Control Alert Monitoring Data, LCTCAMD), and the time, type and related parameters of each early warning event are recorded. According to the linked constant temperature control early warning monitoring data (LCTCAMD), past early warning events are analyzed to identify potential safety hazards and common failure modes. A safety protection mechanism is formulated, including measures and operating procedures for responding to early warnings. For example, when the water temperature or water pressure is abnormal, the mixing valve or heat source output is automatically adjusted, or the emergency shutdown program is started, and a control protection strategy for bathing equipment (Control Protection Strategy for Bathing Equipment, CPSBE) is generated. The strategy should include response measures for different early warning levels. The control protection strategy for bathing equipment (CPSBE) is implemented in the control system of the bathing equipment to ensure that it can respond quickly when the early warning is triggered. Establish a feedback mechanism in the monitoring system. When the control and protection strategy is implemented, the system status is updated in real time to ensure that the monitoring data (such as water temperature and pressure) returns to a safe range. Record the execution of the linkage constant temperature control operation, including the response time, measures and effects after each warning, and generate the corresponding execution record data (ERD). Store the generated linkage constant temperature control early warning monitoring data (LCTCAMD), bathing equipment control and protection strategy (CPSBE) and execution record data (ERD) in the database for subsequent analysis and audit. Generate early warning monitoring and control strategy execution reports regularly and provide them to equipment maintenance personnel and management to evaluate the stability and safety of the system.

[0035] Preferably, step S1 comprises the following steps: Step S11: Acquire target temperature data for setting the bathing equipment; Step S12: using a temperature sensor to collect the real-time temperature of the water inlet pipe of the bathing equipment to obtain real-time water temperature data; using a water pressure sensor to collect the real-time water pressure of the water outlet of the bathing equipment to obtain real-time water pressure data; Step S13: performing data preprocessing on the real-time water temperature data and the real-time water pressure data to generate standard real-time water temperature data and standard real-time water pressure data, wherein the data preprocessing includes data cleaning, data denoising and data standardization; Step S14: Identify the water temperature difference between the target temperature data set for the bathing equipment and the standard real-time water temperature data to generate water temperature difference identification data for the bathing equipment; analyze the potential fluctuation impact of the standard real-time water pressure data based on the water temperature difference identification data for the bathing equipment to generate water pressure fluctuation data for the bathing equipment.

[0036] In an embodiment of the present invention, the target temperature data is obtained through the user's input interface. The specific operation is as follows: the user sets the desired target water temperature on the digital or mechanical control panel of the bathing device, usually in degrees Celsius (°C), for example, the set temperature is 40°C. The set target temperature is uploaded to the temperature control module through the control system of the device to generate target temperature data. The data is then stored in the system memory for subsequent comparison with the real-time water temperature data. The main purpose of this step is to determine the user's desired water temperature and provide benchmark data for subsequent control processes. The temperature sensor is installed on the water inlet pipe of the bathing device, usually a thermistor or a thermocouple. These sensors can detect the temperature of the water flow in the pipe with high precision and fast response. The temperature sensor reads the water temperature at regular time intervals (such as once per second) and generates real-time water temperature data, and the data format is a curve of temperature change over time or a temperature data point marked by a timestamp (such as: [time, temperature]->[12:01:30,38.5°C]). These data are transmitted to the central control unit via the sensor signal line or wireless connection for subsequent processing. The water pressure sensor is installed near the water outlet of the bathing equipment to measure the water pressure at the outlet of the equipment. The sensor is usually a strain gauge water pressure sensor that can detect pressure when water flows through. Similar to temperature acquisition, the water pressure sensor also collects data at fixed time intervals to generate real-time water pressure data. The data format is a record of pressure values ​​changing over time (such as: [time, water pressure] -> [12:01:30, 1.5bar]). The data is transmitted to the control unit via wired or wireless means and processed synchronously with the water temperature data. First, the collected raw water temperature and water pressure data are detected to identify outliers or unreasonable values ​​(such as: temperature suddenly changes to too high or too low). Statistical methods (such as the triple standard deviation method) or machine learning models can be used to identify abnormal data. If the sensor fails to record some data due to some reasons (such as short-term signal loss), the system will use interpolation methods to supplement these lost points to maintain data continuity. Use filters (such as Kalman filters or sliding average filters) to process temperature and water pressure data to eliminate small fluctuations caused by environmental noise. For example, a sliding average filter can average several consecutive water temperature data points to eliminate the impact of sudden noise on the data. Convert data of different ranges to the same standard range, usually between 0 and 1. The benefit of standardization is that it can reduce the impact of different units, making subsequent analysis and calculations easier. For example, water pressure is expressed in kilopascals (kPa) and water temperature is expressed in degrees Celsius. Standardization converts these data into dimensionless data. After preprocessing, standard real-time water temperature data and standard real-time water pressure data are generated, which are more reliable and have analytical value. Compare the set target temperature of the bathing equipment (for example, 40°C) with the standard real-time water temperature data. For example, when the real-time water temperature is 38°C, the system will calculate a temperature difference of 2°C.This difference data will serve as an important reference for subsequent water pressure fluctuation analysis. The system determines whether the water temperature difference needs to be corrected based on the set threshold. For example, if the temperature difference is less than 1°C, the water temperature is considered to be within the normal range and does not need to be adjusted; if the temperature difference is greater than 1°C, the device's adjustment mechanism is triggered to try to adjust the water temperature to the target value. The result of the temperature difference calculation will generate water temperature difference identification data, which can reflect the difference between the current water temperature and the target value. Based on the water temperature difference identification data, the standard real-time water pressure data is analyzed for potential fluctuations. For example, if the temperature difference is large, the water flow will be unstable, which will affect the water pressure. Therefore, the system will analyze the real-time water pressure and evaluate its fluctuations. The system can analyze the impact of temperature difference on water pressure fluctuations based on historical data or prediction models. For example, when the temperature difference increases, the water pressure will fluctuate periodically, and the system will calculate the potential water pressure fluctuation range through the prediction model. The water pressure fluctuation data is generated through analysis to reflect whether the water pressure is stable. If the water pressure fluctuation exceeds the safe range, the system will activate an alarm or automatic adjustment mechanism to ensure that the water pressure and water temperature are within the safe range.

[0037] Preferably, step S14 includes the following steps: Step S141: Calculate the water temperature difference between the target temperature data set for the bathing equipment and the standard real-time water temperature data to obtain water temperature difference data; compare the water temperature difference data with a preset water temperature difference threshold value; when the water temperature difference data is greater than or equal to the preset water temperature difference threshold value, mark the water temperature difference data to generate bathing equipment water temperature difference identification data; Step S142: Analyze the water pressure time series change of the standard real-time water pressure data to generate historical water pressure data; calculate the flow rate of the bathing equipment based on the standard real-time water pressure data and the historical water pressure data to obtain the flow rate data of the bathing equipment; Step S143: performing a water pressure stability impact analysis on the standard real-time water pressure data and the water temperature difference identification data of the bathing equipment according to the bathing equipment flow data, and generating bathing equipment water pressure fluctuation data; The formula for calculating the flow rate of bathing equipment is as follows: In the formula, Expressed as flow rate, Expressed as the water flow resistance coefficient, Expressed as real-time water pressure, Expressed as historical water pressure.

[0038] In an embodiment of the present invention, the difference between the real-time water temperature and the set target temperature is calculated by comparing the target temperature (e.g., 40°C) set by the device with the pre-processed standard real-time water temperature data. For example, if the target temperature is 40°C and the standard real-time water temperature is 38°C, the water temperature difference is 2°C. The calculated water temperature difference is compared with the preset water temperature difference threshold. Assuming that the preset threshold is 1°C, when the water temperature difference reaches or exceeds 1°C, the system will mark the difference data. The water temperature difference data greater than or equal to the preset threshold is marked to generate bathing equipment water temperature difference identification data. These data indicate that the water temperature deviation of the bathing equipment exceeds the acceptable range, and the system needs to automatically adjust or prompt the user to manually adjust the water temperature. The system performs a time series change analysis on the pre-processed standard real-time water pressure data to generate historical water pressure data. By comparing the water pressure data at different time points, the system can understand the fluctuation of water pressure and establish a time series model of water pressure change. By analyzing the historical data, the system can detect whether the water pressure presents periodic changes or sudden changes. Based on standard real-time water pressure data and historical water pressure data, the system calculates the flow rate of the bathing equipment through the following formula: In the formula, Expressed as flow rate, Expressed as the water flow resistance coefficient, Expressed as real-time water pressure, It is expressed as the historical water pressure. This formula is a flow estimation formula based on the Bernoulli principle and water pressure difference, where the water flow resistance coefficient It reflects the water flow resistance in the equipment or pipeline. The system is set according to the structure and material of the actual equipment. value. The greater the difference between the real-time water pressure and the historical water pressure, the greater the pressure difference of the water flow, and the flow rate increases accordingly. Based on the flow data of the bathing equipment, the standard real-time water pressure data, and the water temperature difference identification data, the system performs a water pressure stability analysis. The specific analysis includes: If the water pressure is stable and the flow rate is stable, the system believes that the equipment is in normal working condition and will not have a negative impact on the user's bathing experience. If the water temperature difference identification data shows that the water temperature deviation is large and the water pressure is unstable (for example, the time-series water pressure fluctuation is large), the system determines that the water pressure fluctuation has a potential impact on the stability of the equipment flow and water temperature. The system generates water pressure fluctuation data for bathing equipment based on the above analysis. The goal of this step is to identify whether water pressure fluctuations affect the user's bathing experience. For example, if there are large fluctuations in water temperature and water pressure at the same time, the system will issue an alarm or automatically adjust the water temperature or water pressure to maintain the normal operation of the equipment.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Intelligently adjust the water mixing of the bathing equipment by using the water temperature difference recognition data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; Step S22: recalculate the water temperature difference between the real-time mixed water adjustment data and the target temperature data set for the bathing equipment to obtain the positive difference data of the mixed water adjustment water temperature and the negative difference data of the mixed water adjustment water temperature; perform heat source output control based on the positive difference data of the mixed water adjustment water temperature and the negative difference data of the mixed water adjustment water temperature to generate the heat source output control data for the bathing equipment; Step S23: using the real-time water pressure dynamic adjustment data to perform heat source reaction regulation on the bathing equipment heat source regulation data, and generating the bathing equipment heat source reaction regulation data; integrating the heat source regulation data based on the bathing equipment heat source output regulation data and the bathing equipment heat source reaction regulation data, and generating the heat source linkage regulation data; Step S24: Analyze the user usage behavior pattern of the bathing equipment according to the heat source linkage control data to generate user bathing behavior pattern analysis data; perform non-contact temperature control on the heat source linkage control data based on the user bathing behavior pattern analysis data to generate non-contact constant temperature control data.

[0040] In the embodiment of the present invention, the system automatically adjusts the water mixing system of the equipment through the water temperature difference identification data and water pressure fluctuation data of the bathing equipment generated in the early stage. Specifically, the water mixing system will adjust the ratio of cold and hot water according to the data feedback of the water temperature difference and water pressure fluctuation to ensure that the equipment maintains stable water temperature and water pressure under dynamic conditions. In the process of executing intelligent water mixing regulation, the system generates two key data: dynamically adjust the water mixing system based on the difference between the current water temperature and the set target temperature. Adjust the water pressure accordingly according to the water pressure fluctuation to ensure the balance of water pressure and flow. According to the real-time water mixing regulation data and the set target temperature data of the equipment, the system recalculates the water temperature difference and obtains: Positive difference data of water mixing regulation water temperature: indicates the positive difference that the real-time water temperature is higher than the set target temperature. Negative difference data of water mixing regulation water temperature: indicates the negative difference that the real-time water temperature is lower than the set target temperature. The system uses the above positive and negative difference data to accurately control the heat source output. When the positive difference of the water temperature in the mixed water adjustment is large, the system will reduce the heat source output; when the negative difference is large, the heat source output will be increased to ensure that the water temperature can quickly reach the target temperature. The heat source output control data is generated to further optimize the system's control of the heat source. Based on the real-time water pressure dynamic adjustment data generated in the previous step, the system performs heat source response control on the generated heat source output control data. This control dynamically adjusts the heat source output according to the water pressure fluctuation to ensure that the water pressure change does not affect the temperature control of the equipment. The system integrates the heat source output control data and the heat source response control data to generate heat source linkage control data, so that the water temperature regulation and water pressure regulation of the equipment are closely combined to achieve a more accurate and stable control effect. Based on the heat source linkage control data, the system analyzes the user's bathing behavior pattern. For example, the user's usage habits, usage time, temperature and water pressure preferences in different time periods, etc., generate user bathing behavior pattern analysis data. Based on the user's behavior pattern data, the system performs non-contact intelligent temperature control on the equipment. By learning the user's bathing habits, the system can automatically adjust the water temperature and water pressure so that it is in the best condition before the user starts using the device. This process generates non-contact constant temperature control data, ensuring that users can enjoy a stable constant temperature experience without having to manually adjust the device. Ultimately, through the analysis of user behavior patterns and non-contact constant temperature control, the system can not only adapt to user needs, but also provide a more intelligent bathing experience.

[0041] Preferably, step S21 includes the following steps: Step S211: Analyze the hot and cold water temperatures of the bathing equipment to generate cold water temperature data and hot water temperature data; perform cold and hot water ratio mixing requirements on the cold water temperature data and the hot water temperature data based on the water temperature difference identification data of the bathing equipment to obtain cold and hot water ratio mixing requirement data; Step S212: Calculate the water flow demand of the cold water temperature data and the hot water temperature data according to the cold and hot water ratio mixing demand data to obtain the cold water demand data and the hot water demand data; perform water mixing adjustment calculation on the cold water demand data and the hot water demand data through the water mixing ratio adjustment formula to obtain real-time water mixing adjustment data; Step S213: adjusting the water mixing valve control of the bathing equipment based on the real-time water mixing adjustment data, thereby generating water mixing valve control adjustment data; adjusting the valve opening water pressure of the water mixing valve control adjustment data based on the water pressure fluctuation data of the bathing equipment, thereby generating real-time water pressure dynamic adjustment data; The calculation formula of the mixing water ratio adjustment formula is as follows: In the formula, Represented as real-time mixed water regulation data, Expressed as the cold and hot water regulation ratio coefficient, Expressed as real-time temperature deviation, Expressed as the adjustment gain coefficient, Expressed as the adjustment differential coefficient, Expressed as the error between the set value and the actual value, Represents the current moment, Represented as a time variable.

[0042] In an embodiment of the present invention, the temperature data of cold water and hot water are obtained in real time through the sensor in the bathing equipment. The reading frequency of the sensor can be set to a fixed interval (such as reading once per second) according to the demand to ensure the real-time and accuracy of the temperature data. The system stores the obtained temperature data as cold water temperature data and hot water temperature data respectively. Based on the previously generated water temperature difference identification data of the bathing equipment, the system analyzes the temperature difference of cold water and hot water to determine the mixing ratio of cold and hot water. The calculation process will comprehensively consider the set target temperature and the current water temperature to generate cold and hot water ratio mixing demand data, which is used to guide subsequent water mixing operations to ensure that the water temperature can be adjusted towards the target temperature. If the water temperature difference is large, the system will adjust the cold and hot water ratio and increase the supply of hot water or cold water until the temperature is close to the set target temperature. The system calculates the required flow of cold water and hot water through the cold and hot water ratio mixing demand data. This calculation is based on the cold water temperature and the hot water temperature, as well as the set water temperature difference ratio demand, to obtain cold water demand data and hot water demand data. The system uses the following formula to adjust the cold water and hot water demand in real time and generate real-time water mixing adjustment data: In the formula, Represented as real-time mixed water regulation data, Expressed as the cold and hot water regulation ratio coefficient, Expressed as real-time temperature deviation, Expressed as the adjustment gain coefficient, Expressed as the adjustment differential coefficient, Expressed as the error between the set value and the actual value, Represents the current moment, is expressed as a time variable. This formula applies the PID control principle to ensure that the mixing ratio of cold and hot water can be adjusted quickly and accurately during the operation of the system to achieve the set target water temperature. According to the real-time water mixing adjustment data, the system automatically adjusts the opening degree of the water mixing valve. Through the actuator, the system can accurately control the opening and closing degree of the valve and generate water mixing valve control adjustment data, which is used to guide the specific action of the water mixing valve to achieve stable temperature regulation. The system further combines the water pressure fluctuation data of the bathing equipment to adjust the relationship between the opening state of the valve and the water pressure. When the valve opening changes, the system adjusts the water pressure in real time to ensure that the water pressure remains stable during the process of adjusting the water temperature, thereby generating real-time water pressure dynamic adjustment data. The system automatically adjusts the opening and closing of the valve according to the user's current water flow demand. The adjustment process needs to be completed within milliseconds to ensure that the water temperature and water pressure change smoothly and avoid users from perceiving sudden changes in temperature or water pressure.

[0043] Preferably, regulating the heat source output by using the positive difference data of the mixed water temperature and the negative difference data of the mixed water temperature comprises: The heat source output power of the bathing equipment is collected to obtain heat source output power collection data, wherein the heat source output power collection data includes current heat source output power data and maximum heat source output power data; Performing data detection on the mixed water adjustment water temperature positive difference data and the mixed water adjustment water temperature negative difference data, when the mixed water adjustment water temperature positive difference data is detected, the heat source output power data is reduced based on the mixed water adjustment water temperature positive difference data to generate the heat source first control data; When negative difference data of mixed water adjustment water temperature is detected, the current heat source output power data and the maximum output power data of the heat source are converted into a heat source power fluctuation curve to generate a heat source power fluctuation curve; the maximum fluctuation peak value of the heat source power fluctuation curve is extracted to obtain the maximum available power data of the heat source; the negative difference data of mixed water adjustment water temperature is increased according to the maximum available power data of the heat source to generate the second control data of the heat source; The first heat source control data and the second heat source control data are integrated to generate the bathing equipment heat source output control data.

[0044] In an embodiment of the present invention, the output power of the heat source of the bathing equipment is collected to obtain the current output power of the heat source and the maximum output power of the heat source, and generate the heat source output power collection data. The current heat source output power data refers to the power actually output by the device in the current state. The maximum output power data of the heat source is the maximum output power that the device can reach under working conditions. The collected positive difference data of the mixed water adjustment water temperature and the negative difference data of the mixed water adjustment water temperature are detected. The positive difference data indicates that the actual water temperature is higher than the set water temperature. The negative difference data indicates that the actual water temperature is lower than the set water temperature. When the positive difference data of the mixed water adjustment water temperature is detected, it indicates that the current water temperature is too high. At this time, the current heat source output power is reduced according to the positive difference data to reduce the output of the heat source, and the first control data of the heat source is generated, which is used to reduce the data of the heat source output power to ensure that the water temperature is adjusted to the target temperature. When the negative difference data of the mixed water adjustment water temperature is detected, it indicates that the current water temperature is too low and the heat source power needs to be increased. In this case, the system will perform the following operations: Analyze the current heat source output power data and the maximum heat source output power data to generate a heat source power fluctuation curve. Analyze the generated heat source power fluctuation curve, extract the maximum fluctuation peak value, and obtain the maximum available power data of the heat source, that is, the maximum power that the heat source can achieve under the current conditions. According to the extracted maximum available power data of the heat source, analyze the negative difference data of the mixed water adjustment water temperature, increase the output power of the heat source, and generate the second heat source control data, which is used to increase the data of the heat source output power to ensure that the water temperature can quickly rise to the target temperature. Integrate the generated first heat source control data and the second heat source control data to generate complete bathing equipment heat source output control data. This data will be used to control the output of the heat source in real time to ensure that the water temperature is always maintained within the set range.

[0045] Preferably, step S24 includes the following steps: Step S241: classifying the non-contact user feedback mode of the bathing equipment according to the heat source linkage control data to generate an image feedback mode and an audio feedback mode; collecting user bathing images using a camera based on the image feedback mode to obtain a user bathing image set; performing user expression recognition on the user bathing image set to generate user expression recognition data; Step S242: marking the user image behavior of the user bathing image set according to the user expression recognition data to generate a user behavior marking image, wherein the user behavior marking image includes a comfort marking image and a discomfort marking image; performing a first non-contact feedback control on the bathing equipment based on the comfort marking image and the discomfort marking image until the number of the comfort marking images is greater than the number of the discomfort marking images, and generating expression feedback control data; Step S243: Based on the audio feedback mode, the recording device is used to collect the user's bathing audio to obtain the user's bathing audio; the audio acoustic feature extraction is performed on the user's bathing audio to obtain the user's bathing audio acoustic feature data; the user's bathing audio acoustic feature data is converted into text to generate the user's bathing acoustic text; the user's bathing acoustic text is semantically recognized to generate user feedback semantic recognition data; Step S244: Use the user feedback semantic recognition data to perform a second non-contact feedback regulation on the bathing equipment to generate audio feedback control data; merge the expression feedback control data and the user feedback semantic recognition data to generate user bathing behavior pattern analysis data; perform non-contact temperature control on the heat source linkage regulation data based on the user bathing behavior pattern analysis data, thereby generating non-contact constant temperature control data.

[0046] In the embodiment of the present invention, the non-contact user feedback mode of the bathing equipment is classified according to the heat source linkage control data, mainly including two modes: the image feedback mode obtains the user's bathing status through the camera. The audio feedback mode obtains the sound information of the user during the bathing process through the recording device. Based on the image feedback mode, the user's bathing image is collected by the camera to obtain the user's bathing image set. The user's expression recognition is performed on the user's bathing image set to generate user expression recognition data to judge the user's emotional state during the bathing process. According to the user expression recognition data, the user's bathing image set is marked with user behavior to generate a user behavior mark image. This image set includes: comfort mark image: an image showing that the user is in a comfortable state. Uncomfortable mark image: an image showing that the user is in an uncomfortable state. Based on the comfort mark image and the uncomfortable mark image, the bathing equipment is first non-contact feedback regulated. This process continues until the number of comfort mark images is greater than the number of uncomfort mark images, and finally the expression feedback control data is generated. Based on the audio feedback mode, the user's bathing audio is collected by the recording device to obtain the user's bathing audio. The acoustic features of the user's bathing audio are extracted to obtain the user's bathing audio acoustic feature data, including information such as pitch, loudness, and frequency. The user's bathing audio acoustic feature data is converted into text to generate the user's bathing acoustic text. Subsequently, the user's bathing acoustic text is semantically recognized to generate user feedback semantic recognition data to understand the user's specific needs and feedback. Using the user feedback semantic recognition data, the bathing equipment is subjected to a second non-contact feedback regulation to generate audio feedback control data to adjust the working state of the equipment to meet the user's needs. The expression feedback control data and the user feedback semantic recognition data are merged to generate user bathing behavior pattern analysis data. This data can be used to analyze the user's behavior patterns and preferences during the bathing process. Based on the user's bathing behavior pattern analysis data, the heat source linkage control data is non-contact temperature controlled to generate non-contact constant temperature control data. This data will guide the temperature adjustment of the equipment to ensure that the user always maintains a comfortable water temperature during the bathing process.

[0047] Preferably, marking the user image behavior of the user bathing image set according to the user expression recognition data includes: Performing face region detection on the user bathing image set to generate user face region detection data; performing facial feature point extraction on the user face region detection data to obtain user facial feature points, wherein the user facial feature points include user eyes, user nose and user mouth; Performing facial region division on the user face region detection data through the user face feature points to generate user face division region data; performing multi-dimensional feature vector extraction on the user face division region data to obtain the user face multi-dimensional feature vector, wherein the multi-dimensional feature vector extraction includes facial geometry extraction, facial muscle movement extraction and facial texture feature extraction; Perform an emotion score on the multi-dimensional feature vector of the user's face according to the user's expression recognition data to generate the emotion score data of the user's facial region; perform an average score on the emotion score data of the user's facial region to generate the comprehensive emotion score data of the user's face; The user's facial comprehensive emotion score data is used to mark the user's image behavior on the user's bathing image set. When the user's facial comprehensive emotion score data is greater than or equal to the preset comprehensive emotion score threshold, the corresponding user's bathing image set is marked as a comfortable emotion image to generate a comfortable marked image; when the user's facial comprehensive emotion score data is less than the preset comprehensive emotion score threshold, the corresponding user's bathing image set is marked as a non-comfortable emotion image to generate a non-comfortable marked image.

[0048] In an embodiment of the present invention, by performing face region detection on a user bathing image set, computer vision technology is used to identify faces in the image, and user face region detection data is generated. This data includes the position, size and shape of the detected face. Based on the user face region detection data, facial feature points are extracted to identify key points of the user's face, including: user eyes, user nose, user mouth, and these feature points are helpful for subsequent facial region division and emotion analysis. According to the user's facial feature points, the user face region detection data is divided into facial regions to generate user face division region data. The data defines the specific positions of various facial regions (such as eyes, mouth, nose, etc.). Feature extraction is performed on the user face division region data to obtain a multi-dimensional feature vector of the user's face. The extracted features include: facial geometric features such as facial contour, proportion and symmetry. Facial muscle movement features such as muscle movement caused by changes in facial expressions. Facial texture features such as skin color, wrinkles and other details. According to the user expression recognition data, the user's facial multi-dimensional feature vector is emotionally scored to generate user facial region emotion scoring data. This score can be calculated by a machine learning model to evaluate the user's emotional state. The user's facial area emotion score data is averaged to generate the user's facial comprehensive emotion score data. This step ensures the comparability of emotion scores in different images and eliminates accidental factors by calculating the average value. When the user's facial comprehensive emotion score data is greater than or equal to the preset comprehensive emotion score threshold, the corresponding user bathing image set is marked with a comfortable emotion image to generate a comfortable mark image. This indicates that the user feels comfortable during the bathing process. When the user's facial comprehensive emotion score data is less than the preset comprehensive emotion score threshold, the corresponding user bathing image set is marked with a non-comfortable emotion image to generate a non-comfortable mark image. This indicates that the user feels uncomfortable during the bathing process.

[0049] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: performing time series analysis on the heat source linkage control data through the non-contact constant temperature control data to generate heat source linkage control time series data; Step S32: dividing the heat source linkage control time series data into data sets to generate a model training set and a model test set; training the model training set using a random forest algorithm to generate a heat source linkage control fluctuation prediction pre-model; optimizing and iterating the heat source linkage control fluctuation prediction pre-model using the model test set, thereby generating a heat source linkage control fluctuation model; Step S33: importing the heat source linkage control data into the heat source linkage control fluctuation model to perform heat source control fluctuation prediction, thereby generating heat source control fluctuation prediction data; Step S34: setting the equipment warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control equipment warning threshold.

[0050] In the embodiment of the present invention, the heat source linkage control data is cleaned to process missing values ​​and outliers. A suitable time series model (such as LSTM or ARIMA) is applied to analyze the heat source linkage control data to generate heat source linkage control time series data. The characteristics of the time series (such as seasonality, trend, etc.) are extracted to improve the accuracy of the subsequent model. The heat source linkage control time series data is divided into a training set (usually accounting for 70-80%) and a test set (20-30%). The training set is trained using a random forest algorithm to generate a heat source linkage control fluctuation prediction pre-model. The model is optimized using the test set, and hyperparameters (such as the number of trees, depth, etc.) are adjusted to improve model performance. The heat source linkage control data is imported into the trained heat source linkage control fluctuation model. Heat source control fluctuation prediction data is generated through model prediction. The heat source control fluctuation prediction data is analyzed to calculate statistics such as the mean and standard deviation. The equipment warning threshold is set according to the statistical characteristics of the predicted data, such as the mean plus or minus 2 times the standard deviation.

[0051] In this specification, a linkage constant temperature control system based on a smart bathing device is provided, which is used to execute the linkage constant temperature control method based on the smart bathing device. The linkage constant temperature control system based on the smart bathing device includes: The environmental analysis module is used to obtain the target temperature data set for the bathing equipment; collect environmental data from the water inlet pipe and the water outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identify the water temperature difference between the target temperature data set for the bathing equipment and the real-time water temperature data to generate water temperature difference identification data for the bathing equipment; analyze the potential fluctuation impact of the real-time water pressure data based on the water temperature difference identification data for the bathing equipment to generate water pressure fluctuation data for the bathing equipment; The constant temperature adjustment module is used to perform intelligent water mixing adjustment on the bathing equipment through the water temperature difference identification data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage adjustment on the real-time water mixing adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage adjustment data; perform non-contact temperature control based on the heat source linkage adjustment data, thereby generating non-contact constant temperature control data; The control warning module is used to perform model training on the heat source linkage control data through non-contact constant temperature control data to generate a heat source linkage control fluctuation model; import the heat source linkage control data into the heat source linkage control fluctuation model to predict the heat source control fluctuation, thereby generating heat source control fluctuation prediction data; set the equipment warning threshold value for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control equipment warning threshold value; The safety monitoring module is used to perform linkage constant temperature control early warning monitoring of bathing equipment based on the warning threshold of the heat source control equipment, and generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data, and generate a bathing equipment control and protection strategy to execute linkage constant temperature control operations.

[0052] The beneficial effect of the present invention is that by collecting environmental data of the water inlet and outlet of the bathing equipment, real-time water temperature and water pressure data are obtained, providing accurate basic data for subsequent analysis. This real-time monitoring ensures the transparency of the equipment operation status and facilitates immediate adjustment. The difference between the set target temperature and the real-time water temperature is identified, and water temperature difference identification data is generated, which helps to quickly discover temperature abnormalities and provide a scientific basis for water mixing regulation. Based on the water temperature difference, the potential fluctuation impact analysis of the real-time water pressure data can identify the impact of water pressure fluctuations on the bathing experience in advance, so as to formulate corresponding control strategies. Intelligent water mixing regulation is performed through water temperature difference identification data and water pressure fluctuation data, and real-time water mixing and water pressure dynamic regulation are realized, so that users can enjoy a more comfortable temperature experience during bathing. Based on real-time water mixing regulation data and water pressure dynamic regulation data, heat source linkage regulation is performed to ensure that the heat source output is highly matched with user needs, thereby reducing energy waste and improving the energy utilization efficiency of the equipment. The generated non-contact constant temperature control data adjusts the water temperature in an intelligent way, so that users can enjoy a constant water temperature without intervention, which improves the convenience and comfort of use. By training the model of the heat source linkage control data and generating the heat source control fluctuation model, the system can continuously learn and optimize, thereby improving the adaptability and flexibility of the system. The heat source linkage control data is imported into the fluctuation model for prediction, and the generated heat source control fluctuation prediction data can help to adjust the equipment operating parameters in time, thereby avoiding equipment failure or performance degradation caused by fluctuations. The equipment warning threshold is set according to the prediction data to ensure that the equipment can respond in time under abnormal conditions, avoid potential safety hazards, and improve the safety of the equipment. Bathing equipment is monitored based on the equipment warning threshold, and abnormal conditions of temperature or water pressure can be discovered and responded to in time, thereby enhancing the real-time monitoring capability of the equipment. A safety protection mechanism is constructed by linking the constant temperature control early warning monitoring data to ensure that protective measures can be taken quickly when the equipment is abnormal, thereby reducing the failure rate and extending the service life of the equipment. The whole process significantly improves the user's bathing experience through intelligent adjustment and monitoring, allowing users to enjoy higher comfort and security when using the equipment. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data acquisition, intelligent water mixing adjustment, heat source linkage control, and non-contact temperature control.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A linkage constant temperature control method based on intelligent bathing equipment, characterized in that: The following steps are involved: Step S1: Obtain target temperature data for bathing equipment; collect environmental data on the water inlet and outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identify water temperature differences between the target temperature data for bathing equipment and the real-time water temperature data to generate water temperature difference identification data for bathing equipment; perform potential fluctuation impact analysis on the real-time water pressure data based on the water temperature difference identification data for bathing equipment to generate water pressure fluctuation data for bathing equipment; Step S2: Intelligently adjust the water mixing of the bathing equipment by using the water temperature difference recognition data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; Perform heat source linkage control on real-time water mixing adjustment data and real-time water pressure dynamic adjustment data to generate heat source linkage control data; Non-contact temperature control is performed based on the heat source linkage control data, thereby generating non-contact constant temperature control data; Step S3: Model training is performed on the heat source linkage control data through non-contact constant temperature control data to generate a heat source linkage control fluctuation model; the heat source linkage control data is imported into the heat source linkage control fluctuation model to perform heat source control fluctuation prediction, thereby generating heat source control fluctuation prediction data; According to the heat source control fluctuation prediction data, the equipment warning threshold is set for the heat source linkage control data, thereby generating the heat source control equipment warning threshold; Step S4: Based on the warning threshold of the heat source control equipment, the bathing equipment is monitored for early warning of the linked constant temperature control, and the linked constant temperature control early warning monitoring data is generated; a safety protection mechanism is constructed through the linked constant temperature control early warning monitoring data, and a bathing equipment control and protection strategy is generated to execute the linked constant temperature control operation.

2. The linkage constant temperature control method based on intelligent bathing equipment according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire target temperature data for setting the bathing equipment; Step S12: using a temperature sensor to collect the real-time temperature of the water inlet pipe of the bathing equipment to obtain real-time water temperature data; using a water pressure sensor to collect the real-time water pressure of the water outlet of the bathing equipment to obtain real-time water pressure data; Step S13: performing data preprocessing on the real-time water temperature data and the real-time water pressure data to generate standard real-time water temperature data and standard real-time water pressure data, wherein the data preprocessing includes data cleaning, data denoising and data standardization; Step S14: Identify the water temperature difference between the target temperature data set for the bathing equipment and the standard real-time water temperature data to generate water temperature difference identification data for the bathing equipment; analyze the potential fluctuation impact of the standard real-time water pressure data based on the water temperature difference identification data for the bathing equipment to generate water pressure fluctuation data for the bathing equipment.

3. The linkage constant temperature control method based on intelligent bathing equipment according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: Calculate the water temperature difference between the target temperature data set for the bathing equipment and the standard real-time water temperature data to obtain water temperature difference data; compare the water temperature difference data with a preset water temperature difference threshold value; when the water temperature difference data is greater than or equal to the preset water temperature difference threshold value, mark the water temperature difference data to generate bathing equipment water temperature difference identification data; Step S142: Analyze the water pressure time series change of the standard real-time water pressure data to generate historical water pressure data; calculate the flow rate of the bathing equipment based on the standard real-time water pressure data and the historical water pressure data to obtain the flow rate data of the bathing equipment; Step S143: performing a water pressure stability impact analysis on the standard real-time water pressure data and the water temperature difference identification data of the bathing equipment according to the bathing equipment flow data, and generating bathing equipment water pressure fluctuation data; The formula for calculating the flow rate of bathing equipment is as follows: In the formula, Expressed as flow rate, Expressed as the water flow resistance coefficient, Expressed as real-time water pressure, Expressed as historical water pressure.

4. The linkage constant temperature control method based on intelligent bathing equipment according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Intelligently adjust the water mixing of the bathing equipment by using the water temperature difference recognition data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; Step S22: recalculate the water temperature difference between the real-time mixed water adjustment data and the target temperature data set for the bathing equipment to obtain the positive difference data of the mixed water adjustment water temperature and the negative difference data of the mixed water adjustment water temperature; perform heat source output control based on the positive difference data of the mixed water adjustment water temperature and the negative difference data of the mixed water adjustment water temperature to generate the heat source output control data for the bathing equipment; Step S23: using the real-time water pressure dynamic adjustment data to perform heat source reaction regulation on the bathing equipment heat source regulation data, and generating the bathing equipment heat source reaction regulation data; integrating the heat source regulation data based on the bathing equipment heat source output regulation data and the bathing equipment heat source reaction regulation data, and generating the heat source linkage regulation data; Step S24: Analyze the user usage behavior pattern of the bathing equipment according to the heat source linkage control data to generate user bathing behavior pattern analysis data; perform non-contact temperature control on the heat source linkage control data based on the user bathing behavior pattern analysis data to generate non-contact constant temperature control data.

5. The linkage constant temperature control method based on intelligent bathing equipment according to claim 4 is characterized in that: Step S21 includes the following steps: Step S211: Analyze the hot and cold water temperatures of the bathing equipment to generate cold water temperature data and hot water temperature data; perform cold and hot water ratio mixing requirements on the cold water temperature data and the hot water temperature data based on the water temperature difference identification data of the bathing equipment to obtain cold and hot water ratio mixing requirement data; Step S212: Calculate the water flow demand of the cold water temperature data and the hot water temperature data according to the cold and hot water ratio mixing demand data to obtain the cold water demand data and the hot water demand data; perform water mixing adjustment calculation on the cold water demand data and the hot water demand data through the water mixing ratio adjustment formula to obtain real-time water mixing adjustment data; Step S213: adjusting the water mixing valve control of the bathing equipment based on the real-time water mixing adjustment data, thereby generating water mixing valve control adjustment data; adjusting the valve opening water pressure of the water mixing valve control adjustment data based on the water pressure fluctuation data of the bathing equipment, thereby generating real-time water pressure dynamic adjustment data; The calculation formula of the mixing water ratio adjustment formula is as follows: In the formula, Represented as real-time mixed water regulation data, Expressed as the cold and hot water regulation ratio coefficient, Expressed as real-time temperature deviation, Expressed as the adjustment gain coefficient, Expressed as the adjustment differential coefficient, Expressed as the error between the set value and the actual value, Represents the current moment, Represented as a time variable.

6. The linkage constant temperature control method based on intelligent bathing equipment according to claim 4 is characterized in that: The heat source output regulation is performed through the mixed water adjustment water temperature positive difference data and the mixed water adjustment water temperature negative difference data, including: The heat source output power of the bathing equipment is collected to obtain heat source output power collection data, wherein the heat source output power collection data includes current heat source output power data and maximum heat source output power data; Performing data detection on the mixed water adjustment water temperature positive difference data and the mixed water adjustment water temperature negative difference data, when the mixed water adjustment water temperature positive difference data is detected, the heat source output power data is reduced based on the mixed water adjustment water temperature positive difference data to generate the heat source first control data; When negative difference data of mixed water adjustment water temperature is detected, the current heat source output power data and the maximum output power data of the heat source are converted into a heat source power fluctuation curve to generate a heat source power fluctuation curve; the maximum fluctuation peak value of the heat source power fluctuation curve is extracted to obtain the maximum available power data of the heat source; the negative difference data of mixed water adjustment water temperature is increased according to the maximum available power data of the heat source to generate the second control data of the heat source; The first heat source control data and the second heat source control data are integrated to generate the bathing equipment heat source output control data.

7. The linkage constant temperature control method based on intelligent bathing equipment according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: classifying the non-contact user feedback mode of the bathing equipment according to the heat source linkage control data to generate an image feedback mode and an audio feedback mode; using a camera to collect user bathing images based on the image feedback mode to obtain a user bathing image set; performing user expression recognition on the user bathing image set to generate user expression recognition data; Step S242: marking the user image behavior of the user bathing image set according to the user expression recognition data to generate a user behavior marking image, wherein the user behavior marking image includes a comfort marking image and a discomfort marking image; performing a first non-contact feedback control on the bathing equipment based on the comfort marking image and the discomfort marking image until the number of the comfort marking images is greater than the number of the discomfort marking images, and generating expression feedback control data; Step S243: Based on the audio feedback mode, the recording device is used to collect the user's bathing audio to obtain the user's bathing audio; the audio acoustic feature extraction is performed on the user's bathing audio to obtain the user's bathing audio acoustic feature data; the user's bathing audio acoustic feature data is converted into text to generate the user's bathing acoustic text; the user's bathing acoustic text is semantically recognized to generate user feedback semantic recognition data; Step S244: Use the user feedback semantic recognition data to perform a second non-contact feedback regulation on the bathing equipment to generate audio feedback control data; merge the expression feedback control data and the user feedback semantic recognition data to generate user bathing behavior pattern analysis data; perform non-contact temperature control on the heat source linkage regulation data based on the user bathing behavior pattern analysis data, thereby generating non-contact constant temperature control data.

8. The linkage constant temperature control method based on intelligent bathing equipment according to claim 7 is characterized in that: The user image behavior tagging of the user bathing image set based on the user expression recognition data includes: Performing face region detection on the user bathing image set to generate user face region detection data; performing facial feature point extraction on the user face region detection data to obtain user facial feature points, wherein the user facial feature points include user eyes, user nose and user mouth; Performing facial region division on the user face region detection data through the user face feature points to generate user face division region data; performing multi-dimensional feature vector extraction on the user face division region data to obtain the user face multi-dimensional feature vector, wherein the multi-dimensional feature vector extraction includes facial geometry extraction, facial muscle movement extraction and facial texture feature extraction; Perform an emotion score on the multi-dimensional feature vector of the user's face according to the user's expression recognition data to generate the emotion score data of the user's facial region; perform an average score on the emotion score data of the user's facial region to generate the comprehensive emotion score data of the user's face; The user's facial comprehensive emotion score data is used to mark the user's image behavior on the user's bathing image set. When the user's facial comprehensive emotion score data is greater than or equal to the preset comprehensive emotion score threshold, the corresponding user's bathing image set is marked as a comfortable emotion image to generate a comfortable marked image; when the user's facial comprehensive emotion score data is less than the preset comprehensive emotion score threshold, the corresponding user's bathing image set is marked as a non-comfortable emotion image to generate a non-comfortable marked image.

9. The linkage constant temperature control method based on intelligent bathing equipment according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing time series analysis on the heat source linkage control data through the non-contact constant temperature control data to generate heat source linkage control time series data; Step S32: dividing the heat source linkage control time series data into data sets to generate a model training set and a model test set; training the model training set using a random forest algorithm to generate a heat source linkage control fluctuation prediction pre-model; optimizing and iterating the heat source linkage control fluctuation prediction pre-model using the model test set, thereby generating a heat source linkage control fluctuation model; Step S33: importing the heat source linkage control data into the heat source linkage control fluctuation model to perform heat source control fluctuation prediction, thereby generating heat source control fluctuation prediction data; Step S34: setting the equipment warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control equipment warning threshold.

10. A linkage constant temperature control system based on intelligent bathing equipment, characterized in that: Used to execute the linkage constant temperature control method based on the intelligent bathing device as claimed in claim 1, the linkage constant temperature control system based on the intelligent bathing device comprises: The environmental analysis module is used to obtain the target temperature data set for the bathing equipment; collect environmental data from the water inlet pipe and the water outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identify the water temperature difference between the target temperature data set for the bathing equipment and the real-time water temperature data to generate water temperature difference identification data for the bathing equipment; analyze the potential fluctuation impact of the real-time water pressure data based on the water temperature difference identification data for the bathing equipment to generate water pressure fluctuation data for the bathing equipment; The constant temperature adjustment module is used to perform intelligent water mixing adjustment on the bathing equipment through the water temperature difference identification data and the water pressure fluctuation data of the bathing equipment, and generate real-time water mixing adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage adjustment on the real-time water mixing adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage adjustment data; perform non-contact temperature control based on the heat source linkage adjustment data, thereby generating non-contact constant temperature control data; The control warning module is used to perform model training on the heat source linkage control data through non-contact constant temperature control data to generate a heat source linkage control fluctuation model; import the heat source linkage control data into the heat source linkage control fluctuation model to predict the heat source control fluctuation, thereby generating heat source control fluctuation prediction data; set the equipment warning threshold value for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control equipment warning threshold value; The safety monitoring module is used to perform linkage constant temperature control early warning monitoring of bathing equipment based on the warning threshold of the heat source control equipment, and generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data, and generate a bathing equipment control and protection strategy to execute linkage constant temperature control operations.

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