A linkage constant temperature control method and system based on intelligent bathing equipment

Abstract

The present invention relates to the technical field of device control, and particularly to a linkage constant temperature control method and system based on intelligent bathing devices. The method includes the following steps: acquiring the set target temperature data of the bathing device; collecting environmental data at the water inlet pipe and the water outlet of the bathing device to obtain real-time water temperature data and real-time water pressure data; identifying the water temperature difference between the set target temperature data and the real-time water temperature data of the bathing device to generate water temperature difference identification data of the bathing device; analyzing the potential fluctuation impact on the real-time water pressure data based on the water temperature difference identification data of the bathing device to generate water pressure fluctuation data of the bathing device. The present invention improves the accuracy and reliability of temperature control through technologies such as real-time data collection, intelligent mixing water regulation, heat source linkage regulation, and non-contact temperature control.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and particularly 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 repeatedly test the water to reach the ideal temperature, which was cumbersome and time-consuming. With the progress of technology, simple electronic temperature control devices gradually emerged, but their stability and response speed were still insufficient. With the rise of the Internet of Things (IoT) and smart homes, intelligent 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 systems mainly used single water source control, but it was found in use that the water temperature fluctuations of different water sources were relatively large, making it difficult to ensure a constant bathing experience. In recent years, with the development of artificial intelligence and big data technologies, the linkage constant temperature control method of intelligent bathing equipment has been significantly improved. Through data analysis and learning, the system can predict users' bathing habits and automatically adjust the water temperature. In addition, the introduction of multi-water source linkage control technology enables the system to real-time adjust the water supply ratio of different water sources, thereby achieving higher-precision temperature control. However, the existing technologies at present have not effectively integrated multiple heat sources, resulting in delays and instability in temperature control. At the same time, physical sensors are usually required for temperature detection, which is easily interfered by the external environment, thus leading to relatively 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 object, a linkage constant temperature control method based on intelligent bathing equipment, the method includes the following steps:

[0005] Step S1: Obtain the set target temperature data of the bathing equipment; collect environmental data of 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 set target temperature data of the bathing equipment and the real-time water temperature data to generate water temperature difference identification data of the bathing equipment; analyze the potential fluctuation impact 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;

[0006] Step S2: Perform intelligent mixing water regulation on the bathing equipment through the data of the water temperature difference of the bathing equipment and the data of the water pressure fluctuation of the bathing equipment to generate real-time mixing water regulation data and real-time water pressure dynamic regulation data; perform heat source linkage control on the real-time mixing water regulation data and the real-time water pressure dynamic regulation data to generate heat source linkage control data; perform non-contact temperature control based on the heat source linkage control data, thereby generating non-contact constant temperature control data;

[0007] Step S3: Perform model training on the heat source linkage control data through the 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 perform heat source control fluctuation prediction, thereby generating heat source control fluctuation prediction data; set 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;

[0008] Step S4: Perform linkage constant temperature control early warning monitoring on the bathing equipment based on the heat source control equipment warning threshold to generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data to generate a bathing equipment control protection strategy to execute the linkage constant temperature control operation.

[0009] The present invention obtains the set target temperature data of the bathing device, collects environmental data of the water inlet pipe and the water outlet, and monitors the water temperature and water pressure in real time. This process can clearly identify the bathing needs of users 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 judge the water temperature that needs to be adjusted. Based on these data, the potential impact of water pressure fluctuations on the water temperature is further analyzed, providing an important basis for subsequent adjustment. This stage ensures a comprehensive understanding of the current state of the system and enhances the response ability 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 mixing water adjustment on the bathing device. This intelligent adjustment is not only fast but also effective, which can optimize the temperature of the mixed water and improve the comfort of users. Subsequently, the system combines the real-time mixing water adjustment data and the water pressure dynamic adjustment data to perform linkage control of the heat source. This mechanism realizes the efficient utilization 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 the non-contact constant temperature control data to train the model of the heat source linkage control data to generate a heat source control fluctuation model. This model can not only learn historical data but also predict future control fluctuations, providing data support for subsequent operations. According to the prediction results, the system sets the warning threshold of the device to prevent abnormal situations from occurring. This link significantly improves the intelligence level of the system, can quickly adapt to the changes in user needs and ensure safe operation. Finally, the fourth step establishes an early warning monitoring system for linkage constant temperature control. By monitoring in real time based on the warning threshold of the heat source control device, the system can identify and handle abnormal situations in time 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 protection strategy for the bathing device to cope 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 use experience. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data collection, intelligent mixing water adjustment, heat source linkage control, and non-contact temperature control.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the set target temperature data of the bathing device;

[0012] Step S12: Use a temperature sensor to collect the real-time temperature of the water inlet pipe of the bathing device to obtain real-time water temperature data; use a water pressure sensor to collect the real-time water pressure of the water outlet of the bathing device to obtain real-time water pressure data;

[0013] Step S13: Perform data preprocessing on the real-time water temperature data and real-time water pressure data to generate standard real-time water temperature data and standard real-time water pressure data, where the data preprocessing includes data cleaning, data denoising, and data standardization;

[0014] Step S14: Identify the water temperature difference between the set target temperature data of the bathing device and the standard real-time water temperature data to generate the water temperature difference identification data of the bathing device; based on the water temperature difference identification data of the bathing device, analyze the potential fluctuation impact on the standard real-time water pressure data to generate the water pressure fluctuation data of the bathing device.

[0015] Through the real-time data collection of the temperature sensor and the water pressure sensor, the present invention can timely understand the working state of the bathing device and ensure that the device operates within the set target temperature and water pressure range. This real-time monitoring can help detect and solve potential problems in a timely manner and improve the safety of the device. 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 set target temperature of the bathing device with the real-time water temperature data, the temperature difference can be identified, and then the operating parameters of the device can be adjusted in a timely manner. This temperature difference identification function can prevent users from feeling uncomfortable during use and improve the user experience. The water pressure fluctuation analysis based on the water temperature difference identification data can reveal potential factors of unstable operation and provide guidance for device maintenance and optimization. By deeply understanding the water pressure fluctuation, the incidence of device failures can be reduced and the service life of the device can be extended.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: Calculate the water temperature difference between the set target temperature data of the bathing device and the standard real-time water temperature data to obtain the water temperature difference data; compare the water temperature difference data with the preset water temperature difference threshold. When the water temperature difference data is greater than or equal to the preset water temperature difference threshold, mark the water temperature difference data to generate the water temperature difference identification data of the bathing device;

[0018] Step S142: Analyze the temporal variation of the water pressure of the standard real-time water pressure data to generate historical water pressure data; calculate the flow rate of the bathing device based on the standard real-time water pressure data and the historical water pressure data to obtain the flow rate data of the bathing device;

[0019] Step S143: Analyze the impact of the flow rate data of the bathing device on the standard real-time water pressure data and the water temperature difference identification data of the bathing device to generate the water pressure fluctuation data of the bathing device;

[0020] The formula for calculating the flow rate of the bathing equipment is as follows:

[0021]

[0022] In the formula, is expressed as the flow rate, is expressed as the water flow resistance coefficient, is expressed as the real-time water pressure, is expressed as the historical water pressure.

[0023] Through the calculation of the water temperature difference and the comparison with the preset threshold value, the present invention can monitor the water temperature condition of the bathing equipment in real time. When the temperature difference reaches the set alarm threshold value, the system can issue a warning in time or automatically adjust the equipment, so as to ensure that the water temperature is always maintained within a comfortable range and improve the bathing experience of users. Through the analysis of the standard real-time water pressure data and the 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 utilization of water resources. The flow rate calculation can help identify any abnormal flow rate and ensure that the equipment operates at the best performance. By combining the flow rate data with the water temperature difference identification data for the analysis of the influence on the water pressure stability, the root cause of the water pressure fluctuation can be deeply understood. This analysis helps to find out potential water pressure fluctuation problems, so as to take preventive measures and reduce the risk of system failure. The generation of the water pressure fluctuation data makes the equipment maintenance and management more scientific. Through the real-time monitoring of the water pressure and the flow rate, the maintenance personnel can formulate a more reasonable maintenance plan, optimize the operation cycle of the equipment and reduce the unnecessary downtime. The generation of the water pressure fluctuation data makes the equipment maintenance and management more scientific. Through the real-time monitoring of the water pressure and the flow rate, the maintenance personnel can formulate a more reasonable maintenance plan, optimize the operation cycle of the equipment and reduce the unnecessary downtime. The real-time monitoring of the water temperature and the water pressure, especially through the water temperature difference marking system, can timely discover and handle the problems that cause potential safety hazards to users, reduce the risk of accidents during the bathing process and ensure the safety of users.

[0024] Preferably, step S2 includes the following steps:

[0025] Step S21: Perform intelligent mixing water adjustment on the bathing equipment through the bathing equipment water temperature difference identification data and the bathing equipment water pressure fluctuation data to generate real-time mixing water adjustment data and real-time water pressure dynamic adjustment data;

[0026] Step S22: Re-calculate the water temperature difference between the real-time mixing water adjustment data and the set target temperature data of the bathing equipment to obtain the positive water temperature difference data for mixing water adjustment and the negative water temperature difference data for mixing water adjustment; Perform heat source output regulation through the positive water temperature difference data for mixing water adjustment and the negative water temperature difference data for mixing water adjustment to generate the heat source output regulation data of the bathing equipment;

[0027] Step S23: Use the real-time water pressure dynamic adjustment data to perform heat source response regulation on the heat source regulation data of the bathing device, and generate the heat source response regulation data of the bathing device; Integrate the heat source output regulation data and the heat source response regulation data of the bathing device to generate the heat source linkage regulation data;

[0028] Step S24: Analyze the user's usage behavior pattern of the bathing device according to the heat source linkage regulation data, and generate the 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, so as to generate non-contact constant temperature control data.

[0029] Through the water temperature difference identification data and the water pressure fluctuation data, the bathing device can intelligently adjust the mixed water. This automatic adjustment ensures that users can always enjoy the ideal water temperature during bathing, avoiding discomfort caused by temperature fluctuations. Through the recalculation of the water temperature difference, the positive and negative difference data of the mixed water adjustment water temperature are generated, enabling precise regulation of the heat source output. 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. Using the real-time water pressure dynamic adjustment data to perform reaction regulation on the heat source output can flexibly adjust the working state of the heat source according to the actual water pressure change situation, avoiding the problem of temperature instability caused by water pressure changes. By integrating the heat source output regulation data and the heat source response regulation data, the generated heat source linkage regulation data realizes the overall optimization of the heat source. This integration can effectively improve the operation efficiency of the system and reduce energy waste. Analyzing the user bathing behavior pattern according to the heat source linkage regulation data helps to understand the user's usage habits, and then optimize the heat source regulation strategy of the device. This analysis can help the device better adapt to the needs of different users and improve user satisfaction. The non-contact temperature control realized based on the user bathing behavior pattern analysis data enables the device to automatically adjust the water temperature without user intervention, providing a more convenient and comfortable bathing experience.

[0030] Preferably, step S21 includes the following steps:

[0031] Step S211: Analyze the cold and hot water temperatures of the bathing device to generate cold water temperature data and hot water temperature data; Based on the water temperature difference identification data of the bathing device, obtain the cold and hot water ratio mixing requirements for the cold water temperature data and the hot water temperature data, and obtain the cold and hot water ratio mixing requirement data;

[0032] Step S212: Calculate the water flow demand for the cold water temperature data and the hot water temperature data according to the cold and hot water ratio mixing requirement data, and obtain the cold water demand data and the hot water demand data; Perform mixed water volume adjustment calculation on the cold water demand data and the hot water demand data through the mixed water ratio adjustment formula to obtain the real-time mixed water adjustment data;

[0033] Step S213: Based on the real-time mixing water adjustment data, control and adjust the mixing valve of the bathing device to generate mixing valve control adjustment data; based on the water pressure fluctuation data of the bathing device, adjust the valve opening water pressure for the mixing valve control adjustment data to generate real-time water pressure dynamic adjustment data;

[0034] The calculation formula of the mixing ratio adjustment formula is as follows:

[0035]

[0036] In the formula, represents the real-time mixing water adjustment data, represents the cold and hot water adjustment ratio coefficient, represents the real-time temperature deviation, represents the adjustment gain coefficient, represents the adjustment differential coefficient, represents the error between the set value and the actual value, represents the current moment, represents the time variable.

[0037] Through the analysis of the cold and hot water temperatures, the present invention can generate accurate cold and hot water temperature data. This provides a basis for subsequent calculation of the mixing ratio, ensuring that the finally mixed water temperature meets the user's set requirements. Based on the cold and hot water ratio mixing requirements identified from the water temperature difference recognition data, the ratio of cold water and hot water can be precisely adjusted. Through effective cold and hot water ratio mixing, it ensures that users obtain the desired comfortable water temperature during bathing, improving user satisfaction. By calculating the demand for cold and hot water, the water flow can be reasonably distributed, ensuring the efficient utilization of water resources during the operation of the device. 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 device to perform dynamic adjustment according to user needs and real-time environmental conditions. This real-time response ability improves the intelligence level of the device, ensuring a smoother experience for users during use. Based on the generation of data for controlling and adjusting the mixing valve, the bathing device can automatically adjust the mixing 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 device. By real-time monitoring of the water pressure fluctuation data and adjusting the 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, enhancing the overall use experience. Applying the mixing ratio adjustment formula and using the feedback control mechanism to adjust the real-time temperature deviation enhances the automatic adjustment ability of the system. This feedback-based adjustment method improves the adaptability of the system to external environmental changes and reduces the discomfort caused by water temperature fluctuations. This technology makes it possible to integrate the intelligent home system of the bathing device. Through linkage with other intelligent devices, users can remotely adjust the water temperature and water pressure through mobile applications and other means, enhancing the convenience of life.

[0038] Preferably, the regulation of the heat source output through the positive difference data of the mixing water temperature regulation and the negative difference data of the mixing water temperature regulation includes:

[0039] Collect the heat source output power of the bathing device to obtain heat source output power collection data, where the heat source output power collection data includes the current heat source output power data and the maximum heat source output power data;

[0040] Detect the positive difference data of the mixing water temperature regulation and the negative difference data of the mixing water temperature regulation. When the positive difference data of the mixing water temperature regulation is detected, the heat source output power data is reduced based on the positive difference data of the mixing water temperature regulation to generate the first heat source regulation data;

[0041] When negative difference data of the mixed water regulating water temperature is detected, the current heat source output power data and the maximum heat source output power data are subjected to heat source power fluctuation curve conversion 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 heat source power data; according to the maximum available heat source power data, the negative difference data of the mixed water regulating water temperature is used to increase the heat source output power to generate second heat source regulation data;

[0042] The first heat source regulation data and the second heat source regulation data are integrated to generate heat source output regulation data for the bathing device.

[0043] Through the real-time acquisition of the heat source output power, the present invention can obtain the data of the current heat source output power and the maximum output power, providing basic data support for 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 regulating water temperature. When positive difference is detected, the system will automatically reduce the heat source output power to avoid overheating; in the case of negative difference, the system can appropriately increase the heat source output power to achieve a rapid temperature rise. This adaptive regulation improves the intelligent level of the system and ensures the comfort of users during bathing. By performing fluctuation curve conversion on the current heat source output power data and the maximum output power data, 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 extends the service life of the equipment. Integrating the first heat source regulation data and the second heat source regulation data, the generated heat source output regulation data for the bathing device 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 device smoother. Through the reasonable adjustment of the heat source power, unnecessary energy waste is avoided. When high-temperature water is not required by the user, the measure of reducing the heat source power not only saves electricity but also reduces the operating cost. The intelligent heat source output regulation enables users to enjoy a more comfortable water temperature change during bathing and avoid discomfort caused by water temperature fluctuations. This good user experience improves the user satisfaction of the bathing device and enhances the user's trust in intelligent devices. By real-time monitoring the heat source power and performing reasonable regulation, the safety hazard caused by overheating of the equipment is reduced. This improvement in safety not only protects the equipment itself but also provides guarantee for the safe use of users.

[0044] Preferably, step S24 includes the following steps:

[0045] Step S241: Classify the non-contact user feedback modes of the bathing equipment according to the heat source linkage control data to generate an image feedback mode and an audio feedback mode; collect user bathing images using a camera based on the image feedback mode to obtain a set of user bathing images; perform user facial expression recognition on the set of user bathing images to generate user facial expression recognition data;

[0046] Step S242: Mark the user images in the set of user bathing images according to the user facial expression recognition data to generate user behavior marked images, where the user behavior marked images include comfortable marked images and uncomfortable marked images; perform the first non-contact feedback control on the bathing equipment based on the comfortable marked images and the uncomfortable marked images until the number of comfortable marked images is greater than the number of uncomfortable marked images to generate facial expression feedback control data;

[0047] Step S243: Collect user bathing audio using a recording device based on the audio feedback mode to obtain user bathing audio; extract the audio acoustic features of the user bathing audio to obtain user bathing audio acoustic feature data; perform text conversion on the user bathing audio acoustic feature data to generate user bathing acoustic text; perform semantic recognition on the user bathing acoustic text to generate user feedback semantic recognition data;

[0048] Step S244: Perform the second non-contact feedback control on the bathing equipment using the user feedback semantic recognition data to generate audio feedback control data; merge the facial 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 control data based on the user bathing behavior pattern analysis data to generate non-contact constant temperature control data.

[0049] Through the classification of image and audio feedback modes, the system can collect users' feedback information in multiple dimensions. Image feedback captures the user's emotional state through the user's facial expressions, while audio feedback provides linguistic information about the user's satisfaction and preferences. This multi-level feedback collection method enhances the understanding of the user experience. By using user expression recognition technology, the comfort level of the user during the bathing process can be recognized in real time. This emotion-based feedback mechanism allows the system to perform personalized regulation, enabling the bathing equipment to make corresponding adjustments according to the actual feelings of the user, thus enhancing the user's comfort and satisfaction. By combining the analysis of user behavior-labeled images with equipment regulation, the system can perform the first non-contact feedback regulation to ensure that adjustments can be made in a timely manner when the user feels uncomfortable. This dynamic feedback mechanism enhances the intelligence level of the equipment, enabling it to adapt to user needs. By merging the expression feedback control data with the user feedback semantic recognition data, the generated analysis data of the user's bathing behavior pattern provides a comprehensive understanding of the user's behavior. This data fusion can provide a more accurate basis for subsequent equipment regulation, further improving the regulation effect. The extraction of the acoustic features 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, thereby helping to optimize the equipment regulation strategy. The non-contact temperature control based on the analysis data of the user's bathing behavior pattern ensures that the user can enjoy a stable temperature when using the bathing equipment. This intelligent temperature control enhances the user experience, making the user feel more comfortable and satisfied during the bathing process. Through the non-contact image and audio feedback collection method, the privacy of the user can be protected to a certain extent, avoiding the discomfort and embarrassment brought by the traditional contact feedback method.

[0050] Preferably, the user image behavior labeling of the user bathing image set according to the user expression recognition data includes:

[0051] Detect the face area of the user bathing image set to generate user face area detection data; extract facial feature points from the user face area detection data to obtain user facial feature points, where the user facial feature points include the user's eyes, the user's nose, and the user's mouth;

[0052] Divide the face area of the user based on the user facial feature points in the user face area detection data to generate user facial division area data; extract multi-dimensional feature vectors from the user facial division area data to obtain user facial multi-dimensional feature vectors, where the extraction of multi-dimensional feature vectors includes facial geometry extraction, facial muscle movement extraction, and facial texture feature extraction;

[0053] Perform an emotional scoring on the user's facial multi-dimensional feature vector according to the user's facial expression recognition data to generate the emotional scoring data for the user's facial area; average the emotional scoring data for the user's facial area to generate the comprehensive emotional scoring data for the user's face.

[0054] Use the comprehensive emotional scoring data of the user's face to perform user image behavior marking on the user's bathing image set. When the comprehensive emotional scoring data of the user's face is greater than or equal to the preset comprehensive emotional scoring threshold, perform comfortable emotional image marking on the corresponding user's bathing image set to generate comfortable marked images; when the comprehensive emotional scoring data of the user's face is less than the preset comprehensive emotional scoring threshold, perform non-comfortable emotional image marking on the corresponding user's bathing image set to generate non-comfortable marked images.

[0055] Through face region detection and extraction of facial feature points from the user's bathing image set, the system can accurately locate the user's facial region and precisely capture key parts such as the user's eyes, nose, and mouth. This provides a reliable data basis for subsequent emotion recognition. The extraction of multi-dimensional feature vectors of the geometric structure, muscle movement, and texture features of the user's face enriches the accuracy of emotion recognition. This multi-dimensional feature analysis not only improves the system's ability to perceive subtle emotional changes but also enables comprehensive evaluation of the user's emotional state based on information from different dimensions. Through the emotion scoring mechanism, the system can generate emotion scoring data for each facial region and obtain the comprehensive emotion score of the user's face through the averaging process. This scoring system ensures the accuracy and flexibility of emotion recognition and can conduct emotion assessment in real-time according to the actual emotional changes of the user. Using the comprehensive emotion scoring data for user image behavior marking, the user's emotional state can be divided into two categories: comfortable and uncomfortable. Through the preset emotion scoring threshold, the system can automatically mark the user image set accordingly, providing a direct basis for subsequent intelligent regulation. Based on the results of image behavior marking, the system can dynamically adjust the settings of bathing equipment, such as water temperature, heat source output, etc. When it detects that the user is in a comfortable state, the system will maintain the current equipment state; when the user shows discomfort, the system will perform corresponding regulation optimization based on the marked image to ensure that the user always enjoys a comfortable bathing experience. Through the linkage of emotion recognition and marked images, the system can continuously adjust equipment parameters, enabling the user to enjoy a personalized and user-friendly bathing experience. This emotion-based regulation method not only enhances the user's sense of participation but also increases the intelligence of the equipment and user satisfaction. The system can analyze the emotional changes in the user's facial expressions in real-time and make timely adjustments when detecting an uncomfortable state. This adaptive regulation mechanism can dynamically adjust the bathing experience according to the user's real-time feedback, providing a more accurate and immediate adjustment effect. The multi-dimensional 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 sensitively capture subtle emotional changes, ensuring the accuracy of marking.

[0056] Preferably, step S3 includes the following steps:

[0057] Step S31: Perform time series analysis on the heat source linkage regulation data through non-contact constant temperature control data to generate heat source linkage regulation time series data;

[0058] Step S32: Divide the heat source linkage regulation time series data into data sets to generate a model training set and a model test set; perform model training on the model training set through the random forest algorithm to generate a pre-model for predicting heat source linkage regulation fluctuations; use the model test set to perform model optimization iteration on the pre-model for predicting heat source linkage regulation fluctuations, thereby generating a model for predicting heat source linkage regulation fluctuations;

[0059] Step S33: 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;

[0060] Step S34: Set the device warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control device warning threshold.

[0061] Through time series analysis of the non-contact constant temperature control data and the heat source linkage control data, the system of the present invention 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 the behavior changes of the heat source, enabling more accurate prediction of subsequent control 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 construct a model in the context of big data. In particular, using the random forest algorithm to train the training set to generate a pre-model for heat source linkage control fluctuation prediction improves the robustness and generalization ability of the model. Using the test set to optimize and iterate the pre-trained fluctuation prediction model not only improves the prediction accuracy but also ensures the dynamic adjustment ability of the model. Through continuous iterative optimization, the system can adapt to different heat source fluctuation situations, making the prediction results more in line with the actual control requirements. Applying the optimized heat source linkage control fluctuation model to real-time data prediction can generate heat source control fluctuation prediction data. This prediction mechanism based on machine learning provides forward-looking data support for device control, enabling the system to respond to heat source fluctuations in advance and avoid the occurrence of emergencies. According to the prediction data, the system can automatically set the warning threshold of the device to ensure that when the heat source linkage control fluctuation exceeds the safe range, the device can take corresponding measures in time. This function effectively improves the safety of device operation and reduces the risk of device failures caused by abnormal heat source fluctuations. The entire process realizes automatic heat source control through intelligent algorithms and prediction models without manual intervention, greatly improving the autonomous control ability of the device. This intelligent control method not only reduces the complexity of manual operation but also enables the system to respond faster and more flexibly to user needs and environmental changes. The device control based on prediction can avoid the situation of excessive or insufficient heat source, thereby improving the energy utilization efficiency and reducing energy waste. This not only extends the service life of the device but also saves energy costs for users.

[0062] In this specification, a linkage constant temperature control system based on intelligent bathing equipment is provided for implementing the above-mentioned linkage constant temperature control method based on intelligent bathing equipment. The linkage constant temperature control system based on intelligent bathing equipment includes:

[0063] An environmental analysis module, configured to obtain the set target temperature data of the bathing device; collect environmental data from the water inlet pipe and the water outlet of the bathing device to obtain real-time water temperature data and real-time water pressure data; identify the water temperature difference between the set target temperature data and the real-time water temperature data of the bathing device to generate bathing device water temperature difference identification data; analyze the potential fluctuation impact on the real-time water pressure data based on the bathing device water temperature difference identification data to generate bathing device water pressure fluctuation data;

[0064] A constant temperature regulation module, configured to perform intelligent mixing water regulation on the bathing device through the bathing device water temperature difference identification data and the bathing device water pressure fluctuation data to generate real-time mixing water regulation data and real-time water pressure dynamic regulation data; perform heat source linkage control on the real-time mixing water regulation data and the real-time water pressure dynamic regulation data to generate heat source linkage control data; perform non-contact temperature control based on the heat source linkage control data to generate non-contact constant temperature control data;

[0065] A control warning module, configured to perform model training on the heat source linkage control data through the 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 device warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating a heat source control device warning threshold;

[0066] A safety monitoring module, configured to perform linkage constant temperature control early warning monitoring on the bathing device based on the heat source control device warning threshold to generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data to generate a bathing device control protection strategy to execute the linkage constant temperature control operation.

[0067] The beneficial effects of the present invention are as follows: By collecting environmental data of the water inlet pipe and outlet of the bathing device, 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 device operation state, facilitating immediate adjustment. Identifying the difference between the set target temperature and the real-time water temperature to generate water temperature difference identification data helps quickly detect temperature anomalies and provides a scientific basis for mixing water adjustment. Analyzing the potential fluctuation impact on the real-time water pressure data based on the water temperature difference can identify in advance the impact of water pressure fluctuations on the bathing experience, thereby formulating corresponding regulation strategies. Intelligent mixing water adjustment is carried out through the water temperature difference identification data and water pressure fluctuation data, realizing dynamic adjustment of real-time mixing water and water pressure, enabling users to enjoy a more comfortable temperature experience during bathing. Based on the real-time mixing water adjustment data and water pressure dynamic adjustment data, heat source linkage control is carried out to ensure a high degree of matching between the heat source output and user needs, thereby reducing energy waste and improving the energy utilization efficiency of the device. The generated non-contact constant temperature control data adjusts the water temperature in an intelligent manner, enabling users to enjoy a constant water temperature without intervention, improving the convenience and comfort of use. By training the heat source linkage control data to generate a heat source regulation fluctuation model, the system can continuously learn and optimize, improving the adaptability and flexibility of the system. Importing the heat source linkage control data into the fluctuation model for prediction, the generated heat source regulation fluctuation prediction data can help adjust the device operation parameters in a timely manner, thereby avoiding device failures or performance degradation caused by fluctuations. Setting the device warning threshold according to the prediction data ensures that the device can respond in a timely manner in case of anomalies, avoiding potential safety hazards and improving the safety of the device. Monitoring the bathing device based on the device warning threshold can promptly detect and respond to anomalies in temperature or water pressure, enhancing the real-time monitoring ability of the device. By linking the constant temperature control early warning monitoring data to construct a safety protection mechanism, it is ensured that protective measures can be quickly taken when the device malfunctions, thereby reducing the failure rate and extending the service life of the device. The whole process significantly improves the user's bathing experience through intelligent adjustment and monitoring, enabling users to enjoy higher comfort and security when using the device. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data collection, intelligent mixing water adjustment, heat source linkage control, and non-contact temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a schematic diagram of the step flow of a linkage constant temperature control method based on an intelligent bathing device;

[0069] Figure 2 is Figure 1 a detailed implementation step flow schematic diagram of step S2 in

[0070] Figure 3 is Figure 1Schematic diagram of the detailed implementation steps of step S3 in

[0071] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0072] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts belong to the scope of protection of the present invention.

[0073] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0074] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0075] To achieve the above object, please refer to Figures 1 to 3 , a linkage constant temperature control method based on an intelligent bathing device, the method comprising the following steps:

[0076] Step S1: Obtain the set target temperature data of the bathing device; collect environmental data of the water inlet pipe and the water outlet of the bathing device to obtain real-time water temperature data and real-time water pressure data; identify the water temperature difference between the set target temperature data and the real-time water temperature data of the bathing device to generate water temperature difference identification data of the bathing device; analyze the potential fluctuation impact on the real-time water pressure data based on the water temperature difference identification data of the bathing device to generate water pressure fluctuation data of the bathing device;

[0077] Step S2: Perform intelligent mixing water regulation on the bathing equipment through the bathing equipment water temperature difference identification data and the bathing equipment water pressure fluctuation data to generate real-time mixing water regulation data and real-time water pressure dynamic regulation data; perform heat source linkage regulation and control on the real-time mixing water regulation data and the real-time water pressure dynamic regulation data to generate heat source linkage regulation and control data; perform non-contact temperature control based on the heat source linkage regulation and control data, thereby generating non-contact constant temperature control data;

[0078] Step S3: Perform model training on the heat source linkage regulation and control data through the non-contact constant temperature control data to generate a heat source linkage regulation and control fluctuation model; import the heat source linkage regulation and control data into the heat source linkage regulation and control fluctuation model for heat source regulation and control fluctuation prediction, thereby generating heat source regulation and control fluctuation prediction data; set the equipment warning threshold for the heat source linkage regulation and control data according to the heat source regulation and control fluctuation prediction data, thereby generating the heat source regulation equipment warning threshold;

[0079] Step S4: Perform linkage constant temperature control early warning monitoring on the bathing equipment based on the heat source regulation equipment warning threshold to generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data to generate a bathing equipment control protection strategy to perform linkage constant temperature control operations.

[0080] The present invention acquires the set target temperature data of the bathing device, collects environmental data of the water inlet pipe and the water outlet, and monitors the water temperature and water pressure in real time. This process can clearly identify the bathing needs of users 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 judge the water temperature that needs to be adjusted. Based on these data, further analyze the impact of potential fluctuations in water pressure on the water temperature, providing an important basis for subsequent adjustment. This stage ensures a comprehensive understanding of the current state of the system and enhances the response ability 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 mixing adjustment on the bathing device. This intelligent adjustment is not only fast but also effective, which can optimize the temperature of the mixed water and improve the comfort of users. Subsequently, the system combines the real-time mixing adjustment data and the water pressure dynamic adjustment data to perform linkage control of the heat source. This mechanism realizes the efficient utilization 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 the non-contact constant temperature control data to train the heat source linkage control data to generate a heat source control fluctuation model. This model can not only learn historical data but also predict future control fluctuations, providing data support for subsequent operations. According to the prediction results, the system sets the warning threshold of the device to prevent abnormal situations from occurring. This link significantly improves the intelligent level of the system, can quickly adapt to the changing needs of users and ensure safe operation. Finally, the fourth step establishes an early warning monitoring system for linkage constant temperature control. By real-time monitoring based on the warning threshold of the heat source control device, 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, formulates the control protection strategy of the bathing device to cope 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 use experience. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data collection, intelligent mixing adjustment, heat source linkage control, and non-contact temperature control.

[0081] In the embodiment of the present invention, refer to Figure 1 As described, it is a schematic diagram of the step flow 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:

[0082] Step S1: Obtain the set target temperature data of the bathing device; collect environmental data from the inlet pipe and the outlet of the bathing device to obtain real-time water temperature data and real-time water pressure data; identify the water temperature difference between the set target temperature data of the bathing device and the real-time water temperature data to generate the water temperature difference identification data of the bathing device; analyze the potential fluctuation impact on the real-time water pressure data based on the water temperature difference identification data of the bathing device to generate the water pressure fluctuation data of the bathing device.

[0083] In the embodiment of the present invention, the set target temperature (TargetTemperature, TT) is obtained from the control system of the bathing device. Record the target temperature data and store it in the data management system for subsequent analysis. The inlet water temperature (InletTemperature, IT) and the outlet water temperature (Outlet Temperature, OT) are collected in real time through the temperature sensors installed at the inlet pipe and the outlet of the bathing device. The water pressure of the inlet pipe (Inlet Pressure, IP) and the water pressure of the outlet (Outlet Pressure, OP) of the bathing device are monitored in real time by using pressure sensors. The real-time water temperature and water pressure data are transmitted to the data storage system to form time series data. Compare the set target temperature data (TT) with the real-time water temperature data (IT and OT), and calculate the water temperature difference (Temperature Difference, TD): 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 device is generated. Based on the water temperature difference identification data (TDD) of the bathing device, analyze the fluctuation impact on the real-time water pressure data (IP and OP). Apply the fluctuation analysis algorithm to identify the potential fluctuation sources of the water pressure change, such as the change of the inlet water temperature, the fluctuation of the outlet water flow, etc., to generate the water pressure fluctuation data (Pressure Fluctuation Data, PFD) of the bathing device 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 of the water temperature and water pressure in real time, as well as the corresponding difference identification and fluctuation analysis results, for the user to monitor and adjust.

[0084] Step S2: Perform intelligent mixing water adjustment on the bathing device through the water temperature difference identification data and the water pressure fluctuation data of the bathing device to generate real-time mixing water adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage control on the real-time mixing water adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage control data; perform non-contact temperature control based on the heat source linkage control data to generate non-contact constant temperature control data.

[0085] In the embodiments of the present invention, by identifying the temperature difference data (TDD) and the water pressure fluctuation data (PFD) of the bathing device, the required mixing ratio (Mixing Ratio, MR) is calculated to ensure that the outlet water temperature (OT) reaches the set target temperature (TT). MR = (TT - OT) / (IT - OT). The inlet valve and the hot water valve are controlled, and the flow rate is adjusted according to the calculated mixing ratio to achieve intelligent mixing adjustment and generate real-time mixing adjustment data (Real-time 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 the safe range. According to the real-time water pressure dynamic adjustment data (Real-time Pressure Adjustment Data, RPAD), the water pressure at the inlet is automatically adjusted to maintain a stable outlet water pressure and prevent the pressure fluctuation from affecting the user. Based on the real-time mixing adjustment data (RMAD) and the real-time water pressure dynamic adjustment data (RPAD), it is judged 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 linkage control of the heat source is carried out to generate heat source linkage control data to ensure that the temperature of the heat source is consistent with the water flow. A non-contact infrared temperature sensor is used to monitor the outlet water temperature (OT) of the bathing device 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 generate non-contact constant temperature control data (Non-contact Constant Temperature Control Data, NCTCD) to ensure that the water temperature during the user's bath always remains within a comfortable range. The real-time mixing adjustment data (RMAD), the real-time water pressure dynamic adjustment data (RPAD), the heat source linkage control data (HSCCD), and the non-contact constant temperature control data (NCTCD) are stored in the database. A user interface is developed to display the mixing adjustment, the water pressure dynamic adjustment, and the constant temperature control in real time so that the user can monitor and adjust the settings.

[0086] Step S3: Model training is performed on the heat source linkage control data through the 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 for heat source control fluctuation prediction, thereby generating heat source control fluctuation prediction data; the device warning threshold of the heat source linkage control data is set according to the heat source control fluctuation prediction data, thereby generating a heat source control device warning threshold;

[0087] In the embodiments of the present invention, by collecting non-contact constant temperature control data (NCTCD) and heat source coordination control data (HSCCD), a training data set is formed. The data set should include temperature, pressure, and control decisions within different time periods. Select a suitable machine learning algorithm (such as regression analysis, decision tree, or neural network) to train the Heat Source Coordination Fluctuation Model (HSCFM). Train the model and optimize the model parameters to improve the accuracy and robustness of the prediction. Use methods such as cross-validation to evaluate the model performance and make adjustments according to the evaluation results. Import the new heat source coordination control data (HSCCD) into the trained Heat Source Coordination Fluctuation Model (HSCFM). Use the model to predict the heat source control fluctuations and generate Heat Source Control Fluctuation Prediction Data (HCFPD). This data should include the temperature and pressure fluctuations within the future time period. Analyze the Heat Source Control Fluctuation Prediction Data (HCFPD) to determine the amplitude and frequency of the fluctuations to identify potential risk areas. Set the device alarm threshold (Device Alarm Threshold, DAT) based on the prediction data. This threshold should consider the normal operating range and the fluctuation range of the system to ensure that an alarm can be issued in a timely manner when the fluctuation exceeds this threshold, generate the Heat Source Control Device Alarm Threshold (HCDAT), and store it in the device monitoring system. Store the generated Heat Source Control Fluctuation Prediction Data (HCFPD) and the device alarm threshold (HCDAT) in the database. Develop a real-time monitoring system to display the heat source control status and the alarm threshold settings to ensure that users can respond to the system alarms in a timely manner.

[0088] Step S4: Based on the heat source control device alarm threshold, perform linkage constant temperature control early warning monitoring on the bathing device to generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data to generate a bathing device control protection strategy to execute the linkage constant temperature control operation.

[0089] In the embodiments of the present invention, by integrating a monitoring system into the bathing equipment, the warning threshold (HCDAT) of the heat source control equipment and the actual operation data (such as water temperature, pressure, etc.) are monitored in real time. Set the threshold logic of the monitoring system. When the actual operation data exceeds the equipment warning threshold (HCDAT), trigger the early warning mechanism to generate linked constant temperature control warning monitoring data (LinkedConstant Temperature Control Alert Monitoring Data, LCTCAMD), and record the time, type and relevant parameters of each warning event. According to the linked constant temperature control warning monitoring data (LCTCAMD), analyze the past warning events to identify potential safety hazards and common failure modes. Develop a safety protection mechanism, including measures and operation procedures for dealing with warnings. For example, when the water temperature or water pressure is abnormal, automatically adjust the mixing valve or the heat source output, or start the emergency shutdown procedure to generate a control protection strategy for the bathing equipment (Control Protection Strategy for Bathing Equipment, CPSBE), and this strategy should include response measures for different warning levels. Implement the control protection strategy for the bathing equipment (CPSBE) in the control system of the bathing equipment to ensure that it can react quickly when a warning is triggered. Establish a feedback mechanism in the monitoring system. When the control protection strategy is implemented, update the system status in real time to ensure that the monitoring data (such as water temperature, pressure) returns to the safe range. Record the execution situation of the linked constant temperature control operation, including the reaction time, measures and effects after each warning, and generate the corresponding execution record data (Execution Record Data, ERD). Store the generated linked constant temperature control warning monitoring data (LCTCAMD), the control protection strategy for the bathing equipment (CPSBE) and the execution record data (ERD) in the database for subsequent analysis and auditing. Regularly generate execution reports of the warning monitoring and control strategy and provide them to the equipment maintenance personnel and management to evaluate the stability and safety of the system.

[0090] Preferably, step S1 includes the following steps:

[0091] Step S11: Obtain the set target temperature data of the bathing equipment;

[0092] Step S12: Use 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; use 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;

[0093] Step S13: Perform 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, where the data preprocessing includes data cleaning, data denoising and data standardization;

[0094] Step S14: Identify the water temperature difference between the target temperature data and the standard real-time water temperature data set for the bathing device to generate bathing device water temperature difference identification data; analyze the potential fluctuation impact on the standard real-time water pressure data based on the bathing device water temperature difference identification data to generate bathing device water pressure fluctuation data.

[0095] In the embodiments of the present invention, target temperature data is obtained through the user's input interface. The specific operations are 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 device's control system to generate target temperature data. This 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 water temperature desired by the user and provide reference data for the subsequent control process. Temperature sensors are installed on the inlet pipe of the bathing device, usually thermistors or thermocouples. These sensors can detect the temperature of the water flow in the pipe with high precision and rapid response. The temperature sensors read the water temperature at regular time intervals (such as once per second) and generate real-time water temperature data in the form of a curve of temperature changing with time or temperature data points marked with timestamps (e.g., [time, temperature] -> [12:01:30, 38.5°C]). These data are transmitted to the central control unit through sensor signal lines or wireless connections for subsequent processing. A water pressure sensor is installed near the outlet of the bathing device to measure the water pressure at the device outlet. The sensor is usually a strain-type water pressure sensor that can detect the pressure when the water flow passes 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 the pressure value changing with time (e.g., [time, water pressure] -> [12:01:30, 1.5 bar]). The data is transmitted to the control unit by wired or wireless means for processing synchronously with the water temperature data. First, the collected original water temperature and water pressure data are detected to identify outliers or unreasonable values (such as sudden changes in temperature to too high or too low). Statistical methods (such as the three-standard-deviation method) or machine learning models can be used to identify abnormal data. If the sensor fails to record some data due to certain reasons (such as short-term signal loss), the system will use interpolation methods to supplement these missing points to maintain the continuity of the data. Filters (such as Kalman filters or moving average filters) are used to process the temperature and water pressure data to eliminate small fluctuations caused by environmental noise. For example, moving average filtering can average several consecutive water temperature data points to eliminate the impact of sudden noise on the data. Data in different ranges are converted into the same standard interval, usually between 0 and 1. The advantage of standardization is that it can reduce the impact of different units, making subsequent analysis and calculations more convenient. For example, the water pressure is expressed in kilopascals (kPa), while the water temperature is expressed in degrees Celsius. The standardization process converts these data into dimensionless data. After preprocessing, the generated standard real-time water temperature data and standard real-time water pressure data are more reliable and valuable for analysis. The set target temperature of the bathing device (such as 40°C) is compared with the standard real-time water temperature data. For example, when the real-time water temperature is 38°C, the system will calculate that the temperature difference is 2°C.This differential data will serve as an important reference for subsequent analysis of water pressure fluctuations. The system determines whether the water temperature difference needs to be corrected based on a set threshold. For example, if the temperature difference is less than 1°C, it is considered that the water temperature is within the normal range and no adjustment is required; if the temperature difference is greater than 1°C, the adjustment mechanism of the device is triggered to attempt 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, a potential fluctuation impact analysis is performed on the standard real-time water pressure data. For example, if the temperature difference is large, it may cause unstable water flow, which in turn affects the water pressure. Therefore, the system will analyze the real-time water pressure and evaluate its fluctuation situation. The system can analyze the impact of the temperature difference on water pressure fluctuations based on historical data or prediction models. For example, when the temperature difference increases, the water pressure will show periodic fluctuations, and the system calculates the potential range of water pressure fluctuations through the prediction model. By analyzing, water pressure fluctuation data is generated to reflect whether the water pressure is stable. If the water pressure fluctuation exceeds the safe range, the system will activate an alarm or an automatic adjustment mechanism to ensure that the water pressure and water temperature are within the safe range.

[0096] Preferably, step S14 includes the following steps:

[0097] Step S141: Calculate the water temperature difference between the target temperature data and the standard real-time water temperature data set for the bathing device to obtain water temperature difference data; compare the water temperature difference data with a preset water temperature difference threshold. When the water temperature difference data is greater than or equal to the preset water temperature difference threshold, the water temperature difference data is marked to generate water temperature difference identification data for the bathing device;

[0098] Step S142: Analyze the time-series change of the water pressure of the standard real-time water pressure data to generate historical water pressure data; calculate the flow rate of the bathing device based on the standard real-time water pressure data and the historical water pressure data to obtain the flow rate data of the bathing device;

[0099] Step S143: Analyze the impact of the flow rate data of the bathing device on the stability of the standard real-time water pressure data and the water temperature difference identification data of the bathing device to generate water pressure fluctuation data for the bathing device;

[0100] The formula for calculating the flow rate of the bathing device is as follows:

[0101]

[0102] In the formula, represents the flow rate, represents the water flow resistance coefficient, represents the real-time water pressure, represents the historical water pressure.

[0103] In the embodiments of the present invention, by comparing the target temperature set by the device (e.g., 40°C) with the preprocessed standard real-time water temperature data, the difference between the real-time water temperature and the set target temperature is calculated. 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 a preset water temperature difference threshold. Assuming the preset threshold is 1°C, when the water temperature difference reaches or exceeds 1°C, the system will mark this difference data. Data marking is performed on the water temperature difference data greater than or equal to the preset threshold to generate water temperature difference identification data for the bathing device. These data indicate that the water temperature deviation of the bathing device exceeds the acceptable range and requires the system to automatically adjust or prompt the user to manually adjust the water temperature. The system performs a time-series change analysis on the preprocessed 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 the water pressure and establish a time series model of the water pressure change. Through the analysis of historical data, the system can detect whether the water pressure shows periodic changes or sudden changes. Based on the standard real-time water pressure data and the historical water pressure data, the system calculates the flow rate of the bathing device through the following formula: In the formula, is expressed as the flow rate, is expressed as the water flow resistance coefficient, is expressed as the real-time water pressure, is expressed as the historical water pressure. This formula is a flow rate estimation formula based on Bernoulli's principle and water pressure difference, where the water flow resistance coefficient reflects the water flow resistance in the device or pipeline. The system sets the value according to the structure and materials of the actual device. 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 corresponding increase in the flow rate. Based on the flow rate data of the bathing device, the standard real-time water pressure data, and the water temperature difference identification data, the system performs water pressure stability analysis. The specific analysis content includes: If the water pressure is stable and the flow rate is stable, then the system considers that the device is in a normal working state and will not have a negative impact on the user's bathing experience. If the water temperature difference identification data shows a large water temperature deviation, and at the same time the water pressure is unstable (e.g., the time-series water pressure fluctuates greatly), then the system judges that the water pressure fluctuation has a potential impact on the stability of the device's flow rate and water temperature. The system generates water pressure fluctuation data for the bathing device according to the above analysis. The goal of this step is to identify whether the water pressure fluctuation affects the user's bathing experience. For example, if there are large fluctuations in both 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 state of the device.

[0104] As an example of the present invention, as shown in reference Figure 2 In this example, the step S2 includes:

[0105] Step S21: Perform intelligent mixing water regulation on the bathing equipment based on the water temperature difference identification data and the water pressure fluctuation data of the bathing equipment, and generate real-time mixing water regulation data and real-time water pressure dynamic regulation data;

[0106] Step S22: Recalculate the water temperature difference between the real-time mixing water regulation data and the set target temperature data of the bathing equipment to obtain the positive difference data of the mixing water regulation water temperature and the negative difference data of the mixing water regulation water temperature; Regulate the heat source output through the positive difference data of the mixing water regulation water temperature and the negative difference data of the mixing water regulation water temperature, and generate the heat source output regulation data of the bathing equipment;

[0107] Step S23: Use the real-time water pressure dynamic regulation data to perform heat source reaction regulation on the heat source regulation data of the bathing equipment, and generate the heat source reaction regulation data of the bathing equipment; Integrate the heat source regulation data based on the heat source output regulation data and the heat source reaction regulation data of the bathing equipment, so as to generate the heat source linkage regulation data;

[0108] Step S24: Analyze the user's usage behavior pattern of the bathing equipment according to the heat source linkage regulation data, and generate the 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, so as to generate the non-contact constant temperature control data.

[0109] In the embodiments of the present invention, the system automatically adjusts the mixing system of the device through the water temperature difference recognition data and water pressure fluctuation data generated in the early stage. Specifically, the mixing system will adjust the proportion of cold and hot water according to the data feedback of the water temperature difference and water pressure fluctuation to ensure that the device maintains stable water temperature and water pressure under dynamic conditions. During the intelligent mixing adjustment process, the system generates two key data: based on the difference between the current water temperature and the set target temperature, the mixing system is dynamically adjusted. The water pressure is adjusted accordingly according to the water pressure fluctuation to ensure the balance of water pressure and flow. According to the real-time mixing adjustment data and the set target temperature data of the device, the system recalculates the water temperature difference and obtains: Mixing adjustment water temperature positive difference data: indicating the positive difference that the real-time water temperature is higher than the set target temperature. Mixing adjustment water temperature negative difference data: indicating 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 precisely control the heat source output. When the positive difference of the mixing adjustment water temperature 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 thus generated for further optimizing 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 device. The system integrates the heat source output control data and the heat source response control data to generate heat source linkage control data, enabling the water temperature adjustment and water pressure control of the device to be closely combined, achieving a more precise 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 duration, preferences for temperature and water pressure at different time periods, etc., to 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 device. By learning the user's bathing habits, the system can automatically adjust the water temperature and water pressure to be in the best state before the user starts using the device. This process generates non-contact constant temperature control data to ensure that the user can enjoy a stable constant temperature experience without manually adjusting the device. Finally, through the analysis of the user's behavior pattern and non-contact constant temperature control, the system can not only adapt to the user's needs but also provide a more intelligent bathing experience.

[0110] Preferably, step S21 includes the following steps:

[0111] Step S211: Analyze the cold and hot water temperatures of the bathing device to generate cold water temperature data and hot water temperature data; based on the water temperature difference recognition data of the bathing device, obtain the cold and hot water ratio mixing requirement data for the cold water temperature data and the hot water temperature data;

[0112] Step S212: Calculate the water flow demand for the cold water temperature data and the hot water temperature data according to the cold-hot water ratio mixing demand data, and obtain the cold water demand data and the hot water demand data; perform mixing water volume adjustment calculation on the cold water demand data and the hot water demand data through the mixing ratio adjustment formula to obtain the real-time mixing adjustment data;

[0113] Step S213: Based on the real-time mixing adjustment data, perform mixing valve control adjustment on the bathing device to generate mixing valve control adjustment data; based on the water pressure fluctuation data of the bathing device, perform valve opening water pressure adjustment on the mixing valve control adjustment data to generate real-time water pressure dynamic adjustment data;

[0114] The calculation formula of the mixing ratio adjustment formula is as follows:

[0115]

[0116] In the formula, represents the real-time mixing adjustment data, represents the cold-hot water adjustment ratio coefficient, represents the real-time temperature deviation, represents the adjustment gain coefficient, represents the adjustment differential coefficient, represents the error between the set value and the actual value, represents the current moment, represents the time variable.

[0117] In the embodiment of the present invention, the temperature data of cold water and hot water are obtained in real time through the sensors in the bathing device. The reading frequency of the sensors can be set to a fixed interval (such as once per second) according to requirements 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 device, the system analyzes the temperature difference between 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-hot water ratio mixing demand data, which is used to guide the subsequent mixing operation 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-hot water ratio and increase the supply of hot water or cold water until the temperature approaches the set target temperature. The system calculates the required cold water and hot water flow rates through the cold-hot water ratio mixing demand data. This calculation is based on the cold water temperature, the hot water temperature, and the set water temperature difference ratio requirement to obtain the cold water demand data and the hot water demand data. The system uses the following formula to perform real-time adjustment on the cold water and hot water demands to generate real-time mixing adjustment data: In the formula, represents the real-time mixing adjustment data, is represented as the cold and hot water regulation ratio coefficient, is represented as the real-time temperature deviation, is represented as the regulation gain coefficient, is represented as the regulation differential coefficient, is represented as the error between the set value and the actual value, is represented as the current moment, is represented as the 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 mixing water regulation data, the system automatically adjusts the opening degree of the mixing water valve. Through the actuator, the system can precisely control the opening and closing degree of the valve, generate the control adjustment data of the mixing water valve, and this data is used to guide the specific actions of the mixing water valve, thereby realizing the stable regulation of the temperature. 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 the real-time water pressure dynamic regulation data. The system will automatically adjust the opening and closing of the valve according to the user's current water flow demand, and the adjustment process needs to be completed within milliseconds to ensure the stable change of water temperature and water pressure, and avoid the user perceiving sudden temperature or water pressure changes.

[0118] Preferably, the heat source output regulation through the positive difference data of the mixing water temperature regulation and the negative difference data of the mixing water temperature regulation includes:

[0119] Collect the heat source output power of the bathing equipment to obtain the heat source output power collection data, where the heat source output power collection data includes the current heat source output power data and the maximum heat source output power data;

[0120] Detect the positive difference data of the mixing water temperature regulation and the negative difference data of the mixing water temperature regulation. When the positive difference data of the mixing water temperature regulation is detected, the heat source output power data is reduced based on the positive difference data of the mixing water temperature regulation to generate the first heat source regulation data;

[0121] When the negative difference data of the mixing water temperature regulation is detected, the heat source power fluctuation curve is converted for the current heat source output power data and the maximum heat source output power data to generate the heat source power fluctuation curve; extract the maximum fluctuation peak value from the heat source power fluctuation curve to obtain the maximum available heat source power data; increase the heat source output power for the negative difference data of the mixing water temperature regulation according to the maximum available heat source power data to generate the second heat source regulation data;

[0122] Integrate the first heat source regulation data and the second heat source regulation data to generate the heat source output regulation data of the bathing equipment.

[0123] In an embodiment of the present invention, by collecting the heat source output power of the bathing device, the current output power of the heat source and the maximum output power of the heat source are obtained, and heat source output power collection data is generated. The current heat source output power data refers to the power actually output by the device in the current state. The heat source maximum output power data 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 regulation data of the heat source is generated, which is used to reduce the heat source output power, so as 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 means 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 heat source maximum 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 therein, and obtain the heat source maximum available power data, that is, the maximum power that the heat source can achieve under the current conditions. According to the extracted heat source maximum available power data, analyze the negative difference data of the mixed water adjustment water temperature, increase the output power of the heat source, and generate the second regulation data of the heat source, which is used to increase the heat source output power, so as to ensure that the water temperature can quickly rise to the target temperature. Integrate the generated first regulation data of the heat source and the second regulation data of the heat source to generate complete bathing device heat source output regulation data. This data will be used to regulate the output of the heat source in real time to ensure that the water temperature is always maintained within the set range.

[0124] Preferably, step S24 includes the following steps:

[0125] Step S241: Classify the non-contact user feedback mode of the bathing device according to the heat source linkage regulation data to generate an image feedback mode and an audio feedback mode; based on the image feedback mode, use a camera to collect user bathing images to obtain a set of user bathing images; perform user expression recognition on the set of user bathing images to generate user expression recognition data;

[0126] Step S242: Mark the user images in the set of user bathing images according to the user expression recognition data to generate user behavior marked images, where the user behavior marked images include comfortable marked images and uncomfortable marked images; perform the first non-contact feedback regulation on the bathing device based on the comfortable marked images and the uncomfortable marked images until the number of comfortable marked images is greater than the number of uncomfortable marked images, and generate expression feedback control data;

[0127] Step S243: Based on the audio feedback mode, use a recording device to collect the user's bathing audio to obtain the user's bathing audio; extract the audio acoustic features of the user's bathing audio to obtain the user's bathing audio acoustic feature data; perform text conversion on the user's bathing audio acoustic feature data to generate the user's bathing acoustic text; perform semantic recognition on the user's bathing acoustic text to generate the user feedback semantic recognition data;

[0128] Step S244: Use the user feedback semantic recognition data to perform the second non-contact feedback regulation on the bathing device to generate the audio feedback control data; merge the expression feedback control data and the user feedback semantic recognition data to generate the user's bathing behavior pattern analysis data; perform non-contact temperature control on the heat source linkage regulation data based on the user's bathing behavior pattern analysis data to generate the non-contact constant temperature control data.

[0129] In the embodiments of the present invention, by classifying the non-contact user feedback mode of the bathing device according to the heat source linkage control data, there are mainly two modes: the image feedback mode obtains the bathing state of the user through a camera. The audio feedback mode obtains the sound information of the user during the bathing process through a recording device. Based on the image feedback mode, the user's bathing images are collected by the camera to obtain the user's bathing image set. The user's facial expressions in the user's bathing image set are recognized to generate user facial expression recognition data to judge the emotional state of the user during the bathing process. According to the user facial expression recognition data, the user's bathing images in the user's bathing image set are marked with user behaviors to generate user behavior marked images. This image set includes: comfortable marked images: images showing the user in a comfortable state. Uncomfortable marked images: images showing the user in an uncomfortable state. Based on the comfortable marked images and the uncomfortable marked images, the first non-contact feedback control of the bathing device is performed. This process continues until the number of comfortable marked images is greater than the number of uncomfortable marked 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 acoustic feature data of the user's bathing audio, including information such as pitch, loudness, and frequency. The acoustic feature data of the user's bathing audio is converted into text to generate the acoustic text of the user's bathing. Subsequently, the semantics of the acoustic text of the user's bathing is recognized to generate user feedback semantics recognition data to understand the specific needs and feedback of the user. Using the user feedback semantics recognition data, the second non-contact feedback control of the bathing device is performed to generate audio feedback control data to adjust the working state of the device to meet the needs of the user. The expression feedback control data and the user feedback semantics recognition data are merged to generate user bathing behavior pattern analysis data. This data can be used to analyze the behavior patterns and preferences of the user during the bathing process. Based on the user bathing behavior pattern analysis data, non-contact temperature control is performed on the heat source linkage control data, thereby generating non-contact constant temperature control data. This data will guide the temperature adjustment of the device to ensure that the user always maintains a comfortable water temperature during the bathing process.

[0130] Preferably, the user image behavior marking of the user's bathing image set according to the user facial expression recognition data includes:

[0131] Detect the face area of the user's bathing image set to generate user face area detection data; extract facial feature points from the user face area detection data to obtain the user's facial feature points, where the user's facial feature points include the user's eyes, the user's nose, and the user's mouth;

[0132] The facial area of the user's face detection data is divided through the user's facial feature points to generate the user's facial division area data; multi-dimensional feature vectors of the user's face are extracted from the user's facial division area data to obtain the multi-dimensional feature vectors of the user's face, where the extraction of multi-dimensional feature vectors includes facial geometry extraction, facial muscle movement extraction, and facial texture feature extraction;

[0133] An emotional score is given to the multi-dimensional feature vectors of the user's face according to the user's expression recognition data to generate the emotional score data of the user's facial area; the score of the emotional score data of the user's facial area is averaged to generate the comprehensive emotional score data of the user's face;

[0134] The user's bath image set is marked with the user's image behavior using the comprehensive emotional score data of the user's face. When the comprehensive emotional score data of the user's face is greater than or equal to the preset comprehensive emotional score threshold, the corresponding user bath image set is marked as a comfortable emotion image to generate a comfortable marked image; when the comprehensive emotional score data of the user's face is less than the preset comprehensive emotional score threshold, the corresponding user bath image set is marked as an uncomfortable emotion image to generate an uncomfortable marked image.

[0135] In the embodiments of the present invention, by detecting the face region in the user's bathing image set, computer vision technology is used to identify the faces in the images and generate user face region detection data. This data includes the detected face position, size, and shape. Based on the user face region detection data, facial feature points are extracted to identify the key points on the user's face, including: the user's eyes, the user's nose, and the user's mouth. 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 facial division region data. This data defines the specific positions of each facial region (such as the eye region, the mouth region, the nose region, etc.). Feature extraction is performed on the user facial division region data to obtain the user facial multi-dimensional feature vector. The extracted features include: facial geometric features such as facial contour, proportion, and symmetry. Facial muscle movement features such as muscle movement caused by facial expression changes. Facial texture features such as skin color, wrinkles, and other details. According to the user expression recognition data, an emotion score is given to the user facial multi-dimensional feature vector to generate user facial region emotion score data. This score can be calculated through a machine learning model to evaluate the user's emotional state. The user facial region emotion score data is averaged to generate user 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 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 marked image. This indicates that the user feels comfortable during the bathing process. When the user facial comprehensive emotion score data is less than the preset comprehensive emotion score threshold, the corresponding user bathing image set is marked with an uncomfortable emotion image to generate an uncomfortable marked image. This indicates that the user feels uncomfortable during the bathing process.

[0136] As an example of the present invention, refer to Figure 3 shown, in this example, step S3 includes:

[0137] Step S31: Perform time series analysis on the heat source linkage control data through non-contact constant temperature control data to generate heat source linkage control time series data;

[0138] Step S32: Divide the heat source linkage control time series data into data sets to generate a model training set and a model test set; perform model training on the model training set through the random forest algorithm to generate a heat source linkage control fluctuation prediction pre-model; use the model test set to perform model optimization iteration on the heat source linkage control fluctuation prediction pre-model to generate a heat source linkage control fluctuation model;

[0139] Step S33: Import 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;

[0140] Step S34: Set the device warning threshold for the heat source linkage control data according to the heat source regulation fluctuation prediction data, so as to generate the heat source regulation device warning threshold.

[0141] In the embodiment of the present invention, the heat source linkage control data is cleaned to process missing values and outliers. Apply a suitable time series model (such as LSTM or ARIMA) to analyze the heat source linkage control data to generate heat source linkage control time series data. Extract the features of the time series (such as seasonality, trend, etc.) to improve the accuracy of the subsequent model. Divide the heat source linkage control time series data into a training set (usually accounting for 70-80%) and a test set (20-30%). Use the random forest algorithm to train the training set to generate a heat source linkage control fluctuation prediction pre-model. Use the test set to optimize the model, adjust the hyperparameters (such as the number of trees, depth, etc.) to improve the model performance. Import the heat source linkage control data into the trained heat source linkage control fluctuation model. Generate heat source regulation fluctuation prediction data through model prediction. Analyze the heat source regulation fluctuation prediction data and calculate statistics such as mean and standard deviation. Set the device warning threshold according to the statistical characteristics of the prediction data, such as the mean plus or minus 2 times the standard deviation.

[0142] In this specification, a linkage constant temperature control system based on intelligent bathing equipment is provided for implementing the above-mentioned linkage constant temperature control method based on intelligent bathing equipment. The linkage constant temperature control system based on intelligent bathing equipment includes:

[0143] An environment analysis module, configured to obtain the set target temperature data of the bathing equipment; collect environmental data of 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 set target temperature data of the bathing equipment and the real-time water temperature data to generate bathing equipment water temperature difference identification data; analyze the potential fluctuation impact on the real-time water pressure data based on the bathing equipment water temperature difference identification data to generate bathing equipment water pressure fluctuation data;

[0144] A constant temperature adjustment module, configured to perform intelligent mixing water adjustment on the bathing equipment through the bathing equipment water temperature difference identification data and the bathing equipment water pressure fluctuation data to generate real-time mixing water adjustment data and real-time water pressure dynamic adjustment data; perform heat source linkage control on the real-time mixing water adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage control data; perform non-contact temperature control based on the heat source linkage control data, thereby generating non-contact constant temperature control data;

[0145] The control and 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, so as to generate heat source control fluctuation prediction data; set the device warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, so as to generate the heat source control device warning threshold.

[0146] The safety monitoring module is used to perform linkage constant temperature control early warning monitoring on the bathing equipment based on the heat source control device warning threshold to generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data to generate a bathing equipment control protection strategy to execute the linkage constant temperature control operation.

[0147] The beneficial effects of the present invention are as follows: By collecting environmental data of the water inlet pipe and outlet of the bathing device, 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 device operation status, facilitating immediate adjustment. Identifying the difference between the set target temperature and the real-time water temperature to generate water temperature difference identification data helps quickly detect abnormal temperature conditions and provides a scientific basis for mixing water adjustment. Analyzing the potential fluctuation impact on the real-time water pressure data based on the water temperature difference can identify in advance the impact of water pressure fluctuations on the bathing experience, thereby formulating corresponding control strategies. Through intelligent mixing water adjustment using the water temperature difference identification data and water pressure fluctuation data, dynamic adjustment of real-time mixing water and water pressure is achieved, enabling users to enjoy a more comfortable temperature experience during bathing. Based on the real-time mixing water adjustment data and water pressure dynamic adjustment data, heat source linkage control is carried out to ensure a high degree of matching between the heat source output and user needs, thereby reducing energy waste and improving the energy utilization efficiency of the device. The generated non-contact constant temperature control data adjusts the water temperature in an intelligent manner, enabling users to enjoy a constant water temperature without intervention, enhancing the use convenience and comfort. By training the heat source linkage control data to generate a heat source control fluctuation model, the system can continuously learn and optimize, improving the adaptability and flexibility of the system. Importing the heat source linkage control data into the fluctuation model for prediction, the generated heat source control fluctuation prediction data can help adjust the device operation parameters in a timely manner, thereby avoiding device failures or performance degradation caused by fluctuations. Setting the device warning threshold according to the prediction data ensures that the device can respond in a timely manner under abnormal conditions, avoiding potential safety hazards and enhancing the safety of the device. Monitoring the bathing device based on the device warning threshold can promptly detect and respond to abnormal conditions of temperature or water pressure, enhancing the real-time monitoring ability of the device. By linking the constant temperature control early warning monitoring data to construct a safety protection mechanism, it is ensured that protective measures can be quickly taken when the device is abnormal, thereby reducing the failure rate and extending the service life of the device. The entire process significantly enhances the user's bathing experience through intelligent adjustment and monitoring, enabling users to enjoy higher comfort and security when using the device. Therefore, the present invention improves the accuracy and reliability of temperature control through technologies such as real-time data collection, intelligent mixing water adjustment, heat source linkage control, and non-contact temperature control.

[0148] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0149] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can 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 these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A linkage constant temperature control method based on an intelligent bathing device, characterized in that, Including the following steps: Step S1: Obtain the set target temperature data of the bathing device; collect environmental data of the water inlet pipe and the water outlet of the bathing device to obtain real-time water temperature data and real-time water pressure data; identify the water temperature difference between the set target temperature data and the real-time water temperature data of the bathing device to generate the water temperature difference identification data of the bathing device; analyze the potential fluctuation impact on the real-time water pressure data based on the water temperature difference identification data of the bathing device to generate the water pressure fluctuation data of the bathing device; Step S2: Perform intelligent mixing water adjustment on the bathing device through the water temperature difference identification data and the water pressure fluctuation data of the bathing device to generate real-time mixing water adjustment data and real-time water pressure dynamic adjustment data; Perform heat source linkage control on the real-time mixing water adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage control data; Perform non-contact temperature control based on the heat source linkage control data to generate non-contact constant temperature control data; Step S2 includes the following steps: Step S21: Perform intelligent mixing water adjustment on the bathing device through the water temperature difference identification data and the water pressure fluctuation data of the bathing device to generate real-time mixing water adjustment data and real-time water pressure dynamic adjustment data; Step S22: Recalculate the water temperature difference between the real-time mixing water adjustment data and the set target temperature data of the bathing device to obtain the positive water temperature difference data and the negative water temperature difference data of the mixing water adjustment; perform heat source output control through the positive water temperature difference data and the negative water temperature difference data of the mixing water adjustment to generate the heat source output control data of the bathing device; Step S23: Use the real-time water pressure dynamic adjustment data to perform heat source reaction control on the heat source control data of the bathing device to generate the heat source reaction control data of the bathing device; integrate the heat source output control data and the heat source reaction control data of the bathing device to generate heat source linkage control data; Step S24: Analyze the user's usage behavior pattern of the bathing device based on the heat source linkage control data to generate the 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; Step S3: Train a model on the heat source linkage control data through the 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 device warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control device warning threshold; Step S4: Perform linkage constant temperature control warning monitoring on the bathing device based on the heat source control device warning threshold to generate linkage constant temperature control warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control warning monitoring data to generate a bathing device control protection strategy to execute the linkage constant temperature control operation.

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

3. The linkage constant temperature control method based on an intelligent bathing device according to claim 2, wherein Step S14 includes the following steps: Step S141: Calculate the water temperature difference between the set target temperature data of the bathing device 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. When the water temperature difference data is greater than or equal to the preset water temperature difference threshold, mark the water temperature difference data to generate water temperature difference identification data for the bathing device; Step S142: Analyze the temporal variation of the water pressure of the standard real-time water pressure data to generate historical water pressure data; calculate the flow rate of the bathing device based on the standard real-time water pressure data and the historical water pressure data to obtain the flow rate data of the bathing device; Step S143: Analyze the impact of the flow rate data of the bathing device on the stability of the standard real-time water pressure data and the water temperature difference identification data of the bathing device to generate water pressure fluctuation data for the bathing device; The formula for calculating the flow rate of the bathing device is as follows: In the formula, is expressed as the flow rate, is expressed as the water flow resistance coefficient, is expressed as the real-time water pressure, is expressed as the historical water pressure.

4. The linkage constant temperature control method based on an intelligent bathing device according to claim 1, characterized in that, Step S21 includes the following steps: Step S211: Analyze the cold and hot water temperatures of the bathing device to generate cold water temperature data and hot water temperature data; based on the water temperature difference identification data of the bathing device, obtain the cold and hot water proportion mixing requirements for the cold water temperature data and hot water temperature data to obtain cold and hot water proportion mixing requirement data; Step S212: Calculate the water flow demand for the cold water temperature data and hot water temperature data according to the cold and hot water proportion mixing requirement data to obtain cold water demand data and hot water demand data; perform mixing water volume adjustment calculation on the cold water demand data and hot water demand data through the mixing water ratio adjustment formula to obtain real-time mixing water adjustment data; Step S213: Based on the real-time mixing water adjustment data, control and adjust the mixing valve of the bathing device to generate mixing valve control adjustment data; based on the water pressure fluctuation data of the bathing device, adjust the water pressure for the opening of the valve for the mixing valve control adjustment data to generate real-time water pressure dynamic adjustment data; The calculation formula of the mixing water ratio adjustment formula is as follows: Wherein, is expressed as real-time mixing water regulation data, is expressed as the cold and hot water regulation ratio coefficient, is expressed as the real-time temperature deviation, is expressed as the regulation gain coefficient, is expressed as the regulation differential coefficient, is expressed as the error between the set value and the actual value, is expressed as the current moment, is expressed as the time variable.

5. The linkage constant temperature control method based on the intelligent bathing device according to claim 1, wherein, Regulating the heat source output through the positive difference data of the mixing water temperature and the negative difference data of the mixing water temperature includes: Collect the heat source output power of the bathing device to obtain heat source output power collection data, where the heat source output power collection data includes the current heat source output power data and the maximum heat source output power data; Data detection is performed on the positive difference data of the mixed water temperature adjustment and the negative difference data of the mixed water temperature adjustment. When the positive difference data of the mixed water temperature adjustment is detected, the heat source output power data is reduced based on the positive difference data of the mixed water temperature adjustment to generate the first heat source control data; When the negative difference data of the mixed water temperature adjustment is detected, the heat source power fluctuation curve conversion is performed on the current heat source output power data and the maximum heat source output power data to generate the heat source power fluctuation curve; the maximum fluctuation peak value of the heat source power fluctuation curve is extracted to obtain the maximum available heat source power data; the heat source output power is increased based on the maximum available heat source power data for the negative difference data of the mixed water temperature adjustment to generate the second heat source control data; The first heat source control data and the second heat source control data are integrated to generate the heat source output control data for the bathing device.

6. The linkage constant temperature control method based on the intelligent bathing device according to claim 1, wherein Step S24 includes the following steps: Step S241: Classify the non-contact user feedback mode of the bathing device according to the heat source linkage control data to generate the image feedback mode and the audio feedback mode; collect the user bathing images using a camera based on the image feedback mode to obtain the user bathing image set; perform user facial expression recognition on the user bathing image set to generate the user facial expression recognition data; Step S242: Perform user image behavior marking on the user bathing image set according to the user facial expression recognition data to generate the user behavior marked image, where the user behavior marked image includes the comfortable marked image and the uncomfortable marked image; perform the first non-contact feedback control on the bathing device based on the comfortable marked image and the uncomfortable marked image until the number of comfortable marked images is greater than the number of uncomfortable marked images to generate the expression feedback control data; Step S243: Collect the user bathing audio using a recording device based on the audio feedback mode to obtain the user bathing audio; extract the audio acoustic features of the user bathing audio to obtain the user bathing audio acoustic feature data; perform text conversion on the user bathing audio acoustic feature data to generate the user bathing acoustic text; perform semantic recognition on the user bathing acoustic text to generate the user feedback semantic recognition data; Step S244: Perform the second non-contact feedback control on the bathing device using the user feedback semantic recognition data to generate the audio feedback control data; merge the expression feedback control data and the user feedback semantic recognition data to generate the 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 the non-contact constant temperature control data.

7. The linkage constant temperature control method based on the intelligent bathing device according to claim 6, wherein Performing user image behavior marking on the user bathing image set according to the user facial expression recognition data includes: Detect the face area of the user bathing image set to generate the user face area detection data; extract the facial feature points from the user face area detection data to obtain the user facial feature points, where the user facial feature points include the user's eyes, the user's nose, and the user's mouth; Divide the user's face area detection data through the user's facial feature points to generate user facial division area data; extract multi-dimensional feature vectors from the user's facial division area data to obtain the user's facial multi-dimensional feature vectors, where the multi-dimensional feature vector extraction includes facial geometry extraction, facial muscle movement extraction, and facial texture feature extraction; Perform an emotional score on the user's facial multi-dimensional feature vectors according to the user's expression recognition data to generate user facial area emotional score data; average the scores of the user's facial area emotional score data to generate user facial comprehensive emotional score data; Use the user's facial comprehensive emotional score data to perform user image behavior marking on the user's bath image set. When the user's facial comprehensive emotional score data is greater than or equal to the preset comprehensive emotional score threshold, mark the corresponding user bath image set as a comfortable emotion image to generate a comfortable marked image; when the user's facial comprehensive emotional score data is less than the preset comprehensive emotional score threshold, mark the corresponding user bath image set as an uncomfortable emotion image to generate an uncomfortable marked image.

8. The linkage constant temperature control method based on the intelligent bathing device according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Perform 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: Divide the heat source linkage control time series data into a model training set and a model test set; perform model training on the model training set through the random forest algorithm to generate a heat source linkage control fluctuation prediction pre-model; use the model test set to perform model optimization iteration on the heat source linkage control fluctuation prediction pre-model to generate a heat source linkage control fluctuation model; Step S33: 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; Step S34: Set the device warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, thereby generating the heat source control device warning threshold.

9. A linkage constant temperature control system based on an intelligent bathing device, characterized in that, For implementing the linkage constant temperature control method based on intelligent bathing equipment as described in claim 1, the linkage constant temperature control system based on intelligent bathing equipment includes: An environment analysis module for obtaining the set target temperature data of the bathing equipment; collecting environment data from the water inlet pipe of the bathing equipment and the water outlet of the bathing equipment to obtain real-time water temperature data and real-time water pressure data; identifying the water temperature difference between the set target temperature data of the bathing equipment and the real-time water temperature data to generate bathing equipment water temperature difference identification data; analyzing the potential fluctuation impact on the real-time water pressure data based on the bathing equipment water temperature difference identification data to generate bathing equipment water pressure fluctuation data; A constant temperature adjustment module for performing intelligent mixing water adjustment on the bathing equipment through the bathing equipment water temperature difference identification data and the bathing equipment water pressure fluctuation data to generate real-time mixing water adjustment data and real-time water pressure dynamic adjustment data; performing heat source linkage control on the real-time mixing water adjustment data and the real-time water pressure dynamic adjustment data to generate heat source linkage control data; performing non-contact temperature control based on the heat source linkage control data to generate non-contact constant temperature control data; The control and 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 for heat source control fluctuation prediction, so as to generate heat source control fluctuation prediction data; set the device warning threshold for the heat source linkage control data according to the heat source control fluctuation prediction data, so as to generate the heat source control device warning threshold. The safety monitoring module is used to perform linkage constant temperature control early warning monitoring on the bathing equipment based on the heat source control device warning threshold to generate linkage constant temperature control early warning monitoring data; construct a safety protection mechanism through the linkage constant temperature control early warning monitoring data to generate a bathing equipment control protection strategy to execute the linkage constant temperature control operation.

Images