Intelligent heating control method of foot bath barrel and foot bath barrel

Abstract

The present application relates to the technical field of temperature control of household health care appliances, and particularly to an intelligent heating control method of a foot bath bucket and the foot bath bucket. The method performs a micro-heat pulse self-check on each temperature sensor according to a preset self-check period to obtain a thermal response time constant. Trend components and residual components are decomposed within a preset time window, a sudden change statistical index and a cross-sensor consistency index are calculated based on the residual components, a temperature measurement reliability coefficient and a fusion weight are determined in combination with the thermal response time constant, the weight is reduced or set to zero when distortion occurs, and a fused water temperature and a temperature difference index are obtained. The heating assembly and the circulating assembly are controlled according to the fused water temperature and the temperature difference index, a global reliability index is calculated, and the conditions are met to enter a reliability limited control mode and to be cooperatively adjusted. The micro-heat pulse self-check quantifies the thermal response and identifies distortion. The residual sudden change and consistency evaluation generate a weight to suppress abnormalities. The global reliability triggers the limited mode to cooperatively adjust the heating and the circulation, thereby improving uniformity and stability.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology for household health care appliances, and more specifically, to an intelligent heating control method for foot bath tubs and a foot bath tub. Background Technology

[0002] Foot bath tubs, a type of home care appliance, typically include a tub body, a heating element, a circulation element, and a controller. After the user sets the target water temperature through the operating interface, the controller adjusts the heating element in a closed loop based on temperature feedback and can drive the circulation element to promote water flow, thereby improving temperature uniformity and comfort. To obtain water temperature information, common solutions place temperature sensors at the bottom, sides, or near the return water path of the tub. The controller reads the temperature sampling values ​​at a fixed sampling period and outputs the heating control quantity using methods such as on / off control, proportional-integral-derivative control, or segmented control. Some products also increase the circulation intensity based on temperature differences or empirical rules to alleviate excessively rapid local temperature rise or temperature stratification.

[0003] In actual operation, the thermal and flow environment inside the foot bath tub exhibits significant time-varying and spatial non-uniformity: localized high-temperature zones form near the heating components, scouring disturbances exist near the circulation path and return port, and the heat exchange conditions between the tub wall and bottom vary with water level, foot entry, and the generation of foam and bubbles. Simultaneously, temperature sensors may experience short-term abnormal fluctuations, slow drift, or reduced dynamic response due to bubble isolation, wall-mounted thermal conductivity deviation, localized eddy current impact, scale buildup, or changes in the installed heat transfer medium. Existing solutions, even when using multiple temperature sensors, commonly employ simple averaging, taking the maximum value, or rejection strategies based on fixed thresholds to obtain the controlled water temperature. These strategies may struggle to distinguish between normal spatial temperature differences and sensor distortion when the temperature distribution is inherently uneven or the disturbances are intermittent, resulting in fusion results that remain sensitive to abnormal measurement points. On the other hand, existing controls mostly drive the heating control quantity directly by the deviation between the fusion temperature and the target water temperature. Cyclic control is more based on experience improvement. If there is a lack of quantitative evaluation of the reliability of temperature measurement and online identification of the dynamic performance of the sensor, when the reliability of temperature measurement decreases or the number of effective measurement points is insufficient, the control may still exhibit phenomena such as heating overshoot, local overheating, slow heating or temperature oscillation.

[0004] Therefore, a control approach is needed that can perform online self-testing, reliability assessment, and weight adaptive fusion of temperature sensors under multi-measuring conditions, and achieve coordinated regulation of heating and circulation under limited reliability conditions, so as to improve temperature control stability and safety margin, while taking into account temperature uniformity and user experience. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an intelligent heating control method for foot bath tubs and a foot bath tub itself.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A smart heating control method for a foot bath tub, the foot bath tub comprising a tub body, a heating component, a circulation component, and a controller; the method is executed by the controller and includes the following steps:

[0008] Step 1: Receive the target water temperature input by the user and receive the start command; acquire the temperature sampling values ​​of multiple temperature sensors to form a temperature sampling sequence corresponding to each temperature sensor;

[0009] Step 2: Perform a micro-thermal pulse self-test on each temperature sensor according to the preset self-test cycle to obtain the thermal response time constant of each temperature sensor;

[0010] Step 3: For each temperature sensor, decompose the temperature sampling sequence into trend and residual components within a preset time window to obtain the residual term corresponding to that temperature sensor; calculate the mutation statistics index and cross-sensor consistency index based on the residual term; determine the temperature measurement reliability coefficient corresponding to each temperature sensor based on the mutation statistics index, cross-sensor consistency index, and thermal response time constant; determine the fusion weight of multiple temperature sensors based on the temperature measurement reliability coefficients corresponding to multiple temperature sensors; when any preset distortion judgment condition is met, reduce the corresponding fusion weight to a preset lower limit or set it to zero; obtain the fused water temperature based on the fusion weight and determine the temperature difference index.

[0011] Step 4: Based on the fusion water temperature, target water temperature and temperature difference index, control the heating component and circulation component, and calculate the global reliability index; when the preset entry conditions are met, enter the reliability-limited control mode and perform dynamic regulation, and coordinate the heating control quantity and circulation control quantity based on the global reliability index and temperature difference index.

[0012] Furthermore, step one includes: during the effective period of the start command, acquiring temperature sampling values ​​from multiple temperature sensors according to a preset sampling period, forming a temperature sampling sequence corresponding to each temperature sensor, and recording the working status of the heating component and the circulation component.

[0013] Furthermore, the micro-thermal pulse self-test in step two includes: when the preset self-test trigger conditions are met, applying a micro-thermal pulse with a preset amplitude and preset pulse width to the self-test circuit, and continuously acquiring temperature sampling values ​​before, during and after the application of the micro-thermal pulse; estimating the thermal response time constant based on the temperature response curve caused by the micro-thermal pulse.

[0014] Furthermore, the preset self-test trigger conditions include at least the heating component being in a preset stable state and the circulation component being in a preset stable state, so as to reduce the interference of the state changes of the heating component and the circulation component on the estimation of the thermal response time constant.

[0015] Furthermore, the mutation statistics index is the mutation confidence or cumulative statistics obtained by performing online change point detection on the residual term, and the cross-sensor consistency index is the normalized cross-correlation peak or mutual information value between the residual term and the corresponding residual term of at least one other temperature sensor.

[0016] Furthermore, the preset distortion judgment conditions include at least the thermal response time constant being greater than a preset time constant threshold, the mutation statistical index being greater than a preset mutation threshold, or the cross-sensor consistency index being less than a preset consistency threshold.

[0017] Furthermore, a heating control quantity is generated based on the fused water temperature and the target water temperature and output to the heating component; a circulation control quantity is generated based on the temperature difference index and output to the circulation component; at the same time, a global credibility index is calculated based on the number of temperature sensors participating in the weighted fusion and the corresponding temperature measurement credibility coefficient.

[0018] Furthermore, the preset entry conditions include at least the global credibility index being less than a preset credibility threshold, or the number of temperature sensors participating in the weighted fusion being less than a preset number threshold, or the thermal response time constant of any temperature sensor being greater than a preset time constant threshold.

[0019] Furthermore, dynamic control includes at least limiting the rate of change of the heating control quantity or limiting the increase of the heating control quantity, and adjusting the cyclic control quantity to a de-isolation cyclic strategy to reduce the probability of the temperature sensor being isolated by bubbles; when the global confidence index is not less than the preset confidence threshold and the temperature difference index is greater than the preset temperature difference threshold, the cyclic control quantity is increased first and the increase of the heating control quantity is limited; when the global confidence index is not less than the preset confidence threshold and the temperature difference index is not greater than the preset temperature difference threshold, the heating control quantity is adjusted in a closed loop according to the deviation between the fused water temperature and the target water temperature, so that the fused water temperature converges to the target water temperature.

[0020] Furthermore, a foot bath tub includes a tub body, a heating component, a circulation component, and a controller. Multiple temperature sensors are installed at different locations inside the tub body, and each temperature sensor is equipped with a self-test circuit that generates micro-thermal pulses under the control of the controller.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] This invention performs micro-thermal pulse self-tests on each temperature sensor according to a preset self-test cycle to obtain the thermal response time constant, which reflects the thermal conductivity and dynamic response capability of the installation. This constant is then used for distortion discrimination and weight adjustment, enabling the controller to promptly identify risk measurement points when the response is slowed down due to factors such as bubble isolation, changes in wall thermal conductivity, or scaling, thereby improving the reliability of temperature measurement and the safety margin of control.

[0023] By decomposing the temperature sampling sequence into trend components and residual components within a preset time window, the abrupt change statistical index and cross-sensor consistency index are calculated based on the residual term. The temperature measurement reliability coefficient and fusion weight are generated by combining the thermal response time constant. When there is an abrupt change or cross-sensor inconsistency, the corresponding fusion weight is reduced to a preset lower limit or set to zero, thereby suppressing the interference of distorted measurement points on the fused water temperature and improving the ability of the fused water temperature to represent the real water temperature state.

[0024] By calculating the global reliability index and entering the reliability-limited control mode when the preset entry conditions are met, dynamic regulation is implemented by limiting the rate of change or increase of heating control quantity and adopting a de-isolation loop strategy. Furthermore, under different combinations of global reliability index and temperature difference index, the heating control quantity and the loop control quantity are coordinated to achieve priority enhancement of loop when the temperature difference is large and closed-loop convergence to the target water temperature when the temperature difference is small, thereby taking into account temperature uniformity, temperature control stability and user experience. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the foot bath tub of the present invention;

[0026] Figure 2 This is a flowchart of the intelligent heating control method for the foot bath tub of the present invention;

[0027] Figure 3 This is a schematic diagram of the micro-thermal pulse self-test process for a temperature sensor.

[0028] Figure 4 This is a flowchart of the switching between heating / circulation coordinated control and reliability-limited control modes based on the integration of water temperature, temperature difference, and global reliability indicators. Detailed Implementation

[0029] Example 1: Refer to Figures 2 to 4 A smart heating control method for a foot bath tub, the foot bath tub comprising a tub body, a heating component, a circulation component, and a controller; the method is executed by the controller and includes the following steps:

[0030] Step 1: Receive the user-inputted target water temperature and the start command; acquire temperature sampling values ​​from multiple temperature sensors to form a temperature sampling sequence for each sensor; establish "control target + measured basic data" for subsequent fusion and control. The target water temperature clarifies the target value for this foot bath tub heating control, and the start command triggers the controller to enter the workflow. Simultaneously, acquiring temperature sampling values ​​from multiple temperature sensors and forming a temperature sampling sequence allows the controller to obtain not only instantaneous temperature information at a specific moment but also information on temperature changes over a period of time. This temperature sampling sequence directly supports subsequent decomposition, index calculation, and reliability evaluation within a preset time window, ensuring control is based on continuous data rather than single-point data, and utilizing the spatial redundancy information provided by multiple temperature sensors to reflect the temperature state at different locations within the tub.

[0031] In one specific implementation, step one is executed by the controller. The controller first receives the target water temperature input by the user and a start command through the foot bath tub's operating interface. The target water temperature is, for example, 42 degrees Celsius. After the start command takes effect, the controller enters the sampling process during the effective period of the start command. It polls and reads multiple temperature sensors located at different positions inside the tub according to a preset sampling period, obtains the temperature sampling value corresponding to each temperature sensor at the same sampling time, and associates and stores the time information of each sampling with the temperature sampling value, thereby forming a temperature sampling sequence corresponding to each temperature sensor. At the same time, in order to ensure that the subsequent changes in the temperature sampling sequence have a consistent correspondence with the execution state, the controller performs sampling at each sampling time. The controller synchronously records the operating status of the heating and circulation components. The operating status includes at least the operating and non-operating states of the heating and circulation components, and preferably also includes the corresponding gear information or operating intensity information. For example, when four temperature sensors are arranged in the tank, located near the bottom of the tank, in the middle of the tank, near the water return path, and on the side of the tank wall, the controller continuously acquires four temperature sampling values ​​at a preset sampling period of one second and writes them into four temperature sampling sequences. At the same time, it records the state combination that the heating component is in the operating state and the circulation component is in the operating state, so as to ensure that subsequent steps can perform consistent analysis and control based on the complete temperature sampling sequence and the operating status of the heating and circulation components.

[0032] Step Two: Perform a micro-thermal pulse self-test on each temperature sensor according to a preset self-test cycle to obtain the thermal response time constant of each sensor. Periodically quantify and evaluate the operating state of each temperature sensor to obtain a dynamic characteristic quantity—the thermal response time constant—that can be used for reliability assessment. Through the micro-thermal pulse self-test, the controller can consistently observe the stimulation and response of the temperature sensors within the preset self-test cycle, thereby characterizing the speed of the temperature sensor's response to the micro-thermal pulse and its thermal coupling state using the thermal response time constant. This thermal response time constant, along with the mutation statistics index and the cross-sensor consistency index, is used in subsequent Step Three to determine the temperature measurement reliability coefficient. This incorporates the temperature sensor's self-test results into the fusion weight allocation and distortion handling logic, avoiding relying solely on the temperature readings to determine reliability.

[0033] In one specific embodiment, step two is executed by the controller after the temperature sampling sequence corresponding to each temperature sensor is formed in step one. The controller performs micro-thermal pulse self-tests on each temperature sensor sequentially according to a preset self-test cycle to obtain the thermal response time constant of each temperature sensor. Specifically, the controller first determines whether a preset self-test trigger condition is met. The preset self-test trigger condition includes at least that the heating component and the circulation component are in a preset stable state, thereby reducing the interference of the state changes of the heating component and the circulation component on the estimation of the thermal response time constant. Preferably, the preset stable state includes that the heating component and the circulation component maintain the same working state for multiple consecutive preset sampling cycles, and the rate of change of the fused water temperature is lower than a preset rate of change threshold. When the preset self-test trigger condition is met, the controller applies a micro-thermal pulse with a preset amplitude and preset pulse width to the self-test circuit of the corresponding temperature sensor. At the same time, the controller continuously acquires the temperature sampling value of the temperature sensor before, during and after the application of the micro-thermal pulse. The continuous acquisition preferably covers the baseline segment before the application of the micro-thermal pulse, the rising segment during the application of the micro-thermal pulse, and the recovery segment after the application of the micro-thermal pulse, so as to form a temperature response curve caused by the micro-thermal pulse.

[0034] The controller estimates the thermal response time constant based on the temperature response curve. Preferably, it uses the average temperature of the baseline segment as the initial value and the time when the temperature response reaches a steady-state increment after the micro-thermal pulse ends as the characteristic point for estimation. Alternatively, it uses exponential fitting of the temperature response curve to obtain the time constant characterizing the response speed. For example, when the preset self-test cycle is sixty seconds, the preset amplitude is the current amplitude that generates a detectable small temperature rise around the temperature sensor, and the preset pulse width is two hundred milliseconds, after confirming that the heating component and the circulation component have maintained a preset stable state, the controller applies micro-thermal pulses to each of the four temperature sensors one by one, and records the temperature sampling values ​​of no less than twenty preset sampling cycles before and after the micro-thermal pulse to construct the temperature response curve. Then, the thermal response time constant of each temperature sensor is calculated, thus providing a quantifiable basis for subsequent determination of the temperature measurement reliability coefficient based on the thermal response time constant.

[0035] In one specific implementation, let the start time of the micro-thermal pulse application be... The end time is The controller continuously takes micro-thermal pulses before applying them. The average of the temperature samples from each sampling point is used as the baseline temperature. ; after the micro-thermal pulse ends, continue The average of the temperature samples from each sampling point is taken as the steady-state temperature. The steady-state increment is During the rising phase, the temperature response curve can be approximated as a first-order inertial response: ;in This is the thermal response time constant. The controller can employ... Feature point method estimation :when First time reaching The corresponding time is Then take Preferably, to reduce the impact of noise, the controller can perform a moving average filter on the temperature response curve before determining the final value. Alternatively, perform an exponential fit on the rising segment data and use the time constant obtained from the fit as... .

[0036] Step 3: For each temperature sensor, decompose the temperature sampling sequence into trend and residual components within a preset time window to obtain the residual term corresponding to that temperature sensor; calculate the mutation statistics index and cross-sensor consistency index based on the residual term; determine the temperature measurement reliability coefficient corresponding to each temperature sensor based on the mutation statistics index, cross-sensor consistency index, and thermal response time constant; determine the fusion weight of multiple temperature sensors based on the temperature measurement reliability coefficients corresponding to multiple temperature sensors; when any preset distortion judgment condition is met, reduce the corresponding fusion weight to a preset lower limit or set it to zero; obtain the fused water temperature based on the fusion weight and determine the temperature difference index.

[0037] The data from multiple temperature sensors is upgraded from a coarse judgment of availability to a complete chain of "quantified reliability - fusion weight - robust fusion result", thereby improving the reliability of fused water temperature under complex operating conditions, and simultaneously outputting temperature difference indicators that can be used for cyclic control and coordinated regulation.

[0038] Within a preset time window, the temperature sampling sequence is decomposed into trend and residual components to separate the slow temperature change trend from short-term disturbances and abnormal fluctuations. The residual term more sensitively reflects sudden fluctuations, local disturbances, or inconsistent changes, providing input for subsequent anomaly detection and consistency assessment.

[0039] The mutation statistics index is used to characterize whether there are abnormal mutations or fluctuations in the residual term corresponding to the temperature sensor; the cross-sensor consistency index is used to characterize the degree of consistency between the residual term corresponding to the temperature sensor and other temperature sensors. Both indexes characterize the temperature measurement quality from two dimensions: single-sensor self-consistency / mutation and multi-sensor consistency.

[0040] The mutation statistics index, cross-sensor consistency index, and thermal response time constant obtained in step two are all incorporated to determine the temperature measurement reliability coefficient for each temperature sensor. Factors such as short-term anomalies, cross-point inconsistencies, and sensor dynamic response characteristics are also considered. The fusion weights are determined based on the temperature measurement reliability coefficients, ensuring that temperature sensors with higher reliability contribute more to the fusion results.

[0041] Weight protection under preset distortion judgment conditions: When any preset distortion judgment condition is met, the corresponding fusion weight will be reduced to the preset lower limit or set to zero. This actively suppresses the influence of the distortion sensor on the results at the fusion level, avoids a few abnormal readings from deviating the fusion water temperature, and thus improves the robustness and safety boundary of the overall control.

[0042] The fusion water temperature obtained based on the fusion weight is used to form a unified temperature characterization for control, serving as a key input for subsequent heating and coordinated regulation. At the same time, the temperature difference index is determined to reflect the degree of spatial difference or non-uniformity of the temperature values ​​participating in the fusion, providing a basis for cyclic control and coordinated strategies, so that the controller not only focuses on whether the target water temperature is reached, but also on whether the temperature distribution inside the tank is balanced.

[0043] In one specific implementation, the controller decomposes the temperature sampling sequence corresponding to each temperature sensor into trend components and residual components within a preset time window. The trend component characterizes the slow temperature rise or slow temperature fall caused by the combined action of the heating and circulation components within the preset time window, while the residual component characterizes short-term disturbances deviating from the trend component, thereby obtaining the residual term corresponding to the temperature sensor. For example, when the preset time window is 30 seconds and the preset sampling period is 1 second, the controller can fit the most recent 30 temperature sampling values ​​into a smooth trend component and use the difference between the temperature sampling value and the trend component as the residual term, so as to more sensitively expose abnormal fluctuations in situations such as surface bubble obstruction, local backflow scouring, or sensor adhesion to the wall. In one specific implementation, let the first... Each temperature sensor in discrete sampling sequence number Temperature sampling value Preset time window corresponds to Each sampling period. The trend component can be obtained using a moving average:

[0044]

[0045] The residual term is defined as follows When the preset sampling period is 1 second and the preset time window is... hour, The trend component is the most recent The average temperature of each sampling point, and the residual term are used to highlight short-term disturbances and abnormal fluctuations.

[0046] The controller calculates abrupt change statistics and cross-sensor consistency indices based on the residual terms. The abrupt change statistics are the confidence level or cumulative statistics of the abrupt change obtained by performing online change point detection on the residual terms. Preferably, the abrupt change of the residual terms is characterized by accumulating the residual terms point by point and generating a significant increase in the cumulative statistics when abrupt changes occur. The cross-sensor consistency index is the normalized cross-correlation peak value or mutual information value between the residual terms and the corresponding residual terms of at least one other temperature sensor. Preferably, the normalized cross-correlation peak value is calculated for multiple residual terms within the same preset time window to reflect whether the temperature disturbances at different locations are synchronous and consistent, or the mutual information value is calculated to reflect the degree of statistical correlation when there are nonlinear disturbances. For example, when four temperature sensors are arranged in the tank and the circulation component is in operation, if one of the temperature sensors is isolated by a bubble, causing a short-term fluctuation in the reading, the online change point detection of its residual terms will often give a high abrupt change confidence level or a large cumulative statistics. At the same time, the normalized cross-correlation peak value or mutual information value between its residual terms and the residual terms of the other temperature sensors will decrease, thus showing anomalies in both the abrupt change statistics and the cross-sensor consistency index.

[0047] In one specific implementation, the online change point detection employs a residual term-based approach. Cumulative statistical method: Let The cumulative statistic is ;in This is a drift parameter used to suppress the cumulative growth of normal noise. When When a mutation is detected, the mutation statistical index can be taken as follows: Alternatively, take the normalized mutation confidence level. ;in and It can be derived from the standard deviation of the residual term under steady-state conditions. Settings, such as taking To be sensitive to significant mutations with a low false alarm probability.

[0048] The controller determines the temperature measurement reliability coefficient for each temperature sensor based on mutation statistics, cross-sensor consistency, and thermal response time constant. It then determines the fusion weights of multiple temperature sensors based on these reliability coefficients, preferably such that the reliability coefficients decrease with increasing mutation statistics, increase with increasing cross-sensor consistency, and decrease with increasing thermal response time constant. The normalized reliability coefficients are then used as fusion weights or for generating fusion weights. Furthermore, when any preset distortion judgment condition is met, the controller reduces the corresponding fusion weight to a preset lower limit or sets it to zero. The conditions include at least the thermal response time constant being greater than the preset time constant threshold, the mutation statistical index being greater than the preset mutation threshold, or the cross-sensor consistency index being less than the preset consistency threshold, so as to suppress the influence of distorted temperature sensors on the fused water temperature at the fusion level; the preset time constant threshold can be set based on the common practices of existing temperature sensor dynamic response detection, according to the thermal response time constant calibration value or factory specification range of the temperature sensor under normal installation and normal thermal conduction conditions, and considering the slowdown margin caused by tank material and water flow disturbance, preferably taking the upper limit of the normal thermal response time constant plus the preset safety margin to balance missed detection and false judgment.

[0049] In one specific implementation, the controller first normalizes the mutation statistics, consistency metrics, and thermal response time constant: ; and trim the consistency index to Interval Based on this, the temperature measurement reliability coefficient can be generated as follows: ;in Preset positive coefficients are used to adjust the impact of abrupt changes, consistency, and slowed response on reliability, respectively. The controller normalizes multiple temperature measurement reliability coefficients to obtain a fusion weight: ;

[0050] Furthermore, when any preset distortion determination condition is met (e.g.) or or When ), the controller commands the corresponding Or reduce it to a preset lower limit. Then normalization is performed to achieve robust suppression of distorted measurement points;

[0051] The preset mutation threshold can be set based on the threshold selection principles of existing online change point detection. During periods when the heating and circulation components are operating stably and temperature changes are gradual, the noise level of the residual terms is statistically analyzed. The mutation statistical index threshold is set as a low false alarm probability threshold corresponding to the noise level, ensuring that normal fluctuations do not trigger distortion judgment, while significant mutations do. The preset consistency threshold can be set based on existing multi-sensor consistency testing and correlation discrimination methods. When the circulation component can form a basic mixture, the residual terms of temperature disturbances at multiple locations usually have high correlation. Therefore, the lower bound of the normalized cross-correlation peak value or mutual information value under normal mixing conditions can be obtained through actual measurement or calibration. This lower bound is used as the preset consistency threshold, or a preset margin is set based on it to resist changes in water flow pattern. The controller obtains the fused water temperature based on the fusion weights and determines the temperature difference index. The temperature difference index is used to characterize the dispersion between the temperature sampling values ​​participating in the fusion, for example, reflecting the degree of uneven temperature distribution or enhanced local disturbances within the tank, thus providing direct input for subsequent control and coordinated adjustment based on the fused water temperature, target water temperature, and temperature difference index.

[0052] In one specific implementation, the cross-sensor consistency index is represented by the normalized cross-correlation peak value: within a preset time window, the residual sequence... and Calculate the normalized cross-correlation function

[0053]

[0054] in It is time-delayed and satisfies The controller is available. As the first Cross-sensor consistency metrics for individual temperature sensors A larger value indicates that the residual is more consistent with at least one other measuring point. Preferably, Pick The sampling period is adjusted to accommodate the small delay caused by water circulation. As an alternative implementation, cross-sensor consistency metrics can employ mutual information: binning statistics are performed on the residual terms to obtain the joint probability. With marginal probability Mutual information is .

[0055] In one specific implementation, the fusion water temperature can be defined as... .

[0056] The temperature difference index can be determined using any of the following methods: First, using the range form. ;in To participate in integration and The first is a set of temperature sensors; the second is a weighted discreteness method. The controller can use the temperature difference index as a direct input for generating and coordinating cyclic control quantities.

[0057] Step 4: Based on the integrated water temperature, target water temperature, and temperature difference index, control the heating and circulation components and calculate the global reliability index. When the preset entry conditions are met, enter the reliability-limited control mode and perform dynamic regulation. The heating and circulation control quantities are then coordinated based on the global reliability index and temperature difference index. By integrating "integrated water temperature, target water temperature, temperature difference index, and reliability" into the controller's decision-making process, control not only pursues the temperature target but also adopts more robust strategies when temperature measurement reliability is insufficient or the temperature difference is large.

[0058] The controller uses the fused water temperature as the current water temperature status and the target water temperature as the control target. It also combines the temperature difference index to reflect the degree of temperature non-uniformity, thereby controlling the heating and circulation components to simultaneously achieve both heating efficiency and temperature uniformity.

[0059] By calculating a global reliability index, the controller can obtain a comprehensive characterization of the overall reliability of the current temperature measurement. This index is used to further consolidate the reliability of multi-sensor fusion into a global quantity, which is convenient for subsequent direct use in mode switching and coordinated adjustment logic, avoiding control decisions based solely on the status of a single temperature sensor.

[0060] In one specific implementation, let the number of effective temperature sensors participating in the weighted fusion be... The global credibility index can be calculated using any of the following methods: First, by using the mean. Secondly, a weighted average method is adopted. .when At that time, the controller command And it directly enters the credibility-restricted control mode. Used to compare with a preset confidence threshold to trigger mode switching, and subsequently used to coordinate with the temperature difference index to adjust the heating control quantity and the circulation control quantity.

[0061] When the preset entry conditions are met, the system enters the reliability-limited control mode and performs dynamic adjustment. When the system determines that the temperature measurement reliability is insufficient or the corresponding entry conditions are met, the control strategy is more robust and risk-controlled, avoiding over-control or accumulation of deviations due to temperature measurement distortion.

[0062] In one specific implementation, the de-isolation loop strategy is used to reduce the probability of thermal coupling isolation caused by bubble adhesion or foam obstruction on the temperature sensor surface by enhancing local scouring and turbulence. In reliability-constrained control mode, the controller sets the loop control quantity to pulse-like operation: periodically... Repeatedly run high-level operation With low speed ,in Furthermore, the flow rate or speed during high-speed operation should not be less than twice that during low-speed operation; for example, it can be taken as follows: If the loop component supports forward and reverse rotation, the controller preferably alternates between directions during high-speed operation to further break up air bubbles attached near the sensor. The de-isolation loop strategy continues until an exit condition is met: for example... And continuous Within a preset time window Then it resumes the normal cycle control mode.

[0063] The controller further coordinates the heating control quantity and the circulation control quantity based on the global reliability index and the temperature difference index, and explicitly uses the reliability of temperature measurement and the degree of temperature non-uniformity as the basis for coordinated adjustment: when the reliability is high and the temperature difference is small, it can focus more on approaching the target water temperature; when the temperature difference is large or the reliability is limited, it matches the heating control quantity and the circulation control quantity through coordinated adjustment to improve uniformity, suppress risks and improve the overall control effect.

[0064] In one specific implementation, the controller generates a heating control quantity based on the fused water temperature and the target water temperature and outputs it to the heating component. The generation method can be to convert the deviation between the fused water temperature and the target water temperature into an increase or decrease command for the heating control quantity. This means that when the fused water temperature is lower than the target water temperature, the heating control quantity is increased; when the fused water temperature is close to the target water temperature, the heating control quantity is maintained or decreased; and when the fused water temperature is higher than the target water temperature, the heating control quantity is decreased or heating is stopped. Simultaneously, the controller generates a circulation control quantity based on the temperature difference index and outputs it to the circulation component. The circulation control quantity increases with the increase of the temperature difference index to promote temperature uniformity within the tank and decreases with the decrease of the temperature difference index to balance comfort and energy consumption. Furthermore, the controller simultaneously calculates a global reliability index based on the number of temperature sensors participating in the weighted fusion and their corresponding temperature measurement reliability coefficients. For example, the number of temperature sensors participating in the weighted fusion and the statistical results of each temperature measurement reliability coefficient are jointly mapped to the global reliability index. This reduces the global reliability index when the number of temperature sensors participating in the weighted fusion is small or the overall temperature measurement reliability coefficients are low, thus providing a quantitative basis for subsequent mode switching.

[0065] The controller determines whether preset entry conditions are met. These preset entry conditions include at least the global reliability index being less than a preset reliability threshold, or the number of temperature sensors participating in weighted fusion being less than a preset number threshold, or the thermal response time constant of any temperature sensor being greater than a preset time constant threshold. If these conditions are met, the controller enters a reliability-limited control mode and performs dynamic adjustment. The preset reliability threshold is set based on existing multi-sensor fusion reliability threshold selection methods. Through prototype testing, the global reliability index distribution is statistically analyzed under normal and abnormal operating conditions. A threshold that can significantly distinguish between reliable and unreliable states with a low probability of false triggering is selected, while also prioritizing safety in the control process. The preset number... The threshold setting can be based on the common practice of existing multi-point temperature measurement redundancy verification, at least ensuring that there is necessary spatial redundancy in the fusion process for mutual verification. For example, the preset threshold can be set to the number that can maintain the most basic cross-validation capability, so that when there is not enough temperature sensor to participate in the weighted fusion, it will automatically switch to more conservative control. The preset time constant threshold setting can be based on the common idea of ​​existing temperature sensor dynamic response judgment, based on the temperature sensor's thermal response time constant calibration range or factory specifications under normal installation and heat conduction conditions, and superimposed on the margin brought by the tank material, flow heat transfer and installation differences, so as to identify the significantly slowed thermal response time constant as a potential distortion risk.

[0066] In the reliability-constrained control mode, the controller performs dynamic regulation and coordinates the heating control quantity and the cyclic control quantity based on the global reliability index and the temperature difference index. Dynamic regulation includes at least limiting the rate of change or the magnitude of increase of the heating control quantity to prevent the heating component from being rapidly pushed up when the temperature measurement reliability is insufficient, thus avoiding overshoot risk. Simultaneously, the cyclic control quantity is adjusted to a de-isolation cyclic strategy to reduce the probability of the temperature sensor being isolated by air bubbles, allowing the cyclic component to improve the heat transfer conditions around the sensor through a cyclic method that is more conducive to breaking local isolation. Furthermore, when the global reliability index is not less than a preset reliability threshold and the temperature difference index is greater than a preset temperature difference threshold, the controller prioritizes increasing the cyclic control quantity and limits the magnitude of increase of the heating control quantity to prioritize the improvement of local isolation. The controller steadily increases the heating level to address uneven temperature distribution within the tank. When the global reliability index is not less than the preset reliability threshold and the temperature difference index is not greater than the preset temperature difference threshold, the controller performs closed-loop adjustment of the heating control quantity according to the deviation between the fused water temperature and the target water temperature, so that the fused water temperature converges towards the target water temperature, and keeps the cyclic control quantity at a level conducive to maintaining uniformity under the premise that the temperature difference index is small. The preset temperature difference threshold can be set based on the engineering criteria for temperature uniformity in the temperature control of existing liquid heating appliances. By measuring the temperature difference index distribution at different locations within the tank under different working conditions of the circulation component, and selecting an acceptable temperature difference threshold based on user comfort and safety requirements, the control strategy tends to strengthen the circulation first to improve uniformity when the temperature difference index exceeds the threshold.

[0067] For example, if the target water temperature is set to 42 degrees Celsius, when the fused water temperature is 38 degrees Celsius, the temperature difference index is small, and the global reliability index is not less than the preset reliability threshold, the controller gradually increases the heating control quantity according to the deviation and keeps the cyclic control quantity at a uniform level. When the fused water temperature is close to 42 degrees Celsius but the temperature difference index is greater than the preset temperature difference threshold, the controller prioritizes increasing the cyclic control quantity and limits the increase of the heating control quantity to reduce local overheating. When the number of temperature sensors participating in the weighted fusion is reduced to less than the preset number threshold due to distortion judgment or the global reliability index is less than the preset reliability threshold, the controller enters the reliability-limited control mode, restricts the change of the heating control quantity, and adjusts the cyclic control quantity to a de-isolation cyclic strategy to improve the reliability of subsequent temperature measurements and reduce control risks.

[0068] In one specific implementation, the following parameter values ​​are exemplary and not limiting: preset sampling period Can be The preset time window length can be: (correspond to (number of sampling points); the preset self-test cycle can be [number]. The pulse width of the micro-thermal pulse can be... To balance detectability and comfort, the steady-state increment caused by micro-thermal pulses Preferred Preset time constant threshold Can be obtained according to the factory specifications mean with standard deviation Settings, for example ;in A value of 2 to 4 can be selected; preset mutation threshold. It can be based on the standard deviation of the residual under stable operating conditions. Settings, for example ;in A value of 3 to 10 can be used; a preset consistency threshold is required. The lower bound of the consistency index obtained from actual measurements under normal mixed operating conditions can be determined by adding a margin, for example, taking 0.3 to 0.8; the preset confidence threshold can be taken as 0.3 to 0.7; the preset quantity threshold... A lower limit can be set for the total number of sensors (e.g., at least two effective sensors participate in fusion); a preset temperature difference threshold can be used. The temperature range can be 0.5–3℃. The above thresholds can be determined by statistically analyzing the distribution of indicators under normal and abnormal operating conditions through prototype testing, and selecting a threshold that can distinguish between credible and uncredible conditions while meeting the safety priority principle.

[0069] Example 2: Refer to Figure 1A foot bath tub includes a tub body, a heating component, a circulation component, and a controller. Multiple temperature sensors are installed at different locations inside the tub body, and each temperature sensor is equipped with a self-test circuit that generates micro-thermal pulses under the control of the controller.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart heating control method for a foot bath tub, characterized in that, The foot bath tub includes a tub body, a heating component, a circulation component, and a controller; the method is executed by the controller and includes the following steps: Step 1: Receive the target water temperature input by the user and receive the start command; acquire the temperature sampling values ​​of multiple temperature sensors to form a temperature sampling sequence corresponding to each temperature sensor; Step 2: Perform a micro-thermal pulse self-test on each temperature sensor according to the preset self-test cycle to obtain the thermal response time constant of each temperature sensor; Step 3: For each temperature sensor, decompose the temperature sampling sequence into trend components and residual components within a preset time window to obtain the residual term corresponding to that temperature sensor; calculate the mutation statistics index and cross-sensor consistency index based on the residual term; determine the temperature measurement reliability coefficient corresponding to each temperature sensor based on the mutation statistics index, cross-sensor consistency index, and thermal response time constant; determine the fusion weight of multiple temperature sensors based on the temperature measurement reliability coefficients corresponding to multiple temperature sensors; when any preset distortion judgment condition is met, reduce the corresponding fusion weight to a preset lower limit or set it to zero; The fusion water temperature is obtained based on the fusion weight, and the temperature difference index is determined. Step 4: Based on the fusion water temperature, target water temperature and temperature difference index, control the heating component and circulation component, and calculate the global reliability index; when the preset entry conditions are met, enter the reliability-limited control mode and perform dynamic regulation, and coordinate the heating control quantity and circulation control quantity based on the global reliability index and temperature difference index.

2. The intelligent heating control method for a foot bath tub according to claim 1, characterized in that, Step one includes: during the effective period of the start command, acquiring temperature sampling values ​​from multiple temperature sensors according to a preset sampling period, forming a temperature sampling sequence corresponding to each temperature sensor, and recording the working status of the heating component and the circulation component.

3. The intelligent heating control method for a foot bath tub according to claim 1, characterized in that, Step two of the micro-thermal pulse self-test includes: when the preset self-test trigger conditions are met, applying a micro-thermal pulse with a preset amplitude and preset pulse width to the self-test circuit, and continuously acquiring temperature sampling values ​​before, during and after the application of the micro-thermal pulse; estimating the thermal response time constant based on the temperature response curve caused by the micro-thermal pulse.

4. The intelligent heating control method for a foot bath tub according to claim 3, characterized in that, The preset self-test trigger conditions include at least the heating component being in a preset stable state and the circulation component being in a preset stable state, so as to reduce the interference of the state changes of the heating component and the circulation component on the estimation of the thermal response time constant.

5. The intelligent heating control method for a foot bath tub according to claim 1, characterized in that, The mutation statistics index is the mutation confidence or cumulative statistics obtained by performing online change point detection on the residual term, and the cross-sensor consistency index is the normalized cross-correlation peak or mutual information value between the residual term and the corresponding residual term of at least one other temperature sensor.

6. The intelligent heating control method for a foot bath tub according to claim 1, characterized in that, The preset distortion judgment conditions include at least the thermal response time constant being greater than the preset time constant threshold, the mutation statistical index being greater than the preset mutation threshold, or the cross-sensor consistency index being less than the preset consistency threshold.

7. The intelligent heating control method for a foot bath tub according to claim 1, characterized in that, Heating control quantities are generated based on the fused water temperature and the target water temperature and output to the heating component. Circulation control quantities are generated based on the temperature difference index and output to the circulation component. Simultaneously, a global reliability index is calculated based on the number of temperature sensors participating in the weighted fusion and their corresponding temperature measurement reliability coefficients.

8. The intelligent heating control method for a foot bath tub according to claim 1, characterized in that, The preset entry conditions include at least the global credibility index being less than the preset credibility threshold, or the number of temperature sensors participating in the weighted fusion being less than the preset number threshold, or the thermal response time constant of any temperature sensor being greater than the preset time constant threshold.

9. The intelligent heating control method for a foot bath tub according to claim 1, characterized in that, Dynamic control includes at least limiting the rate of change of the heating control quantity or limiting the increase of the heating control quantity, and adjusting the cyclic control quantity to a de-isolation cyclic strategy to reduce the probability of the temperature sensor being isolated by bubbles; when the global confidence index is not less than the preset confidence threshold and the temperature difference index is greater than the preset temperature difference threshold, the cyclic control quantity is increased first and the increase of the heating control quantity is limited; when the global confidence index is not less than the preset confidence threshold and the temperature difference index is not greater than the preset temperature difference threshold, the heating control quantity is adjusted in a closed loop according to the deviation between the fused water temperature and the target water temperature, so that the fused water temperature converges to the target water temperature.

10. A foot bath tub, characterized in that, The intelligent heating control method for foot bath tubs according to any one of claims 1-9 includes a tub body, a heating component, a circulation component, and a controller. Multiple temperature sensors are arranged at different positions inside the tub body, and each temperature sensor is equipped with a self-test circuit that generates micro-thermal pulses under the control of the controller.

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