Intelligent control system of water dispenser

Abstract

The invention discloses an intelligent control system for a water dispenser, and relates to the technical field of water dispenser control, and the system comprises a data collection module which is used for obtaining parameters; the first processing module is used for calculating a brewing quality index; the second processing module is used for calculating a system comprehensive performance loss factor; the third processing module is used for generating adaptive control parameters; and the adaptive control module is used for executing compensation control. Through real-time quantification of the brewing quality, dynamic generation of the adaptive control parameters, prediction of the health state and early warning in advance, accurate control of the water quality and the water temperature in an infant brewing scene is realized, the operation period of equipment in a safe range is prolonged, and the equipment safety and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water dispenser control, and in particular to an intelligent control system for a water dispenser. Background Art

[0002] Preparing infant formula has stringent requirements for water temperature and quality, requiring precise control of target temperature and water purity. Traditional water dispensers operate independently from their heating and filtration systems, lacking coupled monitoring of performance degradation. This makes it difficult to dynamically adapt to complex operating conditions such as fluctuating water quality, filter aging, and heating element scaling, hindering long-term stable brewing quality.

[0003] Traditional water dispenser control systems mostly rely on fixed programs to operate and can only passively respond to single parameter anomalies. They are unable to quantify the correlation between decreased heating efficiency and attenuated filter purification capacity. Their maintenance relies on empirical regular replacement and cannot provide early warning of related failure risks such as accelerated scaling of heating elements and filter overload caused by poor water quality. The water temperature or water quality may suddenly exceed the standard, posing a safety hazard to infants and young children. In addition, it is difficult for users to perceive the internal performance degradation of the equipment. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control system for a water dispenser, which solves the problems existing in the background technology.

[0005] In order to solve the above technical problems, the present invention provides an intelligent control system for a water dispenser, comprising: a data acquisition module for acquiring system performance benchmark parameters, system real-time operating status parameters and preset brewing target parameters; A first processing module is used to calculate a brewing quality index; The brewing quality index is generated based on the real-time operating state parameters of the system and the preset brewing target parameters, and is used to characterize the real-time deviation of the produced water quality; The second processing module is used to calculate the system comprehensive performance loss factor; The system comprehensive performance loss factor is generated based on the system performance benchmark parameter and the system real-time operating status parameter, and is used to quantify the cumulative attenuation of system performance; A third processing module is used to generate adaptive control parameters; The adaptive control parameters are generated based on the system comprehensive performance loss factor and include adaptive heating power and adaptive filter element backwash frequency; an adaptive control module for performing compensation control; The compensation control is an adjustment action performed on the water dispenser by applying the adaptive control parameter when the brewing quality index exceeds a preset quality threshold.

[0006] Preferably, the system performance benchmark parameters include initial thermal response time and initial purification efficiency; The initial thermal response time is used to characterize the initial heating capacity of the heating module; The initial purification efficiency is used to characterize the initial purification capacity of the filtration system; The system's real-time operating status parameters include source water quality, cumulative water production, and single heating power consumption; The preset brewing target parameters include target brewing temperature and target water purity.

[0007] Preferably, the first processing module is specifically used to generate a brewing quality deviation vector; The brewing quality deviation vector is generated by comparing the real-time water output parameter with the preset brewing target parameter; The first processing module is further configured to calculate the brewing quality index based on the brewing quality deviation vector.

[0008] Preferably, the system is further configured to preset a maximum allowable deviation vector; The maximum allowable deviation vector is set based on health and safety standards; The brewing quality index is determined by calculating the ratio of the modulus of the brewing quality deviation vector to the modulus of the maximum allowable deviation vector.

[0009] Preferably, the second processing module first calculates a plurality of attenuation characterization factors; The attenuation characterization factors include the filter element physical blocking factor, the filter element efficiency failure factor and the thermal element aging factor; Then, the multiple attenuation characterization factors are weighted and summed to generate the system comprehensive performance loss factor.

[0010] Preferably, the physical blocking factor of the filter element is determined by analyzing the relationship between the total impurity input of the source water and the total filtered water volume; The filter element efficiency failure factor is determined by comparing the current outlet water purity with the attenuation degree of the initial purification efficiency.

[0011] Preferably, the third processing module first obtains a preset initial heating power and an initial backwashing frequency; Setting the initial heating power and the initial backwash frequency as compensation adjustment reference values; The system comprehensive performance loss factor is applied to the compensation adjustment reference value to generate the adaptive heating power and the adaptive filter element backwash frequency.

[0012] Preferably, the adaptive control module is further used to set a warning quality threshold and a failure quality threshold; When the brewing quality index does not exceed the warning quality threshold, the system maintains the current operating parameters; When the brewing quality index exceeds the warning quality threshold but does not exceed the failure quality threshold, the compensation control is performed by starting the secondary adaptive compensation; When the brewing quality index exceeds the failure quality threshold, the compensation control is performed by starting three-level strong compensation and safety intervention.

[0013] Preferably, it also includes: Health index update module, used to generate and update the system comprehensive health index; The system comprehensive health index is updated according to the system comprehensive performance loss factor and the source water quality, and is used to quantify the overall health level of the system; Health status prediction module, used to calculate the health index decay rate; The health index decay rate is calculated based on the historical changes of the system comprehensive health index; The health status prediction module is further configured to predict the future system health status based on the health index decay rate.

[0014] Preferably,, further comprising: a first-level forward-looking warning module, configured to compare the predicted future system health status with a preset health threshold; When the predicted future system health state is not lower than the preset health threshold, the first-level forward-looking warning module does not perform a warning operation; When the predicted future system health status is lower than the preset health threshold, and the brewing quality index is within a qualified range, the first-level forward-looking warning module pushes a maintenance reminder to the user.

[0015] Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: 1. By constructing a brewing quality index, multi-dimensional deviations in water temperature and water quality are converted into intuitive grading results, achieving real-time quantification and dynamic evaluation of brewing quality. Users can clearly perceive the performance status of the device rather than just receiving vague prompts, improving their ability to control the safety of drinking water for infants and young children.

[0016] 2. Through the system's comprehensive performance loss factor, the adaptive parameters of heating power and filter element backwash frequency are dynamically generated. They can be adjusted in real time according to attenuation conditions such as heating element aging and filter element clogging. They have adaptive compensation and precise control capabilities, extending the equipment's operating cycle within a safe range.

[0017] 3. By updating and predicting the system's comprehensive health index, the risk of future health status decline can be identified in advance before the brewing quality exceeds the standard, and maintenance reminders can be pushed to achieve forward-looking warnings and proactive maintenance, transforming maintenance from experience-driven to data-driven, minimizing safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention, and those skilled in the art can derive other drawings based on these drawings without inventive effort. Figure 1 It is a logic block diagram of the system of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Example 1: See also Figure 1 , the present invention provides an intelligent control system for a water dispenser, comprising: a data acquisition module for acquiring system performance benchmark parameters, system real-time operating status parameters and preset brewing target parameters; A first processing module is used to calculate a brewing quality index; The brewing quality index is generated based on the real-time operating state parameters of the system and the preset brewing target parameters, and is used to characterize the real-time deviation of the produced water quality; The second processing module is used to calculate the system comprehensive performance loss factor; The system comprehensive performance loss factor is generated based on the system performance benchmark parameter and the system real-time operating status parameter, and is used to quantify the cumulative attenuation of system performance; A third processing module is used to generate adaptive control parameters; The adaptive control parameters are generated based on the system comprehensive performance loss factor and include adaptive heating power and adaptive filter element backwash frequency; an adaptive control module for performing compensation control; The compensation control is an adjustment action performed on the water dispenser by applying the adaptive control parameters when the brewing quality index exceeds a preset quality threshold; The present invention provides an intelligent control system for a water dispenser. The control system uses a built-in data acquisition module to acquire system performance baseline parameters during the initialization phase and continuously acquires system real-time operating status parameters and user-preset brewing target parameters during operation. Compared to traditional water dispensers that can only operate according to a fixed program, the system utilizes a first processing module to compare real-time parameters with target parameters, generating a brewing quality index that can instantly reflect both water quality and water temperature. Simultaneously, the second processing module calculates a comprehensive system performance loss factor that reflects long-term core hardware loss based on the accumulation of system performance baseline parameters and real-time operating data. Based on this loss factor, the third processing module dynamically generates adaptive control parameters for compensating for performance degradation. Finally, when the adaptive control module detects that the brewing quality index exceeds the standard, it applies these adaptive control parameters to perform compensatory control. Through this multi-module collaborative approach, the previously isolated heating and filtering functions are integrated into a closed-loop intelligent system capable of self-diagnosis, evaluation, and active adjustment. This system achieves a leap from passive use to active protection of infant brewing quality, ensuring drinking water safety throughout the device's lifecycle.

[0021] Example 2: The system performance benchmark parameters include initial thermal response time and initial purification efficiency; The initial thermal response time is used to characterize the initial heating capacity of the heating module; The initial purification efficiency is used to characterize the initial purification capacity of the filtration system; The system's real-time operating status parameters include source water quality, cumulative water production, and single heating power consumption; The preset brewing target parameters include target brewing temperature and target water purity; The first processing module is specifically configured to generate a brewing quality deviation vector; The brewing quality deviation vector is generated by comparing the real-time water output parameter with the preset brewing target parameter; The first processing module is further configured to calculate the brewing quality index based on the brewing quality deviation vector; The system performance benchmark parameters acquired by the data acquisition module in this system are specifically defined as initial thermal response time and initial purification efficiency. The initial thermal response time is measured under ideal conditions and serves as a baseline for the inherent performance of the heating module. The initial purification efficiency calibrates the optimal filtration level of the filter element in its new state. The system's real-time operating status parameters continuously track the source water quality, cumulative water production, and single heating power consumption. These data together describe the dynamic operating conditions of the equipment in the actual use environment. The preset brewing target parameters are set to a specific target brewing temperature and target water purity based on the brewing standards of infant formula. The function of the first processing module is further refined. It compares the real-time monitored outlet water temperature and outlet water purity with the preset target values ​​to construct a brewing quality deviation vector. This vector is mathematically expressed as: ; Indicates the instantaneous outlet water temperature; Indicates the purity of the filtered water; Indicates the target brewing temperature; Indicates target water purity; By constructing this two-dimensional vector, the first processing module no longer views temperature or purity as a single indicator in isolation, but instead unifies the two into a single evaluation system. This approach can more comprehensively and accurately capture any subtle changes in brewing quality, providing a basis for subsequent precise evaluation and control, and ensuring strict quality control in the high-demand scenario of infant drinking water.

[0022] Example 3: The system is further configured to preset a maximum allowable deviation vector; The maximum allowable deviation vector is set based on health and safety standards; The brewing quality index is determined by calculating the ratio of the modulus of the brewing quality deviation vector to the modulus of the maximum allowable deviation vector; The second processing module first calculates a plurality of attenuation characterization factors; The attenuation characterization factors include the filter element physical blocking factor, the filter element efficiency failure factor and the thermal element aging factor; Then performing weighted summation on the multiple attenuation characterization factors to generate the system comprehensive performance loss factor; The system is configured to preset a maximum allowable deviation vector, which is quantified according to infant health and safety standards and defines the maximum acceptable deviation range in the two dimensions of temperature and purity. Based on this vector, the first processing module calculates the ratio of the modulus of the brewing quality deviation vector to the modulus of the maximum allowable deviation vector, ultimately determining the brewing quality index. The calculation formula for this index is: ; Indicates brewing quality index; Indicates the modulus of the current brewing quality deviation vector; Indicates the modulus of the maximum allowed deviation vector; At the same time, the second processing module is designed to calculate multiple attenuation factors, including the filter element physical blockage factor, the filter element efficiency failure factor, and the thermal element aging factor. These factors accurately describe the attenuation of core components from three different dimensions: physical blockage, chemical efficacy, and thermodynamic efficiency. The second processing module then performs a weighted summation of these three independent attenuation factors to generate a unified system comprehensive performance loss factor. The calculation formula is: ; Represents the system comprehensive performance loss factor; Indicates the physical blocking factor of the filter element; Indicates the filter element efficiency failure factor; Indicates the thermal element aging factor; are the weight coefficients of each attenuation factor respectively; To quantify the aging of a heating element (usually manifested as a decrease in heating efficiency due to scaling), the change in its heating efficiency can be monitored; the heating element aging factor It can be calculated by comparing the current heating power consumption with the initial baseline power consumption; specifically, it is defined as: ; This formula calculates the growth rate of heating power consumption relative to the initial state under unit water production; It refers to the actual heating power consumption currently required by the system when heating a unit volume of water from a standard initial temperature to the target temperature; Refers to the initial baseline power consumption required for the equipment to complete the same task in a new state (or after the last maintenance); as the thermal components scale and age, will gradually increase, leading to The value increases, which truly reflects the attenuation of performance; this factor is a dimensionless pure number; By unifying multi-dimensional performance deviations into a dimensionless brewing quality index and combining it with a system-wide comprehensive performance loss factor that quantifies the actual hardware loss, this system has established a dual-track parallel evaluation mechanism. This not only determines whether the current water quality is qualified from a macro perspective, but also reveals the root causes of quality changes from a micro perspective, laying a data foundation for achieving precise and adaptive compensation control.

[0023] Example 4: The physical blocking factor of the filter element is determined by analyzing the relationship between the total impurity input of the source water and the total filtered water volume; The filter element efficiency failure factor is determined by comparing the current water purity with the attenuation degree of the initial purification efficiency; The third processing module first obtains a preset initial heating power and an initial backwashing frequency; Setting the initial heating power and the initial backwash frequency as compensation adjustment reference values; Applying the system comprehensive performance loss factor to the compensation adjustment reference value to generate the adaptive heating power and the adaptive filter element backwash frequency; When calculating the attenuation characterization factor in the second processing module, the physical blocking factor of the filter element is determined by analyzing the relationship between the total impurity input of the source water and the total filtered water volume. The specific calculation is as follows: ; is the dynamic physical blockage degree of the filter element; is the empirical coefficient related to the physical structure of the filter element; Real-time TDS value of source water About time The cumulative integral of represents the total impurity input; is the total filtered water volume; The filter efficiency failure factor is determined by comparing the current water purity with the attenuation of the initial purification efficiency. The formula is: ; It is a quantitative indicator of the degree of chemical efficiency failure of the filter element; The current real-time water purity; The current source water purity; It is the initial purification efficiency of the filter element; Based on the calculated system comprehensive performance loss factor The third processing module obtains the preset initial heating power and initial backwash frequency and uses them as the reference values ​​for compensation adjustment. By applying the system comprehensive performance loss factor to the reference value, the adaptive heating power and adaptive filter element backwash frequency are dynamically generated. The correction formula is: ; ; is the corrected adaptive heating power; is the initial heating power; is the corrected adaptive filter element backwash frequency; is the initial backwash frequency; is the adjustment coefficient of backwash frequency; This design enables the system to quantify invisible hardware degradation (such as filter pore blockage and decreased adsorption capacity) into specific mathematical factors, and directly use these factors to generate precise compensation instructions; this means that the control strategy is no longer fixed, but can be adjusted in real time according to the equipment's own conditions, thereby maximizing the guarantee of water quality even when hardware performance degrades.

[0024] Example 5: The adaptive control module is also used to set a warning quality threshold and a failure quality threshold; When the brewing quality index does not exceed the warning quality threshold, the system maintains the current operating parameters; When the brewing quality index exceeds the warning quality threshold but does not exceed the failure quality threshold, the compensation control is performed by starting the secondary adaptive compensation; When the brewing quality index exceeds the failure quality threshold, the compensation control is performed by starting three-level strong compensation and safety intervention; It also includes: a health index update module for generating and updating a comprehensive health index of the system; The system comprehensive health index is updated according to the system comprehensive performance loss factor and the source water quality, and is used to quantify the overall health level of the system; Health status prediction module, used to calculate the health index decay rate; The health index decay rate is calculated based on the historical changes of the system comprehensive health index; The health status prediction module is further used to predict the future system health status based on the health index decay rate; The adaptive control module sets the warning quality threshold (e.g. ) and failure quality thresholds (e.g. ); When the brewing quality index is within the safe range ( ), the system does not intervene; once the brewing quality index exceeds the warning quality threshold, the system immediately initiates secondary adaptive compensation and applies the adaptive parameters generated in the previous step for adjustment; if the quality further deteriorates and exceeds the failure quality threshold, the highest level of tertiary strong compensation and safety intervention is initiated; in addition, the system also includes a health index update module and a health status prediction module; the health index update module is responsible for generating and updating a system comprehensive health index that quantifies the overall health level of the system; the update of this index not only takes into account the current system comprehensive performance loss factor, but also innovatively couples the influence of source water quality. Its update rule is: ; is the health index at the current moment; is the health index of the previous calculation cycle; is the comprehensive performance loss factor; is the scale formation rate coefficient; Real-time purity of source water; For reference purity; Scale formation rate coefficient Is a key empirical coefficient used to characterize the effect of different source water qualities on the scaling rate of the heating element; the determination of this coefficient should be based on experimental data; specifically, in a laboratory environment, using source water of different hardness (or total dissolved solids, TDS), running the equipment under a set working cycle, and regularly measuring the decay rate of the heating efficiency; by fitting the data, the source water purity (such as the one used here) is established. ) and the scaling rate; in practical applications, It can be selected from a preset lookup table based on the initially set source water type (such as TDS range), or through an empirical function fitted from experimental data are calculated dynamically; for example, a simple linear model could be: ; and is a constant determined by experiment; This coefficient effectively introduces the external variable of water quality into the health status assessment model; The health status prediction module calculates the health index decay rate based on the historical data of the health index, and uses this to linearly extrapolate and predict the system health status at a certain point in the future. Its prediction model is: ; For the predicted future health index; It is the latest health index; is the health index decay rate; is the prediction time interval; Health index decay rate It is a quantification of the trend of system health deterioration. In order to achieve effective prediction, its calculation method should be clear. A robust and easy-to-implement method is based on historical data from the last one or more calculation cycles. The most direct calculation method is to use the first-order difference, that is, to use the health index of the previous calculation cycle. and the health index of the current cycle To calculate: ; The health index at the current moment, indicating the comprehensive health assessment value of the system at the current time point; The health index of the last calculation cycle, indicating the health index value of the system at the last calculation; The health index decay rate indicates the value of the health index decrease per unit time, reflecting the speed of system performance deterioration; is the time interval between two consecutive health index calculation points (e.g., one day or a certain amount of water production); This method calculates the average decay rate within the most recent cycle. To increase the smoothness and anti-interference ability of the prediction, a moving average method can also be used, for example, to calculate the average decay rate over the most recent N cycles. This clear calculation method makes health status prediction operational. By introducing hierarchical control logic and a health index prediction mechanism, the system has achieved an intelligent upgrade from post-event remediation to pre-event prediction. It can not only perform real-time, graded intervention based on the current water quality, but also provide users with forward-looking maintenance recommendations based on deep insights into their own health status and predictions of future trends, thereby taking measures before problems occur and achieving a higher level of safety assurance.

[0025] Example 6: Also included: a first-level forward-looking warning module for comparing the predicted future system health state with a preset health threshold; When the predicted future system health state is not lower than the preset health threshold, the first-level forward-looking warning module does not perform a warning operation; When the predicted future system health status is lower than the preset health threshold, and the brewing quality index is within the qualified range, the first-level forward-looking warning module pushes a maintenance reminder to the user; The system further integrates a first-level forward-looking early warning module, the core function of which is to compare the predicted future system health status calculated by the health status prediction module with a preset health threshold. The uniqueness of this early warning mechanism lies in its triggering conditions. The first-level forward-looking early warning module does not respond only when the water quality has already had problems, but intervenes in advance when it predicts that the future health status is about to fall below the health threshold. More importantly, this warning is triggered when the current brewing quality index is still within the excellent or qualified range (for example, ); when the predicted future system health status is lower than the preset health threshold, the module will proactively push flexible maintenance reminders to the user, such as "the health of the core components has declined, and it is recommended to schedule maintenance"; in this way, the first-level forward-looking warning module will predict the invisible internal component performance degradation process and the future potential risks caused by this process in advance in a way that users can understand; this completely changes the traditional mode of passively waiting for faults to occur before repairing household appliances, and realizes true predictive maintenance. Without affecting the current user experience, it elevates the safety of infant and young children's drinking water to an unprecedented level of proactive prevention.

[0026] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A water dispenser intelligent control system, characterized in that: include: Data acquisition module, used to obtain system performance benchmark parameters, system real-time operating status parameters and preset brewing target parameters; A first processing module is used to calculate a brewing quality index; The brewing quality index is generated based on the real-time operating state parameters of the system and the preset brewing target parameters, and is used to characterize the real-time deviation of the produced water quality; The second processing module is used to calculate the system comprehensive performance loss factor; The system comprehensive performance loss factor is generated based on the system performance benchmark parameter and the system real-time operating status parameter, and is used to quantify the cumulative attenuation of system performance; A third processing module is used to generate adaptive control parameters; The adaptive control parameters are generated based on the system comprehensive performance loss factor and include adaptive heating power and adaptive filter element backwash frequency; an adaptive control module for performing compensation control; The compensation control is an adjustment action performed on the water dispenser by applying the adaptive control parameter when the brewing quality index exceeds a preset quality threshold.

2. The intelligent control system for a water dispenser according to claim 1, characterized in that: The system performance benchmark parameters include initial thermal response time and initial purification efficiency; The initial thermal response time is used to characterize the initial heating capacity of the heating module; The initial purification efficiency is used to characterize the initial purification capacity of the filtration system; The system's real-time operating status parameters include source water quality, cumulative water production, and single heating power consumption; The preset brewing target parameters include target brewing temperature and target water purity.

3. The intelligent control system for a water dispenser according to claim 1, characterized in that: The first processing module is specifically configured to generate a brewing quality deviation vector; The brewing quality deviation vector is generated by comparing the real-time water output parameter with the preset brewing target parameter; The first processing module is further configured to calculate the brewing quality index based on the brewing quality deviation vector.

4. The intelligent control system for a water dispenser according to claim 3, characterized in that: The system is further configured to preset a maximum allowable deviation vector; The maximum allowable deviation vector is set based on health and safety standards; The brewing quality index is determined by calculating the ratio of the modulus of the brewing quality deviation vector to the modulus of the maximum allowable deviation vector.

5. The intelligent control system for a water dispenser according to claim 2, characterized in that: The second processing module first calculates a plurality of attenuation characterization factors; The attenuation characterization factors include the filter element physical blocking factor, the filter element efficiency failure factor and the thermal element aging factor; Then, the multiple attenuation characterization factors are weighted and summed to generate the system comprehensive performance loss factor.

6. The intelligent control system for a water dispenser according to claim 5, characterized in that: The physical blocking factor of the filter element is determined by analyzing the relationship between the total impurity input of the source water and the total filtered water volume; The filter element efficiency failure factor is determined by comparing the current outlet water purity with the attenuation degree of the initial purification efficiency.

7. The intelligent control system for a water dispenser according to claim 1, characterized in that: The third processing module first obtains a preset initial heating power and an initial backwashing frequency; Setting the initial heating power and the initial backwash frequency as compensation adjustment reference values; The system comprehensive performance loss factor is applied to the compensation adjustment reference value to generate the adaptive heating power and the adaptive filter element backwash frequency.

8. The intelligent control system for a water dispenser according to claim 1, characterized in that: The adaptive control module is also used to set a warning quality threshold and a failure quality threshold; When the brewing quality index does not exceed the warning quality threshold, the system maintains the current operating parameters; When the brewing quality index exceeds the warning quality threshold but does not exceed the failure quality threshold, the compensation control is performed by starting the secondary adaptive compensation; When the brewing quality index exceeds the failure quality threshold, the compensation control is performed by starting three-level strong compensation and safety intervention.

9. The intelligent control system for a water dispenser according to claim 2, characterized in that: Also includes: Health index update module, used to generate and update the system comprehensive health index; The system comprehensive health index is updated according to the system comprehensive performance loss factor and the source water quality, and is used to quantify the overall health level of the system; Health status prediction module, used to calculate the health index decay rate; The health index decay rate is calculated based on the historical changes of the system comprehensive health index; The health status prediction module is further configured to predict the future system health status based on the health index decay rate.

10. The intelligent control system for a water dispenser according to claim 9, characterized in that: Also includes: a first-level forward-looking warning module, configured to compare the predicted future system health status with a preset health threshold; When the predicted future system health state is not lower than the preset health threshold, the first-level forward-looking warning module does not perform a warning operation; When the predicted future system health status is lower than the preset health threshold, and the brewing quality index is within a qualified range, the first-level forward-looking warning module pushes a maintenance reminder to the user.

Images