A water dispenser sterilization method and system

Abstract

The present disclosure provides a water dispenser sterilization method and system. The covariance is calculated using the ion concentration time series and the data of the microorganism concentration obtained at the water inlet to form a water inlet covariance matrix, and the covariance is calculated using the microorganism concentration and the impurity concentration obtained at the inlet of the ozone module to form an ozone inlet covariance matrix; the water inlet covariance matrix is compared with the ozone inlet covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier. The sterilization and purification processes are optimized to improve the response speed and adaptability of the equipment to water quality changes and to achieve efficient resource utilization.

Description

Technical Field

[0001] The present disclosure belongs to the field of data monitoring, and particularly relates to a water dispenser sterilization method and system. Background Art

[0002] In the current sterilization and cleaning technologies of water purification equipment, usually a single type of sensor, such as a UV sensor or a conductivity sensor, is used to detect a specific index in water, such as the microbial concentration or the ion concentration, and the water quality state is judged through a simple threshold. The application scenarios are small household water purification equipment and commercial water purification equipment. Most water purification equipment uses a fixed time interval or preset conditions, such as automatically cleaning every 6 hours, for waterway flushing and sterilization, lacking dynamic response ability, especially for water purification equipment in areas with stable tap water quality. Many water purification equipment are equipped with an ozone module or an ultraviolet germicidal lamp, but the sterilization efficiency depends on the environment during the operation of the module, such as water quality and pollutant concentration, and usually the sterilization intensity cannot be dynamically adjusted, which is mostly seen in commercial or industrial water treatment equipment.

[0003] Most devices detect the water quality through the absolute value of a single sampling. The single detection includes but is not limited to the microbial concentration, the ion concentration, etc., lacking the trend analysis of the water quality change over time. Each module of the water purification equipment, such as the water inlet and the ozone module inlet, usually operates independently and detects specific indexes respectively, lacking the synergistic effect between modules.

[0004] In the prior art, the detection indexes are single and the water quality evaluation is not comprehensive. For example, in the patent document with the publication number of CN117401784A, a sterilization system and its control method are provided. A single sensor can only detect a certain type of index. Among them, the UV sensor can only detect the microbial concentration, and the conductivity sensor can only detect the ion concentration. It is difficult to comprehensively evaluate the overall situation of the comprehensive state of the water quality, such as the microbial, ion, and impurity concentrations. It cannot dynamically adapt to complex water quality conditions. When the microbial and ion concentrations in the water source exceed the standard at the same time, it may lead to misjudgment or missed judgment. There is insufficient feedback on the sterilization or purification process, and the operation parameters of the equipment cannot be optimized.

[0005] Flushing in a fixed cycle wastes resources or has insufficient efficiency. The flushing strategy in a fixed cycle does not consider the water quality change and only relies on time to trigger the flushing program. In the case of good water quality, frequent flushing wastes water and energy consumption. In the case of deteriorated water quality, the fixed flushing time may not be able to remove the accumulated pollutants, affecting the purification effect.

[0006] For another example, in the patent document with the publication number CN113100631B, the sterilization control system and method for a water dispenser are provided. However, the modules lack coordination in operation. The water inlet and the ozone module inlet in the water purification device usually operate independently, each collecting data, and no clear correlation is established. It is impossible to predict the working state of the ozone module based on the water quality change at the water inlet. There is a lack of systematic optimization, which may lead to unsatisfactory sterilization effects or equipment damage.

[0007] Many existing water purification devices lack dynamic water quality analysis. Only single sampling values are used as the judgment basis, and the dynamic changes in water quality, such as short-term fluctuations in water quality, cannot be captured. If sampling is done outside the pollution peak, the water quality may be misjudged as safe. It is impossible to respond to water quality changes in real time, resulting in delayed treatment.

[0008] The flushing parameters cannot be dynamically adjusted according to the actual pollution degree. The flushing time and flow rate are usually preset values and cannot be adjusted according to the actual pollution degree of the water quality. For slightly polluted situations, the flushing intensity is too high, resulting in waste of resources; for severely polluted situations, the flushing intensity may be too low to clean thoroughly. Summary of the Invention

[0009] The purpose of the present invention is to provide a sterilization method and system for a water dispenser to solve one or more technical problems in the prior art and at least provide a beneficial option or create conditions.

[0010] The present disclosure provides a sterilization method and system for a water dispenser. The covariance is calculated using the data of the ion concentration time series and the microbial concentration obtained at the water inlet to form a water inlet covariance matrix, and the covariance is calculated using the microbial concentration and the impurity concentration obtained at the ozone module inlet to form an ozone inlet covariance matrix; the water inlet covariance matrix is compared with the ozone inlet covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier. Optimize the sterilization and purification processes, improve the response speed and adaptability of the equipment to water quality changes, and achieve efficient resource utilization.

[0011] To achieve the above object, according to one aspect of the present disclosure, a sterilization method for a water dispenser is provided. The method includes the following steps:

[0012] Obtain data on the microbial concentration and ion concentration in the water flow at the water inlet of the water purification device, and obtain data on the microbial concentration and impurity concentration in the water flow at the water inlet of the ozone module;

[0013] Calculate the covariance using the data of the ion concentration time series and the microbial concentration obtained at the water inlet to form a water inlet covariance matrix, and calculate the covariance using the microbial concentration and the impurity concentration obtained at the ozone module inlet to form an ozone inlet covariance matrix;

[0014] Compare the water inlet covariance matrix with the ozone inlet covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier.

[0015] In the embodiments of the present invention, the microbial concentrations are obtained at both inlets because microorganisms (such as bacteria and viruses) are the core pollution indicators in the water purification process, and their concentrations directly affect the disinfection target of the water purification equipment. Obtaining the microbial concentration at the water inlet can determine the initial pollution level of the external water source; obtaining the microbial concentration again at the ozone module inlet can evaluate the remaining microbial level after partial purification inside the equipment. In this way, the dynamic monitoring of the microbial purification effect is well handled. By comparing the changes in the microbial concentrations at the water inlet and the ozone module inlet, the working efficiency of the front-end treatment such as the filter element or other sterilization modules can be judged in real time. If the microbial concentration at the ozone module inlet is too high, the enhanced ozone sterilization process can be triggered to improve the disinfection effect. The double-point detection helps to detect the problem of microbial growth in the system at an early stage and avoid secondary pollution caused by internal pollution during the purification process.

[0016] The reason for obtaining the ion concentration at the water inlet and the impurity concentration at the ozone module inlet is that ions such as calcium and magnesium mineral ions are detected by a conductivity sensor and are mainly used to evaluate the mineral content of the raw water and the hardness of the water, which is particularly important at the water inlet. High ion concentrations may affect the working life of the front-end filter element or the ozone module, so it is necessary to monitor them at the inlet. Impurities such as heavy metals and chlorides are usually detected by chemical sensors, and their detection significance lies mainly in ensuring the water quality safety. Impurities will have a direct impact on the ozone disinfection process before entering the ozone module, such as reacting with ozone to form by-products, so it is necessary to detect them at the module inlet.

[0017] The advantage of obtaining the ion concentration at the inlet is that a high ion concentration will increase the risk of equipment scaling, and front-end detection can start pretreatment before entering the ozone module, such as softening the water. The ion concentration data can be used to dynamically adjust the life prediction of the filter element and avoid the loss of equipment performance due to hard water.

[0018] The advantage of obtaining the impurity concentration at the module inlet is that ozone disinfection is sensitive to impurities. For example, heavy metals may react with ozone to form harmful by-products, and this situation can be avoided by monitoring the impurity concentration. The impurity concentration directly reflects the potential harmful components in the water quality, and detecting before entering the module helps to dynamically adjust the purification strategy.

[0019] The similarity between the two inlets is that the microbial concentration is detected because the microbial concentration needs to be monitored at both inlets, which reflects the quality control of the entire water purification process. The water inlet is the input gateway for the external water source, while the ozone module inlet is the key node before sterilization. In this way, the change in the microbial concentration detected at the two points helps to dynamically optimize the sterilization process and improve the overall sterilization efficiency of the equipment.

[0020] The difference is that the ion concentration is detected at the water inlet, and the impurity concentration is detected at the module inlet. This is because the ion concentration reflects the overall mineral characteristics of the raw water and is suitable for monitoring at the system inlet; the impurity concentration is a direct factor affecting the effect of active oxygen sterilization, and is more suitable for detection at the sterilization module inlet. In this way, monitoring the ion and impurity concentrations separately can not only ensure the stable operation of the equipment, but also dynamically adjust the water purification strategy, taking into account both equipment protection and water quality safety.

[0021] That is to say, the monitoring of ion concentration and microbial concentration at the water inlet focuses on the overall characteristics of the external water source input; the detection of microbial concentration and impurity concentration at the inlet of the active oxygen module focuses on the key working conditions of the disinfection module. Sensors are arranged in a targeted manner at different inlets to avoid unnecessary repeated data collection and reduce testing costs. The dual-point detection forms a closed loop, covering the full process monitoring of the water purification equipment from the inlet to the disinfection module, ensuring water quality safety and equipment life.

[0022] Furthermore, the method is applied to a water purification device comprising a voltage regulator, a solenoid valve, an active oxygen module group, a cold cathode UV mercury lamp, a control center, a bacteria sensor and / or a UV-LED lamp, wherein the water flow in the water purification device is divided through at least two solenoid valves, wherein at least one path passes through the active oxygen module group, and at least another path bypasses the active oxygen module group, and the divided water flows converge at the cold cathode UV mercury lamp.

[0023] Furthermore, the method of obtaining the data of microbial concentration and ion concentration in the water flow at the water inlet of the water purification device and obtaining the data of microbial concentration and impurity concentration in the water flow at the water inlet of the oxygen module is specifically as follows:

[0024] Data collection is performed at multiple different sampling times within a sampling period, and the values ​​of ion concentration and microbial concentration in the water are obtained at the water inlet of the water purification equipment at each sampling time, and the values ​​of microbial concentration and impurity concentration in the water are obtained at the inlet of the oxygen module;

[0025] For the data obtained at the water inlet of the water purification equipment, the array of the values ​​of the ion concentration corresponding to each sampling time is used as the ion concentration time series obtained at the water inlet, and the array of the values ​​of the microbial concentration corresponding to each sampling time is used as the microbial concentration time series obtained at the water inlet. The ion concentration time series and the microbial concentration time series obtained at the water inlet are used to calculate the covariance values ​​with themselves and each other respectively, and then form a covariance matrix called the water inlet covariance matrix;

[0026] For the data obtained at the inlet of the ozone module, the array formed by arranging the numerical values of the microbial concentration corresponding to each sampling moment in chronological order is used as the time series of the microbial concentration obtained at the water inlet, and the array formed by arranging the numerical values of the impurity concentration corresponding to each sampling moment in chronological order is used as the time series of the impurity concentration obtained at the water inlet. The covariance values are calculated respectively for the time series of the microbial concentration and the time series of the impurity concentration obtained at the inlet of the ozone module with respect to themselves and each other, and then the covariance matrix formed is called the ozone inlet covariance matrix.

[0027] Furthermore, comparing the water inlet covariance matrix with the ozone inlet covariance matrix is used to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier, specifically including:

[0028] Calculate the probability distribution distance of the water inlet covariance matrix relative to the ozone inlet covariance matrix, which is the water quality co-distribution difference distance; calculate the difference matrix obtained by subtracting the water inlet covariance matrix from the ozone inlet covariance matrix dimension by dimension point by point, which is the distribution difference matrix;

[0029] By comparing the water quality co-distribution difference distance with the distribution difference matrix, a pollution distribution normalization value is obtained;

[0030] The pollution distribution normalization value is used to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier.

[0031] Furthermore, by comparing the water quality co-distribution difference distance with the distribution difference matrix, a pollution distribution normalization value is obtained, specifically as follows:

[0032] Use cross-entropy or KL divergence to calculate the water quality co-distribution difference distance. Among them, it is necessary to first perform preprocessing of normalizing the matrix elements in the water inlet covariance matrix and the ozone inlet covariance matrix;

[0033] Calculate the sum of the absolute values of the distribution difference matrix as the total pollution intensity to quantify the pollution degree;

[0034] Divide the numerical value of the water quality co-distribution difference distance by the total pollution intensity, and the obtained numerical value is used as the pollution distribution normalization value.

[0035] Furthermore, the pollution distribution normalization value is used to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier, specifically as follows:

[0036] When the numerical value of the pollution distribution normalization value is greater than 1, it is determined that the water purifier needs to be flushed; when the water purifier needs to be flushed, use the numerical value of the pollution distribution normalization value as the weight to adjust the flushing time and flushing flow rate.

[0037] The present disclosure also provides a water dispenser sterilization system. The water dispenser sterilization system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the water dispenser sterilization method are implemented. The water dispenser sterilization system can run on computing devices such as desktop computers, laptop computers, mobile phones, palm computers, and cloud data centers. The operable system may include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program and runs in the following system units:

[0038] A data acquisition unit, configured to acquire data on the microbial concentration and ion concentration in the water flow at the water inlet of the water purification device, and acquire data on the microbial concentration and impurity concentration in the water flow at the water inlet of the ozone module;

[0039] A data analysis unit, configured to calculate the covariance using the ion concentration time series and microbial concentration data acquired at the water inlet to form a water inlet covariance matrix, and calculate the covariance using the microbial concentration and impurity concentration acquired at the ozone module inlet to form an ozone inlet covariance matrix;

[0040] A control and judgment unit, configured to compare the water inlet covariance matrix with the ozone inlet covariance matrix, determine whether the water purifier needs to be flushed, and adjust the flushing of the water purifier.

[0041] The beneficial effects of the present disclosure are as follows: The present disclosure provides a water dispenser sterilization method and system. The covariance is calculated using the ion concentration time series and microbial concentration data acquired at the water inlet to form a water inlet covariance matrix, and the covariance is calculated using the microbial concentration and impurity concentration acquired at the ozone module inlet to form an ozone inlet covariance matrix; The water inlet covariance matrix is compared with the ozone inlet covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier. The sterilization and purification processes are optimized, the response speed and adaptability of the device to water quality changes are improved, and efficient resource utilization is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] By describing the embodiments shown in the accompanying drawings in detail, the above and other features of the present disclosure will become more obvious. The same reference numerals in the drawings of the present disclosure denote the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0043] Figure 1 Shown is a flowchart of a water dispenser sterilization method;

[0044] Figure 2 Shown is the system structure diagram of a water dispenser sterilization system. Specific implementation manners

[0045] The following will clearly and completely describe the concept, specific structure and technical effects generated by the present disclosure in combination with embodiments and drawings, so as to fully understand the purpose, solution and effects of the present disclosure. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0046] In the description of the present invention, the meaning of several is one or more, the meaning of multiple is more than two, greater than, less than, exceeding, etc. are understood as not including the present number, above, below, within, etc. are understood as including the present number. If the first and the second are described only for the purpose of distinguishing technical features, they cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0047] As Figure 1 Shown is the flowchart of a water dispenser sterilization method according to the present invention. The following will describe a water dispenser sterilization method and system according to the implementation manner of the present invention in combination with Figure 1 to elaborate.

[0048] The present disclosure proposes a water dispenser sterilization method, and the method specifically includes the following steps:

[0049] Obtain data on the microbial concentration and ion concentration in the water flow at the water inlet of the water purification device, and obtain data on the microbial concentration and impurity concentration in the water flow at the water inlet of the ozone module;

[0050] Calculate the covariance to form the Alpha covariance matrix using the ion concentration time series and the data on the microbial concentration obtained at the water inlet, and calculate the covariance to form the Omega covariance matrix using the microbial concentration and the impurity concentration obtained at the inlet of the ozone module;

[0051] Compare the Alpha covariance matrix with the Omega covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier.

[0052] In the embodiments of the present invention, microbial concentrations are obtained at both inlets because microorganisms (such as bacteria and viruses) are the core pollution indicators in the water purification process, and their concentrations directly affect the disinfection target of the water purification equipment. Obtaining the microbial concentration at the water inlet can determine the initial pollution level of the external water source; obtaining the microbial concentration again at the inlet of the ozone module can evaluate the remaining microbial level after partial purification inside the equipment. This can dynamically monitor the microbial purification effect, and by comparing the changes in microbial concentrations at the water inlet and the ozone module inlet, the working efficiency of the front-end treatment such as the filter element or other sterilization modules can be judged in real time. If the microbial concentration at the inlet of the ozone module is too high, the enhanced ozone sterilization process can be triggered to improve the disinfection effect. The double-point detection helps to detect the problem of microbial growth in the system at an early stage and avoid secondary pollution caused by internal pollution during the purification process.

[0053] The reason for obtaining the ion concentration at the water inlet and the impurity concentration at the inlet of the ozone module is that ions such as calcium and magnesium mineral ions are detected by a conductivity sensor and are mainly used to evaluate the mineral content of the raw water and the hardness of the water, which is particularly important at the water inlet. High ion concentrations may affect the working life of the front-end filter element or the ozone module, so it is necessary to monitor them at the inlet. Impurities such as heavy metals and chlorides are usually detected by chemical sensors, and their detection significance lies mainly in ensuring the water quality safety. Impurities will have a direct impact on the ozone disinfection process before entering the ozone module, such as reacting with ozone to form by-products, so it is necessary to detect them at the module inlet.

[0054] The advantage of obtaining the ion concentration at the inlet is that a high ion concentration will increase the risk of equipment scaling, and front-end detection can initiate pretreatment such as water softening before entering the ozone module. The ion concentration data can be used to dynamically adjust the life prediction of the filter element and avoid the loss of equipment performance due to hard water.

[0055] The advantage of obtaining the impurity concentration at the module inlet is that ozone disinfection is sensitive to impurities. For example, heavy metals may react with ozone to form harmful by-products, and this situation can be avoided by monitoring the impurity concentration. The impurity concentration directly reflects the potential harmful components in the water quality, and detection before entering the module helps to dynamically adjust the purification strategy.

[0056] The similarity between the two inlets is that the microbial concentration is detected because the microbial concentration needs to be monitored at both inlets, which reflects the quality control of the entire water purification process. The water inlet is the input gateway for the external water source, while the inlet of the ozone module is the key node before sterilization. In this way, detecting the changes in the microbial concentration at two points helps to dynamically optimize the sterilization process and improve the overall sterilization efficiency of the equipment.

[0057] The difference is that the ion concentration is detected at the water inlet, and the impurity concentration is detected at the module inlet. This is because the ion concentration reflects the overall mineral characteristics of the raw water and is suitable for monitoring at the system inlet; the impurity concentration is a direct factor affecting the effect of active oxygen sterilization, and is more suitable for detection at the sterilization module inlet. In this way, monitoring the ion and impurity concentrations separately can not only ensure the stable operation of the equipment, but also dynamically adjust the water purification strategy, taking into account both equipment protection and water quality safety.

[0058] That is to say, the monitoring of ion concentration and microbial concentration at the water inlet focuses on the overall characteristics of the external water source input; the detection of microbial concentration and impurity concentration at the inlet of the active oxygen module focuses on the key working conditions of the disinfection module. Sensors are arranged in a targeted manner at different inlets to avoid unnecessary repeated data collection and reduce testing costs. The dual-point detection forms a closed loop, covering the full process monitoring of the water purification equipment from the inlet to the disinfection module, ensuring water quality safety and equipment life.

[0059] In the embodiments provided by the present invention:

[0060] The AO covariance probability distribution distance is the water quality covariance distribution difference distance, which is used to emphasize the degree of difference in covariance distribution and highlight the function of comparing water quality distribution characteristics.

[0061] SBeta is the total pollution intensity, which is used to quantify the intensity value of the pollution degree and reflect the overall pollution situation of water quality.

[0062] The Beta matrix is ​​a distribution difference matrix, which is used to represent the characteristic differences between the water inlet and the oxygen module inlet, highlighting the core functions.

[0063] The Alpha matrix is ​​the water inlet covariance matrix, which is used to represent the multidimensional correlation data source of water inlet. The name is intuitive and easy to understand.

[0064] The Omega matrix is ​​the covariance matrix of the active oxygen inlet, which is used to represent the correlation of the pollution characteristics of the active oxygen module inlet, emphasizing the data source and function.

[0065] sdAOB is the normalized value of pollution distribution, which is used to represent the normalized distribution difference value and intuitively reflects whether the pollution exceeds the standard.

[0066] At the water inlet of the water purification equipment, the microbial concentration and ion concentration in the water flow are obtained; at the water inlet of the active oxygen module, the impurity concentration in the water flow is obtained.

[0067] In some embodiments, the water purification device is composed of components including a voltage regulator, a solenoid valve, an oxygen module group, a cold cathode UV mercury lamp, a control center, a bacteria sensor and / or a UV-LED lamp. The water flows through the solenoid valve, flows through the oxygen module group, and then converges at the cold cathode UV mercury lamp. The water purification device sterilizes the water flow with the oxygen module group, UV-LED lamp, and cold cathode UV mercury lamp.

[0068] In the bacterial sensor, an ultraviolet sensor can be used to detect the concentration of microorganisms in water, a conductivity sensor can detect the concentration of ions in water to judge the degree of water pollution, and a chemical sensor can detect the concentration of harmful chemical substances in water, such as the concentration of impurities including heavy metals, chlorides, etc.

[0069] At the water inlet of the water purification device, preferably, a conductivity sensor is used to detect the ion concentration in water, and an ultraviolet sensor is combined to detect microorganisms to form a basic judgment.

[0070] At the inlet of the ozone module, the concentration of microorganisms in water is also obtained, and at the same time, a sensor is added to detect the concentration of impurities to ensure that the impurity level is detected before ozone disinfection, thereby avoiding ozone water pollution.

[0071] Data collection is carried out at multiple different sampling times within a sampling time period. At each sampling time, the ion concentration, microorganism concentration, etc. in water are obtained at the water inlet of the water purification device, and the microorganism concentration, impurity concentration, etc. in water are obtained at the inlet of the ozone module.

[0072] In some embodiments, for the data obtained at the water inlet of the water purification device, the array formed by arranging the values of the ion concentration corresponding to each sampling time in chronological order is used as the ion concentration time series obtained at the water inlet, and the array formed by arranging the values of the microorganism concentration corresponding to each sampling time in chronological order is used as the microorganism concentration time series obtained at the water inlet. The covariance values are calculated between the ion concentration time series and the microorganism concentration time series obtained at the water inlet with themselves and each other, and then the covariance matrix formed is called the Alpha covariance matrix.

[0073] For the data obtained at the inlet of the ozone module, the array formed by arranging the values of the microorganism concentration corresponding to each sampling time in chronological order is used as the microorganism concentration time series obtained at the water inlet, and the array formed by arranging the values of the impurity concentration corresponding to each sampling time in chronological order is used as the impurity concentration time series obtained at the water inlet. The covariance values are calculated between the microorganism concentration time series and the impurity concentration time series obtained at the inlet of the ozone module with themselves and each other, and then the covariance matrix formed is called the Omega covariance matrix.

[0074] In some embodiments, the numpy.cov function can directly calculate the covariance matrix, and the returned covariance matrix is a 2x2 matrix, where: the values on the diagonal are the respective variances (the variances of the ion concentration and the microorganism concentration); the values on the non - diagonal are the covariances.

[0075] In one of the embodiments, a double-moment sampling inspection method can be adopted: within a sampling time period, only the data at two sampling moments are collected, and the data at the two sampling moments can be used for calculation, but the effect needs to be tested.

[0076] Existing water purification equipment usually relies on the absolute value of a single sampling (such as the result of a single microbial concentration measurement) to evaluate water quality, lacking attention to the time-varying trend. For the ozone module and the water inlet, the prior art mostly analyzes them in isolation and does not attempt to establish a synergistic relationship. Single sampling is difficult to reflect the dynamic changes of water quality and may miss short-term fluctuations. There is no clear correlation analysis between the detection results of the ozone module and the water inlet, and the water purification process cannot be optimized through the relationship between the two. Dynamic sampling analysis converts the microbial concentration, ion concentration, and impurity concentration into a dynamic data matrix through time-series sampling to capture the real-time changes of water quality. Synergistic evaluation uses the covariance matrix to establish the correlation between the data at the water inlet and the ozone module inlet to more accurately evaluate the water quality characteristics and treatment effect. The time-series analysis method can quantify the change of variables over time and capture instantaneous fluctuations; the covariance matrix can quantify the correlation between two sets of data and provide a data basis for optimizing the treatment strategy.

[0077] Calculate the probability distribution distance of the Alpha covariance matrix relative to the Omega covariance matrix. The probability distribution distance can include probability distribution distances such as KL divergence. The probability distribution distance can preferably be cross-entropy, and the probability distribution distance can also be referred to as the AO covariance probability distribution distance.

[0078] In some embodiments, a fixed threshold is usually used in the prior art, such as microbial concentration > 1000 CFU / ml, to determine whether water purification treatment is required, without in-depth quantitative analysis of the statistical distribution of different water quality indicators. However, the fixed threshold does not consider the possible diversity or dynamic changes of water quality characteristics. The existing methods only rely on a single indicator and ignore the overall difference between the water inlet and the ozone module inlet. The method of the present invention introduces probability distribution and dynamically measures the water quality distribution difference through the AO covariance probability distribution distance (such as cross-entropy, KL divergence) to make up for the limitations of the fixed threshold. The overall correlation analysis directly quantifies the distribution difference between the water inlet and the ozone module inlet and provides a scientific basis for optimizing the disinfection process. KL divergence and cross-entropy can accurately quantify the similarity of two distributions and directly reflect the water quality difference. The dynamic distribution evaluation replaces the fixed threshold and is more adaptable to complex water quality changes.

[0079] Calculate the difference matrix obtained by point-to-point subtraction of the Alpha covariance matrix relative to the Omega covariance matrix in each dimension, which is the Beta difference matrix.

[0080] In the prior art, the pollution degree is usually evaluated by comparing water quality data point by point (such as simple differences), and the sources and scopes of the differences cannot be refined. Compared with the prior art, it is analyzed that the rough and simple differences in granularity cannot refine the polluted area or specific sources. The water quality characteristics at the inlet of the ozone module cannot form a clear relationship with the water inlet through the prior art. By comparing the water quality differences dimension by dimension through the Beta difference matrix, the pollution sources and magnitudes can be located. The difference heat map of the Beta matrix can visually present the water quality problem areas and improve the operation and maintenance efficiency. The matrix difference calculation point by point in dimensions can quantify the changes of each feature. The visualization of the matrix in dimensions makes the difference analysis more intuitive and helps to quickly locate the problem areas.

[0081] Calculate sdAOB according to the AO covariance probability distribution distance and the Beta difference matrix.

[0082] In some embodiments, to calculate the AO covariance probability distribution distance, the cross entropy or KL divergence can be used to calculate the AO probability distribution distance. Among them, it is necessary to first perform preprocessing of normalizing the matrix elements in the Alpha covariance matrix and the Omega covariance matrix. To calculate the Beta difference matrix, the difference matrix between Alpha and Omega can be calculated point by point in dimensions.

[0083] For the calculation of the overall pollution coefficient, the sum of the absolute values of the Beta matrix can be calculated as SBeta, which is used to quantify the pollution degree. Among them, SBeta is used to quantify the average level of the pollution degree.

[0084] Then, divide the value of the AO covariance probability distribution distance by the SBeta, and the obtained value is used as sdAOB.

[0085] In the implementation of the prior art, the current water quality evaluation method is usually based on simple averages or maximum values, and it is difficult to comprehensively evaluate the pollution degree uniformly. However, different water quality indicators (such as microbial concentration and impurity concentration) are difficult to compare through a single value. The prior art lacks tools that can simplify complex water quality evaluations. The method described in the present invention unifies the quantification indicators, normalizes the AO covariance probability distribution distance through SBeta, forms the sdAOB indicator, and unifies the pollution evaluation dimension. sdAOB directly reflects the current pollution level and simplifies complex evaluations. SBeta quantifies the overall pollution level. sdAOB eliminates the dimension differences through normalization, facilitating the unified judgment of the pollution degree.

[0086] Use the value of the sdAOB to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier.

[0087] In some embodiments, when the value of the sDAOB is greater than 1, it is determined that the water purifier needs to be flushed. When the water purifier needs to be flushed, the value of the sDAOB is used as a weight to adjust the flushing time and the flushing flow rate.

[0088] In the prior art, the flushing strategy usually runs in a fixed cycle, and the parameters are difficult to be dynamically adjusted according to the actual pollution situation. Fixed-cycle flushing may cause waste of resources or insufficient flushing. The response lag makes it difficult to adjust the flushing intensity and time according to the real-time water quality. The sDAOB is directly used as the basis for dynamically adjusting the flushing parameters, which significantly improves the flushing efficiency. By controlling the flushing flow rate and time with the sDAOB weight, the resource utilization rate is maximized. The sDAOB being greater than 1 directly reflects the high-pollution state. Moreover, the weight adjustment makes the flushing match the pollution degree, ensuring water quality and reducing energy consumption.

[0089] Further, the method is applied to a water purification device composed of a voltage regulator, a solenoid valve, an ozone module group, a cold cathode UV mercury lamp, a control center, a bacteria sensor, and / or a UV-LED lamp. The water flow in the water purification device is branched through at least two solenoid valves, at least one branch passes through the ozone module group, and at least one other branch bypasses the ozone module group. The branched water flows converge at the cold cathode UV mercury lamp.

[0090] Further, the method for obtaining the data of the microbial concentration and the ion concentration in the water flow at the water inlet of the water purification device and the data of the microbial concentration and the impurity concentration in the water flow at the water inlet of the ozone module is specifically as follows:

[0091] Data collection is carried out at multiple different sampling times within a sampling time period. At each sampling time, the value of the ion concentration and the value of the microbial concentration in the water are obtained at the water inlet of the water purification device, and the value of the microbial concentration and the value of the impurity concentration in the water are obtained at the inlet of the ozone module.

[0092] For the data obtained at the water inlet of the water purification device, the array formed by arranging the values of the ion concentration corresponding to each sampling time in time sequence is used as the ion concentration time series obtained at the water inlet, and the array formed by arranging the values of the microbial concentration corresponding to each sampling time in time sequence is used as the microbial concentration time series obtained at the water inlet. The covariance values are calculated between the ion concentration time series and the microbial concentration time series obtained at the water inlet with themselves and each other, and then the covariance matrix formed is called the Alpha covariance matrix.

[0093] For the data obtained at the inlet of the ozone module, the array composed of the values of the microbial concentration corresponding to each sampling moment in chronological order is used as the time series of the microbial concentration obtained at the water inlet, and the array composed of the values of the impurity concentration corresponding to each sampling moment in chronological order is used as the time series of the impurity concentration obtained at the water inlet. The covariance values are calculated respectively between the time series of the microbial concentration and the impurity concentration obtained at the inlet of the ozone module with themselves and each other, and then the covariance matrix formed is called the Omega covariance matrix.

[0094] Further, the Alpha covariance matrix and the Omega covariance matrix are compared to judge whether the water purifier needs to be flushed and to adjust the flushing of the water purifier, specifically including:

[0095] Calculate the probability distribution distance of the Alpha covariance matrix relative to the Omega covariance matrix, which is the AO covariance probability distribution distance; calculate the difference matrix of the Alpha covariance matrix relative to the Omega covariance matrix by subtracting point by point in each dimension, which is the Beta difference matrix;

[0096] By comparing the AO covariance probability distribution distance with the Beta difference matrix, sdAOB is obtained;

[0097] The sdAOB is used to judge whether the water purifier needs to be flushed and to adjust the flushing of the water purifier.

[0098] Further, by comparing the AO covariance probability distribution distance with the Beta difference matrix, sdAOB is obtained, specifically as follows:

[0099] The cross entropy or KL divergence is used to calculate the AO probability distribution distance. Among them, it is necessary to perform preprocessing of normalizing the matrix elements in the Alpha covariance matrix and the Omega covariance matrix first;

[0100] Calculate the sum of the absolute values of the Beta matrix as SBeta to quantify the degree of pollution;

[0101] Divide the value of the AO covariance probability distribution distance by the SBeta, and the obtained value is used as sdAOB.

[0102] In some embodiments, it is necessary to normalize the covariance matrix because the elements of the covariance matrix represent the linear correlations between different variables, and their numerical ranges may vary greatly due to data distributions. Without normalization, directly using these elements to calculate probability distribution distances, such as cross-entropy or KL divergence, may lead to result biases due to differences in numerical magnitudes and cannot fairly reflect the distribution differences between data. Normalization standardizes the values of the matrix elements to a unified range, such as 0 to 1, to avoid result distortion caused by the dimensions or distribution characteristics of different variables. After normalization, the probability distributions of the Alpha matrix and the Omega matrix can be compared on the same scale, and the calculated AO probability distribution distance is more scientific and representative.

[0103] To calculate the sum of the absolute values of the Beta matrix as SBeta, it is because the Beta matrix represents the point-by-point differences between the Alpha and Omega matrices, directly quantifying the characteristic differences between the water inlet and the inlet of the active oxygen module. Calculating the sum of the absolute values of the Beta matrix SBeta is equivalent to accumulating all the difference amplitudes as a quantitative indicator of the overall pollution level. The role of SBeta can represent the overall quantitative pollution level, and the value of SBeta reflects the average pollution level of the current water quality in multiple dimensions, serving as a measure of "pollution intensity".

[0104] Regarding the normalization of the AO probability distribution distance, the AO covariance probability distribution distance essentially reflects the similarity between two distributions but does not consider the absolute magnitude of the pollution level. Normalizing the AO distance with SBeta can adjust the distribution difference to a comprehensive index related to the pollution level, such as sdAOB.

[0105] The principle of calculating sdAOB by dividing the AO distance by SBeta is that using only the AO probability distribution distance may overly emphasize the distribution difference while ignoring the absolute degree of water quality pollution; normalizing the AO distance to sdAOB and introducing the weight of pollution intensity through SBeta enables the result to contain information on both "distribution similarity" and "pollution level". The significance of sdAOB lies in unifying the pollution judgment standard. sdAOB combines the AO distance and SBeta into a dimensionless index for dynamically judging whether the water quality reaches the flushing threshold.

[0106] When sdAOB is greater than 1, it indicates that the distribution difference (AO distance) has significantly exceeded the relative pollution intensity (SBeta), triggering the flushing operation. Through sdAOB, the sensitivity of the distribution difference is combined with the practical significance of the pollution level, improving the judgment accuracy.

[0107] Furthermore, the sdAOB is used to judge whether the water purifier needs to be flushed and to adjust the flushing of the water purifier, specifically as follows:

[0108] When the value of sDAOB is greater than 1, it is determined that the water purifier needs to be flushed; when the water purifier needs to be flushed, the value of sDAOB is used as a weight to adjust the flushing time and flushing flow rate.

[0109] In some embodiments, the flushing time Tflush is an important parameter for cleaning the water circuit and equipment. Too short a time may result in incomplete cleaning, while too long a time wastes resources. Through sDAOB, the flushing time is dynamically adjusted to be proportional to the severity of pollution. The device sets a default base flushing time TbaseT, for example, 10 seconds, as the minimum flushing time.

[0110] When sDAOB > 1, it indicates that the degree of distribution difference has exceeded the average level of pollution intensity, suggesting that the water quality has changed abnormally and may require flushing treatment. When sDAOB ≤ 1, the distribution difference is still within a reasonable range, and the water quality remains in an acceptable state without the need for flushing. After normalization, the threshold value 1 of sDAOB is a dimensionless unified benchmark that is not affected by the absolute value of the specific pollution intensity, making the flushing determination more universal for different devices and scenarios. When sDAOB > 1, it means that the distribution difference is greater than the average pollution intensity, and this difference is often related to water quality deterioration. The threshold value 1 is selected at the equilibrium point of the distribution difference and pollution intensity, so that the determination is neither too sensitive to cause frequent flushing triggers nor too slow to cause delays in the flushing opportunity.

[0111] According to the value of sDAOB, the flushing time is dynamically increased: Tflush = Tbase * sDAOB. When sDAOB = 1, the flushing time is equal to the base time; when sDAOB > 1, the flushing time increases with the severity of pollution.

[0112] In one implementation, if Tbase = 10s: when sDAOB = 1: Tflush = 10 × 1 = 10s. When sDAOB = 2: Tflush = 10 × 2 = 20s. When sDAOB = 3: Tflush = 10 × 3 = 30s.

[0113] The flushing flow rate Fflush is an important parameter affecting the flushing effect and equipment pressure. Too low a flow rate may result in pollutant residues, while too high a flow rate may damage the equipment. Therefore, the flow rate needs to be dynamically adjusted according to sDAOB. The device sets a default base flushing flow rate Fbase, for example, 1.0 liter per second, as the minimum flow rate. According to the value of sDAOB, the flushing flow rate is dynamically increased, but the growth rate is slightly lower than that of the flushing time to avoid excessive impact on the equipment: Fflush = Fbase · sDAOB. Using the square root growth method makes the adjustment of the flow rate smoother and adapts to the water circuit pressure bearing capacity.

[0114] The dynamic adjustment of the flushing time and flow rate is based on sdAOB and can flexibly respond to different pollution levels: When the pollution is mild (sdAOB is close to 1): Operate with the basic flushing parameters to save resources. When the pollution is severe (sdAOB is significantly greater than 1): Significantly increase the flushing time and flow rate to ensure thorough cleaning. Through the dynamic combination of the two, while ensuring the cleaning effect, waste of water and energy consumption is minimized as much as possible. Through the smooth increase in the flow rate (square root adjustment), the impact on equipment or pipelines caused by excessive flow rate is avoided, and the equipment life is extended.

[0115] During the flushing process, adjust the opening of the solenoid valve or proportional valve to control the water flow rate; run the flushing program and monitor whether the flushing process is completed. After the flushing is completed, return to normal operation.

[0116] The described drinking water sterilization system operates in any computing device such as a desktop computer, laptop, mobile phone, personal digital assistant, or cloud data center. The computing device includes: a processor, a memory, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the described drinking water sterilization method are implemented. The operable system may include, but is not limited to, a processor, a memory, and a server cluster.

[0117] An embodiment of the present disclosure provides a drinking water sterilization system, as Figure 2 shown. The drinking water sterilization system of this embodiment includes: a processor, a memory, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the steps in the above-described embodiment of the drinking water sterilization method are implemented. The processor executes the computer program and runs in the following system units:

[0118] The data acquisition unit is used to acquire data on the microbial concentration and ion concentration in the water flow at the water inlet of the water purification device, and acquire data on the microbial concentration and impurity concentration in the water flow at the water inlet of the ozone module;

[0119] The data analysis unit is used to calculate the covariance using the time series of ion concentration and the data of microbial concentration acquired at the water inlet to form a water inlet covariance matrix, and calculate the covariance using the microbial concentration and impurity concentration acquired at the ozone module inlet to form an ozone inlet covariance matrix;

[0120] The control and judgment unit is used to compare the water inlet covariance matrix with the ozone inlet covariance matrix, determine whether the water purifier needs to be flushed, and adjust the flushing of the water purifier.

[0121] Among them, preferably, for all undefined variables in the present invention, if there is no clear definition, they can all be manually set thresholds.

[0122] Among them, dimensionless numerical calculations are adopted for physical quantities of different units.

[0123] The described drinking water dispenser sterilization system can operate in computing devices such as desktop computers, laptop computers, mobile phones, personal digital assistants, and cloud data centers. The described drinking water dispenser sterilization system includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that the examples are merely examples of a drinking water dispenser sterilization method and system, and do not constitute a limitation on a drinking water dispenser sterilization method and system. It may include more or fewer components than the examples, or combine certain components, or different components. For example, the described drinking water dispenser sterilization system may also include input / output devices, network access devices, buses, etc.

[0124] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete component gate circuits, or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the described drinking water dispenser sterilization system, and uses various interfaces and lines to connect each sub-region of the entire drinking water dispenser sterilization system.

[0125] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the described drinking water dispenser sterilization method and system by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0126] The present disclosure provides a water dispenser sterilization method and system. The covariance is calculated using the ion concentration time series and the data of the microorganism concentration obtained at the water inlet to form a water inlet covariance matrix, and the covariance is calculated using the microorganism concentration and the impurity concentration obtained at the inlet of the ozone module to form an ozone inlet covariance matrix; the water inlet covariance matrix is compared with the ozone inlet covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier. The sterilization and purification processes are optimized to improve the response speed and adaptability of the equipment to water quality changes and to achieve efficient resource utilization.

[0127] Although the description of the present disclosure has been quite detailed and has particularly described several of the described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present disclosure. In addition, the present disclosure has been described above in terms of embodiments foreseeable by the inventors for the purpose of providing a useful description, and non-substantive modifications to the present disclosure that are not currently foreseeable may still represent equivalent modifications of the present disclosure.

Claims

1. A method for sterilizing a water dispenser, characterized in that: The method comprises the following steps: The data of microbial concentration and ion concentration in the water flow are obtained at the water inlet of the water dispenser, and the data of microbial concentration and impurity concentration in the water flow are obtained at the water inlet of the oxygen module. Specifically, data collection is performed at multiple different sampling times within a sampling period, and the values ​​of ion concentration and microbial concentration in the water are obtained at the water inlet of the water dispenser at each sampling time, and the values ​​of microbial concentration and impurity concentration in the water are obtained at the inlet of the oxygen module; For the data obtained at the water inlet of the water dispenser, the values ​​of the microbial concentration corresponding to each sampling time are arranged in chronological order as the microbial concentration time series obtained at the water inlet. The ion concentration time series and the microbial concentration time series obtained at the water inlet are used to calculate the covariance values ​​with themselves and each other respectively, and then form a covariance matrix called the water inlet covariance matrix; For the data obtained at the entrance of the active oxygen module, the array of values ​​of the impurity concentration corresponding to each sampling time is used as the impurity concentration time series obtained at the water entrance. The microbial concentration time series and the impurity concentration time series obtained at the entrance of the active oxygen module are used to calculate the covariance values ​​with each other and then form a covariance matrix called the active oxygen entrance covariance matrix; The covariance matrix of the water inlet is calculated using the ion concentration time series and microbial concentration data obtained at the water inlet, and the covariance matrix of the oxygen inlet is calculated using the microbial concentration and impurity concentration obtained at the oxygen module inlet; The water inlet covariance matrix is ​​compared with the oxygen inlet covariance matrix to determine whether the water dispenser needs to be flushed and to adjust the flushing of the water dispenser, specifically including: calculating the probability distribution distance of the water inlet covariance matrix relative to the oxygen inlet covariance matrix, which is the water quality co-distribution difference distance; calculating the difference matrix of the water inlet covariance matrix relative to the oxygen inlet covariance matrix by dimensional point-to-point subtraction, which is the distribution difference matrix; By comparing the water quality co-distribution difference distance with the distribution difference matrix, the pollution distribution normalization value is obtained, which is: The water quality co-distribution difference distance is calculated using cross entropy or KL divergence, wherein the matrix elements in the water inlet covariance matrix and the oxygen inlet covariance matrix need to be normalized before preprocessing; The absolute value sum of the distribution difference matrix is ​​calculated as the total pollution intensity to quantify the pollution degree; Divide the value of the water quality co-distribution difference distance by the total amount of pollution intensity, and the resulting value is used as the pollution distribution normalization value; The pollution distribution normalization value is used to determine whether the water dispenser needs to be flushed, and to adjust the flushing of the water dispenser. Specifically, when the value of the pollution distribution normalization value is greater than 1, it is determined that the water dispenser needs to be flushed; when the water dispenser needs to be flushed, the value of the pollution distribution normalization value is used as a weight to adjust the flushing time and flushing flow rate.

2. A method for sterilizing a drinking fountain according to claim 1, characterized in that: The method is applied to a water dispenser comprising a voltage regulator, a solenoid valve, an active oxygen module group, a cold cathode UV mercury lamp, a control center, a bacteria sensor and / or a UV-LED lamp. The water flow in the water dispenser is divided by at least two solenoid valves, wherein at least one path passes through the active oxygen module group and at least one path bypasses the active oxygen module group. The divided water flows converge at the cold cathode UV mercury lamp.

3. A water dispenser sterilization system, characterized in that: The water dispenser sterilization system runs in any computing device such as a desktop computer, a laptop computer or a cloud data center. The computing device includes: a processor, a memory and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the water dispenser sterilization method as described in any one of claims 1 to 2 are implemented.

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