Water dispenser sterilization method and system

Abstract

The invention provides a water dispenser sterilization method and system. The method comprises the following steps: calculating covariance by using an ion concentration time sequence and microorganism concentration data obtained at a water inlet to form a water inlet covariance matrix, and calculating covariance by using microorganism concentration and impurity concentration obtained at an active oxygen module inlet to form an active oxygen inlet covariance matrix; and comparing the water inlet covariance matrix with the active oxygen inlet covariance matrix to judge whether the water purifier needs to be washed or not and adjust the washing of the water purifier. The sterilization and purification process is optimized, the response speed and adaptive capacity of the equipment to water quality change are improved, and efficient resource utilization is realized.

Description

Technical Field

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

[0002] In the current sterilization and cleaning technology of water purification equipment, a single type of sensor, such as UV sensor, conductivity sensor, is usually used to detect a specific indicator in the water, such as microbial concentration or ion concentration, and judge the water quality status through a simple threshold. The application scenarios are small household water purification equipment and commercial water purification equipment. Most water purification equipment uses fixed time intervals or preset conditions, such as automatic cleaning every 6 hours, water flushing and sterilization, and lacks dynamic response capabilities, especially water purification equipment in areas with stable tap water quality. Many water purification equipment is equipped with active oxygen modules or ultraviolet sterilization lamps, but the sterilization efficiency depends on the environment when the module is running, water quality, pollutant concentration, etc., and it is usually impossible to dynamically adjust the sterilization intensity. It is more common in commercial or industrial-grade water treatment equipment.

[0003] Most equipment detects water quality through the absolute value of a single sample. Single detection includes but is not limited to microbial concentration, ion concentration, etc., and lacks trend analysis of water quality changes over time. The various modules of water purification equipment, such as the water inlet and the oxygen module inlet, usually operate independently, each detecting specific indicators, and there is a lack of synergy between the modules.

[0004] In the prior art, the detection index is single and the water quality assessment is not comprehensive. For example, in the patent document with announcement number CN117401784A, a sterilization system and its control method are provided. A single sensor can only detect a certain type of indicator, among which the UV sensor can only detect the concentration of microorganisms, and the conductivity sensor can only detect the concentration of ions. It is difficult to comprehensively evaluate the comprehensive state of water quality, such as the overall situation of microorganisms, ions, and impurity concentrations. It is unable to dynamically adapt to complex water quality conditions. If the microorganism and ion concentrations of 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 it is impossible to optimize the equipment operating parameters.

[0005] Fixed-cycle flushing wastes resources or is inefficient. The fixed-cycle flushing strategy does not consider changes in water quality and only relies on time to trigger the flushing procedure. When the water quality is good, frequent flushing wastes water and energy. When the water quality deteriorates, the fixed flushing time may not be able to remove the accumulated pollutants, affecting the purification effect.

[0006] Another example is the sterilization control system and sterilization control method for a water dispenser provided in the patent document with the announcement number CN113100631B. The modules lack coordination in operation. The water inlet and the oxygen module inlet in the water purification equipment usually operate independently, collect data separately, and no clear correlation is established. The working status of the oxygen module cannot be predicted by the change in water quality at the water inlet. The lack of systematic optimization may lead to unsatisfactory sterilization effect or damage to the equipment.

[0007] Many water purification devices in the existing technology lack dynamic water quality analysis and only use single sampling values ​​as the basis for judgment. They are unable to capture dynamic changes in water quality, such as fluctuations in water quality in a short period of time. If sampling is performed outside of the pollution peak, the water quality may be misjudged as safe. It is unable to respond to water quality changes in real time, resulting in delayed processing.

[0008] Flushing parameters cannot be adjusted dynamically according to the actual pollution level. Flushing time and flow rate are usually preset values, and the cleaning intensity cannot be adjusted according to the actual pollution level of the water quality. For light pollution, too high a flushing intensity will lead to a waste of resources; for heavy pollution, too low a flushing intensity may not be able to clean thoroughly. Summary of the invention

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

[0010] The present disclosure provides a water dispenser sterilization method and system, which uses the data of ion concentration time series and microorganism concentration obtained at the water inlet to calculate the covariance to form the water inlet covariance matrix, and uses the microorganism concentration and impurity concentration obtained at the oxygen module inlet to calculate the covariance to form the oxygen inlet covariance matrix; the water inlet covariance matrix is ​​compared with the oxygen 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 process, improve the response speed and adaptability of the equipment to water quality changes, and achieve efficient resource utilization.

[0011] In order to achieve the above object, according to one aspect of the present disclosure, a method for sterilizing a water dispenser is provided, the method comprising the following steps:

[0012] The data of microorganism concentration and ion concentration in the water flow are obtained at the water inlet of the water purification equipment, and the data of microorganism concentration and impurity concentration in the water flow are obtained at the water inlet of the active oxygen module;

[0013] 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;

[0014] The water inlet covariance matrix is ​​compared with the oxygen inlet covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier.

[0015] In an embodiment of the present invention, the microbial concentration is obtained at both inlets because microorganisms (such as bacteria and viruses) are the core pollution indicators in the water purification process, and their concentration directly affects the disinfection target of the water purification equipment. By obtaining the microbial concentration at the water inlet, the initial pollution level of the external water source can be determined; by obtaining the microbial concentration again at the inlet of the active oxygen module, the residual microbial level after partial purification inside the equipment can be evaluated. In this way, the microbial purification effect can be dynamically monitored. By comparing the changes in microbial concentration at the water inlet and the active oxygen module inlet, the working efficiency of the front-end processing such as the filter element or other sterilization modules can be judged in real time. If the microbial concentration at the inlet of the active oxygen module is too high, the enhanced active oxygen sterilization process can be triggered to improve the disinfection effect. Double-point detection helps to detect microbial growth problems in the system early and avoid secondary pollution caused by internal pollution during the purification process.

[0016] The reason why the ion concentration is obtained at the water inlet and the impurity concentration is obtained at the oxygen module inlet is that ions such as calcium, magnesium and other mineral ions are detected by conductivity sensors, which are mainly used to evaluate the mineral content of raw water and the hardness of water, which is especially important at the water inlet. High ion concentration may affect the working life of the front-end filter element or the oxygen module, so it is necessary to focus on monitoring 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 safety of water quality. Impurities will have a direct impact on the oxygen disinfection process before entering the oxygen module, such as reacting with the oxygen to generate by-products, so they need to be focused on at the module entrance.

[0017] The benefit of obtaining ion concentration at the inlet is that high ion concentration increases the risk of equipment scaling. Front-end detection can start pretreatment before entering the active oxygen module, such as softening water. Ion concentration data can be used to dynamically adjust the life estimate of the filter element to avoid loss of equipment performance due to hard water.

[0018] The benefit of obtaining the impurity concentration at the module entrance is that active oxygen disinfection is sensitive to impurities. For example, heavy metals may react with active oxygen to produce harmful byproducts. This can be avoided by monitoring the impurity concentration. The impurity concentration directly reflects the potential harmful components in the water quality. Detection before entering the module helps to dynamically adjust the purification strategy.

[0019] The two inlets are similar in that they both detect microbial concentrations, 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 external water sources, while the oxygen module inlet is the key node before sterilization. In this way, the changes in microbial concentration can be detected at two points, which 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 entrance of the active oxygen module, the array composed of the numerical values ​​of the microbial concentration corresponding to each sampling time in chronological order is used as the time series of the microbial concentration obtained at the water entrance, and the array composed of the numerical values ​​of the impurity concentration corresponding to each sampling time in chronological order is used as the time series of the impurity concentration 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 themselves and each other respectively, and then form a covariance matrix called the active oxygen entrance covariance matrix.

[0027] Furthermore, the water inlet covariance matrix is ​​compared with the oxygen inlet covariance matrix 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 oxygen inlet covariance matrix, which is the water quality co-distribution difference distance; calculate 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;

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

[0032] 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;

[0033] The absolute value sum of the distribution difference matrix is ​​calculated as the total pollution intensity to quantify the pollution degree;

[0034] The value of the water quality co-distribution difference distance is divided by the total amount of pollution intensity, and the resulting 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:

[0036] When the 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, the value of the pollution distribution normalization value is used as a 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 comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the water dispenser sterilization method when executing the computer program, the water dispenser sterilization system can be run in computing devices such as desktop computers, notebook computers, mobile phones, PDAs, and cloud data centers, and the executable system may include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program to run in the following system units:

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

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

[0040] The control judgment unit is used to compare the water inlet covariance matrix with the active oxygen inlet covariance matrix, to judge whether the water purifier needs to be flushed, and to 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, which uses the ion concentration time series and microorganism concentration data obtained at the water inlet to calculate the covariance to form the water inlet covariance matrix, and uses the microorganism concentration and impurity concentration obtained at the oxygen module inlet to calculate the covariance to form the oxygen inlet covariance matrix; the water inlet covariance matrix is ​​compared with the oxygen 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 process is optimized, the response speed and adaptability of the equipment to water quality changes are improved, and efficient resource utilization is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other features of the present disclosure will become more apparent by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present disclosure represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0043] Figure 1 Shown is a flow chart of a method for sterilizing a water dispenser;

[0044] Figure 2 Shown is a system structure diagram of a water dispenser sterilization system. DETAILED DESCRIPTION

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

[0046] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0047] like Figure 1 The flowchart of a method for sterilizing a water dispenser according to the present invention is shown below. Figure 1 A method and system for sterilizing a water dispenser according to an embodiment of the present invention will be described.

[0048] The present disclosure provides a method for sterilizing a water dispenser, the method specifically comprising the following steps:

[0049] The data of microorganism concentration and ion concentration in the water flow are obtained at the water inlet of the water purification equipment, and the data of microorganism concentration and impurity concentration in the water flow are obtained at the water inlet of the active oxygen module;

[0050] The covariance composition of the Alpha covariance matrix is ​​calculated using the ion concentration time series and microbial concentration data obtained at the water inlet, and the covariance composition of the Omega covariance matrix is ​​calculated using the microbial concentration and impurity concentration obtained at the oxygen module inlet;

[0051] The Alpha covariance matrix is ​​compared 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 an embodiment of the present invention, the microbial concentration is obtained at both inlets because microorganisms (such as bacteria and viruses) are the core pollution indicators in the water purification process, and their concentration directly affects the disinfection target of the water purification equipment. By obtaining the microbial concentration at the water inlet, the initial pollution level of the external water source can be determined; by obtaining the microbial concentration again at the inlet of the active oxygen module, the residual microbial level after partial purification inside the equipment can be evaluated. In this way, the microbial purification effect can be dynamically monitored. By comparing the changes in microbial concentration at the water inlet and the active oxygen module inlet, the working efficiency of the front-end processing such as the filter element or other sterilization modules can be judged in real time. If the microbial concentration at the inlet of the active oxygen module is too high, the enhanced active oxygen sterilization process can be triggered to improve the disinfection effect. Double-point detection helps to detect microbial growth problems in the system early and avoid secondary pollution caused by internal pollution during the purification process.

[0053] The reason why the ion concentration is obtained at the water inlet and the impurity concentration is obtained at the oxygen module inlet is that ions such as calcium, magnesium and other mineral ions are detected by conductivity sensors, which are mainly used to evaluate the mineral content of raw water and the hardness of water, which is especially important at the water inlet. High ion concentration may affect the working life of the front-end filter element or the oxygen module, so it is necessary to focus on monitoring 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 safety of water quality. Impurities will have a direct impact on the oxygen disinfection process before entering the oxygen module, such as reacting with the oxygen to generate by-products, so they need to be focused on at the module entrance.

[0054] The benefit of obtaining ion concentration at the inlet is that high ion concentration increases the risk of equipment scaling. Front-end detection can start pretreatment before entering the active oxygen module, such as softening water. Ion concentration data can be used to dynamically adjust the life estimate of the filter element to avoid loss of equipment performance due to hard water.

[0055] The benefit of obtaining the impurity concentration at the module entrance is that active oxygen disinfection is sensitive to impurities. For example, heavy metals may react with active oxygen to produce harmful byproducts. This can be avoided by monitoring the impurity concentration. The impurity concentration directly reflects the potential harmful components in the water quality. Detection before entering the module helps to dynamically adjust the purification strategy.

[0056] The two inlets are similar in that they both detect microbial concentrations, 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 external water sources, while the oxygen module inlet is the key node before sterilization. In this way, the changes in microbial concentration can be detected at two points, which 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] Among the bacterial sensors, ultraviolet sensors can be used to detect the concentration of microorganisms in water, conductivity sensors can detect the concentration of ions in water to determine the degree of water pollution, and chemical sensors can detect the concentration of harmful chemicals in water, such as the concentration of impurities including heavy metals, chlorides, etc.

[0069] At the water inlet of the water purification equipment, preferably, a conductivity sensor is used to detect the ion concentration in the water, and a UV sensor is combined to detect microorganisms to form a basic judgment;

[0070] At the entrance of the oxygen module, the concentration of microorganisms in the water is also obtained, and the concentration of impurities detected by the sensor is increased to ensure that the impurity level is detected before oxygen disinfection, thereby avoiding oxygen water contamination.

[0071] Data collection is performed at multiple different sampling moments within a sampling period, and the ion concentration, microbial concentration, etc. in the water are obtained at the water inlet of the water purification equipment at each sampling moment, and the microbial concentration, impurity concentration, etc. in the water are obtained at the inlet of the active oxygen module.

[0072] In some embodiments, for the data obtained at the water inlet of the water purification equipment, an array composed of the numerical values ​​of the ion concentration corresponding to each sampling moment in chronological order is used as the ion concentration time series obtained at the water inlet, and an array composed of the numerical values ​​of the microbial concentration corresponding to each sampling moment in chronological order 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 Alpha covariance matrix.

[0073] For the data obtained at the entrance of the active oxygen module, the array composed of the numerical values ​​of the microbial concentration corresponding to each sampling time in chronological order is used as the time series of the microbial concentration obtained at the water entrance, and the array composed of the numerical values ​​of the impurity concentration corresponding to each sampling time in chronological order is used as the time series of the impurity concentration 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 themselves and each other respectively, and then form a covariance matrix called the Omega covariance matrix.

[0074] In some embodiments, the numpy.cov function can directly calculate the covariance matrix and return a 2x2 covariance matrix, where: the values ​​on the diagonal are the respective variances (the variances of ion concentrations and microbial concentrations); and the values ​​on the off-diagonal are the covariances.

[0075] In one embodiment, a dual-moment sampling verification method may be used: data at only two sampling moments are collected within a sampling period, and the data at the two sampling moments may be used for calculation, but the effect remains to be tested.

[0076] Existing water purification equipment usually relies on the absolute value of a single sampling (such as a single microbial concentration measurement result) to evaluate water quality, and lacks attention to the trend of time changes. For the oxygen module and the water inlet, the existing technology is mostly isolated analysis, and no attempt is made to establish a synergistic relationship. A single sampling is difficult to reflect the dynamic changes of water quality and may miss fluctuations in a short period of time. There is no clear correlation analysis between the detection results of the oxygen module and the water inlet, and it is impossible to optimize the water purification process through the relationship between the two. Dynamic sampling analysis converts microbial concentration, ion concentration, and impurity concentration into a dynamic data matrix through time series sampling to capture real-time changes in water quality. Collaborative evaluation uses the covariance matrix to establish the correlation between the water inlet and the oxygen module inlet data to more accurately evaluate water quality characteristics and treatment effects. The time series analysis method can quantify the changes of variables over time and capture instantaneous fluctuations; the covariance matrix can quantify the correlation between the two sets of data and provide a data basis for optimizing treatment strategies.

[0077] The probability distribution distance of the Alpha covariance matrix relative to the Omega covariance matrix is ​​calculated, and the probability distribution distance may include a probability distribution distance such as KL divergence, and the probability distribution distance may preferably be a cross entropy, and the probability distribution distance may also be called an AO covariance probability distribution distance.

[0078] In some embodiments, a fixed threshold, such as a microbial concentration of >1000 CFU / ml, is generally used in the prior art to determine whether water purification treatment is needed, without conducting an in-depth quantitative analysis of the statistical distribution of different water quality indicators. However, the fixed threshold does not take into account the possible diversity or dynamic changes of water quality characteristics. The existing method relies only on a single indicator, ignoring the overall difference between the water inlet and the oxygen module inlet. The method described in the present invention introduces probability distribution, and dynamically measures the difference in water quality distribution 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 oxygen module inlet, providing a scientific basis for optimizing the disinfection process. KL divergence and cross entropy can accurately quantify the similarity of the two distributions, directly reflecting the difference in water quality. Dynamic distribution evaluation replaces fixed thresholds, which is more adaptable to complex water quality changes.

[0079] Calculate the difference matrix of the Alpha covariance matrix relative to the Omega covariance matrix by subtracting the dimensions point by point, which is the Beta difference matrix.

[0080] In the prior art, the pollution level is usually assessed by comparing water quality data point by point (such as simple differences), and the source and scope of the differences cannot be refined. Compared with the prior art, the analysis of coarse-grained simple differences cannot refine the pollution area or specific source. The water quality characteristics at the inlet of the oxygen module cannot be clearly related to the water inlet through the prior art. The Beta difference matrix is ​​used to compare water quality differences dimension by dimension to locate the source and magnitude of pollution. The difference heat map of the Beta matrix can intuitively present the water quality problem area and improve operation and maintenance efficiency. The matrix difference calculation based on the point-to-point dimension can quantify the change of each feature. The visualization of the matrix in dimension makes the difference analysis more intuitive and helps to quickly locate the problem area.

[0081] The sdAOB is obtained by calculating according to the AO covariance probability distribution distance and the Beta difference matrix.

[0082] In some embodiments, the AO covariance probability distribution distance can be calculated by using cross entropy or KL divergence. In this case, the matrix elements in the Alpha covariance matrix and the Omega covariance matrix need to be pre-processed to be normalized. To calculate the Beta difference matrix, the difference matrix between Alpha and Omega can be calculated point by point in dimension.

[0083] For the overall pollution coefficient calculation, the absolute value sum 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 pollution degree.

[0084] Then, the value of the AO covariance probability distribution distance is divided by the SBeta, and the resulting value is taken as sdAOB.

[0085] In the implementation of the prior art, the current water quality assessment method is usually based on a simple average or maximum value, which makes it difficult to uniformly and comprehensively assess the degree of pollution. 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 assessments. The method described in the present invention unifies quantitative indicators, normalizes the AO covariance probability distribution distance through SBeta, forms the sdAOB indicator, and unifies the pollution assessment dimension. sdAOB directly reflects the current pollution level and simplifies complex assessments. SBeta quantifies the overall pollution level. sdAOB eliminates dimensional differences through normalization processing, which is convenient for unified judgment of the degree of pollution.

[0086] The value of sdAOB is used 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 flushing flow rate.

[0088] In the prior art, flushing strategies usually run in fixed cycles, and parameters are difficult to adjust dynamically according to actual pollution conditions. 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. sdAOB is directly used as the basis for dynamically adjusting the flushing parameters, significantly improving the flushing efficiency. The flushing flow rate and time are controlled by the sdAOB weight to maximize resource utilization. sdAOB greater than 1 directly reflects a high pollution state. Moreover, the weight adjustment makes the flushing match the pollution level, which not only ensures water quality but also reduces energy consumption.

[0089] 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.

[0090] 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:

[0091] 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;

[0092] 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 Alpha covariance matrix;

[0093] For the data obtained at the entrance of the active oxygen module, the array composed of the numerical values ​​of the microbial concentration corresponding to each sampling time in chronological order is used as the time series of the microbial concentration obtained at the water entrance, and the array composed of the numerical values ​​of the impurity concentration corresponding to each sampling time in chronological order is used as the time series of the impurity concentration 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 themselves and each other respectively, and then form a covariance matrix called the Omega covariance matrix.

[0094] Furthermore, the Alpha covariance matrix is ​​compared with the Omega covariance matrix to determine 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 point-to-point subtraction in dimensions, 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 determine whether the water purifier needs to be flushed, and to adjust the flushing of the water purifier.

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

[0099] The cross entropy or KL divergence is used to calculate the AO probability distribution distance, where the matrix elements in the Alpha covariance matrix and the Omega covariance matrix need to be normalized before preprocessing;

[0100] The absolute value sum of the Beta matrix is ​​calculated as SBeta to quantify the degree of pollution;

[0101] The value of the AO covariance probability distribution distance is divided by the SBeta, and the resulting value is taken as sdAOB.

[0102] In some embodiments, the covariance matrix needs to be normalized because the elements of the covariance matrix represent the linear correlation between different variables, and their numerical ranges may vary greatly depending on the data distribution. If normalization is not performed, directly using these elements to calculate probability distribution distances, such as cross entropy or KL divergence, may cause result deviations due to differences in numerical magnitude, and fail to fairly reflect the distribution differences between data. Normalization standardizes the values ​​of matrix elements to a uniform range such as 0 to 1 to avoid distortion of results due to 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] The absolute sum of the Beta matrix is ​​calculated as SBeta because the Beta matrix represents the point-by-point difference between the Alpha and Omega matrices, directly quantifying the characteristic difference between the water inlet and the oxygen module inlet. Calculating the absolute sum 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 is to represent the overall quantitative pollution level. The value of SBeta reflects the average pollution level of the current water quality in multiple dimensions, as a measure of "pollution intensity".

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

[0105] The principle of calculating sdAOB by dividing AO distance by SBeta is that using AO probability distribution distance alone may overemphasize distribution differences and ignore the absolute degree of water pollution; normalizing AO distance to sdAOB and introducing the weight of pollution intensity through SBeta makes the result contain information on both "distribution similarity" and "pollution degree". The significance of sdAOB is to unify the pollution judgment standard. sdAOB combines AO distance and SBeta into a dimensionless indicator, which is used to dynamically judge whether water quality has reached the flushing threshold.

[0106] When sdAOB is greater than 1, it means 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 actual significance of the pollution degree to improve the accuracy of judgment.

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

[0108] 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.

[0109] In some embodiments, the flushing time Tflush is an important parameter for cleaning waterways 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 the contamination. The device sets a default basic flushing time TbaseT, for example, 10 seconds, as the minimum flushing time.

[0110] When sdAOB>1, it means that the degree of distribution difference has exceeded the average level of pollution intensity, indicating that the water quality has changed abnormally and may require flushing. When sdAOB≤1, the distribution difference is still within a reasonable range, the water quality remains acceptable, and no flushing is required. After normalization, the sdAOB threshold of 1 is a dimensionless unified benchmark that is not affected by the absolute value of the specific pollution intensity, which makes the flushing judgment of different equipment and different scenarios more universal. When sdAOB>1, it means that the distribution difference is greater than the average pollution intensity, and this difference is often related to the deterioration of water quality. Threshold 1 is selected at the balance point between distribution difference and pollution intensity, so that the judgment is neither too sensitive, resulting in frequent triggering of flushing, nor too slow, resulting in delayed flushing.

[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 that affects the flushing effect and equipment pressure. Too low a flow rate may cause pollutants to remain, while too high a flow rate may damage the equipment. Therefore, the flow rate needs to be dynamically adjusted according to sdAOB. The equipment sets a default basic flushing flow rate Fbase, for example 1.0 liters / 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 the flushing time to avoid excessive impact on the equipment: Fflush = Fbase·sdAOB. Using the square root growth method makes the flow rate adjustment smoother and adapts to the waterway pressure bearing capacity.

[0114] The dynamic adjustment of flushing time and flow rate is based on sdAOB, which can flexibly respond to different pollution levels: When the pollution is light (sdAOB is close to 1): operate with basic flushing parameters to save resources. When the pollution is heavy (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, the waste of water and energy consumption is minimized. Through the smooth increase of flow rate (square root adjustment), excessive impact on equipment or pipelines caused by excessive flow rate is avoided, and the life of the equipment is extended.

[0115] During flushing, 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 water dispenser sterilization system runs on any computing device such as a desktop computer, a laptop computer, a mobile phone, a PDA 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 are implemented. The executable system may include, but is not limited to, a processor, a memory, and a server cluster.

[0117] The embodiment of the present disclosure provides a water dispenser sterilization system, such as Figure 2 As shown, a water dispenser sterilization system of this embodiment 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 above-mentioned water dispenser sterilization method embodiment are implemented. The processor executes the computer program to run in the following system units:

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

[0119] A data analysis unit, used to calculate the covariance using the ion concentration time series and microbial concentration data obtained at the water inlet to form a water inlet covariance matrix, and to calculate the covariance using the microbial concentration and impurity concentration obtained at the oxygen module inlet to form an oxygen inlet covariance matrix;

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

[0121] Among them, preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

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

[0123] The water dispenser sterilization system can be run in computing devices such as desktop computers, laptops, mobile phones, PDAs, and cloud data centers. The water dispenser sterilization system includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that the example is only an example of a water dispenser sterilization method and system, and does not constitute a limitation on a water dispenser sterilization method and system, which may include more or fewer components than the example, or a combination of certain components, or different components. For example, the water dispenser sterilization system may also include input and output devices, network access devices, buses, etc.

[0124] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) 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 any conventional processor, etc. The processor is the control center of the water dispenser sterilization system, and uses various interfaces and lines to connect the various sub-areas of the entire water dispenser sterilization system.

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

[0126] The present disclosure provides a water dispenser sterilization method and system, which uses the data of ion concentration time series and microorganism concentration obtained at the water inlet to calculate the covariance to form the water inlet covariance matrix, and uses the microorganism concentration and impurity concentration obtained at the oxygen module inlet to calculate the covariance to form the oxygen inlet covariance matrix; the water inlet covariance matrix is ​​compared with the oxygen 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 process, improve the response speed and adaptability of the equipment to water quality changes, and achieve efficient resource utilization.

[0127] Although the description of the present disclosure has been quite detailed and specifically describes several 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 above description of the present disclosure is based on the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present disclosure that have not yet been foreseen may still represent equivalent changes to the present disclosure.

Claims

1. A method for sterilizing a water dispenser, characterized in that: The method comprises the following steps: The data of microorganism concentration and ion concentration in the water flow are obtained at the water inlet of the water purification equipment, and the data of microorganism concentration and impurity concentration in the water flow are obtained at the water inlet of the active oxygen module; 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 purifier needs to be flushed and to adjust the flushing of the water purifier.

2. A method for sterilizing a drinking fountain according to claim 1, characterized in that: 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. The water flow in the water purification device is divided through at least two solenoid valves, at least one of which passes through the active oxygen module group and at least one of which bypasses the active oxygen module group. The divided water flows converge at the cold cathode UV mercury lamp.

3. A method for sterilizing a water dispenser according to claim 1, characterized in that: in, The method for obtaining the data of microbial concentration and ion concentration in the water flow at the water inlet of the water purification equipment and the data of microbial concentration and impurity concentration in the water flow at the water inlet of the oxygen module is as follows: 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; For the data obtained at the water inlet of the water purification equipment, 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 impurity concentration values ​​corresponding to each sampling time is arranged in chronological order 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 themselves and each other respectively, and then form a covariance matrix called the active oxygen entrance covariance matrix.

4. A method for sterilizing a water dispenser according to claim 1, characterized in that: in, The water inlet covariance matrix is ​​compared with the oxygen inlet covariance matrix to determine whether the water purifier needs to be flushed and to adjust the flushing of the water purifier, including: Calculate 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; calculate 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; 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.

5. A method for sterilizing a water dispenser according to claim 4, characterized in that: in, By comparing the water quality co-distribution difference distance with the distribution difference matrix, the pollution distribution normalization value is obtained, which is: The cross entropy or KL divergence is used to calculate the water quality co-distribution difference distance, wherein the matrix elements in the water inlet covariance matrix and the active 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; The value of the water quality co-distribution difference distance is divided by the total amount of pollution intensity, and the resulting value is used as the pollution distribution normalization value.

6. A method for sterilizing a water dispenser according to claim 4, characterized in that: in, 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: When the 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, the value of the pollution distribution normalization value is used as a weight to adjust the flushing time and flushing flow rate.

7. 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 6 are implemented.

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