A method and system for monitoring water quality of a treatment facility based on small molecule water

Abstract

This invention relates to the field of small molecule water production monitoring technology, and discloses a method and system for water quality monitoring of small molecule water treatment facilities. The system includes a sensing module installed on the product water pipeline, a data processing module for data collection and preliminary sorting, and a central monitoring and analysis platform. The platform, through the coordinated operation of a direct water quality monitoring unit, an indirect water quality monitoring unit, an equipment operation monitoring unit, and an abnormal event diagnosis unit, achieves real-time comparison and alarm of water quality parameters, indirect assessment of small molecule cluster size, predictive maintenance of the operating status of equipment such as electrolyzers, and rapid root cause diagnosis of water quality anomalies. This invention solves the problems of difficulty in real-time monitoring of core quality indicators of small molecule water, reliance on manual experience for equipment maintenance, and low efficiency in troubleshooting water quality anomalies. It enables comprehensive and forward-looking intelligent monitoring, effectively ensuring water quality stability and improving production efficiency.

Description

Technical Field

[0001] This invention relates to the field of small molecule water production monitoring technology, specifically to a method and system for monitoring water quality in small molecule water treatment facilities. Background Technology

[0002] In current small molecule water production processes, while key parameters such as pH and conductivity can be measured, continuous and stable real-time monitoring is difficult. Traditional methods rely on manual, timed sampling and offline laboratory analysis. This approach not only suffers from significant lag, failing to reflect instantaneous fluctuations during production, but also greatly increases labor costs. More importantly, core indicators such as the size of small molecule clusters, which directly determine water functionality, typically require sophisticated instruments from specialized institutions for measurement. Real-time data is virtually impossible to obtain on the production line, leaving a gap in the monitoring of the most critical quality aspects of the final product.

[0003] Furthermore, the operation and maintenance of production equipment, especially core components such as electrolyzers, heavily rely on the personal experience of operators. Health status assessments of the equipment lack quantitative data, and maintenance is often reactive, occurring only after water quality has significantly deteriorated or the equipment has completely failed. Early warning and predictive maintenance are impossible for performance degradation in electrolyzers caused by scaling, electrode wear, etc., frequently leading to unplanned downtime, disrupting production continuity, and resulting in high maintenance costs.

[0004] When water quality anomalies occur, the troubleshooting process using existing technologies is inefficient. Because water quality parameters and equipment operating parameters are scattered and lack correlation analysis, maintenance personnel can only rely on experience to troubleshoot step by step, which is time-consuming and labor-intensive. It is impossible to quickly pinpoint the root cause of the anomaly, such as distinguishing between fluctuations in source water quality, equipment malfunction, or improper process parameter settings. This prolongs the fault recovery time and increases the risk of product defects. Therefore, there is an urgent need for an integrated solution that can achieve comprehensive real-time monitoring, intelligent early warning, and efficient diagnostics. Summary of the Invention

[0005] The purpose of this invention is to provide a water quality monitoring method and system for small molecule water treatment facilities. By deploying multi-parameter sensors in the product water pipeline to collect water quality data in real time, and then standardizing the data through a data processing module, the data is uploaded to a central monitoring and analysis platform for comprehensive intelligent analysis. The platform can not only monitor and alarm for exceeding limits for key indicators such as pH and potential values ​​in real time, but also indirectly assess the size of small molecule clusters that are difficult to measure directly through a built-in model. Simultaneously, the system continuously tracks equipment operating parameters such as the electrolyzer's operating current to achieve predictive maintenance. When an anomaly occurs, the system can automatically correlate and analyze multi-source data to quickly diagnose the root cause of the fault, realizing a shift from passive response to proactive early warning. This effectively solves the core pain points of traditional methods, such as lagging water quality monitoring, reliance on manual experience for equipment maintenance, and low efficiency in fault diagnosis. It significantly improves the intelligence level and monitoring efficiency of the production process, ensures stable and reliable water quality, and reduces operation and maintenance costs and the risk of production stoppage, thus effectively addressing the problems mentioned in the background technology.

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

[0007] A method for monitoring water quality in a treatment facility based on small molecule water includes the following steps:

[0008] Raw water quality parameters are collected by sensor modules deployed on the water production pipeline. These raw parameters include at least oxidation-reduction potential, pH value, conductivity, and water temperature.

[0009] The data processing module collects, verifies, and converts the original parameter data, adds device identifiers and timestamps, generates a standardized water quality parameter data package, and uploads it over the network.

[0010] The water quality parameter data packet is received through the central monitoring and analysis platform, and the following monitoring and analysis are performed:

[0011] Directly monitor water quality parameters to determine whether they exceed the preset acceptable range and trigger an alarm;

[0012] The size of small molecular clusters is indirectly assessed based on real-time water quality parameters using a pre-set estimation model.

[0013] Based on the operating current value of the electrolytic cell, monitor the equipment operating status, perform performance trend analysis, and provide predictive maintenance warnings;

[0014] When an anomaly occurs, the diagnostic process is automatically initiated, and historical data is correlated and analyzed to determine the root cause of the fault.

[0015] Preferably, the step of indirectly assessing the size of small molecular clusters includes:

[0016] Using historical data samples and laboratory-measured small molecule cluster size data, an estimation model is established through machine learning algorithms, with real-time water quality parameters as input and estimated small molecule cluster size as output.

[0017] Input the real-time monitored water quality parameters into the model, and it will automatically calculate the estimated value of the current small molecular cluster size.

[0018] Preferably, the steps for monitoring the operating status of the monitoring device include:

[0019] Continuously collect the operating current value of the electrolyzer; establish a dynamic performance baseline model for each device, which records the typical operating current range required to achieve the water production standard under standard conditions;

[0020] The real-time operating current value is compared with the performance baseline to analyze its long-term trend.

[0021] When an abnormal performance degradation trend is identified, the system predicts future failure risks based on the current degradation rate and generates maintenance warning information.

[0022] Preferably, the steps of automatically initiating the diagnostic process include:

[0023] After receiving the warning, a diagnostic time window is defined; all relevant historical data, including water quality parameter change curves and operating current change trajectories, are retrieved from this time window.

[0024] Analyze the sequence and correlation of parameter changes, and perform pattern matching with typical failure cases in the knowledge base;

[0025] The root cause is comprehensively inferred, and a structured diagnostic report containing a chain of data evidence is generated.

[0026] A water quality monitoring system for a treatment facility based on small molecule water, comprising:

[0027] A sensing module, installed on the water production pipeline, is used to collect raw parameter data of water quality. The sensing module includes an oxidation-reduction potential sensor, a pH sensor, a conductivity sensor, and a temperature sensor.

[0028] A data processing module, connected to the sensing module, is used to verify, convert, package, and transmit the raw data over a network.

[0029] The central monitoring and analysis platform is connected to the data processing module and is used to receive data and perform monitoring and analysis. It includes a direct water quality monitoring unit, an indirect water quality monitoring unit, an equipment operation monitoring unit, and an abnormal event diagnosis unit.

[0030] Preferably, the water quality indirect monitoring unit has a built-in estimation model for assessing the size of small molecular clusters based on real-time water quality parameters; the equipment operation monitoring unit is configured to establish and maintain the performance baseline model and perform predictive maintenance analysis.

[0031] Preferably, the abnormal event diagnosis unit is configured to automatically start the diagnosis process after receiving an early warning, perform multi-parameter correlation analysis and fault case matching, and generate a diagnosis report.

[0032] Preferably, the central monitoring and analysis platform is also connected to a platform monitoring interface for visually displaying real-time data, historical trend charts, alarm information, and diagnostic reports.

[0033] This system not only enables continuous monitoring of key parameters of effluent water quality, but also allows for indirect assessment of the structure of small molecular clusters that are difficult to measure directly online. It can also perform predictive diagnosis of the health status of key components of treatment facilities and intelligent analysis of the root causes of water quality anomalies. Ultimately, it completes the transformation of water quality supervision from passive response to proactive early warning, effectively improving the production efficiency of small molecule water.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] 1. This invention can simultaneously track core water quality indicators and the operating efficiency of key equipment, connecting previously isolated data points into a dynamic performance graph with a time dimension. This not only solves the problem of real-time monitoring of core quality indicators, but also enables intermittent and continuous evaluation of key functional indicators such as the size of small molecular clusters, filling the gap in key quality monitoring on the production line.

[0036] 2. This invention significantly improves the level of intelligence in equipment maintenance. By continuously learning the normal operating status of the equipment, a dynamic performance baseline is established for each piece of equipment, which can keenly capture subtle performance degradation trends that indicate potential failures. This predictive maintenance mechanism changes the passive situation of relying on manual experience judgment in the past, enabling maintenance personnel to receive early warnings before the equipment performance declines significantly or causes water quality abnormalities, and to intervene based on the clear maintenance suggestions provided by the system, effectively avoiding unplanned downtime.

[0037] 3. This invention automatically correlates and analyzes the changes in water quality parameters and equipment operation data over time, and compares them with the built-in fault case library. It can quickly deduce the root cause of the anomaly, providing maintenance personnel with a clear direction for troubleshooting. This greatly reduces the time from discovering a problem to locating the root cause, significantly improves the efficiency of fault diagnosis, and ensures the continuity of production and the stability of product quality. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0039] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] To address the challenges of real-time monitoring of core quality indicators in the production of small molecule water, reliance on manual experience for equipment maintenance, and low efficiency in troubleshooting water quality anomalies, please refer to [link to relevant documentation]. Figure 1-2 This embodiment provides the following technical solution:

[0042] This system is used to monitor the quality of water produced by small molecule water treatment facilities, especially for monitoring the quality of small molecule alkaline negative potential water. The system architecture mainly consists of a sensing module, a data processing module, a central monitoring and analysis platform, and a platform monitoring interface. The specific operation method of the system architecture is as follows:

[0043] Sensing modules, including redox potential sensors, pH sensors, conductivity sensors, and temperature sensors, are installed on the pipelines that ultimately produce water from the treatment facility.

[0044] Specifically, the oxidation-reduction potential sensor is used to measure the potential value of small molecule water. Whether the potential value is negative and the magnitude of the negative value are direct standards for judging whether small molecule water has the reducing property of negative potential, thereby judging the antioxidant capacity of small molecule water; the pH sensor is used to measure the pH value of small molecule water to ensure that it is stable within a reasonable alkaline range; the conductivity sensor is used to assess the total mineral content in the water by measuring the conductivity value; and the temperature sensor is used to measure the water temperature value.

[0045] These sensors together provide a comprehensive understanding of the water quality, continuously capturing the latest raw parameter values ​​of the water flow and converting them into standard electrical signals. The acquired electrical signals are then transmitted to a nearby data processing module.

[0046] The data processing module is used to collect and initially organize the electrical signals representing the parameter values ​​transmitted from each sensor. The specific operation process is as follows:

[0047] The received electrical signals are converted into a unified digital format and subjected to preliminary verification and filtering to ensure the accuracy and validity of the uploaded data.

[0048] These raw parameter values ​​are mapped into meaningful physical quantities according to predefined measurement ranges and calculation rules and used as water quality parameters;

[0049] Label each set of water quality parameters with the source device and the collection time;

[0050] Each set of water quality parameters and its label is packaged and sent to a remote central monitoring and analysis platform at regular intervals via wired or wireless network.

[0051] As the core of the system, the central monitoring and analysis platform is primarily responsible for receiving, analyzing, and storing data packets from the data processing module, enabling comprehensive oversight of the small molecule water treatment facilities. By integrating multiple monitoring units, the platform continuously tracks water quality parameters and equipment operating status, ensuring stable water quality and enhancing the intelligence and automation of the production process.

[0052] The central monitoring and analysis platform includes a direct water quality monitoring unit, an indirect water quality monitoring unit, an equipment operation monitoring unit, and an abnormal event diagnosis unit.

[0053] The specific operation method of the direct water quality monitoring unit is as follows:

[0054] The water quality direct monitoring unit pre-sets a qualified range for each water quality parameter, including potential value, pH value, conductivity value, and water temperature value;

[0055] After the water quality parameters enter the water quality direct monitoring unit, they are compared with the corresponding acceptable range in real time.

[0056] If a water quality parameter is found to be consistently outside the acceptable range, the water quality direct monitoring unit will immediately activate the alarm procedure. On the one hand, it will report the abnormal situation to the abnormal event diagnosis unit for troubleshooting and analysis. On the other hand, it will notify the management personnel to intervene by sending text messages, emails, or popping up a prominent prompt on the platform monitoring interface, thereby ensuring that the basic functions of the functional water produced at every moment meet the standards.

[0057] The water quality indirect monitoring unit employs an indirect assessment method for the indicator of small molecule cluster size. It utilizes real-time measured water quality parameters and modeling techniques to evaluate the condition of small molecule water clusters, which are difficult to measure directly. The specific operational process is as follows:

[0058] By accumulating a large number of historical data samples of water quality parameters, and combining them with the actual size of small molecular clusters in small molecular water samples measured in advance through professional laboratories, machine learning algorithms are used to find the inherent and quantifiable correlation between water quality parameters and small molecular cluster size. Thus, an estimation model is established with potential value, pH value, conductivity value, and water temperature value as inputs and estimated value of small molecular cluster size as output.

[0059] Once the estimation model is deployed on the central monitoring and analysis platform, it can automatically calculate the estimated value of the current water quality small molecule cluster status based on the real-time monitored water quality parameters, thereby achieving a comprehensive understanding and supervision of this key function of small molecule water.

[0060] The equipment operation monitoring unit is used to monitor the operating status of the water treatment facility itself, especially its core component, the electrolyzer, to ensure its healthy operation. The specific operating process is as follows:

[0061] Continuous collection and recording of electrolyzer operating parameters are crucial. The key operating parameter is the operating current applied to the electrolyzer to produce standard-compliant small-molecule water, assuming water quality parameters remain relatively stable. This current value directly reflects the electrolysis efficiency of the electrolyzer and is a core indicator for evaluating its operational effectiveness.

[0062] The equipment operation monitoring unit establishes a dynamic performance baseline model for each device. The performance baseline model records the typical operating current range required to achieve the water production standard under standard influent conditions after the initial installation of the device or the most recent thorough maintenance. As the device continues to operate, the unit continuously compares the real-time collected operating current value with the performance baseline and analyzes its long-term trend.

[0063] Normal component aging is characterized by a very slow and linear increase in the operating current value over time. However, when abnormal conditions occur inside the electrolytic cell, including scaling, electrode coating wear, or blockage by foreign objects, the decline in operating efficiency will accelerate, manifested as a significant increase in the operating current value in a short period of time, or an increase rate exceeding the normal aging threshold.

[0064] Once the equipment operation monitoring unit identifies this abnormal performance degradation trend, it immediately activates the early warning mechanism. Based on the current rate of performance decline, it predicts that at some future point in time, even with the equipment's maximum permissible operating current, it may be unable to produce water that meets quality standards. At this time, the equipment operation monitoring unit generates an early warning message with clear maintenance recommendations and sends it to the platform monitoring interface. Simultaneously, it notifies the abnormal event diagnosis unit, providing data background for possible in-depth analysis.

[0065] The abnormal event diagnosis unit is used to quickly troubleshoot water quality anomalies, analyze the causes of the faults, and shorten the troubleshooting time. Its specific operation process is as follows:

[0066] Upon receiving a fault warning, a diagnostic time window is first defined, which covers a period of time before the abnormal event occurs until the period of abnormality. All relevant historical data are automatically retrieved from this time window, including but not limited to the instantaneous values ​​and change curves of all water quality parameters, the change trajectory of the electrolyzer's operating current, and whether the equipment has any recent warning records.

[0067] Analyze the sequence and correlation of these parameter changes, and perform pattern matching between the current anomaly pattern and typical failure cases stored in the knowledge base. After comprehensive judgment, infer the root cause most likely to cause this anomaly.

[0068] Generate a structured diagnostic report, which includes the most likely root cause and a chain of data evidence supporting that conclusion;

[0069] The diagnostic conclusions are output and displayed through the monitoring interface.

[0070] The abnormal event diagnosis unit provides maintenance personnel with a clear direction for troubleshooting, greatly shortening the fault handling time.

[0071] When the direct water quality monitoring unit, indirect water quality monitoring unit, and equipment operation monitoring unit detect an anomaly, they will immediately transmit the fault data and early warning to the abnormal event diagnosis unit. After the abnormal event diagnosis unit is activated by the early warning, it will conduct in-depth analysis of the fault data.

[0072] During the joint operation of the direct water quality monitoring unit, indirect water quality monitoring unit, equipment operation monitoring unit, and abnormal event diagnosis unit, all input data, analysis data, and output data are saved to the local storage of the central monitoring and analysis platform and displayed in chart form on the platform's monitoring interface. This allows managers to clearly view the current water quality parameters and their historical trends, thereby improving overall regulatory efficiency and fault handling speed, and achieving efficient and intelligent management.

[0073] Working principle: First, the sensing module is installed at the end of the water production pipeline to continuously collect raw water quality parameters. This module includes an oxidation-reduction potential sensor, a pH sensor, a conductivity sensor, and a temperature sensor, which are used to measure the water's potential value, acidity / alkalinity, mineral content, and temperature, respectively. These sensors convert physical parameters into standard electrical signals, ensuring the real-time nature and accuracy of the data.

[0074] Next, the data processing module receives the electrical signals from the sensing module and performs preliminary processing. The module converts the electrical signals into digital format and performs verification and filtering operations to eliminate errors. Then, according to predefined rules, the raw data is mapped to meaningful water quality parameter values, such as specific potential values ​​or pH levels. Each parameter value is appended with the device source and timestamp, forming a structured data packet, which is finally sent to the central monitoring and analysis platform via the network.

[0075] The central monitoring and analysis platform is responsible for in-depth data processing and analysis. The platform's direct water quality monitoring unit checks each water quality parameter in real time to ensure it remains within preset acceptable ranges; if an anomaly is detected, an alarm mechanism is immediately triggered, and management personnel are notified via SMS or an interface notification. Simultaneously, the indirect water quality monitoring unit estimates the size of small molecular clusters based on real-time parameters, thereby indirectly assessing the water's functional characteristics. The equipment operation monitoring unit focuses on the health status of the water treatment facility, tracking the operating current value of the electrolyzer and comparing it with a dynamic performance baseline to identify abnormal aging or failure trends, and generating early maintenance warnings.

[0076] When any monitoring unit detects an anomaly, the anomaly event diagnosis unit automatically activates. This unit defines a time window, retrieves relevant historical data, analyzes the correlation of parameter changes, and matches them with typical fault cases to deduce the root cause. Finally, a diagnostic report is generated, providing maintenance personnel with clear guidance.

[0077] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0078] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for monitoring water quality in a treatment facility based on small molecule water, characterized in that, Includes the following steps: Step 1: Collect raw water quality parameter data through sensor modules deployed on the water production pipeline; Step 2: The raw parameter data is collected, verified, and converted in format using the data processing module to generate a standardized water quality parameter data package and upload it. Step 3: Receive the water quality parameter data packet through the central monitoring and analysis platform, and perform the following monitoring and analysis: The water quality parameters are directly monitored to determine whether they exceed the preset acceptable range and trigger an alarm; based on real-time water quality parameters, the size of small molecular clusters is indirectly assessed through a preset estimation model; the operating status of the equipment is monitored based on the working current value of the electrolyzer, and performance trend analysis and predictive maintenance warnings are performed. Step 4: When an anomaly occurs, the diagnostic process is automatically initiated, and historical data is correlated and analyzed to determine the root cause of the fault.

2. The water quality monitoring method for treatment facilities based on small molecule water according to claim 1, characterized in that, For step three, the steps for directly monitoring water quality parameters include: Preset acceptable ranges for each water quality parameter; The parameters entering the platform in real time are compared with the corresponding range; When a certain water quality parameter continues to exceed the range, an alarm procedure is activated and a report is sent to the abnormal event diagnosis unit.

3. The water quality monitoring method for treatment facilities based on small molecule water according to claim 1, characterized in that, For step three, the steps for indirectly assessing the size of small molecular clusters include: Using historical data samples and laboratory-measured small molecule cluster size data, an estimation model is established through machine learning algorithms, with real-time water quality parameters as input and estimated small molecule cluster size as output. Input the real-time monitored water quality parameters into the estimation model to automatically calculate the estimated value of the current small molecular cluster size.

4. The water quality monitoring method for treatment facilities based on small molecule water according to claim 1, characterized in that, For step three, the steps for monitoring the operating status of the equipment include: Continuously collect the operating current value of the electrolytic cell; A dynamic performance baseline model is established for each device, which records the typical operating current range required to achieve water production standards under standard conditions; The real-time operating current value is compared with the performance baseline to analyze the long-term trend. When an abnormal performance degradation trend is identified, the system predicts future failure risks based on the current degradation rate and generates maintenance warning information.

5. The water quality monitoring method for treatment facilities based on small molecule water according to claim 1, characterized in that, For step one, the raw parameter data collected by the sensing module should include at least the oxidation-reduction potential, pH value, conductivity, and water temperature.

6. The water quality monitoring method for treatment facilities based on small molecule water according to claim 1, characterized in that, For step two, the process by which the data processing module generates standardized water quality parameter data packages includes: The electrical signal is converted into a digital format, verified and filtered, the original value is mapped to a physical quantity value, a device identifier and a timestamp are added, and then sent to the platform via the network.

7. The water quality monitoring method for treatment facilities based on small molecule water according to claim 1, characterized in that, For step four, the steps to automatically initiate the diagnostic process include: A diagnostic time window is defined after receiving an early warning; Retrieve all relevant historical data within this time window, including water quality parameter change curves and operating current change trajectories; Analyze the sequence and correlation of parameter changes, and perform pattern matching with typical failure cases in the knowledge base; The root cause is comprehensively inferred, and a structured diagnostic report containing a chain of data evidence is generated.

8. A water quality monitoring system for a treatment facility based on small molecule water, used to implement the water quality monitoring method for a treatment facility based on small molecule water as described in any one of claims 1-7, characterized in that, The system includes a sensing module, a data processing module, and a central monitoring and analysis platform; The sensing module is installed on the water production pipeline to collect raw water quality parameter data; The sensing module includes a redox potential sensor, a pH sensor, a conductivity sensor, and a temperature sensor. The data processing module is connected to the sensing module and is used to process the raw data and generate standardized data packets; The central monitoring and analysis platform is connected to the data processing module to receive data and perform monitoring and analysis. It includes a direct water quality monitoring unit, an indirect water quality monitoring unit, an equipment operation monitoring unit, and an abnormal event diagnosis unit.

9. The water quality monitoring system for treatment facilities based on small molecule water according to claim 8, characterized in that, The water quality direct monitoring unit is configured to perform parameter range comparison and alarm. The water quality indirect monitoring unit has a built-in estimation model; The equipment operation monitoring unit is configured to establish and maintain a performance baseline model; The abnormal event diagnosis unit is configured to perform correlation analysis and case matching.

10. The water quality monitoring system for treatment facilities based on small molecule water according to claim 8, characterized in that, The central monitoring and analysis platform is also connected to a platform monitoring interface, which is used to visually display real-time data, historical trend charts, alarm information and diagnostic reports.

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